rafmacalaba/data-use-mentions-extended huggingface dataset:
In addition to the evidence already presented, recent developments in behavioral economics also help shed light on the decision-making process that leads to dropout. As described in the 2015 World Development Report, individuals more often make decisions based on mental models, drawing on information in their immediate social contexts and recent experiences, rather than a careful weighing of all costs and benefits. Individuals also differ in what they even consider as being choices within their own power to make. Some recent qualitative research on youth and particularly on dropouts helps to highlight these factors and their potential policy implications.

Dropping out of school is often not a discrete decision, but the culmination of an ongoing process of disengagement. Rich administrative data from the United States and other high-income countries has shown that students who will eventually drop out prior to completing secondary school can be reliably identified in primary school, based on their attendance, behavioral records, and course performance (Gardner Center 2011; Frazelle and Nagel 2015). Interviews with youth in Central America who have dropped out reflect these early origins of their ultimate decision. In Costa Rica and Panama, many dropouts report having had trouble passing courses in the past and sometimes repeating grades (Programa Estado de la Nacion 2013; UNICEF 2015). Students with unaddressed learning deficits may get stuck at a grade level while their peers move on, or they may be promoted to higher grades but unable to follow overly rapid curricula – in either case, they are likely to eventually give up, or to be pushed out by school policies (Pritchett and Beatty 2015).

Poor or excluded youth may lack the agency to actively make the decision to drop out. In contexts of poverty, individuals may suffer from excessive cognitive burdens in navigating the challenges of daily life, leaving them little room for long-term planning (Mullainathan and Shafir 2013). Living in conditions of poverty or social and economic exclusion also affects individuals’ attitudes about themselves and their place in the world: they have lower aspirations and lower selfconfidence, reducing their propensity to deliberatively
increase food supply, whereas minimum wages decrease product supply by increasing labor costs and exert demand-side effects by increasing the income of consumers. By contrast, as an in-kind electronic-benefit-transfers program, SNAP would mostly shift the demand curve without directly shifting the supply curve. Similar to Filmer et al. (2018), we find that transfer programs can have unintended consequences on both recipients and non-recipients through equilibrium effects.

Third, we contribute to a growing literature providing empirical evidence on how demand shocks affect prices. Recent work using retail scanner data suggests price responses to demand shocks can be significant. Stroebel and Vavra (2019) find a strong positive relationship between housing-price shocks and retail price indices at the zip-code level. They argue their results are driven by procyclical markups due to decreases in demand elasticities as a result of increases in housing wealth. Beraja et al. (2019) show a negative relationship between unemployment rates and retail prices at the state level.<sup>10</sup> Our paper shows that another source of demand shocks—in our context driven by government transfers—increases prices. We additionally contribute pass-through formulas that shed light on the factors that determine the size of the price response. We also show that regional pricing can be significant. DellaVigna and Gentzkow (2019) show that uniform pricing in retail chains are prevalent and suggest that the uniform pricing could dampen price response to local demand shocks. Nuancing their evidence, we find that grocery chains in particular price more flexibly or have limited geographic representations with stores spanning at most one or two states, such that prices in the sector show significant responses to locally clustered demand shocks.<sup>11</sup> Furthermore, our analysis provides an informative foil to results by Egger et al. (2022), who find a significant price effect but one that is an order of magnitude smaller than ours in their analysis of a general cash-transfer program provided to three subcounties (653 villages) in rural Kenya. Although some products showed price inflation as large
Georgia’s regulatory requirements are also more flexible with respect to dismissing workers. No third-party notification or approval is required; nor are there priority retraining or rehiring requirements for dismissed workers. Jordan, in contrast, is among the 32 countries with the most stringent regulation of worker dismissals (including prohibiting dismissals for any reason without third party approval), and fixed term contracts are prohibited for permanent tasks (EWD, 2020).

Labor market outcomes entering the crisis also diverged. Unemployment in Jordan was more than 5 percent points higher and labor force participation 23.7 percentage points lower than in Georgia (Table 3-1). Jordan’s female participation rate (13.4 percent in 2019)<sup>8</sup> is among the lowest in the world and female unemployment — 19 percent at the end of Q4 2019 was also relatively high just before the COVID-19 pandemic. Georgia’s female participation and unemployment rates, in contrast, were 54.5% and 10.2%,<sup>9</sup> respectively. Georgia’s population aged 25 or over was more educated, with 59 percent having completed at least post-secondary (2017, WDI/UNESCO) as compared with Jordan’s 27 percent (in 2010, WDI/UNESCO).

