rafmacalaba/datause-encoder-data huggingface dataset:
-83-

# VIII. Conclusions.

# VIIILLl SWmar of Result,.

# I summarize very schematically the key points of the paper:

1. A distinction between moderate and extreme-poverty is required. The moderately-poor lack some goods and services that given the country's wealth everybody should enjoy. The extremely-poor have such low resources as to be at risk of undernutrition, with higher morbidity and potential anthropometric deficiencies.

2. Undernutrition and m.lnutrition should be separated. There are significant difficulties in the measurement of undernutrition. Most estimates of undernutrition in Mexico are based on the comparison between exogenously given intakes and prescribed requirements. These estimates are probably flawed and overestimate the extent of the problem.

3. Indicatois of poverty should incorporate concerns about its severity and distribution; the head-count ratio fails to do this, as do other 'indices of marginalization' computed by government agencies and currently used to identify the poor. The Foster-Greer-Thorbecke poverty index P(a,z) satisfies axioms with respect to severity and distribution of poverty, can be separably decomposed, allows measurement of the contribution of each region to total poverty, can serve to rank regions for delivery of benefits, and can be used to monitor progress in poverty alleviation. The index takes as exogenous the poverty line z and a parameter a that measures societies' concern for the poorest of the poor. A distinction was made between a line of extremepoverty, 1, and a line of moderate-poverty, z. The monetary cost of each was estimated.

4. Based on the 1984 Income-Expenditure Survey I find that: (i) at most 19X of the population is extremely-poor, although it is probably the case that this is an over-estimate, (ii) not only is extreme-poverty mostly a rural problem, but the poorest of the extremely-poor are rural, (iii) the extvemely-poor have larger household sizes, more children and the highest dependency ratios, (iv) not even the extremely-poor allocate more than 60X of total monetary expenditures to food.
Table 8a: Extensions of Table 7 with alternative dependent variables for manufacturing, 2000-2005

||Indicator variable<br>for increase in<br>unorg. sector's<br>employment share|Indicator variable<br>for increase in<br>unorg. sector's<br>employment level|Change in unorg.<br>sector's employment<br>share relative to<br>average values|<br>Change in unorg.<br>sector's employment<br>level relative to<br>average values|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Employment growth in organized sector for state-industry|-0.199+++|0.022|-0.377+++|0.091|
|across the time period relative to starting total employment|(0.049)|(0.051)|(0.101)|(0.099)|
|Indicator for employment growth in organized sector being|-0.047|0.024|-0.027|-0.047|
|greatest in small establishments (39 or fewer employees)|(0.058)|(0.061)|(0.056)|(0.090)|
|Indicator for employment growth in organized sector being|-0.219+++|-0.085|-0.119++|-0.135+|
|greatest in large establishments (100 or more employees)|(0.052)|(0.054)|(0.050)|(0.076)|
|Indicator for entrepreneurship level in organized sector in|-0.075+|-0.105++|-0.096+|-0.174++|
|state-industry at the start of period being above median|(0.044)|(0.046)|(0.050)|(0.071)|
|Observations|563|563|563|563|
|Adjusted R-squared|0.178|0.092|0.184|0.127|
|State and industry fixed effects|Yes|Yes|Yes|Yes|

Notes: See Table 7.
The earnings ratio of non-teachers to teachers increased slightly from 1.1 to 1.3. More of interest is the negative correlation between the pay-off of non-teaching job and the share of college graduates in teaching (Figure 7). The correlation is -0.83, and highly significant ( _P_ =0.01).

**Figure 6: Trend of relative earnings and share of college graduates in**

**teaching, 2001-2008**

<!-- Start of picture text -->
1.7 0.28<br>0.26<br>1.5<br>0.24<br>1.3 0.22<br>0.2<br>1.1<br>0.18<br>0.9 0.16<br>0.14<br>0.7<br>0.12<br>0.5 0.1<br>2001 2002 2003 2004 2005 2006 2007 2008<br>Wnt/Wt % of college graduates in teaching<br>Wnt/Wt teaching<br>% college graduates in<br><!-- End of picture text -->

_Source: SAKERNAS_ .

