rafmacalaba/data-use-mentions-extended huggingface dataset:
Estimation**

We conduct an in IV regression analysis to overcome endogeneity caused from a simple linear regression. The results from the 2020 human capital index (HCI) are positive and significant (i.e. 20.61 points in [2] in Table 4). Consequently, countries with higher levels of human capital (i.e. improved higher education and better health outcomes) have a higher rate of economic growth. Global health security shows a positive and insignificant coefficient, therefore, global health security matters on economic growth. The coefficient estimates of government response of both containment and health measures as well as stringency measures are robustly negative and significant. Therefore, governments measures of stringency and containment may not be providing efficient support or adequately targeting the lives and livelihoods that they want to protect. Ineffective pandemic policies can exacerbate the economy decline.

The results from IV estimations support the findings from the least squares regressions: they confirm the existence of a negative relationship between trade exposure and growth. The deleterious effects of trade exposure can be partly due to vulnerabilities to commodity price volatility. The results from trade openness and export to advanced countries are mostly negative and significant. Natural resource exports and gross capital inflows are negative and statistically not significant. The plunge in global trade as a result of the pandemic has affected more countries participating in global value chains compared with those that resort to intra-regional trade.

15
importance of girls’ education could mitigate potential negative effects of targeting girls for enrollment, rather than focusing on both genders.

To evaluate this program, we use a cluster-randomized experiment in 230 primary schools and individual-level panel data on enrollment, retention, attendance, and test scores in English, Hindi, and Math. We examine whether the program met its objectives: improving enrollment – especially of girls, increasing learning – across all abilities, and supporting school management committees. To measure whether the program was successful at targeting – to girls and students of lower-ability – we examine how the program differentially affected girls (vs. boys), and initially low-performing students (vs. higher performing). We also discuss threats that may arise from differential retention and enrollment.

We find large and statistically significant positive impacts on student enrollment – especially among girls – in the two years of the program. Further, these effects are large, representing increases of 8.1% and 11.7% in the first and second year of the program, respectively. While estimated effects are larger for girls, the differences between girls and boys are not statistically significant. Further, we find no statistically significant effects on the types of students (i.e., high vs. low ability) who enroll in program schools.

In terms of learning, we find large positive gains in all subjects, among both boys and girls. In the first year of the program, the impacts on post-program test scores are highest: 0.323 standard deviation across all subjects (0.317 in Hindi, 0.256 in English, and 0.369 in Math). Students in treatment schools are also 22.7 percentage points more likely to improve their scores in the first year when comparing pre-program with post-program tests. There are no significant differences in the effect of the program on learning gains by student gender. Second year post-program impacts are similar, although somewhat smaller, at 0.156 SD (0.127 in Hindi, 0
of the World Bank database in its considerable specificity. However, this focus is of more general interest because of the importance placed on TRIPS-Plus standards by negotiators and the attention they draw in the policy debate. If, for example, American negotiators place heavy emphasis on patent extensions, test-data protection, and anticircumvention of digital copyrights, their inclusion may bear detectable traces in the trade data. In essence, we explore whether IP-related PTAs with specific BTRIPS standards have trade

> 11 To clarify, we select PTAs in which the IPRs are designated as LE. These are agreements labeled WTOX_ipr_le=2 in the World Bank source.

> 12 Appendix Table A1 lists IPAs involving the United States, the EU or EFTA, and all those considered legally enforceable by the World Bank. We exclude the North American Free Trade Agreement (NAFTA) because it was implemented prior to our sample period. Lists of other groupings are available on request.

14
# **1. Introduction**

Standard welfare measures are based on household consumption and assume an equal and equitable distribution of resources among family members. This presumes that households either distribute resources equally among all family members (equal) or according to the respective dietary or nutrition requirements of all respective family members (equitable). However, existing literature has demonstrated that neither equal nor equitable distributions are observed for most households (Udry, 1996; Brown et al., 2017) and the inferior outcomes of many, particularly women, have been documented (Oster, 2009; van de Walle, 2013). However, disparate intrahousehold distributions may extend further, to differences in the quality and nutritional value of foods consumed between family members. This may ultimately affect nutrition and health outcomes (Coates et al., 2018; Harris-Fry et al., 2018; D’Souza & Tandon, 2020; Brown et al., 2021; De Vreyer & Lambert, 2021).

Due to the potential biases associated with these household-level aggregations, welfare metrics and measures of food consumption and diet quality which are based on individuals may depict a more accurate picture of the conditions of these individuals within households. This has the potential to improve policy recommendations which may otherwise be skewed if everyone in a household is assumed to be allocated equal or equitable resources. There is a growing literature that indicates that the cost of nutritious diets is quite high, suggesting that we may gain richer insight from assessing inequalities in diet quality as opposed to only inequalities in food expenditure (Dizon et al., 2019; SOFI 2020).

