rafmacalaba/datause-encoder-data huggingface dataset:
To facilitate estimation of the net gains to participants and to test the robustness of our results using the NSS, we will also use another household dataset collected in rural areas of the state of Bihar with support from the World Bank. Bihar is India’s poorest state by the government’s official poverty measures (Dutta et al., 2012a). Two rounds of survey data were collected for 3,000 randomly chosen households from 150 random villages spread across Bihar. The first round was between May and July of 2009 and the second during the same months one year later. A two-stage sampling design was followed, using the 2001 Census list of villages as the sampling frame. Data were collected through several survey instruments, including household surveys and individual surveys, the latter for one adult male and one adult female in each household. Dutta et al. (2012b, Chapter 3) contains a fuller description of the survey design.

In this paper we only report results for the first round of the Bihar survey, which is designed to be representative of rural Bihar. However, we did all our analysis for the second round as well and all qualitative findings reported for Round 1 were found to be robust. For comparison purposes we also provide results for the Bihar sub-sample of the NSS.

The World Bank’s Bihar Survey contains similar variables to the NSS data (including on consumption), but provides more detail on participation and other variables related to NREGS.<sup>9</sup> Importantly, the Bihar survey includes reported estimates of forgone work and earnings for NREGS participants. The Bihar Survey contains a block on time spent on casual work during the week ended, with total number of days and incomes for each activity (block 23). Thus we are able to estimate income gains (net of forgone earnings) from NREGS in Bihar, and impacts on poverty. Moreover, we know if household members had enough NREGS working days if they worked on NREGS, or if they did not want to work on the scheme, both during the year ended.

Another attraction of the Bihar Survey is that the questions asked allow us to plausibly identify households who are not rationed in the casual rural labor market. These are identified as the sub-sample who report that they do not want more work on the NREGS than what they have already. Since NREGS pays a higher wage rate (about 10% higher in Bihar) than the casual labor

> 9 One difference is that we do not have the split manual/non-manual work in the Bihar survey: we only know if the activity was or was not casual work. This implies higher participation rate in this type of work in the Bihar Survey (21.1%% in the NSS data on rural Bihar versus 39.8% in the Bihar Survey).

8
<!-- Start of picture text --> 86<br><!-- End of picture text --> <!-- Start of picture text --> 59<br><!-- End of picture text --> <!-- Start of picture text --> 51<br><!-- End of picture text --> <!-- Start of picture text --> 38<br><!-- End of picture text --> <!-- Start of picture text --> 31<br>29<br><!-- End of picture text --> <!-- Start of picture text --> 27<br><!-- End of picture text --> <!-- Start of picture text --> 19 20<br>10 12<br>5 5 6<br>NOT AT ALL 0 LITTLE EXTENT SOME EXTENT 1 LARGE EXTENT<br><!-- End of picture text --> <!-- Start of picture text --> Spouse Family Non-HH family Community<br><!-- End of picture text --> _Notes:_ (1) “Life events” include important events in one’s life related to one’s education, responsibilities, marriage, migration, and the child’s education, their marriage etc.

**Figures B.3(b) Reference groups for working outside the home for pay**

<!-- Start of picture text --> Females<br><!-- End of picture text --> <!-- Start of picture text --> 93<br><!-- End of picture text --> <!-- Start of picture text --> 53<br><!-- End of picture text --> <!-- Start of picture text --> 49<br><!-- End of picture text --> <!-- Start of picture text --> 38<br><!-- End of picture text --> <!-- Start of picture text --> 35<br><!-- End of picture text --> <!-- Start of picture text --> 32<br>23<br>19 19<br>13<br>8 7 6 4<br>NOT AT ALL 1 LITTLE EXTENT SOME EXTENT 1 LARGE EXTENT<br><!-- End of picture text --> <!-- Start of picture text --> Spouse Family Non-HH family Community<br><!-- End of picture text --> 61
Policy Research Working Paper 8609

