rafmacalaba/datause-encoder-data huggingface dataset:
26 TABLE 3

||Summary of Results of<br>To Outp|Studies Relating Expor<br>ut Growth (Group B)|ts Growth|
|---|---|---|---|
|Study|Sam le/Period|Argued Improvement|Estimated Equation|
|Tyler<br>(1981)|-55 middle income<br>LDCs. Eliminating<br>those with income per<br>capita (Y/N) < 300.<br>-Period: 1960-77|-Less restrictive<br>sample of countries.<br>No selection bias<br>with respect to<br>Balassa's studies.|-<br>P<br>+pft<br>+p2t**+**<br>1<br>+_P3X_|
|Feder<br>(1983)|-2 samples of LDCs<br>1) 19 middle income<br>countries<br>2) 31 middle income<br>**+**low income<br>countries|-Distinguishes<br>theor-<br>etically and empiric-<br>ally between: factor<br>productivity differ-<br>ential and external-<br>ity effects of EP<br>policies in a 2-secto<br>model (export and non-<br>export sectors).|2-<br>p1(I/Y) +p2t+<br> <br>+<br>X* X/Y<br>(1)<br>Y-<br>71<sup>(I/Y) +- </sup>2<sup>t+</sup><br>r +<br>X* X/Y +<br> <br>+ 4k<br>(2)|
|Kavouss<br>(1984)|i-73 low andmiddle<br>income LDCs, charac-<br>terized by whether|-Examine whether the<br>positive correlation<br>between X andt|Y-<br>0<br>+ft+ 02+<br> <br>1<br>2<br>+P3<br>(1)|
||(Y/N)60$$360 and<br>excluding oil<br>exporters.<br>-Period 1960-78.|also holds for low<br>income countries.|Y- a +at<br>_+_t+<br>+ a3X + a4km<br>(2)<br>(Xm- share of manu-<br>factured Xs on<br>total exports).|
|Ram<br>(1985)|-73 LDCs, low and<br>middle income dist-<br>inguished by<br>(Y/N)77<br>g$300<br>-2 subper'.ods:<br>1960-<br>70, 1970-77.|-Avoid selection<br>bias.<br>-Test for absence of<br>simultaneity<br>bias.|0-+pii/Y+0t+<br>2<br>+ 3X + P4D (low<br>income dummy).|
Policy Research Working Paper 10890

# **Abstract**

Poverty maps are a useful tool for targeting social programs on areas with high concentrations of poverty. However, a static focus on poverty ignores its temporal dimension. Thus, current nonpoor households still face substantial welfare volatility and are at risk of becoming poor in the face of shocks. This paper combines the methods of poverty mapping and vulnerability estimation to create highly disaggregated vulnerability maps. The maps include predictions of the share of chronically poor households (poverty-induced vulnerability)—the focus of traditional poverty maps—and the share of households showing a significant probability of falling into poverty (risk-induced vulnerability). As an application of the method, the paper estimates a vulnerability map for Senegal that provides quotas for the expansion of the social registry. Accounting for the poor and the population at risk of poverty implies, in practice, the expansion of coverage into urban and peri-urban areas that tend to experience lower poverty rates. The inclusion of nonpoor households also serves as a first step toward supporting a dynamic social registry.

This paper is a product of the Poverty and Equity 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/prwp. The authors may be contacted at obarriga@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
18 countries left the system largely intact after 1945. Japan occupied Korea between 1910 and 1945, and while it was not until the early 1960s that SHI began to take off in Korea, the influence of Japan’s system is much in evidence (Anderson 1989; Bong-Min 1996). By contrast, political leaders in the English-speaking countries pushed for tax-finance early after World War II, though universal tax-financed health care did not immediately become a reality except in the UK where it was adopted in 1948. The relevance of the three instruments is assessed through LM and Wald versions of under-identification and weak identification tests based on the _rk_ statistic recently proposed by Kleibergen and Paap (2006) which account for non-iid structures of the error terms in the estimated equations. Cluster-robust versions of Hansen’s _J_ -statistic tests are used to check the exogeneity of the instruments in the estimated models.

