rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Robustness Checks

||(1)<br>ln NTL pc<br>IV|(2)<br>ln NTL pc<br>IV|(3)<br>ln NTL pc<br>IV|(4)<br>ln NTL pc<br>IV|(5)<br>ln NTL pc<br>IV|(6)<br>ln NTL pc<br>IV|
|---|---|---|---|---|---|---|
|MA=Market Access|Varyi|ng Distance|Decay|Holding pop|ulation fxed|External MA|
|ln MA (_θ_ = 30)|0.184<sup>_∗∗∗_</sup><br>(2.86)||||||
|ln MA (_θ_ = 60)||0.246<sup>_∗∗∗_</sup><br>(2.66)|||||
|ln MA (_θ_ = 80)|||0.367<sup>_∗∗_</sup><br>(2.56)||||
|ln MA (_θ_ = 40,<br> ̄<br>_pop_)||||0.189<sup>_∗∗∗_</sup><br>(2.81)|||
|ln MA (_θ_ = 80,<br> ̄<br>_pop_)|||||0.364<sup>_∗∗_</sup><br>(2.55)||
|ln MA (incl. external)||||||0.357<sup>_∗∗_</sup>|
|(_θ_ = 80,<br> ̄<br>_pop_)||||||(2.53)|
|ln(1+Pal.Fat._<_5km)|0.0165<sup>_∗_</sup>|0.0165<sup>_∗_</sup>|0.0201<sup>_∗∗_</sup>|0.0166<sup>_∗_</sup>|0.0204<sup>_∗∗_</sup>|0.0231<sup>_∗∗_</sup>|
||(1.81)|(1.86)|(2.14)|(1.88)|(2.16)|(2.32)|
|ln(1+Isr.Fat._<_5km)|0.0169|0.0176|0.0162|0.0180|0.0162|0.0121|
||(1.01)|(0.98)|(0.87)|(1.05)|(0.86)|(0.66)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Locality FE|Yes|Yes|Yes|Yes|Yes|Yes|
|_N_|1928|1928|1928|1928|1928|1928|
|adj. _R_<sup>2</sup>|0.342|0.310|0.261|0.345|0.260|0.259|

_∗ ∗∗ ∗∗∗_ Notes: _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Heteroscedasticity robust _t_ -statistics clustered by locality in parentheses. _NTL pc_ measures local (top-coded) Nighttime Lights emissions per capita (Source: NOAA) as described in Section 4. _Market Access_ is calculated using: _MAit_ =<sup>�</sup> _j_<sup>_h_(</sup><sup>_Tijt_)</sup><sup>_Pjt_,where</sup><sup>_P_denotesdestinationpopulation,</sup><sup>_T_denotestraveltime,and</sup><sup>_h_(</sup><sup>_T_)=exp (</sup><sup>_−_</sup><sup><u>1</u></sup> 2<sup>_T_2</sup><sup>_/θ_2)</sup> is a distance decay function. The choice of the distance decay parameter _θ_ is indicated in the variable names (see Section 3 for details). In columns 1, 2 and 3, population counts are allowed to vary over time, whereas in columns 4, 5 and 6, they are kept constant (as is indicated by _pop_ ̄ ), such that all variation in market access is driven by obstacle deployment. The augmented market access measure in column 6 adds Tel Aviv as a 12th destination (in addition to the 11 governorate capitals) and assigns it a population of 10 million in order to proxy for external market access. _Pal.Fat.<5km (Isr.Fat.<5km)_ denotes the number of Palestinian (Israeli) fatalities within a 5km radius in a given calendar year (Source: B’Tselem). _Market Access_ is instrumented using _Checkpoints 10-25km_ , the number of checkpoints deployed in a radius between 10 and 25km away from the locality’s center (Source: UNOCHA).

34
disaggregation of output. In the complete sample, the elasticity of changes in poverty with respect changes in output per capita is not significantly different from zero. When output per capita is separated by sector the coefficient on the change in output per capita of the primary sector is significant if the change in the inflation rate is not included. If we include the change in inflation, column 3, none of the elasticities is statistically significant with the exception of the coefficient on changes in inflation.

The results for the rural sample without the municipalities are quite different. In this case the elasticity of the change in poverty with respect to changes in aggregate output per capita is significant. When we included the output per capita by sectors, only the coefficient associated with the primary sector is significantly different from zero. This result is not affected by the inclusion of the change in inflation as an additional explanatory variable.

