rafmacalaba/datause-encoder-data huggingface dataset:
___________ ( ) US$/household/ year V2. Median per household WTP (annual payment) ___________ ( ) US$/household/ year

**PLAN B (FIGURE 7; a less ambitious, and less expensive, plan than plan A, to protect only 88% of today’s rainforest)**

W1. Mean per household WTP (annual payment) ___________ US$/household/ year W2. Median per household WTP (annual payment) ___________ US$/household/ year X1. In the population survey of the U.S. population, our payment vehicle will be either an increase in households' utility bills, or in their income taxes. Which payment vehicle would, in your opinion, be most appropriate in such a survey: a) The utility bill b) The income tax c) Other (specify) X2. Which payment vehicle would, in your view, yield the higher mean WTP value, if this vehicle alone was used in a survey of the entire U.S. population?

a) The utility bill b) The income tax c) No difference X3. In your opinion, would using either the utility bill or income tax as payment vehicle in such a survey be likely to lead to either an upward or downward bias of population WTP, or to no bias? Please specify for each.

a) The utility bill b) The income tax 42
ASI observations and 655,571 NSSO observations. Finally, we drop observations with any of the following properties: (a) null, missing, negative, or extremely large values of output, output per worker, or employment<sup>14</sup> , (b) NSSO observations from states that are not surveyed by ASI, and (c) observations missing state indicators. These drops results in a final sample of 169,814 ASI observations and 651,808 NSSO observations. A detailed table of these cleaning procedures and sample composition consequences is available from the authors upon request.

Cutting the observations across rural and urban locations, we present the count of plants in each year, sector, and location in Panel A of Table 1. The observation counts are fairly similar over surveys for the organized sector, with a dip in 2000. The unorganized sector has significantly more data collected in 1994 and 2000 than in other years. Aggregating over all five surveys, 63% of our data observations for the organized sector come from urban locations, while 48% of the unorganized sector plants studied in our sample come from an urban location.

In Panel B, we apply sample weights to the plants surveyed by ASI and NSSO surveys. This panel reiterates and extends India’s urbanization story noted in Ghani et al. (2012)—over time, the organized sector in India is migrating to rural locations with the share of urban plants falling from 72% to 62% from 1989 to 2010.<sup>15</sup> In contrast, the unorganized sector is urbanizing, with the share of urban plants increasing from 21% to 42% during the same period. These sample weights also identify clear trends in the growth of each sector and demonstrate the much larger underlying plant count in the unorganized sector.

Panel C of Table 1 presents the output by sector and location for each of the years considered in our sample. These numbers suggest a compound annual growth rate in output of 5% from 19892010 for plants in the organized sector located in urban regions, vis-à-vis 8% growth rate for their rural counterparts. In contrast, in the unorganized sector, we note an average 10% per annum growth during 1989-2010 in output in the urban locations relative to 6% growth in rural regions. The diverging trends noted in plant counts with respect to urbanization are also present in output, with the urban output share for the formal sector declining from 68% to 54% during 1989 to 2011, while the corresponding figure for the informal sector rises from 46% to 66%.

> 14 Maximums thresholds for a plant are US$ 1 billion in output, US$ 1 million in output/worker, and 500,000 employees.

> 15 Ghani et al. (2012) also describe in greater detail the urban/rural classifications the India employs.

17
Figure 15.<sup>27</sup> Since access to contributory pensions is biased towards the better-off, the implementation of universal access to pensions leads to reductions in inequality.

Figure 15. Design of non-contributory pensions during the last decade and change in inequality during 2010-12 due to change on pensions income.

<!-- Start of picture text --> 0.2<br>0.05<br>-0.1<br>-0.25<br>Universal targeted towards universal targeted N.I<br>Annual Gini points<br>Uruguay Chile Brazil Panama Argentina El Salvador Ecuador Bolivia Honduras Costa Rica Colombia Peru Paraguay DR Mexico<br><!-- End of picture text --> Source: Authors’ calculation from SEDLAC (CEDLAS and the World Bank for Changes on inequality and Rofman (2013) for the information about designs of non contributory pensions programs. Note: N.I means no information available about contributory programs.

It is expected that pensions will continue reducing inequality as non-contributory programs and their benefits expand. However, the Latin American population is aging and the expansion needed to cover the growing elderly population puts into question the fiscal sustainability of non-contributory pensions in the mid-term (Bosch, Melguizo and Pages 2013). As a result, policies for social protection for the elderly should focus not only on non-contributory pensions, but also on strengthening labor market institutions and generating more formal employment to increase the number of workers contributing to the region’s pension systems ( Bosch, Melguizo and Pages 2013).

