rafmacalaba/data-use-mentions-extended huggingface dataset:
and the coefficients are increasing with size.<sup>4</sup> The workers employed by firms, employing 1500 or more workers receive the highest wages. In Zambia, Ghana, and Cameroon the highest wages were obtained in firms with more than 200 employees and in Kenya it was in the group with 400 or more workers that earned the most. Velenchik (1996a) also found a large premium associated with working for larger firms in Zimbabwe and the premium is found to be larger for white than blue collar workers. Findings suggest that larger firms use higher wages to increase the quality of the applicant pool, to reduce employment turn over, or to strengthen worker loyalty.

# **3. Data and Methodology**

Wages and productivity determination are examined by matched employee-employer manufacturing sector data; the Regional Program on Enterprise Development (RPED) survey data from 1993. The number of firms and employees interviewed are 201 and 1609, respectively. About 10 randomly selected workers from different occupational categories are interviewed in each enterprise. Matched employee-employer datasets have advantages compared to pure employee or employer data sets. An employee dataset may contain information about the sector in which a worker is employed but limited information about the firm. On the other hand, an employer dataset contains information about firms but limited information about individuals employed in the firms, apart from aggregate wage, education, and training costs. An employer-employee dataset allows detailed analyses of hypotheses related to both firms and individuals.

Empirical studies of determinants of wages and earnings inequality have focused primarily on factors affecting labor supply. Examples of long-run labor supply factors

> 4 Earnings per worker could increase with the size of the enterprise because of institutional factors that are correlated with firm size or economic factors. The non-institutional economic factors relating to wage increases are: (i) the inelasticity of labor supply of individual firms, (ii) factors that can be classified under 'efficiency wages'; (iii) profits, economic rent and labor productivity may increase with firm size and wages
# **Inputs**

The sum of the within elasticities of both types of capital is 0.37, with fixed capital accounting for most of the impact on productivity. Turning to the between regressions, the elasticity on fixed capital is particularly high (0.59) in the between-time regression. This suggests that the pace of the implementation of changes in the available technology was strongly constrained by the level of the fixed capital stock in agriculture. The between-time estimate of capital of agricultural origin is significantly positive, though more modest in value (0.24). While the between-time coefficient on fixed capital is robust to different specifications of the model, the other between-time coefficients are difficult to interpret in light of the limited time series (31 years) and the concern over multicollinearity.

The between-country estimate of fixed capital is smaller than the within estimate, while the between-country coefficient on livestock and treestock is larger (0.14). These estimates are more similar to those obtained from cross-country studies, in which the impact of capital on the changes in technology over time is not considered.<sup>30</sup>

# **State variables**

State variables are included in the analysis to eliminate the bias of the estimated input coefficients that is caused by the jointness property of choosing inputs and implemented technology simultaneously. Looking first at the within estimation, the technology variables give mixed results. The development indicator is quite robust, indicating that the more productive is the economy as a whole, the higher is the productivity of agriculture. Schooling and peak yield have unexpected negative coefficients, though the coefficient on peak yield is not significant. As expected, the price coefficient is positive, and that of the price variability is negative. Macroeconomic instability (in particular, inflation) does not have a significant effect. The institutional measures do not seem to be able to capture any effect beyond what was already reflected by the development indicator.

The between-country results reflect the determinants of cross-country variability at a given point in
for example, tenants may invest less than landowners merely because they are less wealthy. The advantage of looking at land tenancy in India (Bell, 1977; Shaban, 1987)—and, as it happens, in Pakistan—is that a given household will often cultivate two or more plots of land under different contractual arrangements, allowing a comparison of behaviors across plots within the same household. A limitation of this approach is that it does not deal with the possibility that the decision to lease out a plot (or to lease it out under a fi xed rent versus a sharecropping contract) may depend on the unobserved attributes of the plot, such as would be the case if there is adverse selection in the leasing market. We use instrumental variables to address this potential endogeneity problem. Our identi fi cation argument, developed below, is that the assets of the owner of a given cultivated plot, which our data set provides even if the plot is leased in, should determine the leasing status of the plot but not its unobservable type.

The next section of the paper provides the context for our study and sets out a simple two-period model of tenancy, moral hazard, and land-speci fi c investment. Section 3 presents the estimation and identi fi cation strategy as well as our evidence for a leasing effect on investment. The analysis of the tenure security effect follows in section 4, along with conclusions and implications in section 5.