The results for the provinces as a whole show that the elasticity of the change in poverty with respect to changes in output per capita is not significant, as was the case for the rural areas including all the provinces. The results are similar to those obtained in the columns for the rural area without municipalities if we eliminate the municipalities from the overall headcount index (including the urban and rural areas). As shown already in Table 3, the results for the sample of rural and urban areas without the municipalities are very similar to the ones for rural areas without municipalities. Therefore, the relevant difference is the inclusion, or not, of the municipalities and not the use of rural poverty versus overall poverty.

From the previous analysis it seems that only the growth in the primary sector has a significant effect on poverty in rural China, without considering the rural areas of the municipalities. This result is compatible with Ravallion and Chen (2007) who find, using nation-wide data for China, that the primary sector has far higher impact on poverty that either the secondary or the tertiary sectors. However, Ravallion and Chen found significant effects of non-primary growth, which we do not confirm using this sub-national data set.

In Table 5 we present analogous results in which the growth rates for the output of each sector are weighted by the proportion of each sector on total output. This transformation is of interest because if the coefficients with respect to all the (weighted) sectoral outputs are the same across sectors then the estimation collapses in a simple regression of the rate of poverty reduction on the rate of growth of output. Thus we have a straightforward statistical test of the PGH. In order to make the comparison as close as possible to the national results reported by Ravallion and Chen (2007), we eliminate the trends and the inflation rate and work only with the growth rate of the three sectors ( _j_ =1,2,3). In this case there are fewer 10
<!-- Start of picture text --> lo 1 1<br>= ; H dry season<br>I 1 | rainy season<br>wo | i]<br>a11<br>—11<br>II<br>1 I<br>a11<br>3I I<br>DII<br>ai} I<br>c 2 I I<br>Pad ~ i} I<br>oO I I<br>5 I I<br>aw I I<br>i} I<br>i} I<br>ioNIII1II<br>I<br>I<br>oO<br>-— tS SY tS +p<br>0 20 40 60 80 100<br>Total time of electricity outages in hours<br><!-- End of picture text -->
the chemicals are durable in soil (ATSDR, 2002). DDT in soil is breaks down slowly to DDE and DDD by microorganisms. Depending on the type of soil and climate, half of DDT in soil will break down in 2-15 years (ATSDR 2002).<sup>8</sup> Generally, a small amount of DDT goes through the soil into groundwater. DDT does not dissolve easily in water. However, DDT, and especially DDE, bio-accumulate, which means that it builds up in plants and in fatty tissues of fish, birds, and other mammals (ATSDR 2002). This magnification of DDT and its breakdown products through the food chain may increase the incidence of cancer, diabetes, and hormone disruptions that could result in potential reproductive failures in the population exposed to prolonged DDT use. DDT Exposure pathways include (i) eating contaminated foods, such as dairy products, root and leafy vegetables, fish and fatty meat; (ii) drinking breast milk from mothers who have been exposed to DDT; (iii) exposure of fetus through the placenta blood; (iv) breathing or swallowing soil particles near waste sites or landfills that contain DDT; and (v) breathing contaminated air or drinking contaminated water in or near houses- sprayed with DDT (ATSDR, 2002).

In terms of relevant exposure scenarios for the general population to DDT in countries using IRS, a few studies point to a concern about the levels of exposure for any of the end-points that were assessed. In North America, rather relatively high levels of exposure have been recorded in biological samples collected near the time of peak use during the 1960s (Eskenazi et al. 2006). The review by van den Berg (2009, page 1658) also draws attention to the fact that “High levels of human exposure to DDT among those living in sprayed houses, most of whom are living under conditions of poverty and often with high levels of immune impairment, have also been found in recent studies in South Africa and Mexico (Aneck-Hahn et al. 2007; Bouwman et al. 1991; De Jager et al. 2006; Yanez et al. 2002).” Since DDT use is now legally allowed only for use in vector control; the main exposure will be among IRS spray operators<sup>9</sup> and also in houses where it is being used in IRS. In addition, some exposure among the general population can occur if the DDT is not stored and handled safely

> 8 The process of degradation is dramatically slowed down in cooler climates.