# **Abstract**

One reason that poor people may not capture the full benefit from participation in international markets is that the goods they produce tend to be subject to relatively high trade barriers. This paper analyzes market access barriers faced by households in different income deciles by matching household survey data from India based on the industrial classification of their economic activity. Tariffs in international markets are higher, and nontariff measures more numerous, on goods produced by poor workers than on goods produced by rich workers. Tariffs faced by exporters are higher on goods produced in rural and more remote areas than on those in urban centers, on goods produced by informal enterprises than by formal ones, and on goods produced by women than by men. Furthermore, the global reduction in tariffs from 1996 to 2012 failed to ameliorate these differences. How did we get there? Efforts to protect poor workers across countries resulted in a coordination problem. Indeed, tariff protection in China and the United States is higher on goods produced by poor workers than on goods produced by rich workers. Therefore, if poor workers are employed in similar sectors, then each country’s attempts to protect its poor workers by imposing higher tariffs and more nontariff measures on such goods will reduce the access of all poor workers to international markets, and thus limit the gains from trade.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/research. The authors may be contacted at gnayyar@worldbank.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
# **5. References**

Bosetti, V., C. Carraro, M. Galeotti, E. Massetti, M. Tavoni (2006), WITCH — A World Induced Technical Change Hybrid Model Carrara, Samuel and Giacomo Marangoni (2017) Including system integration of variable renewable energies in a constant elasticity of substitution framework: The case of the WITCH model **,** _Energy Economics_ Volume 64, 612-626 Chen, C. and M. Tavoni (2013) Direct air capture of CO2 and climate stabilization: A model based assessment. _Climatic Change_ , 118(1), 59–72.

Clarke, L. Clarke, J. Edmonds, V. Krey, R. Richels, S. Rose, M. Tavoni (2009) S64–S81, 2009: International climate policy architectures: Overview of the EMF 22 International Scenarios. _Energy Economics_ , 31, S64–S81 <mark>Clarke, L., K. Jiang, K. Akimoto, M. Babiker, G. Blanford, K. Fisher-Vanden, J.-C. Hourcade, V. Krey, E. Kriegler, A. Löschel, D. McCollum, S. Paltsev, S. Rose, P.R. Shukla, M. Tavoni, B.C.C. van der Zwaan, and D.P. van Vuuren. (2014). Assessing transformation pathways. In:</mark> _<mark>Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change</mark>_ <mark>. Cambridge: Cambridge University Press.</mark> pp. 413–510 Creutzig, F. et al., 2017: The underestimated potential of solar energy to mitigate climate change. Nature Energy, 2(9), 17140, doi:10.1038/nenergy.2017.140.

Dai, H. S. Fujimori, D. Silva Herran, H. Shiraki, T. Masui, Y. Matsuoka (2016) The impacts on climate mitigation costs of considering curtailment and storage of variable renewable energy in a general equilibrium model, _Energy Economics_ , 64, pp. 627-637 De Boer, H.-S., D.P. Van Vuuren (2017) Representation of variable renewable energy sources in TIMER, an aggregated energy system simulation model, _Energy Economics_ Volume 64, 600-611.

Després, J., S. Mima, A. Kitous, P. Criqui, N. Hadjsaid, I. Noirot (2017) Storage as a flexibility option in power systems with high shares of variable renewable energy sources: a POLES-based analysis, _Energy Economics_ Volume 64, 638-650, Després, J., N. Hadjsaid, P. Criqui, I. Noirot (2015) Modelling the impacts of variable renewable sources on the power sector: reconsidering the typology of energy modelling tools, _Energy_ , 80, pp. 486-495, Dietz, Simon Frederick van der Ploeg, Armon Rezai and Frank Venmans (2020) Are economists getting climate dynamics right and does it matter? <u>https://www.frbsf.org/economicresearch/files/Simon_Dietz_Climate_dynamics_160520_to_share.pdf</u> 37
Table 8: Robustness - Other determinants of productivity

