rafmacalaba/data-use-mentions-extended huggingface dataset:
most-educated-female-adult FHHs (Appendix A, Tables A.12 and A.13).<sup>14</sup> We further consider the overlaps of three main FHH types (self-reported, potential, and most-educated-female-adult FHHs) and all four main FHH types and show the estimation results in Appendix A, Table A.14, which remain qualitatively similar.

# **4.2. FHH poverty dynamics based on synthetic panels**

We turn next to discussing the results on poverty dynamics based on synthetic panels. For each country, Figure 5 reports the conditional upward mobility rates in the second survey year for the four main FHH types, considered separately with and without any children (Equation (3)). Figure 5 shows considerable (conditional) upward mobility at the national average level (dashed line) for some countries. In particular, the upward mobility rate is 45 percent in Iraq during 2007-2012 (i.e., 45 percent of the initial poor in the first year escape poverty in the second year), 54 percent in Jordan during 2010-2013, and 41 percent in Mauritania during 2014-2019. Still, a significant degree of immobility exists in Egypt, the West Bank and Gaza, and Tunisia, where most of the population remained poor in both years and only about one-third (or less) of the poor escaped poverty in the most recent year: 29 percent for Egypt during 2017–2020, 31 percent in the West Bank and Gaza during 2011–2017, and 21 percent in Tunisia during 2015–2021.<sup>15</sup> Unsurprisingly, non-FHHs have upward mobility rates that are almost the same as the national averages, given their large shares in the population (Table 1). But interestingly, FHHs without

> 14 The estimated marginal effects for the interaction terms with children are qualitatively similar (using the Stata command “ginteff” (Radean, 2023)).

> 15 The survey period lengths generally differ for the six countries so the estimated mobility rates are not
, whereas the quality of monitoring with the control of corruption), and the estimated Poverty Space for 2011. We estimate the model parameters and find the optimal spillover rate by calibrating the model with spillover rates ranging from 0 to 1 (in increments of 0.05) and calculating the goodness of fit. The rate providing the best fit on average is our optimal spillover rate. See Appendix 4 for more details on the data cleaning and calibration procedures for our PPI analysis.

We then conduct a prospective analysis until 2030 using data from the final survey year (2019) and Poverty Space data for the same year. We assume that the rule of law and quality of monitoring remain at the 2019 level and that government expenditure remains at its average value from 2011 to 2019. In 27
# **Appendix D. Additional Estimates**

**Table D1A. Difference-in-Difference Intent to Treat Impacts for Domains of Cognitive, Social, Motor and Language Development by Age in Years (at Round 3) and Duration Class** **<u>(without matching), Philippines ECD Study</u>**

|**Age/ECD Indicator**|**Durat**|**ion of Exposure (m**|**onths)**|
|---|---|---|---|
|**_Gross Motor_**|**4 to 12**|**13 to 16**|**17+**|
|2|1.08 (.284)*|1.44 (.224)*|1.41 (.247)*|
|3|.050 (.206)|.420 (.189)+|.482 (.168)*|
|4|-.005 (.172)|.111 (.206)|.243 (.145)~|
|5|.061 (.168)|.467 (.185)+|.405 (.165)+|
|6+|-.122 (.124)|-.019 (.132)|-.056 (.120)|
|**_Fine Motor_**||||
|2|.248 (.303)|.421 (.249)~|.500 (.262)~|
|3|-.057 (.254)|.158 (.246)|.259 (.188)|
|4|.040 (.207)|.090 (.212)|-.156 (.193)|
|5|.429 (.179)+|.472 (.235
# Addressing the Education Puzzle

# The Distribution of Education and Economic Reforms

## Ram6n Lopez, Vinod Thomas, and Yan Wang

## Summarv

No country has achieved sustained economic development without substantial investment in human capital. Previous studies have shown the handsome returns to various forms of human capital accumulation: basic education, research, training, learning-by-doing and capacity-building. Yet history also tells us that education by itself does not guarantee successful development. Examples include the former Soviet Bloc, Sri Lanka, the Philippines, and the Indian states of Kerala and West Bengal. The real question, then, is when and how education can bring high payoffs.

While theories suggested a strong causal link from education to growth, the empirical evidence has not been unanimous and conclusive. This paper focuses on two explanatory factors. First, who gets educated matters a great deal. The distribution of education is a complex issue and not much has been written about it. In this paper we construct an asset allocation model that elucidates the importance of the distribution of education for economic development.

Second, the impact of education on growth is greatly affected by the economic policy environment. Policies determine what people can do with their education. Reforms of trade, investment, labor policies can increase the returns from education. Using panel data from 12 Asian and Latin American countries for 1970 to 1994, the paper investigates the relationship of education, policy reforms, and economic growth. Our empirical results are promising.

