rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6543

# **Abstract**

This paper uses a three-round 4,000-household panel from Andhra Pradesh together with administrative data to explore short and medium-term poverty and welfare effects of the National Rural Employment Guarantee Scheme. Triple difference estimates suggest that participants significantly increase consumption (protein and energy intake) in the short run and accumulate more nonfinancial assets in the medium term. Direct benefits exceed program-related transfers and are most pronounced for scheduled castes and tribes and households supplying casual labor. Asset creation via program-induced land improvements is consistent with a medium-term increase in assets by nonparticipants and increases in wage income in excess of program cost.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org and y.liu@cgiar.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
associated with slightly higher shares in agricultural employment but lower shares in services for districts whose population is close to a main city (on average 8 km away). The relation is opposite for districts from the second quintile, with a population on average 27 km away from a main city. Combined investments are associated with lower agricultural employment share and higher service employment share. The relation between structural change and infrastructure investments seems to be the strongest for the most isolated districts.

# **3.2 Instrumental Variables**

In this section, we use an instrumental variables identification strategy to deal with the potential endogeneity in the placement of the infrastructure. We instrument both roads and electricity, and the interaction terms.<sup>18</sup>

## **3.2.1 Method**

We instrument electrification and access to a paved road. Regarding electrification, the instrumental variable relies on four assumptions. First, electricity generation must be connected to demand, which comes mostly from the main cities. Second, the sources of energy generation that are identified are the main sources of electricity generation. Third, the locations of the supply sources are exogenous to economic geographic development. Finally, the locations between the generation sources and the main demand centers are more likely to be electrified.

We identified two sources of energy generation that can be used for the IV strategy: dams for hydroelectricity, wind farms, gas and geothermal production stations. The main sources of energy supply are hydropower in Cameroon, and gas in Nigeria. Similar to Moneke (2020), we develop an IV which yields a hypothetical electrification status based on a location’s proximity to a straight line corridor from electricity generators to the main cities. First, we identify the locations of the electricity generators using two databases, one on dams opening year and another on power plant locations (Platts database). For Cameroon, we use the geolocalized database including all dams in Africa and their year of opening. For Nigeria, we use the global power plant database that includes all power plants per type of energy (hydro, wind, gas, and geothermal) with their capacity and year of commissioning. From the year of dam opening or power plant commissioning onwards, all districts lying along the straight lines connecting the dams or power plants to the main demand centers are considered as having access to electricity. For Nigeria and Cameroon, the main sources of demand vary across time. At the beginning of our panel, all dams in Cameroon have been opened therefore a panel IV is created by varying the sources of demand rather than the supply sources. For Cameroon, we set the threshold of 500,000 inhabitants for a city to be included as a main source of demand for the hydropower supply sources. In 1990, only Douala and Yaounde are included. In 2000, Garoua in the North

> 18While the previous part also includes Internet investments, this subsection only focuses on finding instruments for paved roads and electricity to keep the IV estimation tractable.

17
et al. (2010).<sup>10</sup> This unconditional gender gap in earnings decreases to 56% after accounting for education and demographics. Both men and women experience positive returns to education.

In aggregate, socio-emotional skills are associated with higher earnings for both men and women in similar magnitude. The gender difference in the relationship between socio-emotional skills and earnings is statistically insignificant<sup>11</sup> (Panel B, Column1). We find some gender differences when we look separately at intrapersonal and interpersonal skills. For men, earnings correlate significantly with intrapersonal skills (0.081 standard deviations in Panel B, Column 2), while the correlation with interpersonal skills is insignificant (Panel B, Column 3). For women, earnings are significantly correlated with both intrapersonal and interpersonal skills, with coefficients of 0.075 and 0.061, respectively. Our results indicate that a 1 standard deviation increase in interpersonal skills (but not in intrapersonal skills) reduces the gender gap in earnings by about 7.8%.

Altogether, we find that one standard deviation increase in socio-emotional skills amounts a 6.8% gain in earnings for men, equivalent to the earnings gain from 2.72 more years of education (0.068/0.025). For women, one standard deviation increase in socio-emotional skills provides an 8.8% increase in earnings, the equivalent earning gain from 5.87 more years of education ((0.068+0.02)/(0.025-0.01)). Our results imply that conditional on similar levels of education for men and women, we would need to increase socio-emotional skills by 9.5 standard deviations (0.647/0.068) to close the gender gap in earnings.

Looking at individual intrapersonal skills (Panel C), we find that positive self-concept and perseverance are positively associated with earnings for both men and women, although two important gender differences arise in the magnitudes of these correlations. While high levels of positive self-concept are less associated with earnings for women than for men, perseverance has a significantly higher correlation with earnings for women. Strikingly again, for all other individual and aggregated skills, gender differences are statistically insignificant. These results potentially highlight the positive impacts of overcoming gender norms for women.