In this paper, we assess the extent of disparity between household and individual measurement of food expenditure and diet quality in the Philippines. We use the National Nutrition Survey (NNS) from the Philippines in 2013, which includes an extensive accounting of consumption for 18,472 individuals, residing in 4,213 households. To better understand intra-household food expenditure and diet quality in the Philippines, we estimate individual- and household-level
**Figure 1. Schooling Composition of the Labor Force, 1996-2015**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1996 2015 1996 2015 1996 2015<br>WAP EAP Sample<br>Incomplete Prim Complete Prim Incomplete JH Complete JH<br>Incomplete SH Complete SH University<br><!-- End of picture text --> _Source:_ Authors’ computations based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

_Note_ : _Prim_ , _JH_ , and _SH_ refer to Primary, Junior High, and Senior High, respectively. _Incomplete Primary_ refers to persons who attended primary school but did not complete it. _Complete Primary_ refers to persons who attended and completed primary school but did not move on to Junior High. _Incomplete Junior High_ refers to persons who attended Junior High but did not complete it. _Complete Junior High_ refers to persons who attended and completed Junior High but did not move on to Senior High. _Incomplete Senior High_ refers to persons who attended Senior High but did not complete it. _Complete Senior High_ refers to persons who attended and completed Senior High but did not move on to University. _University_ refers to persons who attended and obtained a University Degree.

Second, the growth rates of the EAP largely mimic those of the WAP, in contrast with the sample, where employment of workers with incomplete junior high or less schooling falls in absolute terms. Third, as a result, the schooling composition of the sample changes rapidly: in 1996, the share with at most primary education equaled that with at least high school (27.7 percent); by 2015, the first share had fallen to 12.9 percent, and the second risen to 47.4 percent. Finally, the
<!-- Start of picture text --> Micro (0-4) Small (5-19)<br>0 0<br>-20 -20<br>-40 -40<br>-60 -60<br>-80 -80<br>Production shock Demand shock Finance Shock Production shock Demand shock Finance Shock<br>No shock suffered Shock suffered No shock suffered Shock suffered<br>Medium (20-99) Large (100+)<br>0 0<br>-20 -20<br>-40 -40<br>-60 -60<br>-80 -80<br>Production shock Demand shock Finance Shock Production shock Demand shock Finance Shock<br>No shock suffered Shock suffered No shock suffered Shock suffered<br>Change in sales (%) Change in sales (%)<br>Change in sales (%) Change in sales (%)<br><!-- End of picture text --> Figure 9: Change in sales by types of shock

# **4.4 Impact of access to programs**

Support programs matter for a firm’s outlook towards the future, investment decisions and financial fragility.<sup>11</sup> Our analysis of the BPS data suggests that increased access to public support programs is weakly positively associated with firm’s expectations of future sales. This highlights the considerably low access to firm support among South Asian firms and the potential for such intervention to influence their behavior (Figure 10, left panel).

Access to public support is also associated with a decline in the probability of falling into arrears for firms that had initially low access. By contrast, financial fragility cannot be addressed at higher levels of policy access (Figure 10, right panel).<sup>12</sup> This finding can be an indication of difficulties in targeting firms that need the most help and are otherwise viable. Some firms that
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Prior to 2003, fixed asset investment (or capital construction prior to 1981) has been calculated from published data for the infrastructure, government and business sectors. A separate estimate has been made for housing GFCF (Herd _et al_ ., 2013). The sum of these four estimates has then been scaled to be equal to the published data for GFCF.

# _Sectoral estimates from 2003_

From 2003, the movements in the aggregate data for fixed asset investment are no longer a reliable guide for investment in the national accounts. To obtain sectoral estimates of GFCF, the following methodology has been adopted

- Industrial sector investment is set equal to the change in the original value of fixed assets, plus an allowance for scrapping.

- Service sector investment (excluding housing) is set equal to FAI in urban areas, excluding FAI in the asset type _other._

- Rural FAI is added to above urban estimates.

- Housing investment is taken from Herd _et al_ . (2013).