For those who were employed in Jordan pre-COVID, a greater share of jobs was in the public sector and in formal private sector jobs than in Georgia.<sup>10</sup> The share of jobs by type of employment from Jordan’s most recent pre-COVID labor force survey (2016) is shown in Table 3-2. Fifty-four percent of jobs were private sector wage jobs, and approximately half of these were formal and permanent, with the rest being either

> 7 In PPP terms, Jordan’s per capita GDP in 2019 was $10,497.00, substantially lower than Georgia’s level of $15,623.00. 8 National estimate, 2019. ILO models predict 14.6
(3) and direct transfers

- 5) **Consumable income** is calculated as the difference between Disposable income (4) and indirect taxes and indirect subsidies (heating and utility allowance)

- 6) **Final income** is calculated as the sum of Consumable income (5) and in-kind health and education benefits

Comparing the income distribution across these different income concepts helps to evaluate the impacts of the fiscal system and of specific fiscal intervention on inequality and poverty. Market income in this analysis includes imputed rent value for owner-occupied housing to enable cross-country comparison, following the Commitment to Equity method in the Guidebook. The poverty and inequality estimates differ slightly from official estimates based on the SILC, which do not include imputed rent in the income aggregate. Appendix 2 includes a robustness analysis for when imputed rent is excluded, where the direction of the results remains valid even though the levels can differ.

The treatment of pensions and pension contributions is often not straightforward. There is no clear consensus: on the one hand, contributory pensions can be treated as personal savings or deferred income; on the other hand, pensions can be treated as government transfers. Pensions

> 7 Informally employed in this context are workers who do not have a written contract with their employer or workers not paying pension and health insurance contributions.

11
# 3.2. The CEQ Approach

To conduct fiscal incidence analysis for Bhutan, we adopt the Commitment to Equity (CEQ) approach, which was developed by Lustig et al. (2018). This approach can be used to examine the incidence of individual tax and subsidy policies, estimate the poverty and inequality impact of each fiscal intervention, and assess whether taxes and transfers are progressive and pro-poor. The distributional impact of the fiscal system as a whole can also be evaluated.

In the standard CEQ approach, the income concepts are constructed as follows:

- _Market income plus pension:_ market income is the income before any fiscal intervention such as taxes and government transfers. It includes labor income (e.g., wages and salaries), income from capital (e.g., rents, interests, and dividends), income from private and public pensions,<sup>13</sup> and private transfers (e.g., remittances). Old-age pensions exist primarily in the public sector in Bhutan and are considered deferred income given their contributory nature.<sup>14</sup> We align our approach to the standard CEQ methodology whenever possible, while labor income is constructed following the approach described in the next section.

- _Disposable income_ is obtained by adding direct transfers and subtracting direct taxes and social security contributions from market income. Since there are few government-supported direct transfer programs in Bhutan and no information in the household survey to identify beneficiaries, we assume that there are no receipts from direct transfers for the baseline assessment. Direct taxes refer to personal income taxes. Social security contributions comprise of payments into a pension fund and mandatory life insurance.

- _Consumable income_ subtracts indirect taxes from disposable income and adds indirect subsidies. Indirect taxes include green taxes, custom duties, sales taxes, and excise taxes in Bhutan. Indirect subsidies are not modeled in this paper.<sup>15</sup>

- _Final income_ is constructed by adding in-kind transfers to consumable income.
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Bandura, A., Barbaranelli, C., Caprara, G. V. & Pastorelli, C., 2001. Self-efficacy beliefs as shapers of children's aspirations and career trajectories. _Child Development ,_ p. 72(1): 187–206..

Bastianon, C. D., 2019. Youth Migration Aspirations in Georgia and Moldova. _Migration Letters,_ 16(1), pp. 105-121.

