rafmacalaba/data-use-mentions-extended huggingface dataset:
education spending is smaller (2.049), but still large compared to existing studies (the last column of Table 5).

# **5.2.4 Event Study**

Figure 3: Event Study (Dynamic Effects) <!-- Start of picture text --> (a) Attending College (b) Years of Education<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br>Year Year<br>(c) Education Spending<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br>Year<br>.05 .2<br>0 0<br>-.05 -.2<br>DID Estimates DID Estimates<br>-.1 -.4<br>-.15 -.6<br>.5<br>0<br>-.5<br>DID Estimates -1<br>-1.5<br>-2<br><!-- End of picture text --> _Source_ : Iranian Households Income and Expenditure Surveys (HIES). Note: Figure reports the DID estimates of dynamic effects on outcome variables (coefficients of the interaction Treat _×_ year in Eq (3), with 95-percent confidence interval). While the estimated coefficients are not significantly different from zero before 2007, they turn significantly negative after the sanction imposed in 2007. Treated: Oil and Gas, Energy Supply industries. Synthetic control: Information (0.148), Education (0.169), and Health (0.683) industries.

Figure 3 shows the difference-in-difference (DID) estimates of dynamic effects on outcome variables: college attendance, years of schooling, and education spending per child (coefficients of the year treatment interaction terms in Eq (3), with 95-percent confidence interval). The estimated coefficients fluctuate around 0 before 2007 thereby providing evidence that there
China, Indonesia and Korea used for basic descriptive statistics presented above. Summary statistics for important variables used in the analysis can be found in Table 1. Below, we provide some additional discussion of the analysis sample used for each country:

**_China_** . The China Health and Retirement Longitudinal Study (CHARLS), directed by Yaohui Zhao at the National School of Development at Peking University, was developed and implemented by an accomplished team of collaborators from China and abroad. The sample used in this analysis is from the pilot survey conducted in 2008 in Gansu and Zhejiang provinces, and is representative of adults over 45 years of age in these provinces. Gansu, in the Northwest is a relatively poor province and Zhejiang, on the coast south of Shanghai, is relatively affluent. CHARLS follows the model of the HRS, ELSA and KLoSA in the US, UK and Korea, and enumerates information on respondent and spouse socio-economic status, health conditions, work and employment, incomes and pensions, and also includes household and community level information. In the pilot sample used in the analysis below, there are 1570 households and 2685 eligible respondents.<sup>21</sup>

**_Indonesia_** . The Indonesian Family Life Survey (IFLS) is an ongoing project conducted by RAND Corporation and universities in both Indonesia and the US. To date, four waves have been conducted in 1993, 1997/98, 2000, and 2007/08. The survey covers individuals, households, communities, and the community-level health and educational facilities that respondent households access in their daily life. While it is not a formal member of the “HRS family” of aging surveys, the data are comprehensive and include information on respondent health status, employment and labor allocation, pension participation and household and family characteristics, all of which are crucial for study of retirement and labor supply decisions of the elderly. The original survey was representative of 83% of the Indonesian population and 13 of 27 provinces. As it tracks the
2. Poverty Has Slightly Declined Nationally and in the Indigenous Areas.**

||**Po**<br>(perce|**verty**<br>nt of popul|ation)|**Extrem**<br>(percen|**e Pover**<br>t of popu|**ty**<br>lation)|**Ineq**<br>(Gini co|**uality**<br>efficient)|
|---|---|---|---|---|---|---|---|---|
||1997|2003|2008|1997|2003|2008|1997|2003|
|National|37.3|36.8|32.7|18.8|16.6|14.4|48.5|46.9|
|Urban|15.3|20.0|17.7|3.1|4.4|3.2|41.4|42.1|
|Rural (non-<br>indigenous)|58.7|54.0|50.7|27.4|22.0|22.2|41.3|39.0|
|Indigenous|95.4|98.4|96.3|86.3|90.0|84.8|40.2|34.9|

_Source:_ 1997 and 2003 ENV data, reported by World Bank (2007). The 2008 ENV estimates are cited from Panama’s _Instituto Nacional de Estadística y Censo_ data (2008) and from World Bank (2010).

6 The Ngobe-Bugle are the largest group (175,000 people), living mainly in the Bocas del Toro (bordering Costa Rica) and Chiriqui provinces; the Kuna on the Caribbean
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study, the OECD (2007) found no significant effect of maternity leave policies on female wages. Ruhm’s (1998) study of 9 European countries concluded that the leave and wage relationship was non-linear, with leaves of 3 months or less having no effect while leaves of 9 months or more were associated with wage declines.

