Economic liberalisation strategies and poverty reduction across Indian states

Economic liberalisation strategies and povertyreduction across Indian statesapel_ 26..42

Kaliappa Kalirajan and Kanhaiya Singh*

The focus of the study is the pace of poverty reduction across Indian statesand its determinants. In particular, the role of foreign direct investment(FDI) and industrialisation in reducing poverty is examined. Empiricalevidence shows that poverty reduction did occur during the 1990s follow-ing the implementation of India’s economic liberalisation program, whichincluded mainly industrial and FDI policy reform. The empirical analysisshows that, thus far, FDI has not contributed significantly to povertyreduction, but it did influence structural changes in the economy, particu-larly with respect to industry, which is an important driver of povertyreduction. The analysis clearly shows that states with dominant industrialsectors have been able to reduce poverty faster than states dominated byagriculture. It is argued that targeting of FDI in India has been misplaced.Had it been in the more labour-intensive manufacturing, it would havemore effectively contributed to the reduction of poverty.

Introduction

Developing countries undertake economic lib-eralisation to reduce the gap between theirpotential and actual performance and therebyimprove their economic performance. Gener-ally, the gap between the potential and actualperformance of an economy emanates fromits structural and institutional rigidities, whichconstrain the economy from using technologyand resources efficiently. These rigiditiesalso limit the country’s access to modern tech-nology, which is usually transferred fromdeveloped countries through foreign directinvestment (FDI). An effective liberalisationstrategy is expected to lead to structuralchange that improves the performance of the

economy, which in turn attracts more FDIand induces further structural change andimproved economic performance. In thiscontext, the notable example is China, whichbegan its economic liberalisation journey in1978.

Critics of liberalisation argue that it leads toincreased inequality, which may even aggra-vate absolute poverty (Rodriguez and Rodrik2001; Stiglitz 2002). The case of China is againan example, with income inequality still amajor concern to policymakers, although thepace of poverty reduction has been remark-able. These arguments necessitate empiricalstudies to examine the relationship betweenliberalisation and poverty reduction in devel-oping countries. It is in this context thatthe present study examines whether India’s

* Kaliappa Kalirajan, Crawford School of Economics and Government, The Australian National University. Kanhaiya Singh,National Council of Applied Economic Research, New Delhi. Comments and suggestions on an earlier version of thispaper by two anonymous referees are acknowledged with thanks.

doi: 10.1111/j.1467-8411.2010.01248.x

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© 2010 The AuthorsJournal compilation © 2010 Crawford School of Economics and Government,

The Australian National University and Blackwell Publishing Asia Pty Ltd.

liberalisation strategy has contributed to sig-nificant reduction in poverty and whether ithas done so uniformly across states.1

Drawing on convergence growth theory, itmay be argued that latecomers enjoy certainadvantages, such as having access to the latestversions of technology and the chance ofadopting new institutions for the harnessing ofcapital and technology. While China has madegood use of ‘latecomer advantage’, has Indiabenefited from being a latecomer to the libera-lised countries’ club?

In this paper, we restrict the analysis to thequestion: have India’s economic liberalisationstrategies, which mainly comprised industrialand FDI policy reform, contributed signifi-cantly to the pace of poverty reduction acrossits states? The pace of poverty reduction is cal-culated using two data points, 1993–94 and1999–2000, for the poverty ratio published bythe Planning Commission of India (Table 1).2

The following section describes the recentgrowth performance of Indian states followingthe implementation of major economic liberali-sation policies. The poverty reduction flowingfrom India’s liberalisation strategies is analy-sed in the next section. A final section con-cludes with policy implications.

The growth performance of Indianstates following the economic reforms

India began its economic liberalisation strate-gies seriously in July 1991. The reforms canbe classified into two groups: first, ‘internal,behind the border’ liberalisation and, second,‘beyond the border’ liberalisation. Some of themajor components of ‘behind the border’ liber-alising strategies include almost completeliberalisation of private investment, freeingindustries from government controls, and

eliminating licensing in most areas, andopening the financial and banking sectors topromote competition. The ‘beyond the border’liberalisation included promoting trade open-ness by reducing tariffs and eliminating otherimport restrictions, encouraging FDI, andallowing Indian companies to go global.

In the analysis, particular attention is givento the impact of the economic liberalisationstrategies relating to industrial policies andFDI policies (international capital mobility) onpoverty reduction at the state level. Accord-ingly, ‘beyond the border’ economic liberalisa-tion is proxied by the ratio of approved FDI togross state domestic product (GSDP).3

The growth performance due to economicliberalisation in India at the national level is wellknown and has been documented adequately inthe literature (Joshi and Little 1998; Desai 1999;Kalirajan et al. 2001; Bhagwati and Srinivasan2002; Bussolo and Whalley 2003; The WorldBank 2003; Jha 2008). Although use of grossdomestic product (GDP) as a measure ofeconomic welfare is known to have seriouslimitations, for most quantitative analysis,GDP growth remains the most commonlyused measure. However, richer insights maybe obtained by examining the structure ofstate economies and sector-level growth. There-fore, we present the growth in GSDP andits three major sectors, namely Agriculture(GSDP_AGR), Industry (GSDP_IND), and Ser-vices (GSDP_SERV) as shown in Table 2, whilethe economic structure is presented in Table 3.The relative size of the states as of 1999–2000,measured by their share in India’s GDP, isshown in Table 1. The growth rates (in Table 2)indicate the economic performance of thestates during the pre-liberalisation and post-liberalisation periods.

