rafmacalaba/data-use-mentions-extended huggingface dataset:
The lack of employment with the massive reduction in labor income reinforced by the loss of assets and services of a proper dwelling implies a significant increase in the incidence of poverty among the IDPs. Simulation results show that the headcount rate would have grown by 15 percentage points from 23% up to 38% which is twice the impact for the population as whole (Figure 7). In other words, 4 out of 10 internal displaced individuals would have become poor as consequence of the crises. Additionally, the poverty gap and severity would also increase by 5 and 3 percentage points for this population.

Overall, IDPs alone account for half a million of the additional poor people as a consequence of the twin crises (Figure 8). This represents almost 20% of the increase in the total number of poor (i.e. 2.8 million poor). However, the IDP poverty incidence crisis varies significantly across regions. While in Kurdistan IDP would have been contributed to 62% of the growth in the number of poor, in the South this population only would contribute 2%.

Having said this, not all IDP would have become poor as consequence of the twin crises. Poor IDPs only comprise 6% of the total number of 8 million poor people in the crisis scenario and only a third of them would have experienced a worsening of their welfare status large enough to have fallen into poverty as consequence of the crises.

# **IV. Validation**

An important limitation that applies to all ex‐ante approaches is related to the validation of assumptions and hypothesis. The only test of the simulation model proposed in this paper is to combine ex‐ante and ex‐post analysis (see Bourguignon and Ferreira 2003). Fortunately, the Central Statistical Office (CSO) and Kurdistan Region Statistics Office (KRSO) have collected a continuous Income Household and Expenditure Survey (C‐IHSES) during 2014.

The objective of this survey was to provide a more frequent estimate of poverty and welfare in Iraq
the combination of PSW and non-PSW weighting is the best (24.7% versus 25.0% in the reference sample). The combination virtually greatly reduces the bias.

Figure 3 (subfigure 2) shows that the SWIFT model trained with the full sample of the reference data (including both phone owners and non-phone owners) projects the national poverty rate more accurately. Note that in the case of the Saint Lucia experiment, we develop only one model. The model projects a national poverty rate of 25.3 percent, which is within the 95 percent confidence interval of the actual poverty rate of 25.0 percent (the simulation results are available upon request). The details of the model are available in Appendix 1.

Applying the SWIFT model to the phone owner sample reduces the sampling bias in poverty estimation. Using original weights, the SWIFT model projects a national poverty rate of 24.4 percent, which reduces the bias from the actual national average poverty rate (25.0 percent) from the previous 2.8 percentage points to the current 0.6 percentage points.

All reweighting procedures show further but minor improvements in poverty projections. PSW with a nonPSW adjustment (maxentropy) produces a national poverty rate of 26.0 percent, which is 1.0 percentage points apart from the national average estimate (25.0 percent). The standard errors of all SWIFT-based poverty projections are larger than that of the national poverty rate directly estimated from the actual consumption data (displayed as “Reference (direct)” in Figure 3) because of the relatively small number of observations in the phone owner sample and the resulting lack of statistical power.

Lastly, Table 3 displays the summary statistics of variables included in the reweighting procedure, variables included in the SWIFT projection model, and other untargeted indicators not included in either procedure. All reweighting techniques produce close estimates for all the above-mentioned indicators, and the difference between reweighting procedures is minimal.

16
Given most small businesses in developing economies do not keep formal (or any) financial records and administrative data are predominantly unavailable or expensive to collect, it is difficult to determine with certainty what the true value is for sales and profits in the previous month. In this subsection, we explicitly seek out administrative data on businesses across two samples for which we contemporaneously collect AAA data. In this manner, we can directly compare the magnitude and CV of AAA measures against administrative records.