# _Comparison of estimates with official data_

The resulting estimate for aggregate investment is then scaled to be equal to the official estimate of gross fixed capital formation. Given that the estimate of GFCF includes R&D expenditure, this scaling factor incorporates an estimate of R&D expenditure not included in FAI. The reliability of the sectoral estimates can, to some extent, be judged from the overall scaling factors that have to be applied to the data (Figure 4). The scaling factor is quite large prior to the 1980s but it reflects the same error as in published data and cannot be resolved. For the period between 1980 and 2002, the sectoral estimates are close to the GFCF data. After 2002, the method used to estimate sectoral GFCF generates data that is much closer to the GFCF data than the FAI series, which by 2016 was almost double the official data for gross fixed capital formation. By contrast, the difference between the sectoral estimate of GFCF and the published data was smaller at 17% in the same year. Indeed
**Table 5: Inequality Estimates from Partially Synthetic Datasets, by Country/Year, Definition of the Middle Class and Index**

|||**Model**<sup>**a**</sup>|||||**$5.5/day**|||||||**$10/d**|**ay**|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**Year**|||_Middle_<br>_class_|||||||_Middle_<br>_class_|||||||
|||_Consumption_|_Income_|<br>_size_<br>_(%)_||_Gini_<sup>_b_</sup>||_MLD_<sup>_b_</sup>||_Theil_<sup>_b_</sup>|<br>_size_<br>_(%)_||_Gini_<sup>_b_</sup>||_MLD_<sup>_b_</sup>||_Theil_<sup>_b_</sup>|
|Burkina<br>Faso|2014|D|GB2|8.060|0.469|(0.031)|0.384|(0.068)|0.746|(0.255)|1.960|0.418|(0.028)|0.301|(0.058)|0.558|(0.222)|
|Ethiopia|2014|SM|
points higher than that of males, suggesting the increase of inactivity might be caused by discouraged women.

As for inactivity, the reasons women give for staying out of the labor force vary across education levels and marriage status. Married women report childcare and domestic duties as their main reasons, while single women report other voluntary reasons. When incorporating the education variable to the analysis, women with less education state family opposition and domestic chores as the main constraints, which is related to the fact that social norms worsen FLFP. In rural areas, the non-educated population has the highest labor participation. For women with higher education, one-third are unemployed, which may be related to the lack of suitable jobs for educated workers.

Overall, these patterns reveal the existence of two different labor markets: one in rural areas where employment and participation are higher for both genders, and another in urban areas where inactivity is extremely high for women and relatively low but increasing for men.

Using data from the LFS we find there are individual and household characteristics keeping women out of the labor force, besides the precondition of an overall decrease in labor force participation rates for both genders, since urban job creation in Morocco has not offset agricultural job destruction. Being married, and the educational attainment of the head of the household—which are variables related with traditional gender roles—are found to deter FLFP. This is also reflected in the later multinomial logit where we find a positive correlation between women who agree that women should be able to work outside home if they wish are and the higher probability of being employed and lower of remaining inactive, suggesting there might be a social stigma toward female keeping women out of the labor force.

36
<!-- Start of picture text --> T tT<br><!-- End of picture text --> <!-- Start of picture text --> o<br><!-- End of picture text --> <!-- Start of picture text --> v<br><!-- End of picture text --> <!-- Start of picture text --> 99<br><!-- End of picture text --> <!-- Start of picture text --> 93 95 97 99 O01 03 O05 OF O9 11 13 93 95 97 99 01 03 O05 OF O9 11 13<br><!-- End of picture text --> <!-- Start of picture text --> ® Difference respectto Labor earnings Gini in 2002 ° Difference respect to Labor earnings Gini in 2002<br>(Confidence interval -95% | Confidence interval -95%<br><!-- End of picture text -->
**(0.1189)**|**0.5867*****<br>**(0.1320)**|**0.6799*****<br>**(0.1688)**|**0.3416*****<br>**(0.1207)**|
|**Initial GDP (1960)**|**-3.8747*****<br>**(0.8375)**|**-3.8616*****<br>**(0.7987)**|**-3.6792*****<br>**(0.8213)**|**-3.8384*****<br>**(0.8858)**|**-3.8537*****<br>**(0.8217)**|**-3.7460*****<br>**(0.8543)**|
|**HC15**|**4.3613****<br>**(1.9441)**||**3.7613****<br>**(1.6161)**||||
|**Sq of HC15**<br>**HC15 with extended**<br>**educational returns**|**-1.0208****<br>**(0.4654)**|**1.6161***<br>**(0.8253)**|**-0.7590***<br>**(0.3870)**||
PotLICY RESEARCH WORKING PAPER _2550_

# Summary findings

In its latest _World Health Report,_ the World Health Organization (WHO) argues that a key dimension of a health system's performance is the fairness of its financing system. The report discusses how policymakers can improve this aspect of performance, proposes an index of fairness, discusses how it should be put into operation, and presents a league table of countries ranked by the fairness with which their health services are financed.

Wagstaff shows that the WHO index cannot discriminate between health financing systems that are regressive and those that are progressive-and cannot discriminate between horizontal inequity and progressiveness or regressiveness.