Tables 1–3 indicate that states had diversepatterns of growth during the period of analysis

1 A recent study that analysed the different responses of the states to the reforms initiated by the central Government is byHowes et al. (2003).

2 Some studies have questioned the comparability of the poverty ratios as measured for 1993–94 and 1999–2000 (see, forexample, Sundaram and Tendulkar 2001; Deaton 2003; Sen and Himanshu 2004). However, when measuring the pace ofpoverty reduction such anomalies as may exist do not affect the conclusions as they exist across all states.

3 Obtaining FDI figures at the state level is problematic. However, experience has shown that the implemented FDI figurestend to mirror the approval figures very closely. This is likely because it is the choice of the investors to select the statesand not otherwise.

KALIRAJAN AND SINGH — ECONOMIC STRATEGIES AND POVERTY REDUCTION

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© 2010 The AuthorsJournal compilation © 2010 Crawford School of Economics and Government,The Australian National University and Blackwell Publishing Asia Pty Ltd.

and their contribution to national GDP alsovaried significantly. States such as Gujarat,Delhi, Goa, Rajasthan, Tamil Nadu, Karnataka,West Bengal, Haryana, and Himachal Pradeshperformed above the national average. Severalstudies (see, for example, Kalirajan and Akita2002; Sachs et al. 2002) reported divergence ingrowth across states rather than convergence.Such divergence is likely to increase inequalityacross states and also within states. Therefore, it

is important to understand whether the pace ofpoverty reduction has been increasing ordecreasing.

Poverty reduction across Indian states

The Planning Commission’s poverty measureis based on consumption expenditure obtainedfrom the National Sample Survey Organiza-

Table 1Poverty across Indian states

StatesGSDP Share

in India

PVT93 PVT99 DPVTA

Percentageof population

below thepoverty line

Percentageof population

below thepoverty line

Annual changein percentage of

population belowthe poverty line1993–94 1999–2000

1 Andhra Pradesh AP 6.93 22.19 15.77 1.072 Arunachal Pradesh AR 0.10 39.35 33.47 0.983 Assam AS 1.49 40.86 36.09 0.794 Bihar BH 4.43 54.96 42.60 2.065 Delhi DL 3.01 14.69 8.23 1.086 Goa GO 0.35 14.92 4.40 1.757 Gujarat GU 6.70 24.21 14.07 1.698 Harayana HY 2.72 25.05 8.74 2.729 Himachal Pradesh HP 0.63 28.44 7.63 3.47

10 Karnataka KT 5.56 33.16 20.04 2.1911 Kerala KL 3.18 25.43 12.72 2.1212 Madhya Pradesh MP 5.98 42.52 37.43 0.8513 Maharashtra MH 14.13 36.86 25.02 1.9714 Manipur MN 0.16 33.78 28.54 0.8715 Meghalaya ME 0.20 37.92 33.87 0.6816 Mizoram MZ 0.10 25.66 19.47 1.0317 Nagaland NG 0.15 37.92 32.67 0.8818 Orissa OR 2.07 48.56 47.15 0.2419 Punjab PU 4.48 11.77 6.16 0.9420 Rajasthan RJ 4.61 27.41 15.28 2.0221 Sikkim SK 0.05 41.43 36.55 0.8122 Tamil Nadu TN 7.37 35.03 21.12 2.3223 Tripura TR 0.24 39.01 34.44 0.7624 Uttar Pradesh UP 10.49 40.85 31.15 1.6225 West Bengal WB 2.02 35.66 27.02 1.44

Notes: The share may not add up to 100 because of inconsistencies in national account and states’ accounts. The other fourstates could not be considered due to non-availability/ inconsistency in data.Source: Planning Commission (2002), departments of economics and statistics of various states and the Central StatisticalOrganization (various years).

ASIAN-PACIFIC ECONOMIC LITERATURE

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Table 2Average growth of GSDP and its major components across Indian states

States

Pre-liberalisationperiod (1985–93) Post-liberalisation period (1993–2000)

