rafmacalaba/datause-encoder-data huggingface dataset:
Table D.1: Placebo Estimates Using Pre-FPE Cohorts and Misplaced Timing of Intensity Measure

||1979|First (M<br>1978|isplaced) <br>1977|Po|st-Reform Co<br>1976|hort<br>1975|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)||(4)|(5)|
|||A.i. Non-|MTI Regio|ns:|Years of Sch|ooling|
|Add’l Years of Free<br>Schooling<br>�<br>_I_<sup>_F P E_</sup><br>_zy_<br>�|-0.042<br>(0.049)|-0.048<br>(0.064)|-0.045<br>(0.072)||0.067<br>(0.061)|0.056<br>(0.056)|
||[0.396]|[0.462]|[0.540]||[0.280]|[0.327]|
|||A.ii. Non-MT|I Regions:|Nu|mber of Chil|dren Born|
|Add’l Years of Free|-0.015|-0.051|-0.004||-0.005|0.047|
|Schooling<br>�<br>_I_<sup>_F P E_</sup><br>_zy_<br>�|(0.022)|(0.029)|(0.031)||(0.045)|(0.049)|
||[0.501]|[0.093]|[0.891]||[0.915]|[0.346]|
|N|8,074|8,074|8,074||8,074|8,074|
||B.i.|National Esti|mates (FP|E|+ MTI): Year|s of Schooling|
|Add’l Years of FPE or MTI|-0.008|-0.016|-0.009||0.057|0.042|
|w/out Script Change|(0.040)|(0.039)|(0.036)||(0.045)|(0.035)|
|(∆_Izy_)|[0.844]|[0.674]|[0.815]||[0.216]|[0.241]|
||B.ii. Na|tional Estimat|es (FPE|+ M|TI): Number|of Children Born|
|Add’l Years of FPE or MTI|0.013|-0.020|-0.016||-0.025|-0.028|
|w/out Script Change|(0.030)|(0.023)|(0.022)||(0.020)|(0.022)|
|(∆_Izy_)|[0.657]|[0.380]|[0.458]||[0.226]|[0.208]|
|N|14,833|14,833|14,833||14,833|14,833|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : MTI = mother tongue instruction; FPE = free primary education. The dependent variable is years of schooling in panels A.i and B.i, and is number of births in panels A.ii and B.ii. All samples include women in birth cohorts from 1963 to 1979, the same number of cohorts as the baseline estimates but moved nine years back. In the baseline sample the first post-reform cohort is 1988, also at least a nine-cohort difference. This timing matches the period in which the FPE intensity measure ( _Izy_<sup>FPE</sup> ) predicts little impact of the reform, as seen in fig. 2 of the main article. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.43
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# **IV. Impact of taxes and social spending on poverty and inequality**

In this section we present the overall impact of taxes and social spending on poverty and inequality and we compare this impact with other countries. The next section then delves deeper to disaggregate the impacts of individual tax and transfers policies.

## **The impact on inequality**

The combined effect of taxes and social spending help to substantially reduce inequality in Romania. Figure 2 shows the change in the Gini coefficient on account of taxes and social spending following the income concepts defined above for 2016. Prior to any fiscal intervention, market income inequality had a Gini of 0.378, if oldage contributory pensions are included and are treated as deferred income, but much higher (0.487) if these pensions are not included. Once direct taxes, social security contributions and noncontributory transfers are accounted for, we end up with a measure of disposable income that has a Gini of 0.334 in both scenarios. The net impact of indirect <!-- Start of picture text --> Figure 2. Romania. Gini Coefficient<br>0.50<br>0.487 Old-age pensions as transfers<br>0.45<br>Old-age pensions as deferred<br>income<br>0.40 0.378<br>0.340<br>0.35 0.334<br>0.310<br>0.30<br>0.25<br>Market Income Disposable Consumable Final Income<br>Income Income<br>Source: Own estimates using the Romanian 2016 HBS.<br><!-- End of picture text --> taxes and subsidies is unequalizing as the Gini increases slightly for consumable income to 0.340, which includes the impact of VAT and excise taxes.<sup>15</sup> Finally, in-kind transfers in the form of education and health helped to reduce inequality. The overall reduction in inequality was equivalent to 0.068 Gini points from market income to final income when old-age pensions are considered as deferred income, but as much as 0.176 Gini points when pensions are treated as transfers.