As countries in Latin America have increased their social spending over the last decade by means of expanding their public transfer programs (particularly conditional cash transfers and non-contributory pensions), the evidence suggests that such expansions may be reaching their limit in terms of inequality gains in the region. This is partially due to these programs reaching near universal coverage of the eligible populations in some countries (Mexico and Brazil). Thus, in the years to come, policy makers will need to rely on additional instruments for achieving redistribution, such as increasing the generosity or targeting of these programs.

> 27 Non-contributory pensions with relatively high benefits, designated as those where benefits exceed 30% of the average contributory pensions, are present in Argentina, Costa Rica, Uruguay, Chile, and Brazil (Rofman 2013).

23
where the thresholds are τ _i_ 1 < τ _i_ 2 < .... < τ _iK_ for the _K_ possible ordinal responses (with _K_ =6 in our case). In standard applications of subjective welfare data the thresholds are taken to be fixed across all _i_ while here we take them to be heterogeneous.

The standard subjective poverty measure designates a respondent to be “poor” if 1 _SWi_ < τ _i_ (<sup>_R_</sup> _i_ = 1). Using this approach we see in Table 1 that 7.5%, 25% and 17% of the samples in Tajikistan, Guatemala, and Tanzania respectively are deemed to be poor (postvignettes).

However, such measures lack concrete meaning in terms of living standards when the scales vary across respondents. With scale heterogeneity, the resulting subjective poverty measures need not be welfare consistent in that rankings across different respondents in terms of their<sup>_R_</sup> _i_<sup>’s need not accord with their rankings in terms of</sup> _SWi_ . Someone responding that<sup>_R_</sup> _i_ = 2 (say) could well have a lower value of _SWi_ than someone responding that<sup>_R_</sup> _i_ = 1. Plainly this could only be ruled out on _a priori_ grounds if the scales are constant.

The vignettes help address this problem by fixing a set of welfare-relevant characteristics so as to limit the variance in the underlying idiosyncratic scales. We assume that _SWi_ is a stable function of a vector of variables, _Z_ , so we can write _SWi_ = _SW_ ( _Zi_ ) . However, _Z_ need not be fully observable. The vignette characteristics are interpreted as a subset of the characteristics in _v o v_ the vector _Z_ . We can partition that vector as _Z_ = ( _Z_ , _Z_ ) where _Z_ is the vector of characteristics identified in the vignettes and _Z o_ are other characteristics. _Z v_ can take various values, denoted _Z vj_ where _j_ =1,..., _J_ , which define each of the _J_ vignettes, where in our case _J_ =4. The values taken by _SW_ ( _Z vj_ ,<sup>_Z_</sup> _o_ ) for vignette _j_ at any given _Z vj_ are treated as a random variable, the distribution of which reflects the respondent’s uncertainty about the omitted characteristics, _o Z_ .

We make two key identifying assumptions. First we assume internal consistency between vignette and own-welfare assessments. Specifically, it is assumed that each respondent uses the same subjective welfare function in assessing the welfare of the vignettes as for the respondent’s 9
# **Welfare and poverty impacts of India's National Rural Employment**

# **Guarantee Scheme: Evidence from Andhra Pradesh**

## Klaus Deininger<sup>1</sup> and Yanyan Liu<sup>2</sup>

1The World Bank; 2International Food Policy Research Institute

## JEL codes: O15, J48, Q15

## Keywords: India, employment guarantee, labor markets, governance, public investment

We thank Centre for Economic and Social Studies (CESS) staff, in particular Prof. S. Galab, Prof. M. Dev, and Dr. P. Reddy, for their support and collaboration in making the data available and Chris Barrett, John Blomquist, Arnab Basu, Nancy Chau, Brian Dillion, Puja Dutta, Rinku Murgai, and seminar participants at Cornell University for helpful comments on an earlier draft. We thank the World Bank Gender Trust Fund, the Bank Netherlands Partnership Program, and the International Initiative for Impact Evaluation for research support. The views expressed in this paper are those of the authors and do not necessarily represent those of the World Bank, its Board of Executive Directors, or the countries they represent.
Table 2: The evolution of the economy and demographic variables, D = 1.