GSDP GSDP_AGR GSDP_IND GSDP_SER GSDP GSDP_AGR GSDP_IND GSDP_SER

Andhra Pradesh 4.67 3.8 4.86 5.46 5.53 2.81 5.66 7.12Arunachal Pradesh — — — — 3.11 -0.79 4.06 10.23Assam — — — — 2.10 0.24 1.02 4.35Bihar 2.08 -0.39 4.19 4.54 4.67 1.48 7.78 7.81Delhi — — — — 8.77 -5.33 1.3 10.54Goa — — — — 9.22 1.25 14.18 9.16Gujarat 5.35 7.82 8.48 6.15 8.01 5.19 8.39 9.17Harayana 5.93 5.42 6.17 7.20 5.96 2.15 6.9 10.17Himachal Pradesh 6.35 4.26 10.35 6.49 7.16 0.25 8.11 9.83Karnataka 5.25 3.50 6.14 6.79 7.66 4.10 8.69 10.2Kerala 4.80 3.39 6.76 5.11 5.62 1.88 4.18 8.15Madhya Pradesh 5.26 3.63 7.79 5.6 4.72 1.73 5.92 7.66Maharashtra 7.59 7.33 7.52 8.39 6.22 3.1 6.81 8.39Manipur — — — — 5.59 2.06 11.61 7.73Meghalaya — — — — 6.95 7.21 9.15 6.84Mizoram — — — — 6.01 5.39 -0.22 8.66Nagaland — — — — 4.3 8.43 4.45 2.24Orissa 4.40 2.28 6.74 6.84 4.4 -0.02 5.73 7.03Punjab 4.82 4.43 7.2 4.06 4.78 2.49 6.47 7.75Rajasthan 6.17 5.51 8.52 7.66 8.34 5.53 10.20 8.50Sikkim — — — — 8.05 1.54 12.62 14.57Tamil Nadu 5.10 3.84 4.38 7.04 6.69 1.80 4.73 9.09Tripura — — — — 7.65 3.72 16.73 9.35Uttar Pradesh 4.19 2.59 5.72 5.33 5.52 3.02 7.58 6.36West Bengal 4.92 4.84 4.52 5.27 7.11 4.10 7.24 9.79India 5.30 3.13 6.02 6.78 6.63 2.97 6.31 9.18

GSDP = gross state domestic product; GSDP_AGR = gross state domestic product of agriculture and allied sectors; GSDP_IND = gross state domestic product of industry;GSDP_SER = gross state domestic product of the services sector.Source: Authors’ calculations using data from departments of economics and statistics of various states and the Central Statistical Organization (various years).

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Table 3Structure of GSDP across Indian states (percentage share of the three major sectors)

States

Pre-liberalisationperiod (1985–93) Post-liberalisation period (1993–2000)

GSDP_AGR GSDP_IND GSDP_SER GSDP_AGR GSDP_IND GSDP_SER

Andhra Pradesh 33.67 24.48 41.86 29.89 25.87 44.25Arunachal Pradesh — — — 37.87 26.32 35.82Assam — — — 37.60 22.71 39.69Bihar 43.47 24.24 32.29 34.99 26.72 38.28Delhi — — — 1.80 22.12 76.08Goa — — — 11.62 35.67 52.71Gujarat 27.11 35.07 37.82 22.80 39.43 37.76Harayana 42.47 27.57 29.97 38.01 27.94 34.06Himachal Pradesh 35.58 25.15 39.27 26.82 33.81 39.37Karnataka 36.65 26.21 37.14 31.04 27.65 41.32Kerala 32.31 18.48 49.21 27.88 21.06 51.06Madhya Pradesh 38.16 26.97 34.86 33.38 30.22 36.40Maharashtra 20.13 35.04 44.83 17.57 34.17 48.26Manipur — — — 32.56 20.32 47.12Meghalaya — — — 25.47 21.26 53.28Mizoram — — — 26.30 14.70 59.00Nagaland — — — 25.27 16.73 58.00Orissa 45.55 23.14 31.31 35.24 26.47 38.29Punjab 46.81 19.84 33.35 42.86 23.09 34.05Rajasthan 38.77 24.23 37.01 33.52 28.61 37.87Sikkim — — — 29.54 21.06 49.40Tamil Nadu 23.76 35.79 40.45 20.51 34.75 44.73Tripura — — — 31.43 14.06 54.51Uttar Pradesh 41.97 21.72 36.30 37.29 24.63 38.08West Bengal 32.44 25.36 42.20 30.50 24.13 45.37India 33.21 26.14 40.65 27.95 27.22 44.83

GSDP = gross state domestic product; GSDP_AGR = gross state domestic product of agriculture and allied sectors; GSDP_IND = gross state domestic product of industry;GSDP_SER = gross state domestic product of the services sector.Source: Authors’ calculations using data from departments of economics and statistics of various states and the Central Statistical Organization (various years).

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tion’s large sample data and the relevant con-sumer price index. The level of poverty acrossthe states, in terms of the headcount ratio, isshown in Table 1. Both the level and the reduc-tion of poverty vary significantly across thestates. The poverty line was defined using percapita monthly expenditure, which variedacross states from Rupees (Rs.) 262.94 in ruralAndhra Pradesh to Rs.374.79 in rural Kerala,and from Rs.344 in urban Assam to Rs.539.71 inurban Maharashtra for 1999–2000 (PlanningCommission 2002).

Dollar and Kraay (2000) have argued thatincomes of the poorest quintile rise in linewith economic growth, in line with the well-known phrase ‘a rising tide lifts all boats’.However, as Sen (1992) has emphasised,poverty may not be adequately reflected inincome or consumption-based measures.Areas with a large concentration of the poorare also those with low levels of income, lownutritional levels, low literacy rates, low lifeexpectancy, and high rates of infant mortality.They also have low standards of physical andsocial infrastructure, particularly in ruralareas, and receive low levels of public expen-diture. Nevertheless, drawing on Anand andRavallion (1993) and Bidani and Ravallion(1995), it may be argued that progress inreducing income poverty is crucial in reducingmost non-income dimensions of poverty.