The overall redistributive effort in Romania is relatively large compared to other developing countries, but lower than other countries in Europe, with most of the reduction in inequality largely being achieved by pensions (Figure 3). When pensions are considered as deferred income, Romania is less redistributive than Poland and Croatia, in line with the existing literature that has so far focused on the impact on disposable income (see Euromod H1.0). However, once indirect taxes and in-kind transfers are considered, Romania is less redistributive than other countries with similar levels of GDP per capita (in PPP terms) such as Croatia and Chile, but also less distributive than some countries with lower levels of GDP per capita, such as Brazil, Costa Rica, Mexico and Tunisia.

> 15 Romania has some indirect subsidy schemes in electricity, heating and transport. However, given their complexity they were difficult to take into account in this exercise.

13
fear to disclose their true opinion about government. Alternatively, when mass media in a country are controlled by the government, people in this country may be indoctrinated to believe and trust those on the top. In the Section 5.2 we analyze these possible effects and their magnitude for the countries from our sample.

# 4.2 Intertemporal comparison

The consistent through time questionnaires of the WVS and repeated surveys during three waves allow us to assess the progress of the governance in certain countries. In particular, citizens of 41 countries were surveyed both during the first wave of WVS (1994-98) and during the second wave (1999-2004). Surveys both from the second wave and the third wave (2005-2008) are available for 33 countries.

In Table 2 we report the countries which achieved the biggest progress in each governance outcome (both from Wave 1 to Wave 2, and from Wave 2 to Wave 3). Not surprisingly, the list is dominated by developing countries and the countries in transition - of 110 positions (10 governance outcomes plus CGIs themselves) only 14 are taken by developed countries (Spain and Germany between waves 1 and 2, and Japan between waves 2 and 3). These numbers clearly reflect increase in the standard of living and stable economic growth in certain parts of the world. Especially it concerns the speedy economic recovery of CEE countries after the horrible post-communist ”hangover” of the 1990s. The most commonly mentioned countries are Nigeria, Venezuela, Latvia, Bangladesh, Moldova between waves 1 and 2, and Turkey, Russian Federation, Jordan, India and South Africa between waves 2 and 3.

The governance in the world (over the sample of countries surveyed by WVS) statistically significantly (at the level of less than 1%) increased from wave 1 to wave 2 (see Figure 5) - in contrast to the WGI’s world of unchanging governance quality - but practically did not change from wave 2 to wave 3. As it can be seen from the figure the main driver of the growth in world’s quality of governance was increasing (in practically all regions) satisfaction of the citizens with their financial situation. This trend was kept from wave 2 to wave 3 as well, but the overall progress was apparently mitigated by the fall of confidence in governments, courts, army, etc. in developing and countries in transition (though CEE countries still ended up progressing from wave 2 to wave 3).

# 4.3 Subnational CGIs

Our estimation procedure as well as dataset collected allows us to extend citizencentric governance indicators from countries to their subnational units. The idea is to aggregate the citizens’ responses not over the whole country, but over its jurisdictions. For the Wave 3 of WVS there are 1121 of them in the sample - usually the second tier of a country’s administrative structure (in some countries - groups of second tier jurisdictions).

The examples of some countries are given in Figure 6. On the left we depict Germany, and on the right - Italy. Both countries were surveyed in 2006. In Germany 14
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**Table 15: FDI Spillovers Take Place through Foreign Backward Linkages**

|Dependent variable: ln labor<br>productivity (**_ln Y_** )|(1)|(2)|(3)|
|---|---|---|---|
|_ln FDI_for_backward_|0.017***||0.016***|
||(3.17)||(2.70)|
|_ln FDI_for_forward_||0.007|0.004|
|||(0.93)|(0.54)|
|_Constant_|3.629***|3.668***|3.628***|
||(92.39)|(94.62)|(90.71)|
|Country-sector FE|Yes|Yes|Yes|
|Country-year FE|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|
|Number of country-sector|2,017|2,017|2,017|
|Observations|11,892|11,986|11,865|
|R-squared|0.407|0.402|0.406|

**Notes:** The dependent variable is the log of labor productivity. The standard errors are clustered at the country-sector level. Country-year, sector-year and year fixed effects are included. *** p<0.01, ** p<0.05, * p<0.1.