|t|λ1,t|lt|c1,t|e<sup>p</sup><br>1,t|y1,t|λ2,t|kt|c2,t|st|e<sup>p</sup><br>2,t|e<sup>s</sup><br>2,t|y2,t|y<sup>′</sup><br>t|y<sup>′′</sup><br>t|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1|1.400|1.020|0.572|0.243|3.219|2.800|1.000|0.783|1.492|0.740|0.502|6.964|0.488|0.512|
|2|1.796|0.697|0.700|0.706|3.566|3.147|1.448|1.106|1.197|0.932|0.632|7.200|0.733|0.804|
|3|2.483|0.421|0.899|1.000|4.177|3.173|1.391|1.459|1.298|1.000|1.000|9.075|0.956|1.033|
|4|3.260|0.338|1.305|1.000|5.907|4.633|1.773|2.100|1.675|1.000|1.000|12.124|1.471|1.547|
|5|4.260|0.327|2.004|1.000|8.801|5.972|2.615|3.092|2.383|1.000|1.000|17.328|2.357|2.431|
|6|5.553|0.407|3.111|1.000|14.640|7.972|4.521|4.537|3.551|1.000|1.000|26.458|3.652|3.735|
|t|q<sup>c</sup><br>i,t|q<sup>a</sup><br>1,t|q<sup>a</sup><br>2,t|N <sup>c</sup><br>1,t|N <sup>c</sup><br>2,t|N <sup>o</sup><br>1,t|N <sup>o</sup><br>2,t|M <sup>a</sup><br>t|ξt|F1,t|F2,t|P1,t|P2,t|Pt|
|0|0.076|0.174|0.174||3.840||||0.423|4.51|0.65||||
|1|0.062|0.206|0.299|4.640|4.150|1.400|0.990|0.391|0.846|5.86|1.19|47.09|8.47|55.56|
|2|0.052|0.162|0.215|3.946|3.516|1.217|0.394|1.028|1.463|8.58|5.04|61.43|29.78|91.21|
|3|0.041|0.126|0.162|3.304|3.012|1.106|1.055|0.183|0.102|14.19|8.47|90.96|51.39|142.35|
|4|0.031|0.104|0.139|2.709|2.586|1.499|1.043|0.499|0.310|17.69|14.87|109.83|83.69|193.52|
|5|0.020|0.100|0.100|2.151|2.151|1.836|1.286|0.427|0.214|18.25|21.45|109.24|116.64|225.89|
|6|0.020|0.100|0.100|2.151|2.151|2.360|1.626|0.392|0.167|14.67|25.03|95.53|144.60|240.13|

> 1 The rural sector: A1,1 = 1.000, gA1,t = 0.222, λ1,0 = 1.150, ω = 0.800, c1,min = 0.500, ep1,min<sup>= 0.001,b1= 0.350,θ1= 0.400.</sup>

> 2 The urban sector: A2,1 = 2.000, gA2,t = 0.000, λ2,0 = 2.300, ω1 = 0.539, ω2 = 0.458, ω3 = 0.001, ω4 = 0.001, c2,min = 0.584, e<sup>p</sup> 2,min<sup>= e</sup> 2<sup>s</sup> ,min<sup>= 0.001,b2= 0.450,θ2= 0.400.</sup>

> † du = 0.500, αi = 0.667, γ = 0.40, δ = 0.20, β = 0.60, ρ = 0.60, φ = 0.75. yt′<sup>growsat1.98%perannum.Fi,t,Pi,tandPtin</sup> millions.
- Jayne, T., T. Yamano, J. Nyoro (2004): “Interlinked Credit and Farm Intensification: Evidence from Kenya.” Agricultural Economics 31(2-3): 209-18.

- Jha, D. and B. Hojjati (1993) “Fertilizer Use on Smallholder Farms in Eastern Province, Zambia.” Research Report 94. Washington: International Food Policy Research Institute.

Johnson, M., P. Hazell, and A. Gulati (2005): “The Role of Intermediate Factor Markets in Asia’s Green Revolution: Lessons for Africa?” American Journal of Agricultural Economics 85(5): 1211-1216.

- Key, N., E Sadoulet, and A. de Janvry (2000): “Transaction Costs and Agricultural Household Supply Response,” American Journal of Agricultural Economics 82(2): 245-259.

- Kimhi, A. (2006): “Plot Size and Maize Productivity in Zambia: Is There an Inverse Relationship?” Agricultural Economics 35(1): 1–9.

- Krugman, P. (1991)” “Increasing Returns and Economic Geography,” Journal of Political Economy 99(3): 485– 499.

Kyriazidou, E. (1997): "Estimation of a Panel Data Sample Selection Model," Econometrica 65(6): 1335-1364 Lamb, R. (2003): “Fertilizer Use, Risk, and Off-farm Labor Markets in the Semi-Arid Tropics of India,” American Journal of Agricultural Economics 85(2): 359-371.