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Integrity Index - Average Politicians|0.240**|0.230**|0.231**|0.239**|0.216*|
||(0.103)|(0.106)|(0.107)|(0.106)|(0.109)|
|Management Perception Index - Avg. Bureaucrats|0.133|||0.141||
||(0.119)|||(0.117)||
|Ethnic Herfndahl (All)||0.0322||||
|||(0.117)||||
|Ethnic Herfndahl (Bureaucrat)|||0.0248|0.00377||
||||(0.145)|(0.136)||
|Ethnic Herfndahl (Politician)|||0.00696|0.0431||
||||(0.128)|(0.126)||
|Integrity Index - Average CAO/RDC|||||0.00433|
||||||(0.0930)|
|Integrity Index - Average Health Bur.|||||0.0921|
||||||(0.0890)|
|Poverty rate 2013|-2.045|-2.280|-2.298|-2.024|-2.251|
||(1.450)|(1.581)|(1.611)|(1.470)|(1.583)|
|Log Total Population|0.0485|0.0503|0.0508|0.0459|0.0705|
||(0.249)|(0.250)|(0.251)|(0.247)|(0.252)|
|Urban population share|-1.368|-1.160|-1.169|-1.386|-1.339|
||(1.708)|(1.729)|(1.750)|(1.756)|(1.735)|
|Average Score UCE - 2011|-0.0615|-0.0617|-0.0620|-0.0618|-0.0699|
||(0.0401)|(0.0428)|(0.0429)|(0.0411)|(0.0418)|
|Voter Attachment to National Party - 2011|-1.473|-1.483|-1.502|-1.462|-1.610|
||(1.095)|(1.107)|(1.168)|(1.160)|(1.123)|
|Average Herfndahl index - Councilor|0.00499|0.00522|0.00526|0.00636|0.00563|
||(0.0135)|(0.0147)|(0.0149)|(0.0146)|(0.0144)|
|Number of radios (HQ)|0.118***|0.111**|0.111**|0.119***|0.110**|
||(0.0410)|(0.0428)|(0.0430)|(0.0421)|(0.0427)|
|Observations|75|75|75|75|74|
|R<sup>2</sup>|0.578|0.565|0.565|0.579|0.572|

_Note:_ This table presents regressions using districts’ health outcomes as dependent variable and individual traits and districts’ characteristics as explanatory variables. Unreported controls include regional dummies.

59
# _Method Description_

Proxy means testing involves two steps. The first step is to estimate the household consumption model using the nationally representative household (or larger) survey, as expressed in Equation (1). Once this is done, the second step is to combine the predicted coefficients β̂ obtained from the first step with the variables xij in the smaller survey to generate the predicted consumption (i.e., estimating the term β̂<sup>′</sup> xij) for household _i_ in the smaller survey _._ In other words, proxy means testing employs the predicted coefficients β̂ that come from the larger consumption survey and the variables xij in the smaller survey to generate the predicted household consumption, which is subsequently utilized to generate poverty estimates.<sup>10</sup>

# _Examples and Remaining Challenges_

A major advantage of proxy means testing over wealth indexes is that the former offers an estimate of household consumption, while the latter an estimate of household wealth. As such, proxy means testing perhaps provides a better estimate of poverty rates (that are based on household consumption data). Still, proxy means testing tends to offer biased poverty estimates. Indeed, Brown, Ravallion, and van de Walle (2016) offer a recent assessment of the performance of various proxy-means testing methods using data for nine African countries. They find that standard proxy-means testing is useful with targeting but excludes many poor people. Furthermore, even with some methodological adjustments, there is room for improvement with proxy-means tests, particularly with identifying the poorest.

The intuition behind this result is rather straightforward: the household poverty status is based on the household consumption that consists of two terms on the right hand side of Equation (1),

> 10 For recent reviews of proxy mean tests, see Grosh _et al._ (2008), Coady _et al._ (2014), and Brown, Ravallion and van de Walle (2016).

12
<!-- Start of picture text --> ~N<br>c<br>s<br>=o<br>2<br>=<br>i=]<br>a<br>=><br>=Q<br>_<br><!-- End of picture text --> <!-- Start of picture text --> Ss<br>Oo<br>Cc<br>el<br>2—<br>or<br>rs<br>ro)<br>=<br>1990 1995 2000 2005 2010<br>year<br><!-- End of picture text -->
How did the different components of the formal system develop over the decade of the 70s? Table 2 shows the structure of rural financial intermediation in 1980/81, with the exception of the regional rural banks.1' The Primary Agricultural Credit Societies (PACs) are specialized rural credit institutions based in individual villages or groups of villages. They have the most developed network, with nearly 95,000 societies, and are present in nearly all villages of India. In 1981/82, 21 percent of cultivators borrowed from PACs (Rath, 1987). During the 1970s, many dormant or illiquid societies were dissolved or merged into larger societies, and therefore, the number of societies has declined by about<sup>42</sup> percent. The PACs mobilize little resources of their own and their credit outstanding exceeds their deposit mobilization by a factor of nearly ten. Instead, they borrow from central and state primary cooperative banks, which together have nearly 9,000 urban and semi-urban branches where they mobilize deposits. The state cooperative banks, in turn, borrow or refinance from NABARD to cover the need of the system in excess of its<sup>deposit</sup> mobilization in rural and urban areas. Because the PACs lend primarily for short-term purposes, their annual lending is about two-thirds of their total portfolio outstanding.<sup>The</sup> PAC system lost about Rs 170 million during the year, although these losses may not fully account for unrecoverable loans.