# 7 Concluding remarks and further research

In this paper we link a project-level FDI database with the Multi-Regional Input and Output Table (MRIOT) to build a database with investment information and GVC participation indices for 61 economies and 35 sectors from 2011 to 2017. Our analysis finds evidence that FDI has an overall positive effect on labor productivity. In our preferred IV regressions, doubling FDI will lead to an 8 percent increase in labor productivity. For a sector with low GVC participation, the effect of FDI is even higher; doubling the FDI leads to a 13 percent increase in labor productivity. Since the data has a wide coverage of countries and sectors, the results have general validity unlike earlier studies that are more narrowly focused. We also find that FDI spillovers take place more through the domestic and foreign backward linkages, suggesting that FDI also has cross-border spillovers by benefitting domestic firms whose suppliers are in foreign countries and are recipients of FDI.

The policy implication should not be to provide more tax incentives, as the cost-benefit of such a policy has been under some scrutiny in recent years. For example, the reduction in the US corporate tax rate in 2018 should have led in theory to US investors reducing their investments abroad to repatriate and invest at home. Instead, US investors engaged in share buybacks and cash
**Table 12: Treatment effect on student performance and learning outcomes – two years into intervention (2010)**

||3|<sup>rd</sup>Grade|5<sup>th</sup>|Grade|
|---|---|---|---|---|
||Standardized<br>test score<sup>a</sup>|Probability that a<br>child can read 45 or<br>more words per<br>minute|Standardized<br>test score<sup>a</sup>|Percentage of<br>students who can<br>read 45 or more<br>wordsper minute|
|WSD group|-0.001|0.01|-0.08|-0.05|
||(0.08)|(0.03)|(0.09)|(0.04)|
|GRANT group|0.01|-0.01|0.03|-0.05|
||(0.08)|(0.02)|(0.09)|(0.04)|
|Number of|4537|1241|4354|1202|
|observations|||||
|Mean of dependent<br>variable in|35.32%|11%|52.06%|38%|
|comparisongroup|||||

Notes: Robust standard error clustered at school level in parenthesis. *** 1% Significance Level, **5% Significance Level, *10% Significance Level.<sup>a</sup> Test score normalized to 100 point. It is standardized only for the calculation of the treatment effect.

**Table 13: Average Treatment Effect on 4th and 6th-Graders – Three to four years into the intervention**

||Math|English|
|---|---|---|
|WSD|-0.05|0.01|
||(0.07)|(0.08)|
|Grant|-0.07|-0.08|
||(0.06)|(0.07)|
|4th Grade Dummy|-0.69***|-0.74***|
||(0.03)|(0.03)|
|Constant|0.40***|0.42***|
||(0.04)|(0.05)|
|P-value WSD = Grant|0.76|0.23|
|Observations|4817|4817|

Notes: Standard errors in parentheses. *** 1% Significance Level,

**5%. Significance Level, *10% Significance Level.

33
transaction costs mute the impact of changes in international prices on local prices (Larson and Mundlak, 1992; Dawe and Maltsoglou, 2014; Yang et al., 2015). Long‐standing trade and food policies affect pass‐through rates, too. However, in the case of the food‐price crisis, a weakening US dollar and new interventions were important as well (Anderson, Ivanic and Martin, 2013).<sup>4</sup> For example, in his study of seven large Asian countries during the crisis, Dawe (2009) found that, on average, exchange rate appreciations muted the impact of rising global prices by 18 percent, roughly $32 per ton. Dawe also reports that the remaining price‐wedge, attributable to markets and policies changed, such as tariffs, trading rules and consumption taxes were adjusted to insulate local markets. The net effect was to lower domestic prices, on average, by $97 per ton, up from an average of $16 a ton during the previous four years. In Indonesia, Dawe estimates that the insulating effect of the domestic policy‐and‐market wedge increased from $13 to $123 per ton, while the protection afforded by an appreciating rupiah declined slightly from $20 to $19 a ton.

Households that do face rising food prices can adjust, although the capacity to do so varies. Wealthier households are better able to smooth their intake of calories and essential nutrients by adjusting other expenditures, by drawing down savings and selling assets, and by borrowing (Morduch, 1995; Dercon, 2005; Kazianga and Udry, 2006; Carter and Lybbert, 2012). In contrast, poor households spend a disproportionate amount of their income on food generally and on staple crops in particular (Jensen and Manrique, 1998; Warr, 2005). Consequently, poor and near‐poor households that purchase food are especially vulnerable to rising food prices.