The index cannot tell policymakers whether it deviates from 1 (complete fairness) because households with similar incomes spend different amounts on health care (horizontal inequity) or because households with different incomes spend different proportions of their income on health care (vertical inequity, given the WHO's interpretation of the ability-to-pay principle)although the two have different policy implications.

With the WHO's index, progressiveness and regressiveness are both treated as unfair. This makes no sense, because policymakers who may be strongly averse to regressive payments (which worsen income distribution) may in the name of fairness be quite receptive to progressive payments (requiring that the better-off, who may be willing to spend proportionately more on health care, are required to pay proportionately more).

Wagstaff compares the WHO index with an alternative and more illuminating approach developed in the income redistribution literature in the early 1990s and used in the late 1990s to study the fairness of various OECD health care financing systems.

He illustrates the differences between the approaches with an empirical comparison, using data on out-ofpocket payments for health services in Vietnam for 1993 and 1998. This analysis is of some interest in its own right, given the large share of health spending from outof-pocket payments in Vietnam and the changes in fees and drug prices over the 1990s.

This paper-a product of Poverty and Human Resources, Development Research Group, and the Health, Nutrition, and Population
# **5 Forced Displacement and Development in Weak States**

Table 1 presents estimates of the effect of forced displacement on economic development across three specifications and three outcomes. Panel A reports OLS estimates of equation (2), Panel B reports 2SLS estimates of equations (2) and (3), and Panel C reports reduced-form estimates obtained by regressing each outcome on Imputed Outflows _mt_ directly. The dependent variables are night-light density in columns (1) and (2), Log(night light) in columns (3) and (4), and the spatial Gini coefficient in columns (5) and (6). Odd-numbered columns exclude baseline controls and even-numbered columns include them.

The estimates consistently show that forced displacement reduced economic activity in origin municipalities. The 2SLS estimates are larger in magnitude than the OLS estimates. This aligns with the intuition that more individuals left municipalities with higher economic growth as they were the ones with greater means to leave, relative to people from other municipalities. The Kleibergen-Paap Wald F-statistic exceeds 600 in all specifications, confirming a strong first stage. Our preferred specification is the 2SLS estimate for Log(night light) with controls, reported in column (4) of Panel B. It indicates that a one-percentagepoint increase in forced displacement outflows as a share of the 1990 municipal population is associated with a 7.5 percent reduction in night-light density. Since night-light density is an established proxy for GDP, with an estimated elasticity of 0.28 (Henderson et al., 2012), this corresponds to a GDP contraction of 2.1 percent for every one-percentage-point increment in forced displacement. To gauge the aggregate magnitude, as noted above, about 7.7 million Venezuelans have left the country since 2013; approximately 2.8 million of them are registered as living in Colombia (UNHCR 2025). Relative to Venezuela’s 1990 census population of 19.83 million, the last census before Ch ́avez’s election, this represents a 14 percent reduction in population
|
|Child controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|
|_Panel B. First-stage estimation_||||MHI-1|MHI-2|MHI-3|
|Shock: Crop or livestock loss||||0.573***|0.616***|0.300***|
|||||(0.165)|(0.179)|(0.088)|
|Observations||||1,095|1,095|1,095|
|Mother controls||||Yes|Yes|Yes|
|Child controls||||Yes|Yes|Yes|
|Household controls||||Yes|Yes|Yes|
|Weak identification F-Stats (Full IV set)<br>Table III presents the effects of maternal mental health on|children vocabula|ry at the age o|f 5 years. Depen|11.53<br>dent variable w|11.69<br>as measured u|11.75<br>sing the|

Table III presents the effects of maternal mental health on children vocabulary at the age of 5 years. Dependent variable was measured using the standardized value of the PPVT test. Mental health indexes are standardized values of the SRQ-20 items using three different estimation approaches. Columns (1)-(3) present estimated coefficients using OLS and columns (4)-(6) show coefficients using IV. Both approaches were implemented using the option PDS-selected variables and full regressor available in the LASSO command. Selected instrument was suffering crop or livestock loss during pregnancy or within the first year of index child. Selected controls are mother's age, wealth index, living in urban area, child's age, consumption of durable goods index, household size, number of children younger than 5 years in the household, and Height for
poverty measures more than urban growth simply because growth is more associated with inequality in urban than in rural areas.

# _IV 3 Possibilitiesforfurther work_

The regressions used above remain descriptive in that we did not attempt to investigate the potentially complex relationships between past, current, and future growth, inequality, and poverty. Completing such an investigation would be beyond the scope of this paper, but a few potential topics can be highlighted in order to show the rich possibilities provided by analyses using regional panel data.