One concern with mandated benefits is that they may have unintended negative consequences for classes of workers covered by the policy. Does this apply to parental leave which still disproportionately covers women? Mandel and Semyonov (2005), analyzing 20 upper-income countries, concluded that maternity leave exacerbated the male-female wage differential because of longer non-employment periods for women and because of employers practising statistical discrimination. In a developing country context, Gindling and Crummett (1997) found that relative wages for women declined after an extension of maternity leave rights in Costa Rica. However, not all researchers find these unintended effects (e.g., Korpi, Ferrarini, and Englund 2009).

# **_6.2  Impacts on productivity_**

The OECD (2007) has summarized the different ways in which parental leave and care-giving leave programs might affect productivity. Positive impacts include improved morale and work commitment and, because of longer tenure, greater firm-specific human capital and investments in training on the part of both firms and workers. On the other hand, these policies could hinder productivity if the leave provisions reduce working time and incentives in invest in human capital or if they lead to inefficient or discriminatory hiring where employers do not hire qualified women or employ them in lower-skilled jobs than they are capable of filling.

Studies on the productivity effects of leave policies are often based on subjective perceptions of managers. According to the OECD (2007), the results are “mixed and difficult to generalise” (p. 79). The OECD’s own analysis, based on quantitative data from 18 countries, finds that parental leave policies are associated with higher productivity levels. An additional week of unpaid leave results in
variance decompositions of global core CPI and PPI inflation (in percent) based on various types of robustness exercises as explained in the paper. Variance decompositions are based on median from 1000 successful Bayesian draws. The numbers in the brackets are 16-84 percentiles draws. “Baseline” indicates the variance decompositions of global inflation based on the global FAVAR model that consists of oil price growth, and the global factors of inflation, output growth, and interest rates extracted from the dataset of G7 countries. “Alternative narrative restriction” indicates that narrative restrictions are imposed on the historical contribution of oil price shocks on oil prices.

54
It is also worth noting that our methodology is not confined to the context of Africa. It can be adapted for various regions, offering policymakers and researchers a valuable tool to understand the dynamics of the informal sector in different settings. Such insights are essential for devising strategies that encourage formalization and foster economic growth.

This paper describes an initial attempt to estimate measures of firm demographics in Africa. The methodology combines several data sources under strong assumptions to overcome the challenges of limited information on business demographics in the region. This is an important avenue for future research and filling the data gap worldwide, particularly in developing countries. Increasing the availability of harmonized establishment and firm-level censuses, along with robust representative surveys for Africa, will further validate and improve these estimates. It would also allow further disaggregation of the indicators and better understanding of the business dynamics in the continent. Three key steps are needed to advance this agenda: 1. Improve the assessment and availability of existing data; 2. Invest in _ex-ante_ and _ex-post_ harmonization of firm-level data globally; 3. Enhance the capacity of National Statistical Agencies to generate and provide business statistics. We plan to continue this effort to improve the availability and quality of harmonized firm-level data across the African continent and globally.

22
often experience some of the largest infrastructure failures, and production of own electricity for organized sector firms is high in both settings. Thus better infrastructure capacity may allow establishments to move to rural locations.

Cost factors also play a critical role in location choice decisions for firms. We would ideally like to model several factors like rental prices, utilities prices, wage rates, and so on, but data on cost factors are very limited. We primarily focus on the wage rate of the district— examining both its average rate and the urban-rural ratio for a district. For a given district, we anticipate that both aspects can push firms to locate in rural areas. Lower average wages in a district may also attract plants searching for lower labor costs from neighboring districts.

District wage rates are calculated using firms in the organized sector in 2000. We focus on the organized sector as many firms in the unorganized sector do not report wages (and may

> 9 In six districts (major cities) which were not further subdivided into separate geographic units, these indicators were not reported in the Census data. In these cases we assign the infrastructure access components as 100%. Our results are fully robust to excluding these major cities from the analysis sample.

11
Almost all food security indicators have confidence intervals excluding zero, and in the expected side of the zero line. The exceptions are the HDDS and the food consumption expenditure estimates.