# **4. Data**

The empirical analysis uses a SHI indicator constructed from various sources, and annual data from OECD Health Data for the period 1960-2003.<sup>11</sup>

## _4.1 Social health insurance status_

_SHIit_ is defined as taking a value of one if in country _i_ at time _t_ if the cell in Figure 1 is shaded gray. The SHI status dummy is equal to 1 in about half (716 observations) of the 1,363 country-year combinations for which there are non-missing values of the indicator.

For the most part, the classifications in Figure 1 are innocuous. However, there are some ambiguities worth mentioning. In group (i), there are several ambiguities. Germany was two countries from 1945-89, and operated two different health systems; no allowance is made for this

> 11 The GDP deflator was taken from the World Bank’s World Development Indicators database.
# **4 Empirical Strategy**

Our primary outcomes of interest are three measures of consumption: log of monthly per capita consumption expenditure: total, food, and non-food. We also show results on an indicator variable for whether the household is poor (headcount ratio). We focus on two measures of weather anomalies in 2018 growing season defined at the cluster level: NDVI, and land surface temperature z-scores, as measures of drought conditions. We also present the main result with rainfall z-scores with the caveat that it is not an ideal measure of drought in Afghanistan (details in section 5).

The relationship between consumption and weather may be nonlinear: while high temperatures may result in crop loss, low-temperature realizations may also adversely affect the harvest. To allow for flexibility in the relationship between consumption and weather anomalies, we use a semiparametric partially linear model, in which we assume a flexible functional form on weather anomalies, whereas the other regressors (controls) enter linearly: _Yicdp_ is the outcome variable for household _i_ in cluster _c_ in-district _d_ of province _p_ in 2019-20 based on past 12 month consumption data (see Appendix B for details). We add district dummies and include several explanatory variables (explained below). _zcdpg,_ 2018 is the weather anomaly constructed as described in Section 3.5. We do not assume a functional form on _f_ ( _._ ). We use Yatchew (1998)’s method to fit a partially linear model. The data is sorted on _z_ ( _weather_ ) _cdpg,_ 2018, and a first difference model from equation 1 is estimated.<sup>21</sup> Under the assumptions that _f_ ( _._ ) is smooth, single-valued with bounded first derivative and _z_ ( _weather_ ) _cdpg,_ 2018 has a compact support, _plim_ ∆ _f_ ( _._ ) = 0 as the sample size increases. In this case, parameters _γ_ 1 and _γ_ 2 can be consistently estimated with OLS, withˆ out explicitly modeling _f_ ( _._ ). Finally, _f_ ( _._ ) is estimated from regressing ( _Yicdp − γ_ 1 _X_ 1 _icdp − γ_ ˆ2 _X_ 2 _cdp_ ) on _z_ ( _weather_ ) _cdpg,_ 2018 nonparametrically.<sup>22</sup>

> 21In a cross section, the first difference here eliminates the observation with the lowest value of z-score from the estimation.

> 22We implement this partial linear model using the plreg command in STATA developed in Lokshin (2006).

12
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anything, retrospective data tend to underestimate the average years of preschool (by around 0.15 years) and to slightly overestimate the probability of ever having attended preschool (by 0.05). Trends across cohorts though are remarkably similar in the two data sets, showing the same increase over time.

In sum, consistent with the evidence from administrative data, the ECH data confirms a strong delay in school progression among urban children and teenagers and a substantial school drop out before completion of compulsory schooling. We find evidence of a rise in preschool attendance across cohorts, and we show that this rise is not a statistical artifact of the data due to recall error. The timing of this increase is also remarkably consistent with the implementation of the preschool reform. We finally find that, in the face of a generalized upward trend in preschool attendance, a faster rise took place among children from more disadvantaged backgrounds (proxied by those whose mother has at most compulsory education).

# **3. Specification and identification**

In this section we present our empirical strategy to estimate the impact of preschool exposure on later school outcomes. Our objective is to devise a strategy that controls for potential spurious correlation between the treatment and the outcome variables.

In the next section we start by regressing school outcomes of child _i_ of age _a_ in household _j_ at time _t_ ( _Yiajt_ ) on a dummy variable ( _PSi_ ) for whether child _i_ attended at least one year of preschool, unrestricted age and cohort dummies and interactions of the two. The model essentially identifies the effect of preschool education by comparing the school trajectories of children and teenagers cohort is then E(PS)= 3*P(Y=3)+2*P(Y=2)+P(Y=1)= 3*P(A=3)+2*[P(A=4)-P(A=3)]+[P(A=5)-P(A=4)]=P(A=3)+ P(A=4)+P(A=5).