> 10 We estimate a 33% earnings gap when we include men and women with non-zero earnings, which is similar to estimates from other studies.

> 11 This result holds whether controlling for education levels or not and after controlling for occupation sector, using data from the Ghana and Kenya STEP surveys, which include information on occupation for a random sample drawn from a general urban population.

21
-48-

- Anand, R. and S.van Wijnbergen (1987), "Inflation and the Financing of Government Expenditure in Turkey: an Empirical Analysis", mimeo, World Bank.

- Anand, R. and S.van Wijr;bergen (1988), "Export Incentives, Exchange Rate Policy and Export Growth in Turkey", in process, World Bank.

- Anand, R., A.Chhibber, R.Rocha and S.van Wijnbergen (1988), "External Debt, Fiscal Policy and Sustainable Growth in Turkey: an Empirical Analysis", mimeo, World Bank.

- Bean, C.( 1984), "The Estimation of "Surprise" Models and the "Surprise" Consumption Function", forthcoming, Review of EconQmic Studies.

- Celasun, M. and D. Rodrik (;987),***, Forthcoming, NBER and University of Chicago Press

- Chhibber, A. and S. van Wijnbergen (1988): "Public Policy and Private Investment in Turkey", mimeo, World Bank.

- Cohen, D., (1985), "How to evaluate the Solvency of an Indebted Nation", Economic Policy, vol.I.

- Cohen, D. (1987), "External and Domestic Debt Constraints of LDCs: a Theory with Numerical Applications to Brazil and Mexico", World Bank Quarterly Economic Review.

- S.van Wijnbergen, R. Anand and R. Rocha (1988),"Inflation, External Debt and Financial Sector Reform in Turkey: a Quantitative Approach to consistent Fiscal Policy", mimeo, World Bank.

ChhibberDisk/chhibber-Turkey Papr/ac :07-28-88: lc
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rabbits. Therefore, livestock sales do not appear to be a coping mechanism that is widely available to Sahelian households either.

## Section 3.3. Social assistance in the Sahel

Few households receive food or cash transfers, although in-kind benefits are more widespread. Distribution of food, cash, or in-kind benefits may further help to smooth consumption between the lean and non-lean season. However, for the survey period, only around 10.5 percent of Sahelians live in a household that received food during the previous 12 months, and 3.6 percent live in a household receiving cash transfers (Figure 5). Yet a much larger share of the population receives in-kind support in the form of care for children under 5 and bed nets. These in-kind benefits may help boost longer-term efforts to build human capital, but they may be less likely to support households in coping with short-term shocks and seasonality.

**Figure 5. Share of the population receiving support from different social safety nets, by country**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Food Non-food<br>Burkina Faso Mali Niger Senegal Chad All countries<br>Percent<br>Cereals Food for work Any food Public works women Any non-food<br>Flour from cereals government<br>Supplements for<br>Food for school students malnourished children Cash transfers from the Program for pregnant Care for children under 5 Distribution of bed nets<br><!-- End of picture text --> Note: Individual-level sampling weights applied. Statistics capture whether any household member received that type of support during the previous 12 months. Source: EHCVM and World Bank estimates.

These descriptive statistics show that Sahelian households’ mechanisms for managing and coping with the effects of seasonality may be limited. All other things equal, this could leave households exposed to large seasonal swings in welfare. We now turn to the main results to assess whether this is the case.

# Section 4. Main results

In this section we present the main results, showing how welfare differs between the lean and non-lean seasons in the EHCVM sample. We first present the effects of seasonality on overall consumption and poverty. We then disaggregate these changes in consumption, showing how the data are consistent with 11
# Table of Contents

|I.|Int|roductio.n<br>, , , , , ,<br>1|
|---|---|---|
|II.|The|Rise of the TVP Sector .<br>4|
||A.|The Production Resnonsibility<br>**Systen**.<br>.<br>,<br>4|
||A.|OutRut GrowthInthe TVP Sector.<br>6|
|III.|Mark|ets and Administrative<br>Structure<br>..<br>8|
||A.|The Market Environment of .e TVPs.<br>8|
||B.|Local Government and Enterprise Hierarchies<br>.10|
|IV.|Ince|ntives for TVP Owners<br>..<br>11|
||A.|The Imolications<br>for Soatial Ineauality<br>.11|
||B.|Incentives for Owners of TVPs .13|
||C.|The Dual Roles of Community Governments<br>.20|
|V.|Labo|r Markets and Incentives .22|
||A.|Labor Markets for Rural Industr .22|
||B.|Incentives and Pay in TVPs .25|
|VI.|Conc|luso<br>..<br>27|
such tradeoffs is not commonly needed in policy making at the country level. But when one faces a tradeoff, because a policy spans more than one dimension, those with a stake in the outcomes will almost certainly be in a better position to do determine what weights to apply than the analyst calibrating a measure of poverty. The specific country and policy context should determine what tradeoff is considered appropriate. This will often be a political decision, though hopefully a well informed one.