The drawback of the above approach is that, by adjusting p-values upwards, it effectively reduces power of any given test. As an alternative, we use the summary index tests which pools all outcomes into a single test and thereby reduces overtesting, increases power, and provides a test on the general effect of the treatment on the outcomes of interest. We implement Kling _et al._ ’s (2007) approach<sup>12</sup> by combining all our food security outcomes into one weighted standardized index, and estimate the mean standardized effect size. To align all variables in the same direction we multiply the consumption expenditure variables by -1 and subtract the food secure status binary variables from 1. We obtain a point estimate of -0.2 standard deviation and a p-value of 0.00 for the summary index. This confirms that the Burkina Faso FIP PES scheme has reduced food insecurity for the beneficiary households.

# **6. Conclusion**

We use data on a sample of households across 32 communes in Burkina Faso and an experimental design to investigate the causal impacts of participation in a Payments for Environmental Services (PES) scheme on a variety of food security outcomes. The specific intervention of interest is a refor-

> 12We use avg ~~e~~ ffect command in STATA.

23
- 33 -

3.32 Overall, the data on objectives suggest that nonform'l components and components in LAC, EMENA and Asia were more complex, seeking to address more issues. African and post-secondary components were least complex. Manpower expansion and improvement in the quality of training were the dominant aims of all investments, with some attention being given to upgrading worker skills and improving access for disadvantaged groups.

3.33 Few differences are observed across country income levels. This may suggest that, in terms of the intended outcomes selected by project designers, the nature of the economy has received relatively little attention.

3.34 Changes Over Time: The data in Table 13 reflect more than twenty years of investment history. The pattern of emphasis on different objectives has changed over this period. Although the changes have been relatively small, they indicate reduced emphasis on manpower expansion and quality improvement and increased attention to upgrading worker skills and improving access for disadvantaged groups. The data, disaggregated for three time periods, are shown in Table 14. Only data for the four most frequently cited client objectives are given.

Table 14: Changes In Percentage Distribution of Client Objectives, Three Time Periods: SLmmary Data

|Type of<br>ObJective|liLme<br>P<br>FY63-70|erods<br>FY71-78|FY79-87|
|---|---|---|---|
|Manwaer Expansion|41|39|35|
|ImproveTraining<br>Quality|41|37|34|
|Upgrade Worker Skills|06|09|14|
|Improve Access of Grouvs|-0-|07|09|
|TOTALS (%)|88|92|92|
1. Poisson regression for the model without interaction ( )

2. Poisson regression for the model with interaction ( ) 3. OLS for the model without interaction ( ) 4. OLS for the model with interaction ( ) The graphs in Annex 2 plot for Poisson and OLS the predicted values for both standards and modified gravity.

# **Table 1 results**

|20710 observations|1|2|3|4|
|---|---|---|---|---|
|R2/ pseudo R2|nd|nd|0.749|0.820|
|Log distance coeff ( β<br>−<br>)|-0.837|-0.802|-1.739|0.837|
|Wald Chi2/ F|68035|55339|175|266|
|,, z statistics|-34.0|-35.9|-77.4|24.2|
|,, standardized “beta”<br>coefficient|-1.008|-0.965|-2.093|1.008|
|Interaction coeff ( γ<br>−)||0.114||0.298|
|,, z statistics||16.1||89.4|
|,, standardized “beta”<br>coefficient||0.013||0.034|

The regression results suggests the following conclusions

1. As expected the Poisson regression has the most consistent result with trade spatial interaction data (Silva & Tenreyro 2006)(Arvis & Shepherd 2013)

2. The improvement in fit provided by the model (R2 or pseudo-R2) is relatively small in both models (also visually apparent in the shape of distributions in Annex 2).

3. In Poisson, the coefficient :

- has a small standardized (“beta”) value meaning that the effect of the non-linear covariance term is
around five main questions that we hope will help to motivate future work on teachers.

_Question 1: Who makes up the teaching workforce?_ This descriptive section explores the demographics of the teaching workforce. We explore various aspects, including gender and age. In some regions of the world, teaching was historically one of the few professions that women could access, although that has changed as other labor market opportunities for women have

> 9 The labor force is defined as people aged between 15 and 64 who worked, looked for a job in the past week, or had a job to return to.

> 10 The statistic of 2.2% for the US is derived from the May 2018 National Occupational Employment and Wage Estimates reported by the U.S. Bureau of Labor Statistics.  It is the share of all employment that is categorized as having the occupations of “Elementary and Middle School Teachers” or “Secondary School Teachers.”

> 11 Formal sector employees are people in the labor force who reported they earned a wage or salary in their primary jobs.

> 12 PASEC stands for Programme d’analyse des systèmes éducatifs de la Confemen.