# 2 Tenancy and Land Speci fi c Investment

## 2.1 Data and context

Ownership of agricultural land is highly concentrated in rural Pakistan, where about half the population is landless. As a result, land lease markets are quite active. According to the latest agricultural census (2000), about a third of total cultivated area was tenant operated, mostly (roughly two-thirds) under crop-sharing arrangements with the remainder under fi xed rent. Land and tenancy reforms have been attempted at various times, but they have been largely ineffectual, leaving tenant cultivators with little legal recourse in the
# **Table 1. Background characteristics of the sample**

||All|Control|Sequence|Coaches|
|---|---|---|---|---|
||sample|Group|Group|Group|
|Number of schools|70|24|23|23|
|Average students per school|301|316|289|297|
|Number of class divisions in 7th grade|136|50|44|42|
|Number of students in 7th grade|2965|1086|917|962|
|Number of Science teachers in 7th grade|99|36|32|31|

_Source:_ Author's analysis based on survey experimental data generated for this study _Note:_ Numbers refer to the schools, class divisions and students involved in the study.

40
# 4.3 RESULTS

# 4.3.1 BASIC TREATMENT IMPACTS ON MENTAL HEALTH

# _Average ITT Effects_

In Table 3, we present the average impact of the CCT and UCT treatment arms on the GHQ-12 binary indicator of psychological distress. Panels A and B present program impacts among _baseline dropouts_ and _baseline schoolgirls_ , respectively. The first two columns investigate psychological distress in Round 2 (i.e. measured during the first follow-up survey while the intervention was still in progress) and the next two columns investigate the same outcome in Round 3 (i.e. measured during the second follow-up survey shortly after the intervention had ended). Regression models presented in columns (1) and (3) do not include any controls other than treatment status, while regressions presented in columns (2) and (4) include the set of baseline characteristics described in Section 4.1 as controls.

Panels A & B show that the GHQ-12 binary indicator of psychological distress classifies approximately 45% of _baseline dropouts_ and 37% of _baseline schoolgirls_ in the control group as (potentially) suffering from psychological distress in Round 2.<sup>16</sup> Panel A shows that CCTs did not affect psychological distress among _baseline dropouts_ in either round. Panel B, on the other hand, shows that CCTs did have a significant impact on the psychological distress of _baseline schoolgirls_ in Round 2, for whom the probability of suffering from psychological distress was 6 to 8 pp (or at least 17%) lower.

> 16  The higher rates of psychological distress among _baseline dropouts_ compared to _baseline schoolgirls_ are consistent with poverty and social disadvantage as important predictors of psychological distress (as _baseline dropouts_ tend to come from poorer socioeconomic backgrounds). However, they could also be the result of a higher likelihood to drop out of school when suffering from a psychological distress.

14
, the improvement in data collection practices has largely contributed to it.

Moreover, at the international level it is also important to address some misclassification problems that may cause either sub- or over-reporting of remittance inflows. For example, are funds borrowed by a migrant in his host country and invested in his home country (e.g. purchase real estate) a remittance? Are guidelines followed by banks sufficiently clear to prevent certain trade-related transactions from being reported as remittances? Clearly, these are issues that need to be addressed at an international level so that remittance data is consistent across countries.

13
of more effective labor market policies in India and elsewhere. Much of the current literature on big data analytics in economic research is limited to methodological reviews and analyses of data sources, and does not examine real-world applications. This report not only reviews the prospective research applications of big data analytics, but also demonstrates their utility through an analysis of actual Babajob data. By using data from India, the study provides a unique case study in a developing country context. Unlike data from online job portals in the United States or Europe, labor market data in India is not regularly updated, making it difficult to assess changes in the labor market. This paper can help shed new light on labor market dynamics in India and expand the possibilities for using big data analytics to enhance labor market policies and promote demand-driven skills development.

The paper discusses five potential applications of big data in formulating and refining labor market policies: (i) conducting labor market monitoring and analysis; (ii) assessing demand for workforce skills; (iii) observing job-search behavior and improving skills matching; (iv) predictive analysis of skills demand; and (v) experimental studies. The paper applies the following analytical methodologies: descriptive-data analysis, time-series analysis with spatial information, text analysis, predictive analysis and transactionaldata analysis.