First, the distribution of education matters. Unequal distribution of education tends to have a negative impact on per capita income in most countries. Moreover, controlling for human capital distribution and the use of appropriate functional form specifications consistent with the asset allocation model makes a difference for the effect of mean education on per capita income. Controlling for education distribution leads to positive and significant effects of average education on per capita income, while failure to do so leads to insignificant and even negative effects of average education.

Second, the policy environment matters a great deal. Our
Table 3 shows we cannot reject equality of impacts on profits across our three follow-up rounds (p>0.90). In addition, for the key outcomes of firm profits and firm sales, the March and December 2011 survey asked for each of the past 3 month’s data, enabling us to run the specification in (2) with 7 months of observations per firm for these outcomes. Standard errors are clustered at the firm level. We instrument for formalization with three variables indicating assignment to the 10,000 LKR, 20,000 LKR, and 40,000 LKR treatments respectively. Since the information and reimbursement only treatment did not lead to any change in registration, it does not serve as an instrument and we drop this treatment group from this part of the analysis.

The treatments are valid instruments for being formal under the assumption that they affect the outcomes of interest only through changing registration status, and not through any other channel. An obvious concern with this assumption is that the grants given to the firm owner may have had

> 9 Recall that we can not reject that attrition is unrelated to treatment status.

16
The first successful integration of a full household survey into a CGE model was by Cockburn (2001) when he integrated the 3,373 households in the household survey into his constant returns to scale CGE model of Nepal. Subsequently, Cororaton and Cockburn (2007) integrated the 24,979 households in the household survey into their CRTS model of the Philippines.<sup>41</sup> The solution techniques used by Cockburn and his co-authors, however, will not typically allow incorporation of larger household data sets; and increasing returns to scale models present greater challenges for robust solutions. In this section, however, we employ a new solution algorithm for an increasing returns to scale CGE model with essentially no significant bound on the number of household agents.

In our central model, we estimate that the mean welfare gains to Russia, averaged over all households, from WTO accession are a rather substantial 7.3 percent of Russian consumption (with a standard deviation of 2.2 percent of consumption) in the medium run. Decomposition analysis reveals that over 70 percent of the gains are due to the liberalization of barriers against foreign investors in services. We estimate that 99.9 percent of the households will gain from 2 percent to 25 percent of their household income. We find that poor households gain slightly more than rich households on average, since the return on capital does not increase as much as the wages of skilled and unskilled labor. We conduct both piecemeal sensitivity analysis and systematic sensitivity analysis (the latter by executing the model 30,000 times with random parameter selection), and find that our results are robust with respect to parameter specification. But without foreign direct investment, services liberalization and Dixit-Stiglitz externalities, the welfare gains are only one-sixth of the gains in our model and seven percent of the households are estimated to lose. Our results show that, while incorporating the diversity of households though a real household model is important, incorporating

> 41 See also Annabi et al (2005), who have incorporated
.1 and 1.8 percentage points of added employment growth for product and process innovators respectively in non-OECD developing countries, while it is statistically unrelated to employment growth for non-innovators. The large firms (with over 200 employees) in both the all-country and non-OECD country samples are different from smaller firms in that while their employment growth is significantly affected, on average, by their product innovations, it is not significantly related to their process innovations or their TFP. In addition, large firms‘ employment growth is unrelated to internet use, ISO certification, use of formal job training programs, membership in a business association and incorporation status. The young firms are different on average since their employment growth rates are not significantly related to process innovation (though they are significantly related to TFP and product innovation), ISO certification, membership in a business association, and their incorporation status. In contrast, internet use and formal job

> 28 ISO management certification refers to a family of internationally recognized management quality standards. The certification status of surveyed establishments is ascertained directly in the Enterprise Surveys. Formal training programs refer to ―beyond the job‖ training opportunities offered to employees of the respondent establishment. Table 1c contains a detailed listing of the definition of these and other enterprise-level business environment indicators used in this study.

20
pattern of investment, indicating that Islamic investing and socially responsible investing are driven (at least in part) by considerations other than fund performance. However, we fail to find evidence that investors view socially responsible funds as substitutes for Islamic funds, with religious investors choosing to invest higher amounts in Islamic funds even when the alternative fund is socially responsible.

Our results provide clear evidence for religiously motivated investing. To the best of our knowledge, ours is the first study that aims to assess the non-pecuniary motivations behind religious investments through a lab experiment. Our study contributes to literature on the role of non-financial information in investment decisions, investor loyalty and faith-based investing. Our findings hold important implications for a growing segment of the mutual fund industry, particularly investment patterns and communication strategies.