In the face of incomplete markets for credit and insurance, poor households also develop alternative informal insurance mechanisms with family members and neighbors to mitigate the risks they face, lending support when asked and seeking assistance when they face difficulties of their own. Such arrangements can be effective when risks are idiosyncratic, but can also fail with dire consequences in the face of systemic events like the food crisis (Townsend, 1994; Larson, Anderson and Varangis, 2004; Skees et al., 2005). To counter this, governments and donors responded by bolstering existing food safety net programs, and launching feeding programs and other new initiatives aimed at helping the most vulnerable (Demeke, Pangrazio, and Maetz, 2008; Wodon and Zaman, 2010). By enhancing the capacity of households and informal insurance networks, these programs can mitigate malnutrition impacts (Coady, Grosh, and Hoddinott, 2004). When all else fails, households apportion limited food supplies among family members, where allocations are affected by family size, age and gender (Lanjouw and Ravallion, 1995; Aromolaran, 2004; Kumar and Quisumbing, 2013).

> 4When governments choose to intervene, the choice of instruments has distributional consequences (Poapongsakorn, 2010; Miranda et al., 2013).

4
# Marital Trajectories and Women’s Well-Being in Senegal

**Sylvie Lambert, Dominique van de Walle and Paola Villar**<sup>**1**</sup>

**JEL Codes** J12; I31; J16.

**Key words:** Marital dissolution, Widowhood, Divorce, Welfare, Gender, Senegal, sub-Saharan Africa.

> 1 This paper is here reproduced with some revisions with acknowledgement of UNU-WIDER in Helsinki which commissioned the original study and holds copyright thereon. Lambert and Villar are at the Paris School of Economics, Paris, France and van de Walle is with the World Bank in Washington, USA. The authors are grateful to Cait Brown for help with the DHS, to Ariel Gruver for excellent research assistance and to the World Bank’s Knowledge for Change Trust Fund for its funding support. We thank two anonymous referees, Jean-Philippe Platteau and the participants at the University of Namur Gender and Development workshop in February 2017 for useful comments. These are the views of the authors and need not reflect those of the World Bank and its affiliated organizations. Corresponding author: Dominique van de Walle, 1818 H St. NW, Washington, DC, 20433; dvandewalle@worldbank.org
∆Yit = - λ uit-1 + eit (7) Alternatively, we could estimate More specifically, since we are particularly interested in the size and significance of the regulatory variable, R, we can impose the estimate of β from the long term levels relationship (2) and estimate From this we derive the following which we estimate, including fixed effects: .

We have alternative estimates of λ from equations (7) and (9b). These can be compared. In addition, we have alternative estimates of γ: firstly, from the _levels_ equation (4); and, secondly, the _differences_ equation 7(b) from which we can calculate the implied value of γ as λγ/λ from the estimated coefficients.

We tested for stationarity using the Pesharan-Shin W-statistic. Applying this test to the differenced equation 7(b), with the regulatory index as our measure of Rit, the test clearly rejects the presence of a unit root in the residuals with a t-statistic of –8.05. Even in the corresponding levels equation, the Pesharan-Shin W-statistic does not appear to suggest non-stationarity in the residuals, implying that our generation capacity variable, GDP and our regulatory variables are co-integrated<sup>30</sup> .

The results are shown in Table 7 below. We report estimates for the levels equation (2), the partial adjustment equation (5) and the differenced model (9b). We report these (a) where the regulatory variable is the 4-element regulatory index and (b) where it is a dummy for all regulators over 3 years old. Equations estimated with alternative definitions of the regulatory variable yielded estimates with similar positive magnitude and statistically significant at the 5% level or better.

> 30 Very similar results on the unit root test applied when we took alternative definitions of the regulatory variables:  regulator over 3 years old and a quadratic in the age of the regulator.

32
**<u>Table 5: Productive performance of smallholders vs. commercial farms in different farm size classes</u>**