At the national level, poverty fell from36 per cent in 1993–94 to 26.1 per cent in 1999–2000 (Planning Commission 2001). Accor-ding to the Planning Commission (2002), theaverage annual rate of poverty reduction afterthe implementation of economic liberalisationis 1.41 per cent, which is higher than the rate ofpoverty (0.85 per cent) during 1983–93. Thisdifference implies that liberalisation measurescontributed to the increase. However, therate of poverty reduction across states variedsignificantly.

This paper examines whether the post-liberalisation industrial production and FDIflows played significant roles in the pace ofpoverty reduction in 25 Indian states duringthe period 1993–94 to 1999–2000.4 Unlike otherstudies cited above, the emphasis here is on thedeterminants of the pace of poverty reductionrather than poverty measures per se.

Determinants of the pace ofpoverty reduction

Drawing on the general-to-specific modellingof Henry (1995), we begin the analysis with thea priori selection of variables that are mostlikely to reduce poverty. We conjecture that thestructure of the economy, measured by theshare of industrialisation (INDZ) and the shareof agriculture (AGRZ), to be important vari-ables besides several others discussed below.The final selection of variables was based ontheir levels of significance and explanatorypower in explaining the variation in the pace ofpoverty reduction.5 All variables are describedin Appendix 1.

According to Schultz (1981), the decisivefactors in improving the welfare of poorpeople are not space, energy, and croplandbut the improvement in population qualityand advances in knowledge. Thus, low basiceducation attainment is an impediment tostrengthening the link between growth andpoverty reduction because it constrains theability of the poor to participate in opportuni-ties generated in agricultural and industrialactivities. Therefore, the change in the literacyrate (DLIT), defined as the annual change inthe literate percentage of the population of astate, is another potential variable to explainvariations in poverty reduction across states.

The 1990s’ economic liberalisation in Indiawas mainly aimed at eliminating the rigidregulations that were constraining industrial

4 Poverty data are available for seven additional states but for these very small states other data needed for the analysis arenot available.

5 For example, state government development expenditure was found to be not significant in the regressions. However, theimpact of development expenditure on poverty is most likely captured through explanatory variables such as literacy rate,industrialisation, welfare schemes for scheduled caste (SC) peoples, and improvements in road density.


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growth and driving away FDI. India’s FDIreform strategies mainly comprised of facilitat-ing the mobility of international capital andaccelerating industrial production and exports.Thus, FDI reform strategies could be expectedto influence poverty reduction directly andindirectly through structural changes in theeconomy. Foreign direct investment (FDIZ) ismeasured as the average ratio of approved FDIto GSDP. Structural change is proxied by theaverage share of industrial production (INDZ)in GSDP.

Given that most of the population in Indiareside in rural areas, it is rational to examinethe role of agriculture in the analysis of the paceof poverty reduction. The impact of agricultureon poverty reduction is captured through thevariable AGRZ, which is the average share ofagriculture and allied sectors in GSDP.

According to Schultz (1978:4), ‘farmers theworld over, in dealing with costs, revenues andrisks, are calculating economic agents. Withintheir small individual allocative domain theyare fine-tuning entrepreneurs, turning so sub-tly that many experts fail to see how efficient

they are’. If this vision of farmers is correct, notonly could agriculture supply wage goods andinputs but also, through technological mod-ernisation, rising productivity, incomes, andrural prosperity, the sector will stimulategrowth in industry. For its part, industry doesnot only supply agriculture with modern pro-duction inputs but also produces consumergoods to satisfy expanding consumer horizons.This interpretation suggests a dynamic two-wayrelationship between agriculture and industry.Support for this approach is drawn from expe-riences in East Asia, particularly post-war Japanand Taiwan and the recent post-1978 reformexperience in China (Hayami 1998).

Simple correlations between productionstructure and poverty reduction. Data pre-sented in Figures 1 and 2 indicate that stateswith higher levels of industrialisation are likelyto have faster reductions in poverty. On theother hand, states where agriculture is the mainsector are likely to experience a slower pace ofpoverty reduction (Figures 3 and 4); althoughthere is a group of states showing a positive

Figure 1Industry share in GSDP and pace of poverty reduction across Indian states

(1993–94 to 1999–2000)

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Note: INDZ and DPVTA are in percentages and percentage points, respectively, while in the regressions these variablesare in fractions.Source: DVPTA—Calculated from basic data obtained from the Planning Commission of India; and INDZ—Authors’calculations using data from departments of economics and statistics of various states and the Central StatisticalOrganization (various years).


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Figure 3Agriculture share in GSDP and the pace of poverty reduction across Indian states

(1993–94 to 1999–2000)

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Note: AGRZ and DPVTA are in percentages and percentage points, respectively, while in the regressions these variablesare in fractions.Source: DVPTA—Calculated from basic data obtained from the Planning Commission of India; and AGRZ—Authors’calculations using data from departments of economics and statistics of various states and the Central StatisticalOrganization (various years).