So we may well be asking too much of a single measure of “poverty” to have it include things like child mortality, schooling, violence or empowerment, on top of material living standards. It is one thing to agree that consumption of market commodities is an incomplete metric of welfare—and that for the purpose of assessing poverty one needs to also account for indicators of non-market goods and services—and quite another to say that a single “poverty” measure should embrace all these things.

This suggests that we may need to focus our efforts and resources on developing the best possible distinct measures of the various dimensions of poverty deemed relevant to a given setting—aiming for a credible set of “multiple indices” rather than a single “multidimensional index.” Granted, that route forgoes the option of identifying a joint distribution, but that is hardly a large loss in practice when that one’s characterization of the joint distribution is so severely data-constrained (notably in finding a single household survey that covers all the dimensions that matter). And, of course, taking the multiple-index route in the context of poverty monitoring does not preclude researching the joint distributions in specific data sets, when feasible.

Some degree of aggregation will almost certainly be needed even within the multipleindex approach. This raises an important but poorly understood issue concerning the space we choose to aggregate multiple dimensions. We must decide whether this is to be done in the space of attainments—the variables we use in assessing individual welfare for the purpose of deciding who is poor and how much—or in the space of deprivations—the assessments we make about whether one is poor in a given attainment space. While arguments can be made for and against each approach, I have questioned the main arguments made in favor of deprivation aggregation, as has become popular in the recent “multidimensional indices of poverty.” 17
Table 3. Information, Education, and the Effect of the Treatment

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|_Panel A: Least squares_|||||||
|Treatment|-0.08***|-0.05|-0.05*|-0.08***|-0.07***|-0.06**|
||(0.025)|(0.029)|(0.027)|(0.023)|(0.020)|(0.026)|
|Daily news consumption|0.16***<br>(0.007)|0.15***<br>(0.014)|0.13***<br>(0.015)||||
|Treatment-news interaction|-0.08***<br>(0.018)|-0.08***<br>(0.024)|-0.07**<br>(0.023)||||
|Education||||0.04***|0.03***|0.03***|
|||||(0.003)|(0.003)|(0.005)|
|Treatment-education interaction||||-0.01**<br>(0.005)|-0.01**<br>(0.004)|-0.01*<br>(0.005)|
|Urban dummy||0.03|0.02||0.02|0.02|
|||(0.021)|(0.019)||(0.020)|(0.019)|
|Own past economic situation|||0.07***<br>(0.006)|||0.07***<br>(0.006)|
|Occupational fixed effects|No|Yes|Yes|No|Yes|Yes|
|Observations|1,196|1,196|1,186|1,194|1,194|1,184|
|R-squared|0.083|0.135|0.211|0.103|0.133|0.205|
|_Panel B: Ordered logit_|||||||
|Treatment|-0.57***|-0.34*|-0.37*|-0.56***|-0.44***|-0.36*|
||(0.164)|(0.203)|(0.203)|(0.157)|(0.133)|(0.189)|
|Daily news consumption|1.11***|1.03***|0.97***||||
||(0.082)|(0.100)|(0.101)||||
|Treatment-news interaction|-0.54***|-0.56***|-0.48**||||
||(0.174)|(0.203)|(0.193)||||
|Education||||0.28***|0.22***|0.20***|
|||||(0.022)|(0.017)|(0.038)|
|Treatment-education interaction||||-0.08**|-0.08***|-0.09**|
|||||(0.035)|(0.026)|(0.044)|

_Source_ : Authors’ analysis based on data sources discussed in text. Treatment denotes the Pula devaluation. Days before devaluation are coded as 0; days after devaluations are coded as 1. All models contain a constant term (not reported to save space). Robust standard errors clustered at the region level in parentheses. *** _p_ < .01, ** _p_ < .05, * _p_ < .1.

25
14 °

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unemployment bnBofLtV (8). All current workers or mbrsu (N) receive equal treatmQnt and those who will bo) oventually unemployed are randomuly selected among membero. The workers or union's utillty function it; U, - _L/f_ u(w) * (N-L)/N u(B) for L.sa (3.1) The production function P(L) iL assumed to have positive but decreasing returns I V'(L)"O, F'(L)40 and V(O)cO In the WCFQ the union maxiemises its utility (eq.3.2) wLth respect to wages and employment subject to a &ero profit conetraint 31 tl(L) - wL - 0 (3.2) where a is a prlce or productlvlty shock if the price iL normalized to 1. We set 4 constant and equal to 1. From the flrst order conditlona we obtains

- (U(w)-LU(B))/U'(w) w-F'(L)

ands umay(L) /L (3.4) Eq. (3.3) lo that for the contract curvo, whlec eq. (3.4) gives the 'rent oharLng' rul(D whleh, ln the caso of the WCY involves the workers approprlatlng all revenues.