11
Tis paper examines whether Tamil Nadu’s PVP program induces women’s participation within the _gram sabha_ . We use _text-as-data_ methods on a matched sample of transcripts from village assembly meetings to examine the effects of the program on political speech at these meetings. We find that PVP significantly increases women’s participation in the _gram sabha_ along numerous dimensions — meeting atendance, propensity to speak, and the length of floor time they enjoy. Our estimates show that the PVP program nearly doubles the number women who come to the _gram sabha_ , and boosts their frequency of speech by nearly 45 percent. Tis is not to say the results are all positive; we find that women in PVP villages are no more likely than women in control villages to drive the broader conversational agenda or elicit a relevant response from government officials. Nevertheless, these substantively significant gains suggest that policy interventions can have a positive impact on what has ofen been thought of as something beyond the reach of small-scale interventions: shaping social norms around gender.

Tis analysis represents one of the first quantitative analyses of self-help groups that measures objective outcomes rather than self-reports. In doing so, it not only contributes to the small but growing body of work on the political impact of self-help groups, which has qualitatively shown largely positive results (Sanyal et al., 2015; Sanyal, 2014; Desai and Joshi, 2014), but also provides a more rigorous foundation for conclusions drawn from studies based on self-reports of empowerment like Prillaman (2016) in Madhya Pradesh. In Tamil Nadu, Khanna et al. (2015) use household survey data from the same villages as this paper and find very similar results: women’s participation in SHGs enhances their intra-household bargaining power and their capacity to participate in the public sphere; but, just as in Prillaman (2016), these results are based not on direct field observations, but on outcomes reported by respondents. As such, we might be concerned that responses are biased by project rhetoric that have imbibed, rather than actual political behavior. To overcome this challenge, we _directly_ examine the effect of PVP on women’s participation using our village assembly rosters and transcripts. Indeed, in Khanna et al. (2015), which uses survey evidence from the same villages studied here, women’s self-reported atendance at the _gram sabha_ is higher than the direct measures collected here, both in control and 4
# **Appendix A: Additional Tables and Figures**

**Table A1: Impacts of mental health on labor outcomes – Full estimation results**

|Dependent variable:|Emplo|yment status|Weekly<br>($1,|labor income<br>000 AUD)|
|---|---|---|---|---|
||Logit-FE<br>(1)|IV-Logit-FE<br>(2)|FE<br>(3)|IV-FE<br>(4)|
|Standardized mental health scores|-0.046***|-0.064***|-0.005***|-0.192***|
||(0.009)|(0.009)|(0.001)|(0.055)|
|_Age groups (Ref: 18-22)_|||||
|23-27|0.221***|0.058|0.012***|0.012|
||(0.074)|(0.646)|(0.004)|(0.018)|
|28-32|0.592***|0.159|0.038***|0.023|
||(0.061)|(0.914)|(0.006)|(0.024)|
|33-37|0.716***|0.251|0.075***|0.053*|
||(0.066)|(0.998)|(0.008)|(0.029)|
|38-42|0.733***|0.312|0.108***|0.061|
||(0.071)|(1.002)|(0.011)|(0.039)|
|43-47|0.737***|0.359|0.131***|0.101**|
||(0.073)|(1.008)|(0.011)|(0.042)|
|48-52|0.739***|0.425|0.158***|0.151***|
||(0.074)|(2.643)|(0.013)|(0.051)|
|53 and older|0.739***|0.494|0.177***|0.175***|
||(0.074)|(3.407)|(0.015)|(0.063)|
|Married|0.034|0.042|0.010**|-0.009|
||(0.023)|(0.026)|(0.004)|(0.016)|
|Household size|-0.003|-0.004|0.004***|0.004*|
||(0.004)|(0.004)|(0.001)|(0.002)|
|English - listening|0.032*|0.043**|0.007***|0.011|
||(0.017)|(0.018)|(0.002)|(0.008)|
|English - speaking|0.056***|0.075***|0.006**|0.010|
||(0.019)|(0.019)|(0.002)|(0.010)|
|English - reading|0.003|0.007|-0.003|0.013|
||(0.018)|(0.019)|(0.002)|(0.011)|
|English - writing|0.023|0.029|0.001|0.002|
||(0.017)|(0.019)|(0.002)|(0.010)|
|Homeowner|0.061|0.083*|0.019***|0.014|
||(0.041)|(0.044)|(0.005)|(0.014)|
|Residence area|0.058*|0.077**|-0.001|-0.009|
||(0.033)|(0.036)|(0.007)|(0.022)|
|_First stage of 2SLS (dependent variable_|_is Standardi_|_zed mental health s_|_cores)_||
|Trauma experience*Year after arrival||-0.039***||-0.058***|
|||(0.009)||(0.016)|
|Kleibergen-Paap F stat.||17.692||19.635|
|<br>AR 95-CIs||[-0.111, -0.031]||[-0.400, -0.124]|
|Adjusted_p_-value|0.000|<br>0.000|0.001|<br>0.011|
|Dep. Mean|0.455|0.455|0.716|0.716|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|