Figure 2Scatter Plot between INDZ and the pace of poverty reduction across Indian states

(1993–94 to 1999–2000)

y = 0.066x-0.248R² =0.312

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Average share of Industry in GSDP

Note: INDZ and DPVTA are in percentages and percentage points, respectively, while in the regressions these variablesare in fractions.Source: Authors’ own estimation.


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relationship between the share of agricultureand the pace of poverty reduction. This calls forfurther examination, which is attempted in thenext section.

Simple correlations between FDI and povertyreduction. Figures 5–7 present data on FDIand the pace of poverty reduction. It is difficultto make any inference a priori about the likelyrelationship between these two variables if all25 states are considered. At least three groupsof states can be distinguished in the data, withtwo different relationships operating. It isalso important to note that FDI and industri-alisation are endogenous to each other and,therefore, states with higher levels of indus-trialisation could distort the relationshippresented in simple diagrams such asFigures 5–7. Interestingly, when the highlyindustrialised states of Maharashtra, TamilNadu, and Karnataka are removed from thesample, the relationship between FDIZ and thepace of poverty reduction (DPVTA) becomesclearly negative (Figure 7). Again, this relation-ship calls for further examination.

Econometric model

The impact of FDIZ on poverty reductiondepends on many factors, such as the sector inwhich the FDI is undertaken, the type ofinvestment (for example, export-oriented orimport-substituting), links of foreign affiliateswith the host economy, and the socioeconomicand political conditions in the host country.Generally, the channels through which FDIwould help reduce poverty are industrial andagricultural growth. Also, in order to maximiseprofits, FDI will flow to states that enjoy highereconomic growth and better infrastructurefacilities. Thus, FDIZ, INDZ, and AGRZ are allendogenous variables, determined by state-specific characteristics.

Infrastructure development that facilitatesthe mobility of labour and goods acrossregions is another factor that may contributeto the reduction in poverty incidence. Giventhe uniform availability of data, infrastructuredevelopment is proxied by average roaddensity (ROADSITY) as measured by thelength of road per 100 km2 of area of the

Figure 4Scatter Plot between AGRZ and the pace of poverty reduction across Indian states

(1993–94 to 1999–2000)

y =-0.010x + 1.749

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Average share of Agriculture in GSDP

Note: AGRZ and DPVTA are in percentages and percentage points, respectively, while in the regressions these variablesare in fractions.Source: Authors’ own estimation.


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Figure 5FDI and the pace of poverty reduction across Indian states

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Note: FDIZ and DPVTA are in percentages and percentage points, respectively, while in the regressions these variablesare in fractions.Source: FDIZ—Authors’ calculation from basic data obtained from the Secretariat of Industrial Assistance (SIA)newsletter, Government of India, various issues; and DVPTA—Calculated from basic data obtained from the PlanningCommission of India.

Figure 6Scatter Plot between FDI and the pace of poverty reduction across Indian states

(1993–94 to 1999–2000)

y = 0.023x + 1.427R² = 0.001

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Note: FDIZ and DPVTA are in percentages and percentage points, respectively, while in the regressions these variablesare in fractions.Source: Authors’ own estimation.


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state.6 The existence of a larger proportion ofthe economically most backward scheduledcaste people in the population attracts moredevelopmental resources from the govern-ment. This, in turn, engages other sectionsof the community through spillover activitiesand, as a result, it is expected to increasethe pace of poverty reduction. Hence, the frac-tion of scheduled caste people in the statepopulation (SCPOP) is included to explain thevariable DPVTA.

Drawing on the above arguments, thesimultaneous equation model specified toexplain the variations in poverty reductionacross states is as follows:

DPVTA = C(1) + C(11)*INDZ + C(12)*FDIZ +

C(13)*SCPOP + C(14)*AGRZ (1)

INDZ = C(2) + C(21)*FDIZ + C(22)*DLIT +

C(23)*ROADSITY + C(24)*AGRZ (2)

AGRZ = C(3) + C(31)*FDIZ + C(32)*DLIT +

C(33)*DRAIN + C(34)*INDZ (3)

FDIZ = C(4) + C(41)*INDZ + C(42)*DLIT +

C(43)*AGRZ + C(44)*ROADSITY (4)

where DPVTA is the annual rate of povertyreduction in percentage points between 1993–94 and 1999–2000 taken in fractions; INDZ is theaverage level of industrialisation in fractions;FDIZ refers to the average value of FDI approv-als during 1993–2000 as fractions of GSDP;ROADSITY refers to the length of road per100 km2 of state area, which is predominantly

6 We do not differentiate between type and width of roads.

Figure 7Scatter Plot between FDI and the pace of poverty reduction across Indian states

(1993–94 to 1999–2000): part sample of 22 States

y =-0.073x +1.422R² = 0.017

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2.00

2.50

3.00

3.50

4.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00

DP

VTA

: an

nu

al c

han

ge

in p

ove

rty

rati

o

bet

wee

n 1

99

3-9

4 a

nd

19

99

-00

FDIZ: average FDI as percentage of GSDP

Note: FDIZ and DPVTA are in percentages and percentage points, respectively, while in the regressions these variablesare in fractions.Source: Authors’ own estimation.