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3 especially illuminating<sup>1</sup> and the keywords in the EconLit metadata are freely chosen by authors and unsurprisingly are highly heterogeneous. We therefore used our own topical classification system, and assigned a topic to a subset of our records manually. We aggregate our metadata to the author, institution and journal levels, showing which of each have published the most, and which of each have had the greatest impact in terms of citations. We show publication counts for both ―country of origin‖ and ―country studied‖. We present frequency distributions of the words used in titles and abstracts and keywords. We also show how the subtopic focus has changed over time and varies across the top institutions.

Two previous exercises are worth mentioning, although neither comes close in terms of comprehensiveness and ambition. Rubin and Chang (2003) undertook a bibliometric analysis of health economics articles over the period 1991-2000 using EconLit metadata. They also report author rankings (using number of articles and number of pages) but in most other respects their analysis is much more limited, covering a shorter time period, excluding citations, using the limited JEL topic classification system, and omitting many of the cuts of the data that we present. Also of note is the exercise Culyer undertook as background for his four-volume collection of 78 articles on health economics (Culyer, 2006). This was based on a survey of teaching members of the International Health Economics Association (iHEA) in 2004 that asked for their student reading lists, the objective being to ensure representation in the 2006 collection of those articles most frequently cited for student reference by people teaching the subject. His e-mail poll of iHEA yielded usable responses from 112 members and 164 articles that were included on at least two reading lists. We use this source as basis for making some intertemporal comparisons between health economics then (2004) and now (2011), and also for comparing the frequency of citations in general (in 2011) with literature recommended by specialist health economics teachers (in
59 VI. CONCLUSION internal efficiency of nutrition policy and programs depends to a substantial degree on appropriate targeting, choice of intervention, and securing appropriate household behavior. All in turn depend on knowledge of the determinants of malnutrition at the household level, the household's potential response to the program, and the family's ability to use program resources.

The paper outlined the potential of household economics and econometrics as frameworks to organize and study determinants of the risk of malnutrition at the household level where the problem occurs.

In many ways the paper constitutes an agenda for research. Relatively iew studies have been conducted, especially in program environments, to yield basic knowledge about the determinants of malnutrition and program impact (e.g. Narangwal). Some unique options for research (Tamil Nadu' have not been fully exploited. After a review of the nutritional consequences of agricultural projects, Martin (1983) remarks: "a research program must be begun which generates the sort of quantitative data needed to permit an analytical determination of the links between adverse nutritional outcomes and their various causes. Initially, such research should probably concentrate on factors which may be considered important for theoretical reasons and for which there is supporting evidence in the existing literature." (p. 46)
Our key independent variable of interest is the political connectivity of each firm. We follow Faccio (2006), and others, and code a firm as politically connected when it has at least one board member or CEO who is a politician, a close relative of one, or a publicly known friend. For that purpose, we leverage online business directories and Lebanon’s commercial registry to look up the name of each firm’s board members in addition to collecting data on their size, age, and paid-in capital.

Our approach to identify political connections takes into account that political connections are a complex phenomenon in a country like Lebanon (Leenders, 2012; Diwan and Haidar, 2020). We go beyond previous studies, which establish objective criteria for the identification of connections, such as by name matching of a company’s shareholder or CEO names with those of political actors. As such approaches have tended to underestimate results,<sup>7</sup> we instead review each firm in our data set manually via an approach outlined in detail in Mahmalat, Atallah and Maktabi (2021). For each firm, we go through a multi-layered search process that relies on media searches on the names of each board member of a firm with a corresponding name of a politician or their political party. This approach allows us to carefully assess a number of common issues in the identification of political connections, such as whether individuals with matching names are related, connections are “deep” enough to matter, or relevant during the period of investigation. We augment and validate the findings with our key informant interviews and code a firm we have not found reliable information on as connected when multiple interviewees correspond in their assessment of a particular firm.<sup>8</sup> Moreover, we review the commercial registries as well as the companies’ websites to identify firm characteristics, notably their age, size (in number of employees), and paid-in capital. As these directories fail to report some of the
- 42 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

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CCT programs in Latin America have been carefully analyzed—see for example, Schultz (2004) and Behrman, Sengupta and Todd (2005) on the PROGRESA program (now re-named Oportunidades) in Mexico, Bourguignon, Ferreira and Leite (2003) on the Bolsa Escola program (now re-named Bolsa Familia) in Brazil, and the reviews by Rawlings and Rubio (2005) and Das, Do, and Ozler (2005). In this paper we assess the impact of a similar program in a context with much lower income, lower enrollment rates, and weaker institutions.