> 9 Occupational exposure

6
an important obstacle to their operations.<sup>4</sup>

# **3.1.3 Living Standards Measurement Study (LSMS)**

The LSMS is a household-survey program led by the World Bank. The LSMS project works in partnership with the national statistics offices of its eight affiliated countries in Sub-Saharan Africa using representative panel, household surveys. The sampling procedure applied is a stratified two-stage (two-stage probability sample). In the first stage, an enumeration area (EA) is selected, according to a probability relative to the total number of households in that area.<sup>5</sup> We analyzed the different survey waves for each country as cross-sectional data. This paper considers seven of the eight countries that are part of the LSMS; Cˆote d’Ivoire is excluded because of a lack of georeferenced data for this country. This sample contains 1,577,278 respondents, with ages ranging from 20 to 65 years old. The average population age is 35.8 years old. About 86.2 percent of the sample lives in rural areas. About 52 percent of the population is female.

# **3.1.4 The Roads Dataset**

We use the road network from Jedwab and Storeygard (2021), who relied on a previous 2004 network by Nelson and Deichmann (2004) and digitized 64 Michelin road maps constructed between 1961 and 2014 to characterize the then-current road outlook.<sup>6</sup> To address the lack of Michelin maps for certain states and periods, we use grid fixed effects. We use georeferenced survey data to estimate the distance between the centroid of each enumeration area and the nearest main road (highway, paved and/or improved road). Throughout the paper, we consider the distance between an individual or a household and the nearest road as the distance between the centroid of the cluster in which that individual or household is located and the nearest road. Similarly, in the WBES, the distance between a firm and the

> 4More details on this sample are displayed in Table (A3) in the Online Appendix.

> 5The LSMS component of our final data set is summarized in the Online Appendix Table (A4).

> 6Figure (A1) in the Online Appendix illustrates the dynamics of the road network of three countries in Sub-Saharan Africa over the period from 1965 to 2014.

13
A Duration Analysis of CONALEP

**Table 7. (cont.)**

||**Prob[****_Activity=j_ |** <br>|**_X, dummy=k_]**<br>|**Marginal**||**Prob[****_Activity=j_ **<br>|**|****_X, dummy=k_]**<br>|**Marginal**|
|---|---|---|---|---|---|---|---|
|**Variable**|**k=1**<br>**Survey 98**|**k=0**<br>**Survey 94**|<br>**Effect**|**Variable**|**k=1**<br>**Survey 98**|**k=0**<br>**Survey 94**|<br>**Effect**|
|_j = Student_||||_j = Housework_||||
|98 Survey|0.06|0.09|-0.031|98 Survey|0.08|0.04|0.034|
|Women|0.07|0.06|0.003|Women|0.17|0.01|0.160|
|Age|||-0.005|Age|||0.001|
|South-East Area|0.06|0.06|0.000|South-East Area|0.06|0.07|-0.009|
|Center-South Area|0.05|0.07|-0.014|Center-South Area|0.05|0.07|-0.021|
|North-East Area|0.03|0.07|-0.040|North-East Area|0.03|0.07|-0.040|
|North-West Area|0.04|0.07|-0.029|North-West Area|0.06|0.07|-0.005|
|Center-North Area|0.04|0.07|-0.033|Center-North Area|0.05|0.07|-0.025|
|Pacific Area|0.05|0.07|-0.014|Pacific Area|0.05|0.07|-0.023|
|92 Cohort||0.07||92 Cohort||0.07||
|93 Cohort||0.06||93 Cohort||0.07||
|94 Cohort|0.10|||94 Cohort|0.04|||
|96 Cohort|0.07|||96 Cohort|0.05|||
|97 Cohort|0.10|||97 Cohort|0.03|||
|Lower-secondary HH|0.08|0.06|0.022|Lower-secondary HH|0.06|0.07|-0.010|
|Upper-secondary HH|0.12|0.06|0.063|Upper-secondary HH|0.07|0.07|0.003|
|University - + HH|0.13|0.06|0.063|University - + HH|0.05|0.07|-0.020|
|Do not know HH|0.04|0.06|-0.028|Do not know HH|0.11|0.07|0.041|
|GDP per capita|||-0.016|GDP per capita|||0.014|

Base categories for independent variables: 94 Survey, men, Center area (D.F. and Mexico), 91 cohort, none or primary school education of household head (HH). Units for GDP thousands of 1998 pesos.