# **3. Literature on Economic Research Using Big Data**

The term “big data” first appeared in the early 2000s, as a surge in data processing capacity, internet use and detailed data collection created an unprecedented wealth of granular, real-time information on the behavior of individuals and organizations. “Volume, variety and velocity” are the three defining properties of big data (Laney 2001). Big data analytics was initially leveraged by firms seeking to adapt their business models and day-to-day decision making to reflect emerging data on consumer behavior. Since then, big data analytics has been increasingly utilized as a critical management tool and knowledge source (McAfee and Brynjolfsson, 2012), occupying a space between academic and applied knowledge (Savage and
Table (G16) Jordanian Neighbor Primary Outcomes Heterogeneity by: Gender

||(1)<br>Social Attitudes<br>and Perceptions|(2)<br>Economic Attitudes<br>and Perceptions|(3)<br>PolicyPreferences|(4)<br>Altruism to Syrians|
|---|---|---|---|---|
|Treat|-0.179|-0.105|0.240|0.144|
||(0.253)|(0.272)|(0.248)|(0.289)|
|Treat*Neighbor Female|-0.236|0.194|-0.117|0.110|
||(0.360)|(0.371)|(0.296)|(0.397)|
|Neighbor Female|0.145|-0.077|0.083|0.027|
||(0.101)|(0.071)|(0.062)|(0.093)|
|p-value: T + T*Het|0.04|0.69|0.50|0.31|
|Control Mean|0.60|0.60|0.60|0.60|
|Control SD|0.49|0.49|0.49|0.49|
|N|1102.00|1102.00|1102.00|1102.00|

This table shows heterogeneity analysis of the primary outcomes for Jordanian neighbors, by whether the Jordanian neighbor respondent is female. The outcomes are as defined in table G1. The regressions have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale
Policy Research Working Paper 9781

# **Abstract**

Recent work draws attention to the fragility of domestic tax revenues—a vital resource for the developing world—to illicit financial flows. To cope with two major challenges in the illicit financial flows–tax revenues relationship— related to the mere illicit financial flows measurement and reverse causality—this paper exploits the Financial Action Task Force data using an impact assessment analysis. Estimations reveal a significant tax revenue loss in countries associated with important illicit financial flows with respect to comparable countries without important illicit financial flows. Moreover, this causal effect—estimated as being economically meaningful—is supported by a large robustness section, and in particular remains unchanged when using several “doubly robust” estimators. Lastly, it unveils heterogeneities in the impact of illicit financial flows on tax revenues, related to the type of tax—a significant loss for indirect but not for direct taxes—and the considered environment. Therefore, policies combating illicit financial flows—for example, by developing institutions or a sound financial system, as shown by the estimations—may provide additional tax revenues for the developing world.

This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at psawadogo2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank
Policy Research Working Paper 10749

# **Abstract**

The 2022 Household Income and Expenditure Survey enhances fieldwork, data management, and information quality but poses comparability challenges with previous rounds. This study proposes a two-step process based on statistical matching to fill the information gap in previous survey rounds. This methodology uses the more comprehensive 2022 information to reconstruct comparable consumption measures over time. This allows for a consistent assessment of poverty and inequality measures, providing insights into the changes for policy makers, researchers, and stakeholders over the years. The results reveal that integrating this correction into previous survey rounds would have reduced poverty rates by around 10.6 percentage points between 2010 and 2016 and a further decrease of 7.8 percentage points between 2016 and 2022. Likewise, extreme poverty rates would have witnessed a decline of approximately 3 percentage points in the earlier period and a more substantial drop of 3.6 percentage points in the more recent one. These poverty reduction trends mirror improvements in other dimensions of well-being, like reductions in infant mortality and stunting and increases in access to electricity, sanitary toilets, and literacy rates.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jfernandezromero@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World
11.1|11.5|12.5|11.1|11.5|12.1|9.5|
|1 child|5.4|5.2|5.7|4.5|5.8|5.5|6.1|5.9|5.6|6.4|7.3|7.6|7.2|7.2|8.3|6.2|
|2 children<br>3 and more|10.8|11.0|11.0|10.3|11.4|13.2|13.9|11.4|12.7|15.4|15.0|17.5|14.5|13.8|14.8|11.1|
|children|28.9|23.6|20.0|21.0|25.5|25.8|26.1|24.4|27.6|30.0|29.4|29.0|27.6|34.8|30.7|29.8|
|singleparent|13.3|14.4|15.1|14.7|13.4|12.8|12.7|12.0|15.9|15.1|15.2|18.7|15.9|14.3|15.0|14.1|

Source: Authors’ estimates based on HSS data.