**Figure 7: Relative earnings vs. share of college graduates in teaching, 2001-2008**

<!-- Start of picture text --> 0.28<br>0.26<br>0.24<br>0.22<br>0.2<br>0.18<br>0.16<br>0.14<br>0.12<br>0.1<br>1 1.1 1.2 1.3 1.4 1.5 1.6<br>Wnt/Wt<br>% of college grad in teaching<br><!-- End of picture text --> 18
Table 8: Effect on Candidate Choice Using Cross Reports from Family Members

||Treated<br>clusters vs<br>control<br>clusters<br>TN-CN<br>(1)|Treated<br>households<br>only vs<br>control<br>clusters<br>T-CN<br>(2)|Control<br>households<br>in treated<br>clusters vs<br>control<br>clusters<br>CTN-CN<br>(3)|
|---|---|---|---|
|Panel A: Treatment||||
|Treatment (T)|-0.007|-0.015|0.024|
||(0.023)|(0.025)|(0.025)|
|Man reporting about woman|-0.012|-0.010|-0.021|
||(0.019)|(0.019)|(0.020)|
|Man reporting x T|-0.085***|-0.082***|-0.104*|
||(0.027)|(0.028)|(0.054)|
|R-Squared|0.065|0.069|0.125|
|Panel B: T1vs T2||||
|Importance of voting (T1)|-0.016|-0.026|0.012|
||(0.026)|(0.028)|(0.025)|
|Importance of voting and secret balloting (T2)|0.002|-0.003|0.024|
||(0.027)|(0.030)|(0.029)|
|Man reporting about woman|-0.012|-0.010|-0.021|
||(0.019)|(0.019)|(0.020)|
|Man reporting x T1|-0.064*|-0.072*|-0.025|
||(0.037)|(0.039)|(0.040)|
|Man reporting x T2|-0.107***|-0.093**|-0.189**|
||(0.032)|(0.035)|(0.084)|
|R-squared|0.064|0.070|0.126|
|N. Observations|3,713|3,200|914|
|Mean dependent variable among CN|0.983|0.983|0.983|
|_P-value_(T1=T2)|0.475|0.431|0.688|
|_P-value_(Male Report x T1=Male Report x T2)|0.313|0.659|0.067|

Note: The dependent variable takes the value 1 if a woman's self-report about candidate choice matches the report of the reportee, either another woman in the household or the male head. Each observation is therefore a pair with several observations for each woman. If a reporter believes that a woman did not vote or does not know whom she voted for, the dependent variable is coded as missing. All specifications include village fixed effects and the following controls: number of registered female voters, whether reportee has a NIC or CNIC, level of schooling, age, zaat status, TV access, number of children between 0-5, total land owned, index of mobility and whether woman seeks advise from a "Pir". Standard errors are reported in parentheses below the coefficient and are clustered at the geographic cluster level. The symbols *, **, *** represent significance at the 10, 5 and 1 % respectively.
17 slgnificant Incentive and bonus schemes, (iv) applying efficient mobile banking systems, (v) achleving high ian recovery, and (vi) making use of some sort of social mechanism In officlont, rapld screening and approving of loans. It should be underscored that beside stable economles, economic policies enhancing more 1iberailzed financlal markets In the two countries contributed to the two schemes kinproved performance.

Chapter 4: The knpact of the Bank's Recent Pollcy Paper on Financial Sector Operations on Future Agricultural Credit Lending 25. The Bank's recent policy paper on financial sector operations considers directed credit to be a potentially useful tool for opening credit access to activities or groups whose access was impeded by Inadequate Information or other market Imperfections. Although there were exceptlons, It Is generally recognized that directed credit programs have often been misused with negative consequence for resource allocation, Income distributlon, and macroeconomic management. Therefore, It Is recommended that financlal sector strategias as well as Bank lending operatlons that Involve directed credit be carefully analyzed and well justified to ensure that objectives are adequately focused and achievable and that the programs would not outlive their usefulness. Past experience has led the Bank to look at the matter more cautiously and to require an explicit justlifcation before embarking on directed credit schemes or lending through specialized sector credit institutions. The conditions for ensuring that they would be engines of growth and consistent wlth officlent financial sector development had to be very carefully
Table A2. FDI inflows <u>and local school enrollment: 2SLS results</u>