17
# **Figure 20. Growth Decomposition in Central American Countries**

<!-- Start of picture text --> (a) SCCIs (b) WCCIs<br><!-- End of picture text --> <!-- Start of picture text --> 0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>‐0.10<br>‐0.20<br>‐0.30<br>‐0.40<br>G EP EL DR<br><!-- End of picture text --> <!-- Start of picture text --> 0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>‐0.10<br>‐0.20<br>‐0.30<br>‐0.40<br>G EP EL DR<br><!-- End of picture text --> Note: All variables are derived using the methodology presented in Section 2.b, taking the logs of the average yearly growth rates over a ten-year moving window. Grouping follows Table 2. _Source: WDI._
36 _Flgure 2.2; Malaysia: Energy Consumption ofArndustrial Sector (share by we offel)_ <!-- Start of picture text --> % Shuc<br>90.<br>80-<br>70.<br>SO- - -<br>50.<br>40.<br>30'. O. ~<br>'L, L1 L, LU hXJiih7d<br>o<br>LiII,<br>1978 199 19W 1961 1962 1963 1964 1965 1986 1987 1968<br>|N.So EWFcarcty *CmaCo -Diued oFudOii -Tn Pet EPd<br><!-- End of picture text -->

_Flgure 2.3: Malaysia: Difference between Dome.tic and Border Pricesfor.Diesel, Subsidy (-), Tar (+)_ <!-- Start of picture text --> ODm .<br>AdDDO<br>-20Dm -<br>0 S I - 1 :-,--_ ,1 0,n a<br>JOOO .<br><!-- End of picture text -->
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 6: 72% of teacher retirees are men between the ages of 42 and 90***

<!-- Start of picture text --> 0.50<br>0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101<br>Age<br>male female<br>Number of pensioners (000')<br><!-- End of picture text -->

**Notes:** * Age distribution of teacher retirees (2003/04). **Source** : MPS.

The gender distribution of both government and teacher retirees is more skewed than the gender distribution of respective active workers. The gender ratio is 2.6 men for every woman for active civil servants and 2 for teachers. The same ratios are 9 and 3 for government retirees and teachers, respectively. The skewed gender distribution is easily explained by the fact that, generally women do not accrue pensionable rights as they exit the labor market early for various reasons including marriage and household care. In Uganda, the marriage gratuity provided under CAP 286 contributes to explaining this gender skewness as, _ceteris paribus_ , it promotes marriages. To some extent, this may also stem from growing labor force participation rates for women which is also widely observed in many developing countries.

Figure 7 shows the age and gender distributions of old age pensions for the stock of retirees derived from the database for the base year of 2003/2004.

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where _i_ denotes individuals, _r_ sub-national regions, and _t_ years as noted above. Other covariates are identical to the ones reported for equation 1. In addition, _c_ denotes countries. _τt ∗ φc_ is a country-by-year fixed effect, which controls for all potentially omitted variables that can vary across countries and years.

These models report the average effects across all treated regions. We then examine the heterogeneity of the effects according to our two dimensions of interest: the _de jure_ right to work policy index (scaled to 0-1) and an indicator for the existence of camps. The question is whether refugee waves in places with camps (or restrictive laws) have the same average effect as places without camps (or liberal laws). This specification now compares across locations, instead of the within-region comparisons across time for the average effects. Places that implement restrictive laws likely differ from places with liberal laws in more dimensions than refugee policy, and these differences could instead explain any differences in the evolution of attitudes across settings. We begin to assess the comparability, as well as scope for reverse causality (i.e. places that had or anticipated more negative effects opened camps), in the results section, with more analysis to follow.

Heterogeneity results are based on the following equations, identical to the ones above but including an interaction term of between our treatment variable and the variable of interest in each case: _Attitudesirt_ = _β_ 1 _Wavert_ + _β_ 2 _Policyr ∗ Wavert_ + _Rrt_<sup>_′θ_+</sup><sup>_X_</sup> _irt_<sup>_′λ_+</sup><sup>_γr_+</sup><sup>_τt_+</sup><sup>_εirt_(3)</sup> _Attitudesirt_ = _β_ 1 _Wavert_ + _β_ 2 _Policyr ∗ Wavert_ + _Rrt_<sup>_′θ_+</sup><sup>_X_</sup> _irt_<sup>_′λ_+</sup><sup>_γr_+</sup><sup>_τt∗φc_+</sup><sup>_εirt_(4)</sup> where, as before, equation (3) uses only treated regions, while specification (4) includes never-treated regions from countries with at least one treated region.