Land development banks are cooperative institutions that lend primarily for long-term purposes. In some states, the land development banks lend to farmers through _/_ **_'The_ regional rural banks, which lend primarily tothe poor, were** left **out of the analytical parts of the paper because time series data did not exist for a sufficiently long period. For an evaluation of poverty-oriented lending** in **India during the 1980's, see Pulley (1989).**

**10**
**Table 9 Macroeconomic and sectoral outcomes, 1975-80, 1980 84, and 1985-91**

||_Cnsis period_|Pie o<br>_}cdJust_|_m_ent period__<br>_|
|---|---|---|---|
|_Item_|<br>19/5-80|PhoseI(_(1980--84)_|Phase 11_(I1985-91)_|
|GDP grovvth (annual percent)|5.6|2.1I|5.0|
|Gross investment/GDP|28.8'|23.8|20.0|
|Incremental capital output ratio (year averages)'|..|10.8|4.2|
|<br>Total factor grovvth/procluctivity (annual percent)|..|0.3|1 .9'|
|Sectoral_GDPGrowth_(annual percent)||||
|<br>AgTiculture|2.6|2,8|3.5|
|Industry|..|2.1|5.0|
|<br>Manufacturing|7.6|3.7|5.3|
|Services||4.1|5.1|
|_External tradSe_||||
|Exportgrowth(annual percent)|0.5|-3.4|4.8|
|<br>Grovvthofvolume of manufacturing exports||||
|<br>(annual percent)|.4.1|-8.7|10.5'|
|<br>Nonoil imports/GDPr|22.5|18.0|**19.1**|
|<br>Domestic expor-ts/GDP|20.6|16.6|1 3.4|
|<br>Manufacturing imports/domestic supply|44.3d|26.8|19.1|
|Manufacturing exports/outpuxt|22.61|1 3.3|6.1|
|_Savintgs_<br>_and investrnent (percentofGDP)_||||
|<br> <br>Fixed investmnent|23.2'|21.6|19.4|
|Public|10.41|8.8|8.1|
|;Private|12.6'|12.8|1 1.3|
|_Financing_||||
|Central govemment savings|2.8'|-1I. I|-1 .5|
|Private sector savingS|1 8.2'|20.2|21.9|
|<br>Foreign savings (grants and net borrovving)y|9.0|1.4|4.6|

.. Not availbbkl.

a. 1977-79.

b, Average of incrernental caprtal output rabos for 1980-84, and 1 985-90 c. 1985-90.

c.l 1972.

e. Shares base on 1979 data f The data are average **d** ifferently to sharpen the changes, the firsn period refers to 1978-8 1, the second to 1 982-86, and the las **t** o 1 987-9 1. _Source_ Stbstiical Abstracts, Economic Surveys (various **Issues),** IMlF, and World Bank staff estimates 6 8
Figure 3 depicts the simulated inequality and income elasticities of poverty reduction if we would use the data from Bourguignon (2003). The patterns found are similar to the ones found in our analysis. There are two main differences, however. First, the estimated values of the elasticities seem to increase at a faster pace as poverty rates change. The estimated value of GEPR goes from approximately 1 to close to 4 when poverty rates are close to 50 and zero, respectively. Increases in the IEPR are more prominent.

To complement our results, we rerun Bourguignon (2003) specification using our data. Specifications are run via Ordinary Least Squares and Fixed Effects for $1.25 and $2 a day poverty lines. The results are presented in the appendix (tables A2-A5). We find similar results across all specifications. Income (Gini) growth has a negative (positive) effect on poverty growth. We also find that both IEPR and (absolute) GEPR are decreasing with poverty, albeit the slopes of the estimated elasticities are more modest. A second difference is that under log normality the IEPR is defined to be negative for all poverty rates above 50 percent. In our results we find that this is not the case for a non-negligible range of poverty rates: 50 – 80 percent. While we recognize that our estimations are not intended to be a formal test of the log normality assumption, it is worth pointing out that such an assumption may be too restrictive in the analysis of elasticities of poverty.