||(1)|(2)|
|---|---|---|
||Panel A: School enrollment second stage results<br>||
|Dependent var|∆Highschool|∆College|
|∆FDI_empshare|0.567***<br>(0.169)|0.826***<br>(0.246)|
|L.FDI_empshare|-0.0255<br>(0.0437)|0.127**<br>(0.0544)|
||Panel B: First stage results||
|FDI_shock|2.047***<br>(0.4978)|1.775***<br>(0.4651)|
|First stage F statistics|1022.45|523.26|
|Observations|660|660|
|Prov-Year FE|Y|Y|
|Lagged city characteristics|Y|Y|

Note: This table reports the effect of changes in FDI employment share on city level school enrollment using 2SLS method. Lagged city characteristics include population, average age, share of Han ethnicity. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

The previous regression specification also controls for lagged dependent variable. While we found initial industry structure/school enrollment rate to be a very significant predictor of changes in industry structure/school enrollment, controlling Ycpt−1 creates further endogeneity in the regression. Below we report the results from an alternative regression specification that does not control Ycpt−1 to see whether and how much the coefficients deviate from the main results in Table 8. The coefficients γ and θ are similar to those in Table 8, the positive effect of FDI on structural transformation and school enrollment still holds.

Table A3. Exclude lagged <u>dependent variables</u>

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Panel A:<br>|Structural transf<br>|ormation|Panel B: School<br>|enrollment<br>|
|Dependent var|∆agri_emps|∆indu_emps|∆serv_emps|∆Highschool|∆College|
|∆FDI_empshare|-1.783**|-0.625|2.408***|1.219***|0.394*|
||(0.823)|(0.544)|(0.856)|(0.375)|(0.214)|
|L.FDI_empshare|0.0204|0.0352|-0.0556|-0.0669**|0.0281|
||(0.0426)|(0.0412)|(0.0422)|(0.0315)|(0.0331)|
|Observations|607|607|607|607|607|
|R-squared|0.542|0.536|0.397|0.811|0.685|
|Prov-Year FE|Y|Y|Y|Y|Y|
|Lagged city characteristics|Y|Y|Y|Y|Y|
|SOE reform|Y|Y|Y|Y|Y|
|Tariff shocks|Y|Y|Y|Y|Y|
|Excludemigrants|Y|Y|Y|Y|Y|

Note: This table reports results based on an alternative specification that excludes lagged dependent variable. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

# Appendix 2. Measuring FDI with output share

This paper primarily measures FDI presence as foreign firms’ employment share in the manufacturing sector. Following the same approach, we use FDI policy changes to predict changes in industry level FDI output share, and estimate the equation below: 37
# **I. Introduction**

Access to reliable electricity has long been recognized as a driver of social and economic development. A growing body of literature shows that low access to and poor quality of electricity affect households’ living standards and social welfare (Chakravorty, Pelli, and Marchand 2014; Samad and Zhang 2016, 2017). Many studies examine the causal link between electricity and development in Bangladesh and India (Khandker, Barnes, and Samad 2012; Rud 2012; van de Walle et al. 2017). To the best of our knowledge, this is the first rigorous empirical study of Pakistan.

Previous analysis of Pakistan shows only correlation between electricity and income (Gellerson 1982; Yazdanie 2010; Majid 2013). Such correlations are often misleading, for at least two reasons. First, people with lower income either put off adopting electricity once it becomes available or are unable to afford the connection fees and other costs of electricity. Second, areas farther from urban centers and areas with limited access to roads and markets are more likely to stay off‐grid. People in these areas are also more likely to be poor. Because it fails to address the endogeneity of program placement and household adoption of electricity, simple correlation analysis cannot explain the direction of causality.

This study uses econometric techniques to determine the net effects of rural electrification on various household and individual welfare outcomes in Pakistan, after controlling for endogeneity using the propensity score matching method. The analysis is based on data from two household surveys. The first—the Pakistan Social and Living Standard Measurement (PSLM) Survey—is a two‐period panel household survey that was carried out in 2007/08 and 2010. It provides information on household demographic characteristics, income, expenditure, education, health, and electrification status. It did not collect information on the quality of electricity supply.