32
- 88 -

22. Six of the projects contained covenants relating to natural gas prices. In Hungary and China gas utilization studies were to be carried out to look at many issues including prices. In India the producer price of gas was to be reviewed to ensure that financial targets were met. A specific price increase was mandated for Pakistan producer prices of associated gas. In Yugoslavia domestic gas prices would be increased in two steps to equal import parity, and in Bangladesh both specific percentage increases in average gas prices and a commitment to base the future price structure on LRMC were agreed. With the exception of the India covenant, all of these are based on efficiency price considerations.

# C. TME HAJOR GAPS

23. Efficiency pricing is clearly relevant to the oil and gas sector and is the subject of much attention both in EGY publications and in the SARs. Definitional issues have been identified in research papers and specific procedures developed to deal with them. The research has been applied in case studies both for natural gas (Pakistan and Bangladesh on LRMC estimation and tariff structure design) and for petroleum products (at least six studies firanced under Bank loans and closely monitored by EGY policy staff). While some of this work has been disseminated through the Departmental Energy Paper series, written guidelines are not yet available on either gas or petroleum product pricing.

24. On the basis of our sample, the areas that such guidelines should cover include, for natural gas:

- the rationale for and calculation of the depletion premium;

- - the calculation of the LRMC for exploration, production, storage, transmission and distribution;

- appropriate fiscal objectives for the sector; and

- the design of tariff structures taking into account LRMCs, fiscal objectives and the depletion premium.

For petroleum products the pricing issues that are still causing problems are:

- appropriate fiscal objectives and tax mechanisms; and

- the basis for setting transfer prices (producer and ex-refinery).

25. Overall, this sector has come a long way toward developing a consistent and widely followed approach to pricing issues. It hap made excellent use of pricing studies financed under its projects, and it xecoumends specific price changes through project covenants more often than most sectors. In the borrowing countries, more work remains to be done on the gas side, both in raising tariff levels toward efficiency levels and in refining tariff structures to reflect LRMCs. In oil most countries already use the sector to raise revenue, but more attention needs to be paid to the methods by which that revenue is raised (e.g., taxes or distorted transfer prices) as well as to the appropriate level of taxation, given the availability (or lack) of other fiscal inst.ruments.
where and 2 denote the mean and variance of the Gini coefficient growth, respectively, and hμμ and ggGGh h respectively symbolize the lower and upper bounds of the Gini coefficients per σσggGGh country typology—in logarithmic terms. The coefficients h and h can be proxied by the first vv bb and largest order statistics of recorded Gini coefficients, respectively. In addition, note that the vv bb thresholds h and h and the random behavior of the growth rates of the Gini coefficient, , vary by country typology h.<sup>20,21</sup> cc,h,tt vv bb uu Our econometric considerations do not model an explicit association between growth in the Gini coefficient and growth in the mean income. The degree of association of the Gini coefficient growth rate with the average annual growth rate of per capita consumption, GDP per capita, and mean income—measured through Pearson’s correlation coefficients—is almost null across country classifications.<sup>22</sup> Thus, we argue that there is not enough information yet to provide evidence of covariance between growth in the Gini coefficients and growth in the mean income.