Figure 3.1 io a standard diagram in wage-employment space contrasting the non-Pareto optimal outcome on the labour demand curve and efficient bargains given by the tangency of tho firm's lsoprof lt curves and the workers or union's lndifference curvsoo. The locus of the polnts of tangency defines the contract curve, The looprof1t curves have a poiLtlve slop, until the wage equates the marginal revenue product of employment, (E"L). The lowest point on the labour demand curve, 0, is the competltlve euillbrlum. Thle can be thought of as that point where the workers' have no bargaining power and where B is the supply prlce of labour or the reservation wage. The contract curve emanates from thiB polnt end slopes upward therafter, given w'w (the _sj_ The firm would fold below thLs levoll ln other words, the hard budget constraint binds,
# Appendix C:  Adjusting GTAP Database to Incorporate Irregular Payments

The existing GTAP model and database are not equipped with the irregular transfer payments in the trading process, which is to be changed as a shock in the simulation. In the text, we have suggested treating such payments as an import tax. The import tariff rates in the existing GTAP database reflect the levels of nominal effective tariff in 2001. These tariff rates in the original database should be adjusted to incorporate the irregular transfer payments, in addition to the nominal tariff rates, as long as information on the levels of trade costs from such payments are available. This Appendix discusses the methodology and results of the adjustments in some detail.

# **_Methodology_**

Malcolm (1998) suggests a procedure for incorporating improved information on taxes into existing model database, while maintaining the internal consistency of the database and minimizing the impacts of the tax changes on the value flows in the database. This procedure can also apply well to our simulation with some minor modification. Based on the suggestion by Malcolm, the GTAP model provides a convenient program, named ALTERTAX, to adjust the tax rates by using the model itself to create a new database, while minimizing the changes in the trade and consumption flows of the original database. A simulation is run where tax rates are shocked to their desired value and the updated post-simulation database is used for subsequent policy experiments.

More specifically, Malcolm (1998) suggests using a special closure and a special parameter file to ensure that the rate-changing simulation left other cost and sales shares as little changed as possible. His closure fixes regional trade balances, whilst his parameter settings amount to "Cobb-Douglas everywhere" -- this keeps nominal budget shares fixed<sup>24</sup> . Accordingly, the Armington elasticities, ESUBM and ESUBT, are set as one. The ALTERTAX closure holds DTBALR (the percentage change in the ration of trade balance over national income) exogenous for all regions except one (in our case, the rest of the world: ROW), and CGDSLACK exogenous for that one region.

> 24 See Help file for RunGTAP program for actual implementation.

36
2000-2005, 2005-2010, and 2010-2015 and examine if being a star is associated with higher average performance in the subsequent five year period. Specifically, for firm _i_ in industry _j_ in year _t_ , the regression we estimate is as follows: _Performanceijt_ = _α_ 0 + _β_ 1 _× Log_ ( _Assets_ ) _it−_ 5 + _β_ 2 _× Log_ ( _Age_ ) _it−_ 5 + _β_ 3 _× Starit−_ 5 We look at the following four performance measures: 5-year average _ROIC_ , _Sales growth_ computed as the five year log difference in sales divided by 5, _Employment growth_ computed as the five year log difference in employment divided by 5, and 5-year average _Labor Productivity_ . Using stacked panel regressions, we examine the association between each of these measures and star firms identified at the beginning of each panel. We also control for size and age at the beginning of each panel. All regressions also include industry and year fixed effects.

Columns 1 and 2 of Table 1 shows that both star status and high _ROIC_ are on average positively associated with higher average _ROIC_ in the subsequent five year period. The predicted value of average 5-year _ROIC_ for firms that were superstars five years ago is 44.01 compared to 7.48 for firms that were not superstars five years ago. Columns 3-8 show that prior star status is also associated with higher sales growth, employment growth, and labor productivity. Replacing _ROIC_ Star by _ROIC_ yields very similar results except for sales growth where it is not significant.

While the above results rely on defining star firms based on returns to equity, as an alternate definition, we define stars in terms of Tobin’s Q. Again following Peters and Taylor [2017], we define _Q_ as the ratio of Firm value to _TOTCAP_ which is the sum of physical ( _PPENT_ ) and intangible capital ( _ICAP_ ): where _V_ is the market value of the firm defined as the market value of equity (=total number of common shares outstanding (Compustat item _CSHO_ ) times closing stock price at the end of the fiscal year (Compustat item _PRCC_ ) plus the book value of debt (sum of Compustat items _DLTT_ and _DLC_ ) minus the firm s current assets (Compustat item _ACT_ ) which includes cash, inventory, and marketable securities. After dropping top and bottom 1% outliers in _Q_ , we define _Q star_ as a 17