rafmacalaba/datause-encoder-data huggingface dataset:
_Table 5: Double-difference estimates of VHI impact on child anthropometrics_

|Outcome|Age in<br>1992/93|Per capita<br>consumption<br>quintile in<br>1992/93|<br>Treatment<br>effects|z-stat|F-test consumption<br>quintile differences|Prob> F|
|---|---|---|---|---|---|---|
|Height-for-age|0-23|all|0.154|3.34|||
||0-4|all|0.456|4.06|F(4, 414) = 0.80|0.527|
||5-23|all|0.031|0.69|F(4, 1029) = 0.51|0.727|
|Weight-for-age|0-23|all|0.113|2.58|||
||0-4|all|0.332|3.57|F(4, 416) = 3.70|0.006|
||0-4|1|-0.115|-0.61|||
||0-4|2|0.293|1.69|||
||0-4|3|0.236|1.42|||
||0-4|4|0.348|2.03|||
||0-4|5|0.869|4.82|||
||5-23|all|0.023|0.49|F(4, 1027) = 0.84|0.499|

The impacts for under-five children are statistically significant, but are they large enough to matter? The estimated treatment effects translate into increases in height and weight of 2.35 cm and 0.75 kg respectively, equivalent to increases on the counterfactual 1997/98 height and weight respectively of 2% and 4%. Assuming the effects occurred continuously throughout the five years following the 1992/93 survey, these effects are equivalent to an extra 0.47 cm extra height and 0.15 kg extra weight per year. To express the effects another way, the annualized VHI impacts are equivalent to 7% and 9% of counterfactual annual growth of height and weight, while the total cumulative impacts are equivalent to 37% and 46% of annual growth of height and weight .<sup>13</sup> How do these impacts compare to those reported for other programs? Behrman and Hoddinott (2000) estimate that young children within Mexico’s PROGRESA program grew by an additional centimeter in the year following the program’s introduction, equivalent to 17% of the counterfactual mean growth per year, a little over twice that achieved by VHI. Case (2002) estimates that five years after its introduction South Africa’s pension scheme had caused a 3-4 cm increase in height among Colored and Black children aged 0-6. This (cumulative) impact was equivalent to 44% of a year’s growth, a little more than VHI’s cumulative impact after a similar period of 37%. South Africa’s pension is estimated to have increased weight among young children as well, but only among Colored children. The estimated 1 kg cumulative impact for this group is a little larger than the 0.75 kg VHI estimated cumulative impact. Chaudhuri (2003) has estimated that Bangladesh’s maternal and child health program increased heights and weights of children under the age of 14 by 0.07 and 0.06 standard deviations respectively. Mackintosh et al. (2002) estimate that a Save-the Children integrated nutrition project in northern Vietnam increased weight-for-age by 0.63 standard deviations, but had no effect on height-forage. Of course, a fully-fledged comparison across these programs would want to take into

> 13 Among the uninsured, the annual change in height and weight among under-five children between 1992/93 and 1997/98 was 6.4 cm and 1.6 kg.

12
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provided collateral worth 4.4 times the value of their loan, much more than the required 1.9 collateral value to loan ratio required for other bank loans of a similar size (Figure 10). This high ratio can in part be explained by the fact that firms were required to submit land titles for loans as small as IDR 15 million (US$ 1000). Nevertheless, KUR’s large collateral requirements are not just a function of a few extreme cases: for the median borrower, collateral requirements for KUR are still larger than for other loans of a similar size (1.7 compared to 1.4).

Figure 9: Almost all KUR borrowers were required to submit collateral to receive KUR **% firms who were required to submit collateral, by type of loan** <!-- Start of picture text --> 100%<br>100% 95.7% 92.5%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Micro KUR Small KUR Non-KUR<br>Commercial Loan<br><!-- End of picture text --> Source: Author calculations using survey of KUR borrowers Figure 10: KUR borrowers present collateral that is valued at multiple times the amount of the loan **Ratio of collateral value to loan amount** <!-- Start of picture text --> 5<br>4.4<br>4.5<br>4<br>3.5<br>3<br>2.5<br>1.9<br>2 1.7<br>1.4<br>1.5<br>1<br>0.5<br>0<br>KUR Other loans KUR Other loans<br>Median Average<br><!-- End of picture text --> Source: For KUR loans, survey of KUR borrowers; For other loans, 2023 World Bank Enterprise Surveys This requirement for substantial collateral limited the inclusivity of KUR loans and in particular hindered women’s access, since female entrepreneurs in Indonesia are less likely than their male counterparts to own buildings or land that can serve as collateral (World Bank 2016). Qualitative interviews revealed that lack of collateral was a principal challenge women faced in accessing KUR,<sup>29</sup> and quantitative data show