- Larson, D.F., and F. Plessmann (2009): “Do Farmers Choose to be Inefficient? Evidence from Bicol,” Journal of Development Economics 90(1): 24-32.

- Larson, D.F., and M. León (2006):”How Endowments, Accumulations, and Choice Determine the Geography of Agricultural Productivity in Ecuador,” World Bank Economic Review 20(3): 449-71.

Larson, D.F., J.R. Anderson, and P. Varangis (2004): "Policies on Managing Risk in Agricultural Markets," World Bank Research Observer 19(2): 199-230.

- Larson, D.F., K. Otsuka, T. Matsumoto, and T. Kilic (2013): “Should African Rural Development Strategies Depend On Smallholder Farms? An Exploration of the Inverse-Productivity Hypothesis,” Agricultural Economics 45(1): 1-13.

- Lele, U.J., R.E. Christiansen, and K. Kadiresan (1989): Fertilizer Policy in Africa: Lessons from Development Programs and Adjustment Lending, 1970-87. Washington: World Bank.

- Lipton, M. (1976): “Agricultural Finance and Rural Credit in Poor Countries,” World Development 4(7): 543553.

Lipton, M. (2006): “Can Small Farmers Survive, Prosper, or be the Key Channel to Cut Mass Poverty?” The Electronic Journal of Agricultural and Development Economics 3(1): 58–85.

- Lipton, M., and M. Lipton (1993): “Creating Rural Livelihoods: Some Lessons for South Africa from Experience Elsewhere,” World Development 21(9): 1515-1548.

- Lucas, D.J. (1993): “Asset Pricing with Incomplete Markets,” Hitotsubashi Journal of Economics, 34 (Special Issue): 163-179.

- Mankiw, N.G. (1986): “The Equity Premium and the Concentration of Aggregate Shocks,” Journal of Financial Economics 17(1): 211-219.

- Marenya, P.P., and C.B. Barrett (2009): “State-conditional Fertilizer Yield Response on Western Kenyan Farms,” American Journal of Agricultural Economics 91(4): 991-1006.

- Marenya, P.P., and C.B. Barrett (2009):” Soil Quality and Fertilizer Use Rates among Smallholder Farmers in Western Kenya,” Agricultural Economics 40(5): 561-72.

25
# Table A.8: Components of Village-level Public Health Infrastructure

## **Panel A: Presence of health facilities**

||Sub<br>center<br>(1)|Primary<br>health center<br>(2)|Block primary<br>health center<br>(3)|Community<br>health center<br>(4)|Government<br>hospital<br>(5)|Government<br>dispensary<br>(6)|
|---|---|---|---|---|---|---|
|Fraction of female leaders|0.398|0.004|0.405***|0.161|0.200|0.731**|
||(0.372)|(0.175)|(0.133)|(0.106)|(0.124)|(0.368)|
|Observations|7,721|7,721|7,722|7,718|7,722|7,713|
|Outcome mean|0.550|0.233|0.061|0.069|0.057|0.114|
|**Panel B: Connection to**|**health facilities**||||||
||Sub|Primary|Block primary|Community|Government||
||centers|health centers|health centers|health centers|hospitals||
||(1)|(2)|(3)|(4)|(5)||
|Fraction of female leaders|0.207|0.633***|1.044***|1.189***|1.101***||
||(0.154)|(0.206)|(0.232)|(0.293)|(0.288)||
|Observations|7,726|7,726|7,702|7,725|7,725||
|Outcome mean|0.865|0.814|0.553|0.724|0.752||
|**Panel C: Health worker**|**s and public prog**|**rams**|||||
||Janani|Janani|Kishori|Balika|||
||Suraksha Yojana|Shishu Suraksha|Shakti Yojana|Samriddhi Yojana|||
||(1)|(2)|(3)|(4)|||
|Fraction of female leaders|0.183*|0.121|0.542*|0.387|||
||(0.101)|(0.265)|(0.295)|(0.274)|||
|Observations|7,726|7,726|7,725|7,725|||
|Outcome mean|0.923|0.631|0.495|0.420|||
|State fxed efects|x|x|x|x|x|x|
|District characteristics|x|x|x|x|x|x|