# **6. Conclusions**

Growth has been the main driver of poverty reduction in the past three decades. Will growth continue to deliver poverty reduction in the future as it has in the past? Our results indicate that the impact of growth in reducing poverty will continue to be important and likely to increase.

However, relative to the role of inequality reduction, that of growth may be diminished in the future. Our results indicate that as poverty declines, the inequality elasticity of poverty reduction increases faster than the growth elasticity. That is, the IEPR/GEPR ratio increases with lower levels of poverty.

14
account at the post office increase if posts serve as cash merchants for remittances or government payments. Notably, posts serving as cash merchants for remittances moreover increases the logoffs of having an account at a financial institution only compared to none albeit to a smaller degree. While this may at first seem counterintuitive, remittances need both a sender and receiver. If remittances can be received at other financial institutions, posts providing remittance services can increase the log-odds of having an account at only a financial institution as well. When we only consider the sample of developing countries (Panel B), we find that there is actually no statistically significant difference between the increase of the log-odds ratios for post office accounts only and financial institutions accounts only compared to none for remittances and government payments.

Finally, we control for the size of the postal network. As expected, the log-odds of having an account at the post office only relative to having no account increase as the size of the postal branch network increases relative to the sum of post and financial institutions access points. At the same time, the log-odds of having an account at a financial institution only versus not having an account decrease. When we consider the number of post office branches per 1,000 inhabitants, we find that the log-odds of having an account at the post office increase compared to none. This time, however, we do not measure a statistically significant impact on the relative log-odds of having an account at a financial institution only.

# **5. Conclusion**

Using data from the Global Financial Inclusion Indicators (Global Findex) database, which collects data on account ownership at post offices for 60 countries where postal accounts are offered, this paper documents and analyzes account ownership patterns at post offices in 14
internet access.<sup>5</sup> The average delegation has a 35 percent (male) internet access rate; the aggregate nation-wide rate is 39 percent because more densely populated urban areas tend to be more connected. Figure 1b shows how geographically heterogeneous internet access is; metropolitan areas closer to the coast have high access rates, while these tend to be low in the interior of Tunisia and in delegations abutting the Algerian border.

To match census data with our Daesh recruit data, we aggregate the census data to a level below the delegation, i.e. the cell level. Recall that a cell is characterized by the delegation _d_ , education level _e_ (primary or below, secondary, and tertiary and higher), and a five-year age group _a_ (starting from at 15-19 through 60-64). We construct demographic, unemployment, and internet and media usage variables for each cell by taking the mean among men in that cell. Our data set thus consists of 7890 observations, but the population in 12 of the _{d, e, a}_ cells is equal to zero, so they are excluded them from the estimation sample, which eventually consists of 7878 cell-level observations. Definitions and sources of the variables used in our analyses are provided in Appendix Table A1 and summary statistics in Table 1.

**Instrumental variables** As an instrumental variable for internet access, we use the average lightning strike intensity per square km per year over the period 1995-2010 obtained from Manacorda and Tesei (2020). They use data collected by the U.S. National Aeronautics and Space Administration (NASA) and processed by the Global Hydrology Resource Center (GHRC). Figure 1c shows the spatial distribution of lightning strikes. The map shows a high degree of spatial correlation, underscoring the need to accommodate this feature in the econometric analysis. Lightning strikes are least prevalent in the south, which is covered by the Sahara desert, and most prevalent in the Chaambi mountains close to the Algerian border and in the Cape Bon peninsula located in the North East. A comparison of the spatial variations in lightning (Figure 1c) and in internet access (Figure 1b) points to a high correlation.

**Other data** We complement census data with delegation-level nighttime light data (Li et al., 2020) for the 1995-2014 period, which we use as a proxy for income. Information on

> 5We only consider metrics for the male population where possible, since the sample of recruits comprises men only. In practice, this distinction makes little difference to our results. The correlation between male internet access rate and overall internet access rate is _ρ_ = _._ 94.

9