36



constructed by central and state governments;DLIT is the annual percentage points change inthe literacy rate, in fractions; AGRZ is theaverage share of agriculture in GSDP over thesample period; SCPOP is the share of scheduledcaste people in the state’s population, in frac-tions; and DRAIN is the deviation of rain fromnormal during the period.

The system of equations contains endog-enous regressors and correlations of dis-turbance terms across equations, whichnecessitates the application of three-stage,least-squares (3SLS) estimation. Moreover, ourobjective of examining the channels throughwhich FDI influences poverty reduction neces-sitates the use of three-stage, least-squaresestimation. The instrumental variables for esti-mating the above system of equations includeSGFD, DLIT, URBAN, DRAIN, ROADSITY,INDZ80-81, AGRZ8081, and METRO, whereSGFD is the state government’s gross fiscaldeficit as a fraction of GSDP; URBAN is urbanpopulation as a fraction of total state popula-tion. METRO is a dummy variable for states’proximity to or encompassing metropolitancities, namely, Delhi, Chennai, Ahmadabad,Bangalore, Lucknow + Kanpur, Kolkata, andMumbai. INDZ80-81 and AGRZ8081 arethe shares of industry and agriculture, res-pectively, during 1980–81, indicating initialstructures. These instrumental variables arecompletely exogenous to the dependent vari-ables including FDIZ, INDZ, AGRZ, andDPVTA and also none of these instrumentalvariables is included in the equation forDPVTA.

Table 4 shows the 3SLS estimates of theabove system of equations. The R-squares ofindividual equations are also presented inTable 4, showing reasonably good fits. Theresults have several findings with policy impli-cations with respect to the pace of povertyreduction.

Role of FDI. The equation for FDIZ is the best-explained variable in the system of equationswith an R-square as high as 0.68, and explainedby INDZ, DLIT, AGRZ, and ROADSITY. FDI is

attracted to states with higher levels of indus-trialisation, low agriculture shares, and higherroad density. However, FDI flows, one of theimportant components of the liberalisationstrategy, are not directly favourable to the paceof poverty reduction; although INDZ, anothercomponent of liberalisation, does increase thepace of poverty reduction.

Although not significant, FDIZ has a nega-tive sign in the equation for DPVTA. This resulthas strong support from quite a few research-ers. In a statement published in Business Line(India) on 23 November 2004, the World Bank’sthen President, Mr James Wolfensohn, said thatforeign investment-driven privatisation of allactivities is not a solution to poverty reductionin India. Our result tends to support his viewand highlights the need for fine-tuning liber-alisation strategies to facilitate greater partici-pation by the unskilled sections of the labourforce.

Nevertheless, FDIZ is significant in explain-ing variations in INDZ and significantly nega-tive in the equation for AGRZ. This means thatFDI did influence the structural changes in theeconomy during the sample period. The impli-cation is that FDI can contribute to povertyreduction only indirectly through structuralchange in the economy. The negative influenceof FDI on agricultural growth can be explainedby the shift of government resources away fromagriculture to those activities considered to beimportant for attracting FDI (Venkatesan 1998).

The coefficient of ROADSITY is highly sig-nificant with a positive sign, which means thatFDI goes to already developed states in termsof infrastructure and large markets.7 Moreover,it is highly uneven across the country. Thelowest density is found in the states of Jammuand Kashmir, Himachal Pradesh, ArunachalPradesh, Nagaland, Mizoram, and Meghalaya.The states with the highest density are Punjab,Haryana, Kerala, and Tamil Nadu (PlanningCommission 2002).

FDI approvals were given mainly to indus-tries such as chemicals and chemical products,metallurgical industries, electrical machinery,and transport equipment, which are capital-

7 The road density in Japan is 14 times higher than in India, and the ratio of the length of roads to total population is 33 timeshigher in the US than in India.


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intensive and not labour-intensive. To put itdifferently, only a small percentage of FDIflows went into export-oriented industries; thebulk went into import-competing or non-traded industries such as power and fuel.India’s experience has been different to that of

many other developing countries, where FDIhas generally been central to the expansion ofexport-oriented industries (Goldar 2002).

It appears that FDI has not increased India’slabour-intensive exports, which could generatethe unskilled jobs required to reduce poverty

Table 43SLS estimation of the model explaining the pace of poverty reduction (25 Indian states)

Estimation Method: 3SLS, Total system (balanced) observations 100

Coefficient Std. Error t-Statistic Prob.