> 29  Another barrier to women’s access to KUR may be the assessment of their husband’s credit history. Some women report their KUR application was denied because their husbands had outstanding loans or had previously defaulted on a loan.

**20**
Once again, the paper follows Firpo et al (2009) and Essama-Nssah et al (2010) in estimating a linear approximation of the RIF regression at each quantile τ _th_ 44, while an OLS regression of log real per capita consumption is also used on the overall sample. Through these regressions, the paper utilizes a wide set of characteristics and links them with the growth incidence curve. This also allows accounting for the heterogeneity of the impact of these characteristics across quantiles. Consequently the paper observes the factors that induced changes in the household living standards whether through widened or reduced welfare differentials among the poor, middle and upper class Jordanians. In the rest of the section, the paper first identifies the variables used as characteristics or determinants of household welfare, building on the work of Shaban et al (2001) but also widening the set of covariates used<sup>45</sup> . Subsequently, the paper dwells on the findings from the empirical estimations of the RIF and OLS regressions. The paper particularly focuses its analysis on the impact of services, income sources and sectoral employment both on a Kingdom and regional levels.

# _V.2. Identifying Determinants of Household Welfare_

The paper identifies seven vectors of socio-economic and regional characteristics as determinants of welfare growth. The first vector is household demographics reflecting the composition of families, as this is expected to have a direct implication on the levels of consumption. Second, characteristics of the household head which captures their education and labor status, as it is often the case that heads are the prime providers of income in their families and have a dominant weight in household spending decisions. Third, household ownership of dwelling, also expected to have a direct impact on household consumption. As for the fourth vector it reflects household income sources. This vector identifies households that are benefitting from any rent sources as opposed to labor income, or whether they receive any cash transfers from the government<sup>46</sup> . This illustrates the implication of access to additional financial resources, or crowding out of labor income if substitution occurs, on consumption and inequality. The fifth vector is household access to selected public and private services _._ The paper uses the

> 44 According to the authors, the expected value of the linear approximation of the RIF regression is equal to the expected value of the true conditional expectation since the expected value of the approximation error is zero.

> 45 The additional covariates used in this paper are those related to income sources, services, and sectoral employment (refer to Tables 8 and 9 for a complete list of these variables). 46 Mainly through the National Aid Fund (NAF).

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- Dhar, D., T. Jain, and S. Jayachandran. 2020. “Reshaping Adolescents' Gender Attitudes: Evidence from a School-Based Experiment in India.” R _evise and resubmit at American Economic Review_ . August 8 version available at: https://bit.ly/32zc2bz

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Dweck, C. 2006. _Mindset: The New Psychology of Success_ . Random House.

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- Fabregas, R. 2020. “Trade-offs of Attending Better Schools: Regression Discontinuity Evidence from Mexico.” _Manuscript_ . December 26 version, available at: https://bit.ly/39KNy12

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- _3.4. The Size of the Welfare State, the Demand for Social Insurance, and Parents’ Incentive to Instill Work Norms in their Children_

A longstanding puzzle is the considerable difference between Western Europe and the US in the size of the welfare state. Several reasons have been put forward as an explanation for this difference; among them are differences in political institutions and in ethnic heterogeneity (Alesina and Glaeser 2004) and self-fulfilling beliefs and multiple equilibria (Piketty 1995, Bénabou and Tirole 2005). However, it may also be the case that the willingness to migrate to the US has historically been associated with different ideologies and these ideologies may have affected the population‘s preferences. In order words, the US population may have a different demand for social insurance and this may be another reason for the differences in the welfare state. A recent paper (Naef _et al_ . 2011) provides evidence for the hypothesis that the US population is less risk averse than the German population. Whether this preference difference holds more generally between the US and Europe is, however, not known.