The two sources of administrate data are loan officer evaluation files in Ghana and professional business coach assessments in Uganda. In Ghana, we identify 50 small firms from a partner bank’s database that had financial statements prepared within the last month by the bank’s loan officers. We complete the AAA performance tool with these 50 firms for the same reporting period and reference month as the financial statements in order to directly compare our performance measures to the administrative data. In Uganda, we use data from an intervention in which professional coaches mentored businesses in our study for 12 comprehensive sessions over a six-month period. In the final session, the consultants were asked to assess the financial performance of the businesses and generate estimates for monthly sales and profits. We compare the coaches’ estimates to the estimates generated from our survey tool for the same reporting period and reference month for 31 firms for which we have data. Firms were not privy to either the loan officer evaluations or the professional coach assessments, so their responses to the AAA survey instrument were not influenced by prior knowledge from these sources.<sup>9</sup> Tables 5A and 5B presents the results of this analysis and show very similar findings in Ghana and Uganda, respectively. For sales, the AAA estimates and administrative estimates are statistically indistinguishable in both settings, although the magnitude of administrative sales is larger. In fact, the CV is lower for the AAA measure, with a value of 0.86 compared to 1.07 in Ghana; and 1.58 compared
26 tional price and its domestic price at the port of Colombo: where _pRoW,k_ is the 2019 price of crop _k_ in the RoW, and _pColombo,k_ is its 2019 “benchmark price” at the port of Colombo. This estimation approach selects between the ratio of international price to domestic price and its reciprocal the one that is larger than unity.<sup>63</sup> Thus, we implicitly assume that an international price that is larger ( _smaller_ ) than its domestic price reveals a crop’s status as an export ( _import_ ) crop. The magnitude of such price gap reveals the severity of the barriers faced by the crop’s exporters ( _importers_ ). Our estimated _B_<sup>ˆ</sup> _k_ are displayed in Table III.

# **VI.A.3 Production Function Parameters**

Since our paper focuses on agriculture, it is important to find context-appropriate values for the parameters _{γk_<sup>_f, γ_</sup> _k_<sup>_l, γ_</sup> _k_<sup>_n}k_that govern Cobb-Douglas agricultural production function (1).Leveraging</sup> 2022 NFS data on fertilizer intensity, we estimate the fertilizer coefficient _{γk_<sup>_f}k_of each crop</sup><sup>_k_as:</sup> where _Mk_<sup>_f,LKA_</sup> is annual fertilizer expenditure by Sri Lankan farmers for production of crop _k_ (in USD), _p_<sup>_LKA_</sup> _k_ is the price of crop _k_ in Sri Lanka (in USD/kg), and _Q_<sup>_LKA_</sup> _k_ is Sri Lanka’s annual production of crop _k_ (in kg).<sup>64</sup> The resulting estimates are displayed on Table III. The remaining Cobb-Douglas coefficients ( _γk_<sup>_l_,</sup><sup>_γ_</sup>
businesses across the urban areas in vey”) registered five largest Kenya (Nairobi, Mombasa, Eldoret, Kisumu, and Nakuru). It was carried out between June and September 2022. The sample frame was drawn from the list of businesses registered with the Kenya National Bureau of Statistics (KNBS), stratified by counties and sectors. To be eligible for the survey, businesses were screened based on their

> 13Using VAT records in Kenya,Wiedemann _et al._ (2024) document that over 40% of businesses and 70% of value-added are generated by businesses headquartered in Nairobi county, even though its population is less than 10% of the country.

17
to the model's predictions (identification code 21_2_7) are, on average, better off than households in the community with the best growth outcomes (identification code _8_6_1). In the former community, the average proportion of indigenous people is 0.02, the average height of mothers is 144.5 centimeters, the average level of mothers' education is 1.1 years, the average level of husbands' education is 1.9 years, and the average per capita household expenditure is 22.8 quetzals. In the latter community, all mothers are indigenous, the average height of mothers is 142.7 centimeters, the average level of mothers' education is 0.6 years, the average level of husbands' education is 0.7 years, and the average per capita household expenditure is 22.9 quetzals. However, the average z- score is -3.2 for children in the former community and -2.8 for children in the latter community.