The second survey was conducted in 2014 under the Lighting Pakistan program of the International Finance Corporation (IFC 2015). It asked households to estimate the average duration of outages. Data from the IFC survey are used to gauge the impact of power outages on household welfare outcomes.

2
depending on weather conditions). Thus, we determine the soybean oil extraction rate to be _β1_ ' = 0.135 (= 3,600/26,582) metric tonnes of oil per one tonne of soybean, while the share of soybean meal is given by the residual, _β2_ ' = 0.865 (=1 – 0.135).<sup>13</sup> Then the production of soybean meal in Zambia, which FAOSTAT does not report, is estimated to be 0.865 x 26,582 = 22,982 mt and is assumed to be all consumed domestically due to high transportation cost and bulkiness, Zambia did not trade in this commodity in 2010. Diesel consumption in Zambia was 597 million liters in 2010.

Because Zambia is a landlocked country remote from the key markets considered in this paper, the market prices in Zambia have to be reflective of the transportation costs and existing trade barriers. We assume all commodities are traded through Dar-Es-Salaam in Tanzania and Durban in South Africa, which are the current traditional ports. Because Zambia is a net exporter of soybeans, the local price in Lusaka ($370.24/mt) is equal to the lowest price in Durban (for soybeans originating in Argentina, $470.24/mt) less the transportation cost ($100/mt).<sup>14</sup> On the other hand, because soybean oil is imported, its domestic price (inclusive of transportation costs and import duties) is equal to $1,284.35/mt, which is the lowest price for soybean oil in Lusaka, Zambia, that originates in Argentina and is delivered to Zambia through Walvis Bay, Namibia. Zambia did not trade soybean in meal in 2010 but plans exist to export the joint product if soybean-based biodiesel is produced. In that case, the price in Lusaka would be equal to the price at Durban, Mozambique ($487.20/mt) less the transportation cost ($100/mt), that is, $387.20/mt.

Zambia imports petroleum products through Tanzania. The diesel market price in Zambia (exclusive of tax) was $0.921/liter and the diesel tax was $0.299/liter in 2010. Because the observed retail price of diesel fuel was $1.459/liter, we ascribe the difference to a (fixed) marketing margin of $0.239/liter. In 2010, the world biodiesel price was determined in the European Union (Germany) (de Gorter et al., 2011; Rajcaniova et al., forthcoming). We therefore take the German biodiesel price of $1.636/liter to be the price faced by Zambian biodiesel exporters in the EU market. Consistent with the relationship between transportation costs and prices of other studied commodities, we set the market price of biodiesel to be 20 percent below the price in the European Union, that is, $1.31/liter.

Econometric estimates of market elasticities for Zambia are not available. Therefore, to

> 13 Contrast these with the corresponding U.S. rates of 0.19 and 0.81, respectively.

> 14 The data on prices and transportation costs come from http://www.unitedsoybean.org/wpcontent/uploads/MarketPotential_SubSaharanAfrica2.pdf

10
CPS and IPS teachers are similar according to the teacher characteristics score (Column 4), while SPS teachers perform significantly better in this dimension (+1.7 SD column 5). However, CPS outperforms IPS on the classroom setting and equipment scores. In these two dimensions, CPS are equivalent and maybe even superior (for equipment) to SPS, confirming that structural quality has been significantly improved by the construction program. The results are strikingly different for the process quality scores. CPS and IPS are not significantly different in terms of curriculum and pedagogy or teacher-child interaction, whereas on both aggregate measures, SPS outperform IPS and CPS.<sup>26</sup> Overall, these results confirm that CPS are of better quality than IPS but maybe not on the dimensions that are the most important for children’s performance. The upgrading from IPS to CPS mostly affected the learning environment by providing better equipment, while teacher quality was not fundamentally affected. In contrast, SPS teachers are significantly more educated and perform better in terms of instructional quality.