29
# 4.2 Results

This section discusses the overall results of the four outcomes reviewed by the study using the database of 762 results. Table 7 provides the full results for the four outcomes classified into positive, non-significant and negative values where positive and negative values are intended as significant. Figure 3 provides an overview of the share of these results by outcome. Table 8 provides the results of the multivariate logit regressions where the dependent variables are dummies for negative and positive significant values and the independent variables are population characteristics. Table 9 provides the same results for the bivariate regressions with the addition of a number of dummies including: the time lag between shock and evaluation, the average share of refugees over the host population for the period considered, dummies for HICs, MICs and LICs and each of the crisis studied.<sup>17</sup> All regressions results in Tables 8 and 9 include weighted and unweighted results where weights are the journals’ impact factors. Results in Tables 8 and 9 are presented for employment and wages only. That is because these are the only two outcomes with sufficient number of observations to run the regressions as the literature rarely provides results by population groups for wellbeing and prices. Below we comment on all results by outcome.

For readers interested in selected forced displacement crises, Annex 2 provides a full discussion of results by crisis. The overview of results organized by crisis and income level of the host countries is useful in as far as this is the most defining feature that could explain differences across results. In particular, the level of economic development of host countries, the absolute and relative scale of the crises and the timing of the inflow are very important factors in determining outcomes.

## 4.2.1 Well‐being and Prices

_Well-being_ . Among all the papers reviewed, 13 papers explicitly measure the impact of displacement on the economic well-being of host communities for a total of 64 distinct results. Of these, 34 are on income, consumption or output, 20 are on
in Mexico.

To the best of our knowledge, this is the first analysis of intergenerational mobility of health in Mexico, and that is based on both anthropometric measurements and information provided directly by individuals from all analyzed generations. A strength of this study is that the analysis uses longitudinal data.

Finally, improving human capital will not suffice to interrupt the intergenerational transmission of poverty in a severely constrained economic context. For example, it helps neither the students nor the economy to train a large cohort of engineers if there are no jobs for new engineers. The improvement in intergenerational mobility, as proposed in the theory of change, rests on assumptions. These assumptions include: the existence of high-quality health and educational services, an economy capable of generating employment opportunities, and equal access to the labor market. With these institutions in place, the improvement in health and human capital estimated here may improve the lives of future generations.

35
data come from the Ministry of Education’s annual Census of Schools (Censo Escolar).<sup>21</sup> The 15% payment to the state fund _did_ count toward the 25% minimum expenditure on education, but 10% more was required. All fund receipts had to go to education. If receipts did not reach a federal minimum per primary school child, the federal government would ‘top off’ the fund, bringing it to this level.<sup>22</sup> This ensured universal primary education of the required quality before municipalities spent the remaining 2/5 of mandated education spending. In 2007, the policy continued under “Fund for the Development and Maintenance of Basic Education” (FUNDEB),<sup>23</sup> which gradually increased the fraction of revenue paid to the fund and added other levels of education to the redistribution algorithm. See Appendix B for more detail.

Municipalities benefitting from the reform looked poor compared to others in their state (even if they were rich in the Brazil-wide sense). In 1998, the average municipality got back 1.1 times what it paid in. At the 25th percentile of net receipts, municipalities got back 1/3 of the amount paid in. At the 75th percentile, they got back 1.6 times the amount paid in. For a municipality paying 15% of revenue, this implied a loss equivalent to 10% of revenue at the 25th percentile, and a gain equivalent to 9% of revenue at the 75th percentile. These are large effects that likely led them to re-optimize spending. Table 2 summarizes the frequency of high, moderate, and low net FUNDEF receipts per child in 1998 by high, moderate, and low average income per capita. Many rich municipalities gained a lot, and many poor ones lost a lot.

A few factors limited FUNDEF crowd-out of local money. First, all fund receipts had to go to education (by law). Beneficiaries could potentially use