Eugster et al. (2011), in this symposium, study the impact of differences across language groups in the demand for social insurance across the cultural boundary defined by German and ―Latin‖ ( _i.e_ . French, Italian, and Romansh) language groups in Switzerland. The authors use a regression discontinuity design to identify cultural differences in the demand for social insurance. The Swiss case is well-suited for this research design because on both sides of the language border exactly the same legal system prevails. This means that the supply of social insurance ( _e.g._ unemployment insurance, the retirement system, maternity leave, etc.) is identical. In addition, the wealth distribution, the probability of becoming unemployed, and other risks are similar on both sides of the border. Eugster _et al._ use data from referenda over the period 1980-2009 to document a persistent difference in the demand for social insurance – the German group expresses a much lower demand than the Latin group. These two groups are at opposite ends of scale defined by average responses in countries to the question whether government should do more to redistribute income. Eugster _et al.’s_ findings suggest that even among groups with the same economic fundamentals, there can be large and long-lasting differences in the demand for social insurance.

A neat example of how the welfare state may shape preferences with regard to labor supply is Lindbeck and Nyberg (2006). They explicitly examine the influence of the welfare state on parents‘ incentive to instill preferences for hard work (―work norms‖ for short) in their children.

17
Head and Mayer (2014) explain that the micro-foundation of gravity equations in trade contributed to the extensive use of these models for a range of other bilateral flows and interactions.

We proceed by specifying our gravity-type relationships between (backward and forward) GVC participation, skills, migration and other control variables such as average distance to global GVC hubs. These gravity-type relationships will then be estimated using fixed-effect estimation techniques to understand the determinants of manufacturing GVC participation in Sub-Saharan Africa. Following Kowalski et al. (2015), we define the backward and forward GVC participation equations for country c in sector s at time t as: Here, we decompose our explanatory variables into two. The first set of variables in matrix X are country-sector specific factors. These factors are skilled and unskilled-labor content in exports all at sector level by country. The second set of variables are country specific factors such as average distance to global GVC hubs, GDP per capita, natural resource endowment, population, net migration as a share of total population, human capital index, fragile and conflict situation (FCS), and net remittances as a share of GDP. Although we are interested in estimating the role of skills and migration in GVC participation in Africa, we include two important controls to increase the robustness of our estimates. The first is natural resource endowment. In the African context, natural resource endowment is an important determinant of a country’s comparative advantage in trade and can potentially stimulate or crowd-out country participation in manufacturing GVCs. We obtain data on natural resource endowment from World Bank’s WDI and use it to classify SSA countries into three groups base on their natural resource endowment.<sup>4</sup> The second important control variable is location/remoteness given the “gravity nature” of global trade. According to

> 4 Using the classification in Allard et al. (2016), SSA countries are first divided into 18 countries that are natural resource-rich and 22 countries that are not. The natural resource-rich countries are subsequently divided into oil exporting and non-oil exporting resource-rich countries. See below:

>  **Oil exporters** : Angola, Cameroon, Chad, the Republic of Congo, Equatorial Guinea, and Nigeria.

>  **Non-oil exporting resource - rich countries** : Botswana, Burkina Faso, Central African Republic, the Democratic Republic of Congo, Ghana, Guinea, Mali, Namibia, Niger, Sierra Leone, South Africa, Zambia, Zimbabwe.

>  **All other countries Sub-Saharan Africa** – Benin, Burundi, Comoros, Cabo Verde, Côte d’Ivoire, Gambia, Guinea-Bissau, Ethiopia, Kenya, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Rwanda, São Tomé and Príncipe, Senegal, the Seychelles, Eswatini, Tanzania, Togo, Uganda.

15
Since the PPPs are measured relative to the price level in the United States in the ICP base year,<sup>29</sup> the � for the US is simply the rate of inflation between 2005 and 2011 (ie. �=1.15 reflecting US inflation of 15%).<sup>30</sup> Consider a country with � > 1.15, say � = 1.20. To purchase the same amount of goods in that country as could be purchased with one USD in 2005 (converted by 2005 PPP into LCU), one would need 1.20 USD in 2011 (converted by 2011 PPP into LCU). If a poverty threshold had been set at 1 dollar in 2005 PPPs for that particular country, then to ensure that the poverty line purchased the same bundle of goods, it would need to be set at 1.20 in 2011 PPP terms (i.e. increasing at a rate greater than the US inflation rate). The purchasing power of the local currency is gaining value relative to the purchasing power of a dollar in real terms (accounting for US inflation).