||**Maize**|**Sorghum**|**Teff**|**Wheat**|**H. beans**|**Soya**|**Sesame**|**Coffee**|**Cotton**|
|---|---|---|---|---|---|---|---|---|---|
||||||**Area cultiva**|<br>**ted**||||
|Smallholder|0.25|0.43|0.46|0.34|0.11||0.69|0.14||
|< 20 ha|4.96|7.08|3.90|6.00|5.81|2.87|8.57|12.66|3.28|
|20-50|14.95|13.59|6.20|15.15|4.88|13.56|24.07|32.07|23.26|
|50-100|29.4|21.9|7.1|46.6|16.4|26.8|45.5|65.4|33.5|
|100-500|96.2|55.5|10.0|118.7|31.3|45.3|114.4|201.7|214.5|
|> 500 ha|339.0|217.1|32.3|1654.6|147.6|496.7|1008.7|672.0|1194.3|
|||||**O**|**utput (quinta**|**l/ha)**||||
|Smallholder|18.73|14.90|8.44|14.59|13.74||6.00|16.79||
|< 20 ha|42.04|30.88|9.18|41.69|23.86|24.39|13.94|4.24|5.18|
|20-50|37.42|24.52|8.79|33.68|38.31|13.91|10.74|8.43|17.75|
|50-100|36.89|25.64|8.61|26.11|20.95|18.82|8.46|6.38|30.81|
|100-500|39.30|28.21|7.75|24.64|30.52|23.71|9.77|6.37|29.31|
|> 500 ha|33.81|29.51|9.90|28.32|34.96|22.50|10.84|7.02|18.92|
|||||**Use**|**d chemical fe**|**rtilizer**||||
|Smallholder|34.52|17.06|61.56|65.01|18.10||10.40|4.82||
|< 20 ha|52.85|19.48|85.44|65.79|30.52|23.83|12.99|0.00|0.00|
|20-50|58.39|20.18|77.00|59.62|20.76|2.05|33.29|0.91|0.00|
|50-100|73.79|18.28|61.34|74.22|18.26|2.88|48.19|3.13|10.46|
|100-500|84.31|33.18|62.44|64.74|25.77|27.83|51.25|14.99|12.32|
|> 500 ha|82.70|44.79|48.01|74.45|31.69|40.82|64.93|59.41|15.09|
|||||**Quantity of ch**|**emical fertil**|**izer used (k**|**g/ha)**|||
|Smallholder|179.32|112.01|133.07|187.57|105.32||77.86|69.98||
|< 20 ha|165.43|84.99|122.63|363.65|32.79|340.37|171.62|||
|20-50|187.22|80.60|155.68|270.65|115.41|48.33|38.53|4.68||
|50-100|151.59|51.86|101.79|97.74|78.08|73.85|32.33|17.99|12.04|
|100-500|187.09|63.42|89.83|118.91|127.90|129.88|40.73|96.37|51.04|
|> 500 ha|163.68|82.05|64.34|72.46|163.64|32.19|59.02|88.05|64.57|
|||||**Us**|**ed improved**|**seed**||||
|Smallholder|20.30|1.77|4.23|12.51|1.59|0.45|1.24|0.38||
|< 20 ha|51.99|17.58|85.44|65.79|30.52|23.83|11.18|0.00|0.00|
|20-50|57.87|18.66|76.23|58.25|17.03|0.82|29.67|0.91|0.00|
|50-100|69.57|15.88|58.66|74.22|18.26|1.44|40.22|2.71|10.46|
|100-500|86.21|4.21|45.86|40.32|26.84|21.19|3.72|0.95|10.14|
|> 500 ha|80.30|42.08|40.93|74.45|21.23|40.82|59.81|56.85|12.64|
|||||**Quantity of**|**improved se**|**ed used (kg/**|**ha)**|||
|Smallholder|40.93|33.09|53.17|<br>183.24|<br>121.14|||73.31||
|< 20 ha|53.90|9.92|36.12|581.88|36.79|125.20|17.24|||
|20-50|46.49|25.71|79.62|262.04|115.11|21.22|7.11||6.38|
|50-100|28.93|17.30|40.67|138.65|59.42|91.68|3.26||8.21|
|100-500|24.47|10.01|52.12|156.68|69.57|148.37|5.35|116.92|8.39|
|> 500 ha|47.44|2.86|33.91|67.52|171.41|60.91|3.27||1.37|
|||||**N**|**o. of observa**|**tions**||||
|Smallholder|1703|1216|1189|794|521||180|838||
|< 20 ha|358|295|291|162|46|79|349|87|73|
|20-50|479|1122|212|109|40|122|1207|220|27|
|50-100|351|833|124|28|100|104|1000|358|90|
|100-500|382|724|165|128|142|178|792|315|234|
|> 500 ha|89|103|34|37|29|33|109|39|96|

_Source:_ Own computation from 2013 CSA large farm and smallholder farm surveys 19
# **Figure 28: FDI Intensity, Percent of GRP, 2000–11**

<!-- Start of picture text --> 20%<br>15%<br>10%<br>5%<br>0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Central (excl. Moscow) Northwestern (excl. Saint‐Petersburg)<br>Southern North‐Caucasian<br>Volga Ural<br>Siberian Far Eastern<br><!-- End of picture text --> _Source_ : Rosstat.

0