- We then compared the information collected by the EGSF community questionnaire in the two communities to investigate the reasons for their deviation from the predicted outcomes. These two communities are very similar in terms of the infrastructure and services that they provide to their residents. They do, however, differ with respect to migration patterns. Specifically, the key informants report that men living in comnmunity 21_2_7 commonly migrate outside the community to work in plantations and farms. Conversely, men in the community _8_6_1 seem to remain, and work, in the community. Three factors help explain why children who depend on fathers' migration to work on plantations may be disadvantaged: (1) these children are generally of poorer families that have less land; (2) they often have long periods without income because their fathers are not able to start eaming and sending money home immediately after they leave; and (3) they may receive little financial help, if any, because some of their fathers may spend their eamings in other activities. Although this
# **2. Data and Empirical Strategy**

In this section we first describe the design of the high-frequency survey which allows us to identify the labor market impacts of unanticipated illness shocks, and then outline the characteristics of sample households and the empirical strategy we use to identify the labor market effects of these shocks.

# **2.1 Data Collection and Identification of Illness Shocks**

The data come from a 10-week high-frequency data collection experiment we conducted in urban Ghana from August to October, 2013. The participants in this experiment were drawn from respondents of the Ghana Household Urban Panel Survey (GHUPS), who had previously been surveyed in 2010 and/or 2012.<sup>4</sup> This section summarizes the key aspects of the data collection that are relevant for estimating the effects of illness shocks on labor outcomes; more details on the overall data collection process are available in Heath et al (2019).

The survey encompassed 949 individuals in 365 households. The experiment was designed to examine how survey modality and the reference period affect the reporting of labor market outcomes. Participants were randomly assigned to one of three treatments – weekly surveys conducted by phone, weekly surveys conducted face to face, or tri-weekly surveys conducted by phone.

While Heath et al (2019) aggregate the triweekly data to weekly data to assess the role of survey modality and reference period on reporting of labor outcomes measured over comparable

> 4 The GHUPS was run by the Centre for the Study of African Economies at the University of Oxford. See <u>https://www.csae.ox.ac.uk/households/the-ghana-and-tanzania-urban-household-panel-surveys-2004-2006</u> for more detail on the original sample selection for the GHUPS.

8
16 is the number of children born in the five years prior to the data collection. In the 2000 RDHS, this corresponds to the period between May 1995 and June 2000. This time span will be compared to the period between May 1987 and June 1992, when data collection for the 1992 RDHS began. Similarly, in the pooled 1992-2005 data, the time span of interest is the ten years prior to the starting date of the 2005 RDHS, which is the period between May 1995 and February 2005. This corresponds to the period from September 1982 to June 1992 in the 1992 RDHS.

To explore the marriage market effects of conflict on fertility, the following equation is estimated: which additionally includes a time dummy _Year_ for the survey year and an interaction term between the proxy for conflict exposure _Conflicti_ and the time dummy. The estimated coefficient _β6_ captures the impact of _actual_ conflict exposure on fertility after the genocide relative to _women with similar risk of exposure to conflict before the genocide_ .

Given that we control for the effects of conflict in addition to the usual socio-economic determinants of fertility and given that conflict may also affect these other variables, the estimated coefficient of the conflict variable can be interpreted as the ‘pure conflict’ effect. This makes our calculations conservative estimates of the total effects of conflict on fertility. For example, conflict is likely to reduce the educational attainment of children exposed to violence during their school-age (Akresh and de Walque 2008) and of girls in particular (Shemyakina 2006), hence inter alia raising their fertility.

In both (1a) and (1b), measures of each woman’s socio-economic characteristics include three age categories (young: 15-24 years, middle: 25-34 years, and old: 35-49 years), the number of sons and daughters born before the time span of interest, her education (no education, some primary education, and some secondary or higher education), a dummy variable indicating whether she is currently in a union,<sup>9</sup>
# 4. Factors associated with the adoption of inputs

It is well established that sustained agricultural growth, paired by an increase in the productivity of the sector, is a necessary condition for a successful structural transformation and for the reduction of poverty. Augmented agricultural productivity is closely linked to the increase in the adoption of improved or modern agricultural inputs, as was the case of the Indian Green Revolution, and to a lesser extent, in countries of Latin America (McCarther and McCord 2017). While in other countries of Sub‐Saharan Africa the use of fertilizer and other agro‐chemicals is higher than often acknowledged, this is not the case in Uganda (Sheahan and Barrett, 2017).