_Policyr_ is either an indicator variable equal to 1 if there was a refugee camp present in region _r_ at any moment in the event window around a large wave of refugees at that region (and 0 if not or if that region is untreated), or the value of the employment policy index from the DWRAP dataset. The policy variable itself 18
**<u>Table 1: Descriptive Statistics of Key Variables</u>**

|<br>Variable|<br>Mean|
|---|---|
|_Whole sample (N =_|_378,691)_|
|Years of schooling|7.5|
|_Educational level:_||
|None|11%|
|Primary|39%|
|Secondary|34%|
|Higher|16%|
|_Secondary school type:_||
|General|20%|
|Vocational|80%|
|Education reform 1997 affected|29%|
|Children aged 15 or less in household|1.0|
|**_Workers (N = 9_**|**_7,050)_**|
|Annual earnings|7,342 $US|
|Years of schooling|10.1|
|Years of labor market experience|21.0|
|Private sector employee|76%|
|Public sector employee|24%|

Source: 2017 Turkish Household Labor Force Survey (HLFS)

# **Earnings function estimates**

Table 2 presents our earnings function estimates of the private overall rate of return to one extra year of schooling by gender, economic sector and secondary school curriculum.

**<u>Table 2: Private rates of return to one year of schooling, Mincerian estimates (%)</u>**

|<br>Reference group|<br>Ordinary least squares|<br>Selectivity corrected|
|---|---|---|
|All workers|8.8||
|Males|8.3||
|Females|10.3|13.4|
|_Economic sector:_|||
|Private|6.5||
|Public|7.9||
|_Secondary school graduates:_|||
|General curriculum|2.4||
|Vocational curriculum|4.2||

Source: 2017 HLFS Note: All coefficients are highly significant. See full results in Annex Table A-2 and Annex Table A-3 There are several points to note in Table 2. First, the selectivity corrected estimate for women is substantially larger than the OLS estimate. We use the number of children under 15 living in the 5
Importantly, the results do not appear to be an artifact of the specific time period, set of countries, or achievement measurement decisions. Extensive robustness tests confirm the strong and significant relationship between educational quality and economic growth.

The results reported in Tables 4.1 and 4.2 are robust to several alternative approaches to measuring the quality of education. The results remain qualitatively the same when using only the tests performed at the level of lower secondary education, excluding any test in primary schooling or in the final year of secondary education. Arguably, test scores at the end of the secondary level, which combine the knowledge accumulated over primary and secondary schooling, may be more relevant for the human capital of the labor force than test scores that capture only the knowledge at the end of either primary or lower secondary school. At the same time, the duration of secondary education differs across countries, so that tests performed in the final year of secondary schooling in each country may not be as readily comparable across countries as the grade-based target populations. Further, given differing school completion rates, the test for the final year of secondary schooling may imply cross-country samples with differential selectivity of test takers. Neither the primary-school tests nor the tests in the final secondary year are crucial for the results.

Furthermore, results are qualitatively the same when using only scores on tests performed since 1995. These recent tests have not been used in the previous analyses and are generally viewed as having the highest standard of sampling and quality control. Likewise, results are robust to using tests scores since 1995 for just lower secondary grades.

A drawback of using only the more recent tests is that such an approach requires a strong version of the assumption that test performance is reasonably constant over time, because it relates test performance measured since 1995 to the economic data for 1960-2000. To make sure that higher previous economic growth is not driving the measured test performance, we also used a test score measure that disregards all tests since the late 1990s. Our results turn out to be robust, with a point estimate on the test score variable that is significantly higher, when we restrict the tests to only those conducted until 1995 (sample reduced to 34 countries) and until 1984 (22 countries).<sup>49</sup> In sum, the results are not driven by either early or late test scores alone.

The results are also robust to performing the analyses in two sub-periods, 1960-1980 and 19802000. The most recent period includes the Asian currency crisis and other major economic disruptions 49 We can also use the average early test scores (either until 1984 or until 1995) as an instrument for the average of all test scores in a two-stage least-squares regression, in order to utilize only that part of the total test-score measure that can be traced back to the early test scores. Again, our results are robust to such an instrumental-variable approach.

37