DPVTAC(1) 0.0040 0.0088 0.4509 0.6533C(11) 0.0634 0.0228 2.7844 0.0067C(12) -0.2453 0.1990 -1.2326 0.2213C(13) 0.0451 0.0253 1.7832 0.0783C(14) -0.0301 0.0259 -1.1648 0.2476

INDZC(2) 0.2957 0.0525 5.6346 0.0000C(21) 3.9705 1.0536 3.7683 0.0003C(22) 1.5048 0.7398 2.0340 0.0453C(23) 0.0167 0.0039 4.2726 0.0001C(24) -0.2173 0.1586 -1.3703 0.1744

AGRZC(3) 0.4611 0.0863 5.3444 0.0000C(31) -5.8828 1.0654 -5.5219 0.0000C(32) 4.4269 1.2078 3.6652 0.0004C(33) 0.0111 0.0654 0.1696 0.8658C(34) -0.5014 0.3495 -1.4347 0.1553

FDIZC(4) -0.0041 0.0129 -0.3200 0.7498C(41) 0.0963 0.0342 2.8171 0.0061C(42) 0.0985 0.0590 1.6687 0.0991C(43) -0.0482 0.0241 -2.0010 0.0488C(44) 0.0023 0.0006 4.0210 0.0001

Determinant residual covariance 8.15E-18Equation: DPVTA = C(1) + C(11)*INDZ + C(12)*FDIZ + C(13)*SCPOP + C(14)*AGRZR-squared = 0.539, Probability (F-statistic) = 1.488Equation: INDZ = C(2)+C(21)*FDIZ + C(22)*DLIT + C(23)* ROADSITY + C(24)*AGRZR-squared = 0.559, Probability (F-statistic) = 2.103Equation: AGRZ = C(3) + C(31)*FDIZ + C(32)*DLIT + C(33)*DRAIN + C(34)*INDZR-squared = 0.572, Probability (F-statistic) = 2.410Equation: FDIZ = C(4) + C(41)*INDZ + C(42)*DLIT + C(43)*AGRZ + C(44)*ROADSITYR-squared = 0.681, Probability (F-statistic) = 2.600Notes: (1) Instrument variables: AGRZ8081, INDZ8081, URBAN, SGFD, ROADSITY, DLIT, METRO, DRAIN.(2) All ratios and percentages were converted into fractions, except for the ROADSITY variable which, being small, is notdivided by 100.(3) For data description, see Appendix 1.Source: Authors’ own estimation.


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quickly. For example, during 1991–2002, 19.8per cent of FDI went to Telecom, 27.2 per centto fuel, including power, 9.8 per cent to electri-cal equipment, 7.4 per cent to the auto sector,and 26.5 per cent to mostly financial services.For example, annual growth in employmentbetween 1993 and 2000 was only one per centin Maharashtra, which is the leading state interms of attracting FDI (Planning Commission2002). Thus, the capacity of FDI to reducepoverty has been limited. But this does notmean that FDI is not capable of contributing tosustained poverty reduction. What is importantfrom a policy perspective is that state govern-ments should improve infrastructure andremove any institutional rigidities. In China,initially, FDI was important in labour-intensivesectors such as textiles, toys, plastic products,small manufacturing, and construction, whichincreased employment substantially. FDI thenmoved on to medium-technology manufactur-ing such as electrical appliances in the secondstage. India does not seem to have benefitedfrom such a process of FDI development.

The role of economic structure (INDZ andAGRZ). The industrialisation and agriculturevariables have negative coefficients in eachother’s equations, although they are not signifi-cant. This result is intuitive in the sense that thegrowth of one sector’s share is at the expense ofthe other. Literacy is significant in both equa-tions with positive signs, which emphasisesthe role of human capital in the growth of thesesectors. FDIZ is also significant in both equa-tions but with opposite signs. FDIZ has a posi-tive sign in the equation for INDZ, indicatingan important channel of poverty reduction; butin the equation for AGRZ, the sign of FDIZis negative. This negative sign can also beexplained by the fact that changes in the shareof one sector is always at the cost or benefit ofthe other two sectors. If FDI strongly favoursindustrialisation, it must reduce the agricultureshare; more so because FDIZ is also likely toincrease the share of the services sector, thethird sector. The positive sign of ROADSITY inthe equation for INDZ implies that states withhigher road density are more likely to be indus-trialised.

With the above interpretation of the equa-tions explaining the shares of agriculture andindustry, we can ponder on the channel ofpoverty reduction. The equation for DPVTA hasa significant positive sign for INDZ and aninsignificant negative sign for AGRZ. Thismeans that the more industrialised a state, thepossibility of reducing poverty faster increases.This process is helped by literacy as well asinvestment. The positive impact of industrialisa-tion has been emphasised by several authors.This finding is in line with that of Datt andRavallion (2002), although they used data fromonly 15 States. In addition, drawing on theLewis (1954) and Ranis-Fei (1961) models ofdevelopment, labour from the agriculture sectorcan more easily migrate and find employmentin more industrialised states than in less indus-trialised states. Urban poverty also declines instates with a higher level of industrialisation.

The share of agriculture has an insignificantnegative sign in the equation for DPVTA,which means agriculture-dominated econo-mies are slow in poverty reduction. However,this does not mean that agricultural growthwould be detrimental to the pace of povertyreduction. In fact, when the system was esti-mated using growth in agriculture (a result notreported here for the sake of brevity), it wasfound to have helped the pace of povertyreduction significantly. In general, it can besafely argued that growth in any sector is likelyto help in reducing poverty (Datt and Ravallion1998), but the pace would be higher if it isobtained through industrialisation.