# **_Parental assessments_**

We use questions from the caregiver survey to compare differences in cancelled classes, perceived teacher quality, financial contributions, and travel time between the types of preschools (Table B4). CPS classes get cancelled significantly more often than SPS classes. Parents of children enrolled at CPS assess the kindness of CPS teachers as higher than that of their SPS counterparts (though these differences tend to be relatively small). This could be explained by the greater likelihood of CPS teachers being from the same community as the parents. At endline, parents of children enrolled at SPS assess the reliability of SPS teachers as higher than that of CPS teachers. While financial contributions for teacher salary and construction tend to be almost zero for all types of schools, contributions for school materials are substantial: On average, parents of children enrolled at CPS have paid $35 in the current school year at midline and $47 at endline (with similar amounts for IPS: $32 at midline, $53 at endline); contributions at SPS were $49 at midline and $68 26Unpacking these comparisons into the individual items that make up the aggregate scores shows that SPS teachers are more experienced, perform better in the Raven’s Progressive Matrices Test and have more training than IPS and CPS teachers (Tables B2 and B3). They are also better paid and receive their salary more regularly. In terms of equipment, CPS significantly dominate IPS and even in some dimension SPS, except for access to electricity, water source and toilet facilities. Compared with IPS, CPS classrooms have less breaks, are more likely to follow a curriculum, have more attendance, more enrollment, have more storybook activities (Table B3). Yet, the quality of the classroom setting, pedagogy and teacher-child interactions remain far from the quality observed in SPS. Crucially, the teacher-interaction individual variables are very similar in CPS and IPS, while SPS engage more with the children and provide significantly more encouragements.

16
We can revisit this question using the tax incidence data introduced earlier. For the six Latin American countries with available data, Figure 8 reports the share of taxes (both direct and indirect) paid by each quintile of the income distribution, as well as the quintile’s effective tax rate (i.e., taxes paid relative to total income). The figure shows that the upper quintiles tend to pay more taxes in all cases. For example, the top quintile pays at least 50 percent of total taxes, and in a couple of cases (Mexico and Colombia) as much as 70 percent -- but this should not be a surprise given that the top quintile of the population also accounts for 55 to 65 percent of the income.

More indicative of the tax burden is the effective tax rate that the different quintiles pay on their disposable income, and in this case it is apparent that poorer households tend to face a heavier tax burden relative to their income than those at the top of the distribution. The estimated incidence of the different taxes is also different. Income taxes are generally progressive and, in some cases (e.g., Brazil or Bolivia), highly so as contributions come solely from the top quintile. Value added taxes on the other hand tend to be regressive, as are overall taxes in most countries (as already shown in section 2).

Table 4 brings together direct and indirect taxes. As already shown, income taxes are modestly progressive or neutral (in all countries except Brazil and Chile they tend to lower income inequality), while the VAT is modestly regressive in all countries. The table also indicates that taxation is roughly neutral overall, so that the Gini coefficients of post-tax income (i.e., after both direct and indirect taxes) are fairly similar to those of gross income.

**Table 4. Income inequality before and after taxation**

||Argentina|Brazil|Chile|Colombia|Mexico|Peru|Average|
|---|---|---|---|---|---|---|---|
|Gini of Gross income (A)|0.49|0.54|0.46|0.53|0.50|0.49|0.50|
|Gini of Disposable income (B)|0.48|0.54|0.46|0.52|0.49|0.48|0.50|
|Gini of Post tax income (C)|0.48|0.55|0.46|0.53|0.49|0.49|0.50|
|(B) - (A)|-0.01|0.00|0.00|-0.01|-0.01|-0.01|-0.01|
|(C) - (B)|0.00|0.00|0.01|0.01|0.00|0.01|0.01|
|(C)-(A)|0.00|0.01|0.01|0.00|-0.01|0.01|0.00|

What is the redistributive potential of a progressive change in the tax structure? Figure 9 reports the results of a counterfactual simulation of a hypothetical tax reform that for each country involves declines of 2 and 1 percent in the effective tax rate of the lowest and second lowest quintile, respectively, and increases of the same magnitudes in the effective tax rates of the highest and second highest quintiles. Tax revenue is kept fixed by adjusting the overall tax structure. Note that this reform is quite significant in terms of how the tax burden is changed. Yet, as figure 9 indicates, the estimated redistributive improvement is quite limited – the Gini coefficient changes by less than half a percentage point. This highlights the limitations of changes in the tax structure as a redistributive tool in a context of low tax collection.

17