If � were 1.15 for all countries,<sup>31</sup> then the overall distribution of wellbeing denominated in USD would not change with the new PPPs. If � were greater than 1.15 in most poor countries, they would appear to be getting better off in dollar terms relative to the US.<sup>32</sup> Between the 2005 and 2011 rounds, � does indeed diverge systematically between rich and poor countries. Figure 3 shows � for all countries that participated in both the 2005 and 2011 rounds of the ICP, plotted against 2011 GNI per capita.<sup>33</sup> It shows how poorer countries saw a large revision (�), in comparison to rich ones.<sup>34</sup> Across all countries for which there exists actual PPP factors in both years,<sup>35</sup> the average � is 1.47, with standard deviation of 0.30. This average value indicates that across most countries, the change in price levels used for measuring local inflation were typically greater than change in the relative price levels reflected in the PPP conversion factors (when

> 29 More accurately, the global poverty estimates are based on an international poverty line denominated in USD, so the PPPs that are used in the poverty estimation are those that convert LCUs into USD. 30 This is because in any given year, the PPP for converting a dollar to a dollar is one and therefore PPP05/PPP11 = 1 for the US.

> 31 More generally, if � were the _same_ for all countries.

32 The mean consumption level for the population would increase in dollar terms when evaluated using the 2011 PPPs relative to its value when evaluated using the 2005 PPPs.

33 We exclude non-benchmark countries (i.e. countries for which regression-based PPPs are reported) in this analysis, as we assume actual PPPs more accurately capture the price levels than imputed PPPs. 34 This divergence is easiest to observe by looking at rich countries with around 50,000 GNI per capita. There is a large mass of countries with � right around one standard deviation below the mean value for the entire sample. It is also useful to note that one standard deviation below the mean is 1.17, and the U.S. has a � equal to 1.15.

> 35 That is a sample of 140 countries.

20
# **Box 1. Standardized sensitivity tests**

_Alternative Scenarios_

- A1.Historical average scenario, where all variables are set equal to their historical averages in the projections.

- A2.No-Policy-Change, where the primary balance is assumed to remain constant at its actual value for the whole projection period.

- A3. Country-specific shock that would lead to a permanent one-standard deviation decline in real growth rate.

- A4. Market forecast scenario, where all variables are set equal to their market/consensus forecasts.

- A5. Oil Prices are set equal to WEO/DECPG projections - 1σ (relevant for oil-rich countries template)

_Bound Tests_

- B1. Real interest rate =  historical mean + 2σ for the first two years of projections

- B2. Real GDP growth rate =historical mean - 2σ for the first two years of projections

- B3. Primary balance = historical mean - 2σ for the first two years of projections

- B4. Real exchange rate based on one year nominal appreciation of 30 percent.

- B5. One-time realization of contingent liabilities

- B6. Oil price shock= WEO/DECPG projections - 2σ for oil-rich countries template B7. A Combined one-standard deviation shock of all variables relative to their historical mean for the first two years of projections.

- _Source: IMF(2003)._

The purpose of the various alternative scenarios is to facilitate discussion of key vulnerabilities of the economy and to ensure more realistic fiscal sustainability assessments.

For example, to check the realism of the baseline policy projections, it is useful to run an alternative scenario, where all variables determining debt dynamics are set equal to their long run historical averages. Furthermore, to account for country specific sources of vulnerability, the fiscal sustainability tool also allows for a specific crisis scenario, to be set up by the user.

The fiscal sustainability tool can be also used to assess the impact of reforms embedded in the medium term projection framework in the base case, by running a counterfactual scenario where the primary balance remains unchanged during the projection period as compared to its actual level during the current year. Finally, the tool can also use market information, whereby financial variables (exchange and interest rates) are set equal to their forward or futures values, while consensus forecasts could be used for variables such as growth and inflation.

13
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