Effect of human capital and other variables.Increases in the literacy rate (human capital)tend to increase the pace of poverty reductionthrough indirect channels related to increases inindustrialisation. Further, literacy and invest-ment, whether the investment is domestic orforeign, are complementary. A literate labourforce will attract investment in productionactivities leading to sustained growth andstrongly increase the poverty reduction benefitfrom that growth. For example, an investmentproject undertaken by Ford Motor Company asa joint venture with the Indian firm Mahindra,in which Ford holds a 90 per cent equity


39


share, is located in Chennai in Tamil Nadu state.The Ford managers said that the reasons theychose Tamil Nadu instead of Maharashtra,which is usually a popular state for FDIs,were, ‘[Chennai] is near an international air-port . . . and in our view Tamil Nadu has a moreliterate, more educated, work force and,perhaps more importantly, has a better labour-relations record than Maharashtra’ (Venkatesan1998:79). Thus, the argument of Drèze and Sen(1995), among others, that improving the lit-eracy rate can significantly increase the pace ofpoverty reduction is supported here.

Conclusions

Theoretically, it can be argued that liberalisa-tion can contribute to human developmentby reducing poverty through its capacity togenerate employment in developing countries.Critics argue strongly that liberalisation canat times aggravate poverty due to the accompa-nying costs of adjustment for labour and entre-preneurs. These arguments demand empiricalwork to examine the relationship between lib-eralisation and poverty reduction in develop-ing countries. In this context, the present studyexamines whether India’s liberalisation strate-gies have reduced poverty uniformly across itsstates. Data on poverty ratios for 1993–94 and1999–2000 covering the period of India’sreform process, have been used for empirical

analysis. The empirical evidence shows thatpoverty reduction occurred at the nationalaggregate level after the implementation ofreforms in 1991. However, FDI does notappear to have had a direct impact on poverty.But FDI has increased the pace of povertyreduction indirectly through structuralchanges in the economy, particularly throughincreased industrialisation.

The study’s findings offer policy sugges-tions for reducing poverty across statesthrough India’s liberalisation strategy. Unlesssignificant affirmative measures are taken tocorrect imbalances in the spread of industriali-sation through regional policies, the pace ofpoverty reduction across states could continueto differ significantly, with economic andpolitical ramifications. There is a need to fine-tune the liberalisation strategies to increase theopportunities for the unskilled labour force byimproving infrastructure, such as road density,which is an important determinant of FDI andindustrialisation. There is also an urgent needto find ways such as reduced corporate tax toattract investment in employment and income-generating activities such as food processing,labour-intensive manufacturing, and agricul-tural exports. State governments need to lookcarefully at the existing institutional rigiditiesand barriers to entering these industries.Without intensifying such efforts in policy-making, the concept of liberalisation may notplay its full role in reducing poverty.

References

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Appendix 1

Variable description for the system of equations

variable Description Data Source

DPVTA Annual rate of poverty reductionpercentage points (fractions)

Calculated from basic data obtained fromthe Planning Commission of India.

INDZ Share of industry in realstate domestic product(GSDP) (fractions)

Authors’ calculations using data fromdepartments of economics and statisticsof various states and the Central StatisticalOrganization (various years).

AGRZ Share of agriculture in real Statedomestic product (GSDP) (fractions)

ditto

SERZ Share of services in real Statedomestic product (GSDP) (fractions)

ditto

INDZ80-81 shares of industry in GSDP during 1980–81 dittoAGRZ8081 shares of agriculture in GSDP during

1980–81ditto

INDG Growth of industrial sector (fractions) Authors’ calculations using data fromDepartments of economics and statisticsof various states and the Central StatisticalOrganization (various years).

AGRG Growth of agricultural sector (fractions) dittoSERG Growth of services sector (fractions) dittoPVT93 Poverty level in 1993–94 (fractions) Planning Commission of India.SCPOP Proportion of scheduled caste population in

total population of the State (fractions)Census of India 1991.

FDIZ Approved foreign direct investment aspercentage of GSDP (fractions)

Authors’ calculation from basic data obtainedfrom Secretariat of Industrial Assistance(SIA) newsletter, Government of India,various issues.

DLIT Annual change in literacy rate (fractions) Basic data Census 1991 and 2001.METRO Dummy for States having proximity to

metropolitan cities or large contiguousurban formations

AP, DL, GO, GU, HY, KT, MH, TN, UP, WB.

ROADSITY Average road density measured kilometresper 100 square kilometres of area during1993–2000

Calculated from basic data obtained fromstatistical abstract of India and the RoadStatistics.

DRAIN Average deviation of rainfall during1993–2000 (in fractions)

National Council of Applied EconomicResearch (NCAER) database.

URBAN Urban population as fraction of totalpopulation

Census 1991.

SGFD State government gross fiscal deficit asfraction of GSDP during sample period.

Reserve Bank of India.


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Economic liberalisation strategies and poverty reduction across Indian states

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