> 20 One moderate alternative of the modeling of Equation (5) would not let reach the thresholds h and h. Thus, Equation (5) could be estimated following this specification: cc,h,tt iff h h] ln GG vv bb . = �<sup>uucc,h,tt</sup> 0 otherwise 21 cc,h,tt−1 cc,h,tt GG One conservative alternative to Equation (6) involves modeling the rate of change in the Gini coefficient following cc,h,tt ln GG + uu ∈[vv , bb gg a truncated normal distribution: 2 0 1 , where, represents a univariate truncated � normal distribution with mean cc,h,tt, variance gg2GGh  , and lower and upper bounds ggGGh 0, and 0, respectively. 22 We account for four important results regarding Pearson’s correlation coefficients pooling our original 1980–2014 ggGGh  uu ~TTNN�μμ ggGGh , σσ , ππ , ππ TTNN(∙) μμ σσ ππ ππ observations by the studied country classifications. First, the significant correlation coefficients of the average growth rate of the Gini coefficient and the average growth rate of per capita consumption vary between -0.077 and -0.048 across the studied country categories. Second, in the case of the growth rate of the Gini coefficient and the growth rate of GDP per capita, all the correlation coefficients are found nonsignificant across the country classifications whereas they vary close to zero in magnitude: from -0.68 to 0.067. Third, few significant correlation coefficients between the Gini coefficient growth rate and the growth rate of the mean income are found across the country typologies: 0.14 pooling all country observations, 0.181 for NROC economies, 0.16 for MOGCs, and 0.4 for LOGCs. Fourth, all the correlation coefficients of the growth rate of the Gini coefficient and the growth rate of the mean income become almost null and/or statistically nonsignificant across the study typologies when we complete our 1980–2014 panel data set using per capita consumption growth rates—or GDP per capita growth rates in the alternative case.

Page **12** of **45**
Table 1: Baseline Population and Expenditure Statistics, in US$ at PPP

|Country|Population|Number of<br>households|Total<br>expenditures|Per capita<br>expenditures|Household<br>expenditures|
|---|---|---|---|---|---|
|Djibouti|939,000|166,966|1,856,869,376|1,977|11,121|
|Iran, Islamic<br>Rep.|77,969,000|21,909 116|582,976,929,792|7,477|26,609|
|Libya|6,213,000|991,549|1, 318,968,832|1,983|12,424|
|Morocco|33,179,000|7,070,798|138,34, 810,176|4,170|19,565|
|Tunisia|11,060,000|2,548,655|43,800,788,992|3,960|17,186|
|Total|129,360,000|10,777,968|628,634,588,160|3,913|17,381|

_Source:_ World Bank estimations from Household Budget Surveys.

_Note:_ PPP = purchasing power parity. Data on household expenditure per capita can be very different from data on GDP per capita and the cross-country ranking made according to these two criteria can be quite different. This is mostly explained by the fact that total household expenditure represents different shares of GDP across countries.

# Baseline Prices and Subsidies

As a reference period for the analysis, we use the very early part of 2014 when oil prices and subsidies peaked at their highest levels. A major wave of subsidy reforms occurred in the MENA region in 2014 but this paper focuses on the extraordinary situation faced by MENA countries before the reforms. We are interested in the prices and subsidies existing in the MENA countries just before the reforms.

Table 2 shows the baseline prices and unit subsidies for energy products. For LPG, prices are the lowest for Libya and the Islamic Republic of Iran in that order and the highest for Djibouti. The highest shares of subsidies as a percentage of the free market price are in Libya and the Islamic Republic of Iran, the two oil-producing countries, with Libya’s LPG subsidies reaching 90.4 percent of the full price. The percentage price increases that would be necessary to eliminate subsidies on LPG are remarkable. In Libya the price would have to be increased by 947 percent to eliminate subsidies and in the Islamic Republic of Iran by 500 percent.

It is interesting to see that in Djibouti, the poorest of the countries considered, the price of LPG is 15 times the price in the Islamic Republic of Iran, the richest country considered. This divergence is also striking because LPG is a product that is typically consumed by the poor and it 5