_Notes_ : Data comes from the 2012-13 District-level Household and Facility Survey (DLHS) of India. The columns present 2SLS estimates specified in equations **??** and **??** . We define close elections as those in which victory margin is less that 3 percent of votes. In Panel A, the dependent variables are indicators for the presence of each health facility. In Panel B, the dependent variables are indicators for whether a village is connected to each health facility with all-weather roads. In Panel C, the dependent variables are indicators for whether each public program has been implemented in the village. All regressions include district controls and state fixed effects. District-level controls include the share of female population, the share of urban population, and the share of SC/ST population in the district, and the female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

35
<!-- Start of picture text --> 2000<br>——— VSP (Asset Replacement)<br>1800 VSP (Insurance Subsidy)<br>3 1600<br>c<<br>£<br>J= 1400<br>7)<br>o<br>© 1200<br>2<br>2 |.<br>= 1000<br>E s o<br>2 800<br>3<br>5 600<br>2<br>5<br>x<br>it<br>®@ 400 lyfe<br>200<br>0<br>0 20 40 60 80 100<br>Time<br><!-- End of picture text -->
demands on health services. Data on health service outputs – measuring organizational performance – and health outcomes are needed to assess the effectiveness of the services, including their governance arrangements. Rather than reviewing the extensive work on inputs, demographics, outputs and outcomes that are addressed elsewhere, the rest of this note focuses on data that is specific to understanding governance, namely measures of (1) governance performance, (2) governance determinants, and (3) governance context.

# **_Measuring governance performance_**

Work on governance performance measures in health is much further advanced than on governance determinants. This is due, in large part, to the fact that governance performance can be measured against reasonably clear standards. Most governance performance indicators in public health care provision involve measuring the gap between expected and actual behaviors such as unexcused absences from work or diversion of funds from intended uses.

WHO 2008 provides a good list of governance performance measures for direct public health care provision:<sup>7</sup>

- Health worker absenteeism in public health facilities

- Proportion of government funds reaching district-level facilities

- Stock-out rates for essential drugs in health facilities

- Proportion of informal payments within the public health care system

- Proportion of pharmaceutical sales that consist of counterfeit drugs

These five indicators measure the gap between expected and actual performance of the public health care services and highlight critical aspects – human resource management, financial management, supply management, drug regulations and enforcement – that affect the outputs and impact of public health care services.

Another recent study, Lewis and Pettersson (2009), presents a longer and more comprehensive list of governance performance measures (See Table 1). For example, they not only propose to measure budget leakages, but also suggest separately reporting payroll leakages and in-kind leakages from financial leakages. Similarly, in addition to tracking absenteeism, they recommend collecting information on job purchasing and health worker performance.

> 7 A sixth measure is also proposed as a governance performance measure, namely, voice and accountability. As discussed earlier, voice and accountability are treated here as governance _determinants_ and not as governance _performance_ indicators.

______________________________________________________________________________________

-18-
clear and transparent framework on who takes the decision and assumes the cost is necessary.

The institutional structure of financial sector supervision can be an important factor as well. The recent trend towards consolidated supervision has been justified with the trend towards financial conglomeration across different segments of the financial system and the need to create an even regulatory playing field. Theory suggests that the separation of responsibility for monetary and financial stability and thus also for lenderof-last resort facilities and bank failure resolution might create stability-enhancing incentives (Kahn and Santos, 2005). Empirical analysis of these questions is still outstanding and previous conclusions on the ideal institutional structure might have been put in doubt by the different reactions to the recent crisis.

The contractual and informational framework can also plays an important role in interacting with the market structure and competition. Take the example of credit information sharing, which numerous studies have shown to be associated with better access to credit (Love and Mylenko, 2003 and Brown, Jappelli and Pagano, 2007), but also with better credit decisions by banks. For instance, Powell et al. (2004) use the actual data in the public Argentine credit registry to show that availability of system-wide registry information can substantially improve the precision of credit decisions even for a large bank. This has important positive repercussions for bank stability. Effective systems of credit information sharing have thus positive ramification for competition, lowering barriers to entry, and stability.

Another important issue for policy makers, though not covered in the previous sections, is foreign bank entry.<sup>9</sup> Claessens and Laeven (2004) show that foreign bank participation is an important dimension of competition in the banking system. Numerous studies have shown that foreign bank participation has contributed to rather than weakened financial sector stability, as often feared by policy makers in developing countries (see Cull and Martinez Peria, 2007, for a literature overview). Specifically, Cull and Martinez Peria (2007) show, using data on the share of banking sector assets held by foreign banks in over 100 developing countries during 1995-2002, that countries that experienced a banking crisis tended to have higher levels of foreign bank 9 Claessens (2006) reviews the effect of cross-border banking on bank competition.