As we show below, the JFPR scholarship program had a large, positive effect on the school enrollment and attendance of girls in Cambodia. Our preferred set of estimates suggest program effects on enrollment and attendance at program schools of 30 to 43 percentage points; scholarship recipients were also more likely to be enrolled at any school (not just program schools) by a margin of 22 to 33 percentage points. The impact of the JFPR program appears to have been largest among girls with the lowest socioeconomic status at baseline. The results we present are robust to a variety of controls for observable differences between scholarship recipients and non-recipients, to unobserved heterogeneity across girls, and to selective attrition out of the sample.

# **2. Data, variables, and program selection rules**

The main sources of data for the analysis in this paper are two: Application forms to the scholarship program, and data on school enrollment and attendance from an unannounced school visit. The application form contains 28 questions about parental education, demographic composition of the household, ownership of various assets, housing materials, and distance to the nearest secondary school; applicants were also asked to specify the name of the secondary school they would like to attend, with the understanding that this should be one of the 93 schools that were eligible for the scholarship program. These forms were completed by a girl in class or at home; some provisions were made for teachers to verify that the data
productivity and earning in countries with different macro environment and protection of property rights.

Another important economic question is how to finance general and specific human capital investments in firms (Willis 1986, Becker 1962, Oi 1962). General human capital raises a worker's productivity regardless of whether she stays at one firm or moves to another. Specific human capital, on the other hand, is lost if she moves to another firm. Efficiency dictates that the employer should sho-ulder much of the costs for specific training, since the worker could not benefit from the training if she moves to another firm. Likewise, there may be little reason for the employer to pay for general training, since the worker could benefit from it even if she moves to another firm. In this case, the worker may pay for the training since she benefits from it wherever she works. It also makes sense for the government to subsidize general training since the economy benefits from more productive workers wherever in the country they work. We have little empirical evidence, however, about the relationship of productivity to labor market experience and how training is financed in different countries. The finding would have policy implications for how governments and firms should approach worker training.

Many Bank surveys cover human capital issues quite well (for instance, RPED and ICS). Most surveys contain the primary measures, although they tend not to explore the distribution of workers in term of labor market experience and seniority structure, and quit rates. Such questions can be important. The turnover rate, for example, can provide an indication of how vigorous a labor market is in one developing country compared to another, or cornpared to industrialized nations. Quit rates may indicate the importance of match-specific human capital and the vitality of a country's labor market (Tybout, 1998). Involuntary turnover rates could indicate the extent to which workers are disciplined or how firms adjust employnient in response to changes in economic conditions.

# **C. Technology**

In the long run, technological
on internal funds to finance their working capital and fixed assets. Due to the issue of data availability, any analysis of other potential measures of access to finance is constrained (for example, rejected loan applications are available only in the post-treatment period in our data sample).

We find that the firms are on average more than two decades old; they have approximately 40 employees; their direct exports account for less than 10 percent of their sales; and they are mostly owned by domestic private individuals. In our baseline regressions, we use the pre-treatment levels of all the firm-level explanatory variables to address potential concerns of reverse causality.

The bank systems in our sample are characterized by relatively high returns on equity, good capitalization, and a reasonably low share of nonperforming loans. However, these averages mask a great deal of variation among the banks. We examine in the following section whether Basel III implementation affects this variation. We present summary statistics for the whole banking industry in table 1. Some of our regressions use overall bank systems data, while in other regressions we identify the supply-side effects of the SME financing market more accurately by employing the matched firm-bank data.

Table 1: Summary statistics

||Observations|Mean|St. dev.|Min|Max|
|---|---|---|---|---|---|
|_SME financing: Dependent variable_||||||
|Access to finance as a constraint|31,014|3.43|1.31|1|5|
|_SME financing: Other variables_||||||
|Line of credit or loan|32,156|0.36|0.48|0|1|
|Working capital financed by banks (%)|29,249|16.71|26.81|0|100|
|Fixed assets financed by banks (%)|9,448|18.81|33.16|
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Mendoza, E. G., and V. Z. Yue. 2012. “A General Equilibrium Model of Sovereign Default and Business Cycles.” Quarterly Journal of Economics 127(2): 889–946.

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