The rest of the paper is organized as follows: Section 2 outlines the design of the lab experiment; Section 3 presents the results; Section 4 discusses the findings; and Section 5 concludes the paper.

# **2 EXPERIMENTAL DESIGN**

Our experiment utilizes a between-subjects design, with a control group and three treatment groups. Our sample consists of university undergraduate students at a private university in Pakistan – the Lahore University of Management Sciences. Students at this university generally belong to the mid to upper income levels of Pakistani society. This sample is ideal for our purposes, as Pakistan has the second largest population of Muslims in the world (following Indonesia) and has a high proportion of Muslims (96.5% according to the 2017 census). Furthermore, the sample is well-educated and likely to be high earners in the future (due to strong employment prospects of the university’s graduates), and hence are likely to engage with the financial services sector, including as investors. Hence, conducting a study on Islamic investing is ideal with this sample. In addition to this, Pakistan is a middle-income country, and has a robust social services and non-governmental organizations sector, indicating that socially responsible investing may also be of interest to the population
’ knowledge, this is one of the first to explicitly examine the dynamics between water availability and conflict in the region. The context of the Sahel region provides a unique setting for empirical investigation.<sup>1</sup> First, high baseline water scarcity has historically defined the livelihood strategies employed by the population (pastoralism, rainfed agriculture, etc.) as well as their cultural institutions. The expansion of centralized irrigation in the region is a more recent phenomenon. Second, while the region has known political instability in the past, the rise in extremism seen over the past years is distinct and ostensibly linked to factors external to the region, namely the geopolitical fallout in the aftermath of the Arab Spring to its north and the rise of Boko Haram in Nigeria in the south. This arguably provides an avenue for causally exploring the mechanisms that link water and fragility in the region.

We present two empirical approaches to document the relationship between water availability and conflict occurrence in the G5 Sahel countries. First, we build on previous findings of Harari and Ferrara (2018), henceforth referred to as HF, to show that the frontier climate of the G5 Sahel is particularly at threat of conflict caused by climate-induced variation in water availability. HF use gridded data for the continent of Africa to document a strong relationship between conflict occurrence and a drought index referred to as the Standardized Precipitation-Evapotranspiration Index, or SPEI. Using their data and a simplified version of their empirical model, augmented with interaction effects, we show that a large portion of the

> 1 A larger body of literature can be found on the Sahel on issues related to climate shocks and malnutrition/ food security. An example of such literature is Alfani et al. (2015).

2
perform physical activity, or to handle moving objects, such as vehicles or machines; requiring workers to repair large mechanical or electronic equipment; entailing worker exposure to diseases or infections; involving work in which email is very seldom used. This measure, however, is based on six-digit-level disaggregation of the Standard Occupational Classification (SOC) system. Given that the EU Labour Force Survey has a coarser disaggregation of occupations, we aggregate our telework measure to three-digit SOC occupations using employment weights based on the Current Population Survey of the US Bureau of Labor Statistics. Therefore, the six-digit-level variable used by Dingel and Neiman (2020) becomes a continuous variable between zero and one in our data; this variable reflects the share of jobs within each broad occupation category that is amenable to telework. We then transform this classification to the International Standard Classification of Occupations (ISCO08) used in the EU labor force survey.

# **Jobs with Greater Income Risks**

Using these criteria, we categorize a job as vulnerable to income risks if it is neither essential nor able to be undertaken via telework. Workers in these jobs are exposed to higher risks of dismissal and loss of income given that mobility restrictions impact their ability to perform their duties at their workplaces.

# **Jobs with Greater Health Risks**

When assessing job-specific exposure to health risks, as our fourth measure, we first quantify the extent of face-to-face interactions required by occupations. We follow Avdiu and Nayyar (2020) who create an index based on O*NET surveys. In this case, we assume that a job requires high face-to-face interactions if it involves tasks such as establishing and maintaining personal relationships, assisting and caring for others, performing for or working directly with the public, or selling to or influencing others through extensive personal interaction (Garrote-Sanchez et al., 2020). We categorize jobs as exposed to health risks, if they require above average face-to-face interaction level, are deemed essential and cannot be performed from home.<sup>5</sup
of the items produced may be consumed. Many FDPs store wealth in jewelry or gold to carry these values with them, and it is unknown how much of this wealth is used for regular or occasional expenditures. These facts make the estimation of income for FDPs rather hard so that most often consumption-based measures are used.