In this section, we shed some light on other factors, besides financial returns, that influence agricultural input use by smallholder farmers in Uganda. We first briefly review the literature on agricultural input use, drawing lessons for the Uganda context. Then, we use the UNPS panel data set, described in Section 2 and spanning 8 years, to assess what factors are correlated with input use. More specifically, we look at the degree to which access to markets, weather conditions and prices affect input use, and also assess how usage rates correlate with extension services and certain household/farm characteristics.

## _4.1 What does the literature on adoption tell us about what constrains adoption?_

The economics literature analyzing the constraints to agricultural technology adoption in a smallholder setting has evolved from studying a farmer’s production function ‐primarily in the US‐ to understanding the limitations that farmers in less developed countries face.<sup>13</sup> Foster and Rosenzweig (2010) provide a comprehensive overview.<sup>14</sup> Initially, studies on input adoption identified labor availability, price uncertainty and household preferences as main constraints to adoption. The first studies on technology adoption, developed within the agricultural economics literature, analyzed the production functions in the context of US agriculture. Farm size (Just and
are undercounted. The wording of questions meant to measure these activities has been shown to affect female labor force statistics more than statistics for the male population. Researchers have shown that keyword questions such as “work”, “job” or “main activities” produce greater variations in female relative to male statistics due to women’s widespread engagement in unpaid agricultural and family work (Bardasi et al. 2010). For instance, the term “main activity” to define employment status might lead women to report that they are housewives even if they produce goods for the market <mark>(Anker, Khan, and Gupta 1987; F</mark> ranck and Olsson 2014). Women who are involved in multiple activities are likely to have more difficulties in recalling and reporting economic activities that are relatively smaller parts of their daily routine (Franck and Olsson 2014). The ambiguity of the use of key terms can result in biases on the part of both the interviewer and the respondent which may exclude from consideration unpaid family labor and even part-time tasks done to earn money (Dixon 1982; Anker 1983). Likewise, the presence of others during interviews might also impact respondents’ answers (Franck and Olsson 2014).

There is a sizable literature on the importance of cultural factors in explaining female labor force participation (Algan and Cahuc 2005; Antecol 2003; Bayanpourtehrani and Sylwester 2013; Clark et al 1991; Feldmann Boeckmann et al 2014; Fortin 2005; Giavazzi et al 2009; Guetto et al 2015; H’madoun 2010; Nordenmark 2004; Steiber and Haas 2009; Uunk 2015). <mark>Cultural norms are important in shaping the perception that women’s work is less important or less appropriate to report (Anker 1983; Bardasi et al. 2011; Beneria 1999; Mata Greenwood 1999;</mark> Franck and Olsson 2014). Therefore, underreporting is more likely to occur in places where female work is considered undesirable for fear of loss in social prestige (Shah 1985;
present long-term evidence on the effectiveness of financial education via a large, randomized controlled trial, and to jointly analyze a diverse set of administrative records on long-term outcomes.

Relying on the comparison of the randomized treatment and control groups, we find no effect of the high-school financial education program on long-run bank account ownership, but a high percentage of students (85 percent) have a bank account after graduating high school. However, treatment students are significantly less likely to use credit than control students, particularly in the most expensive credit categories: credit cards and overdrafts. Treatment students are 1.4 percentage points less likely to have credit card debt<sup>3</sup> and 0.9 percentage point less likely to use overdrafts, compared to 23 percent of control students with credit card debt and 11 percent of control students with overdrafts. Both effects persist over time, that is, they are equally strong eight to nine years after the financial education program ended as they were five to seven years after the program.