However, measuring consumption among FDPs has its own challenges, including misreporting and questionnaire fatigue. It relies on a list of all potential products consumed by a household. For each consumed item, the household needs to provide specific information on the consumed quantity. Usually, additional questions are asked about the amount that was purchased, including the outlet and price, to be able to estimate the monetary value of consumption. However, FDPs have a strong incentive for misreporting if they are aid-dependent and believe that their responses affect future aid. Kaplan, Walsh and Pape (2018) find in numerous rounds of data collection in Somalia and South Sudan that IDPs report significantly lower levels of consumption than non-IDP households. For example, 45 percent of Somalia IDP households report food consumption below subsistence levels and approximately 80 percent below recommended levels. While the data may be accurate, there are two reasons to suspect that it is not. Such high levels of below-subsistence consumption would be associated with high rates of mortality due to starvation, which is not borne out in mortality data. Kaplan, Walsh and Pape (2018) use a randomized experiment to test the effectiveness of a bundled nudge including a truth primer and more investigative reporting controls. They find that the bundled nudge induces higher reporting in lower quintiles of reported consumption. This treatment pattern is driven by aid reliant IDPs and vanishes when considering the comparison group of non-IDPs. This suggests that IDPs – and possibly more generally aid-dependent populations – are indeed misreporting. However, the study does not yield a ‘true’ estimate of consumption, which is needed to assess the overall level of misreporting and the different approaches that could be used to mitigate misreporting.

Questionnaire fatigue is another important issue
# **Figures 9c and 9d. Government Revenue and Growth: Egypt**

<!-- Start of picture text --> Fig. 9c. Gov. budget gains as % GDP (Carbon tax =<br>$25)<br>1.8<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>2022 2024 2026 2028 2030 2032 2034 2036<br>$25 carbon tax phase out fuel subsidies<br><!-- End of picture text --> <!-- Start of picture text --> Fig. 9d. Growth contribution of carbon tax versus<br>removing fossil fuel subsidies<br>0.07<br>0.06<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0<br>2020 2022 2024 2026 2028 2030 2032 2034 2036 2038<br>Baseline GDP growth GDP growth with $25 carbon tax<br>GDP growth: remove subsidies<br><!-- End of picture text --> Note: Figure 9a. Results are similar to those for Saudi Arabia (fig. 7a), but with smaller magnitudes. In Figure 9b, at $50 per ton of CO2, although swapping carbon tax and fossil fuel subsidy policies (imposing the tax but keeping the fuel subsidy -as in orange line- versus no tax but removing the subsidy-as in yellow line) leads to largest gain in CO2 reduction (yellow to orange line shift) , the gain in CO2 reduction by removing the subsidies when there is no tax (shift of gray to yellow line) is substantially larger than doing the same at a carbon tax rate of $
relationships were present, we applied transformations, so that the relationship between continuous predictors and the outcome variable of log per capita household consumption was approximately linear. Examples of these analyses are found in Annex 4.

Table 3 below presents the main results of the Sudan national PMT. During the modeling process, we found that a more parsimonious model, i.e., one with less variables and parameters, performed similar to the complete model, which included all candidate variables. Thus, the final model presented below retains the

> 17 For example, the level of schooling exhibited a stronger relationship with household consumption than years of schooling, due to grade repetitions increasing years of schooling, but not the level earned.

12
show that the evaluation of the impacts of an increase in the price of major agricultural products (like corn) is sensitive to the inclusion of both consumption and income effects, as well as to the allowance for consumption and income responses.

The paper is organized as follows. Section 2 provides a general motivation and develops the econometric model. I provide an overview of the methodology and I introduce the general equilibrium econometric model of consumption and income responses. In section 3, I apply the methods to the Mexican data and I report the results on the demand and wage income price-elasticities. Section 4 simulates the welfare effects of the increase in the price of corn taking into account consumption and income responses of Mexican households. In Section 5, I conclude.

# 2 The Econometric Model

In most studies of price reforms, such as those summarized in Deaton (1997), the analysis is based on the responses of household consumption. Standard arguments of efficiency, based on price elasticities, and equity, based on expenditure elasticities, are used to characterize optimal taxes or to indicate direction of changes in the structure of taxation. This analysis omits important aspects of the effects of any policy, namely the responses of outputs, wages, and household income. To estimate the impacts of the reforms both on the expenditure and income sides, I argue that it is necessary to estimate own- and cross-price elasticities, on the one hand, and wage-price elasticities, on the other.

Discussions about the poverty impacts of trade reforms often make the argument that supply responses are critical for the poor. Specifically, WTO reforms on agricultural trade are expected to boost production opportunities in rural areas in developing countries. Proponents of this view argue that agricultural trade liberalization will bring about increases in international prices of agricultural goods. Faced with higher permanent prices, households will choose to devote more resources to agricultural production and firms will increase their labor demand in agriculture. This higher demand may involve higher employment in rural farms (for planting, weeding, or harvesting),