Interestingly, the long-run effect on using expensive sources of credit is the opposite of the shortrun effect found in Bruhn et al. (2016). That is, in the short run, treatment students were more likely to use expensive sources of credit than control students, while treatment students are less likely than control students to use these sources in the long run. While we do not have further data to analyze mechanisms, we hypothesize that in the short-run, students experimented with expensive credit early and realized that this was not a sound financial decision. It is also likely that the credit they used while still in high school was only for small purchases, whereas in the long

> 2 We do not include data from the post-COVID-19 period in our analysis since the crisis likely altered the typical functioning of the credit and labor markets.

> 3 Revolving debt or installment plans to pay outstanding balances.

3
|R-squared|0.937|0.938|0.943|0.943|0.856|0.857|0.944|0.852|
|<br>Fixed effect|HH|HH|HH|HH|State|State|HH|State|
|Year FE|Yes|Yes|Yes|Yes|No|No|Yes|No|

**Note:** Standard errors are clustered at household or state level. *** p<0.01, ** p<0.05, * p<0.1. The eight states for which employment data are available are Andhra Pradesh, Bihar, Chattisgarh, Haryana, Madhya Pradesh, Maharashtra, Rajasthan, and Tamil Nadu. As explained in the text, the relevant MNREGS variable in cols 1 and 2 is a MNREGS dummy while in cols. 3-6, it is the number of days of work provided under MNREGS in the village since the program was put in place. Outcome variable is log of agricultural output in all colomns.

23
# The Health Effects of Universal Health Care: Evidence from Thailand

## by

## Adam Wagstaff<sup>a</sup> and Wanwiphang Manachotphong<sup>b</sup>

a Development Research Group, The World Bank, Washington DC, USA b Faculty of Economics, Thammasat University, Bangkok, Thailand

**Corresponding author and contact details:** Adam Wagstaff, World Bank, 1818 H Street NW, Washington, D.C. 20433, USA. Tel. (202) 473-0566. Fax (202)-522 1153. Email: awagstaff@worldbank.org.

**Keywords:** Universal health coverage; health insurance.

JEL codes: I13; I15.

**Acknowledgements:** We are grateful to Pummarin Hmokthong at the Thai National Village and Urban Community Fund Office for providing us with the village fund data, and Pornpan Kaewsringam at the Thai National Statistics Office for helping us with the Labor Force Survey data. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.
_presuntos_ had not completed high school.<sup>17</sup> Even if, as Chioda (2015) has alerted from the study of Mexican homicide data, the quality of employment—and not only the availability of jobs, plays an important role in the dynamics of murder rates, the coincidence of a sudden labor market shock in the maquila sector and the demand for criminal activities (originated by the government’s crackdown and the war for territorial control among DTO’s) dramatically shaped the opportunity costs ninis faced in relation to getting involved in legal or illegal activities.<sup>18</sup> We believe that the similarities in characteristics between workers in the manufacturing maquila sector and the alleged criminals help explain the significant correlation between male ninis aged 19 to 24 and homicide rates during the period 2007 - 2013, particularly in the north of Mexico.

# **_5. CONCLUSIONS_**

The results presented here suggest that ninis, on their own, are not associated with crime and violence in Mexico. However, during the period 2007-2013, the relationship is positive and significant. The association between ninis and homicide rates is stronger in states located along the border with the United States, a region particularly affected by organized crime and the international financial crisis of 2008-2009. During the period 2007-2011, which saw a massive rise in violence, the increase in the number of male ninis in Mexico accounted for a quarter of the total escalation in the homicide rate observed during that period. In border states, the share of male ninis almost doubled during the period 2007-2011, a change sufficient to explain a third of the total increase in homicide rates in that region.

> 17 The dataset excludes those below 16, the legal minimum age in Mexico to be processed by conventional penal courts. The administrative records of alleged criminals provide part of the legal statistics on crime. This information is captured during all three stages of the criminal justice procedures: preliminary investigation, pre-instruction, and sentencing. In particular, data