7
(roughly 21 times annual GDP per capita, which is 527 USD), and receive between 5 and 20 percent of the fines they issue as a bonus.<sup>10</sup> Inspectors in Toamasina are employed under a performance monitoring scheme in which they can earn a bonus of 1,000 USD if they are the best performing inspector in terms of fraud detection, revenue mobilization, and speed of clearance in a given quarter. Their performance is evaluated relative to performance targets, which are revised regularly. Particular attention is paid to how they handle declarations allocated to the physical inspection (red) channel as well as declarations for which valuation advice was issued, since such declarations are associated with the largest potential tax losses. The shares of these types of declarations that are deemed fraudulent are among the metrics used to assess inspectors’ performance.

In spite of these performance incentives, corruption appears rampant,<sup>11</sup> perhaps because official compensation is low relative to opportunities for graft or because of coercion by economic operators.<sup>12</sup> The customs department is characterized by a culture of impunity and absence of meritocracy. Inspectors are hardly ever fired, even in cases of obvious corruption.<sup>13</sup> According to a nationwide survey of inspectors that we conducted prior to the RCT, only 23 percent believe that their colleagues act with integrity, only 6 percent claim non-ethical behavior is sanctioned and only 12 percent believe promotions are merit-based.<sup>14</sup> Undervaluation of imports, which is the focus of our study, was widely agreed to be the most important type of customs fraud. This is consistent with formal fraud records which classify 67.2 percent of all fraud as underreporting of value, 27.4 percent as underreporting of quantities, and the remainder as misclassification (4.9 percent) and misreporting country of origin (0.5 percent).<sup>15</sup> However,
# Review of evidence for high-income countries

Tracking socio-economic mobility and inequality across space and time, and building an understanding of where, how, and for whom observed differences have occurred, is both important and challenging. The extensive 2022 NBER conference volume by Raj Chetty and four-co-editors – featuring contributions by many of the world’s leading researchers on income and wealth distributions – is a collection of carefully executed empirical studies that do exactly that, drawing on a wealth of data across countries in the developed world (Chetty et al 2022). These efforts have yielded an overwhelming amount of empirical evidence – some new, others refining, expanding, and consolidating what others have previously reported – the most striking and consequential of which is that both income and wealth inequality are increasing, and intergenerational mobility is declining, at least across most high-income countries.

A wide array of detailed findings supporting and extending these key conclusions are presented across the Introduction and 23 substantive chapters that comprise _Measuring Distribution and Mobility of Income and Wealth_ (hereafter MDMIW); the chapters, in turn, are structured around five themes: Income Inequality, Wealth Inequality, Income and Wealth Mobility, Mitigating Inequality, and Distributional National Accounts. In the process of unpacking these themes, readers are taken on detailed descents into datasets across various high-income countries (mostly from the United States and Europe) on labor and housing markets, earnings and consumption, and inheritance, administrative, tax, and national accounts data. Apparent anomalies and necessary qualifiers are noted along the way, but by the end it’s abundantly clear that, no matter how one analyzes any of the comprehensive datasets, the early 21<sup>st</sup> century has only exacerbated the enduring “worlds of difference” (Alesina and Glaeser, 2004) experienced by the rich and poor in otherwise prosperous countries.

A risk in focusing so intensely on the scale and sophistication of measurement issues, however, is that it leaves vastly less room for broader but important considerations as to why we should care
# **Data Appendix**

## **Appendix I. Description of Outcomes and Other Variables**

## **Outcomes from individual-level administrative data**

## _A. Previous labor market experience_

These variables refer to the 12-month period before graduation (Ecuador) or entry into the program (Brazil). For a given student, her peers are those who graduated from the same program as her in the same year (Ecuador) or who entered the same program as her in the same year (Brazil). Previous labor market experience outcomes include the following: _Percent of time employed before graduation/entry_ is the number of months the individual was employed during that 12-month period divided by 12.

_Peer average percent of time employed before graduation/entry_ is the average of the previous variable for the student’s peers.

_Employed at least one month before graduation/entry_ is a dummy that takes the value of one if the individual was employed for at least one month within the corresponding 12-month period.

_Percent of peers employed at least one month before graduation/entry_ is the average of the previous variable for the student’s peers.

## _B. Graduation_

_Graduation_ (only for Brazil) is a dummy that takes the value of one if the individual graduated within three years of entering the program.

## _C. Labor Market Outcomes_

These variables (for Ecuador and Brazil) refer to the 12-month period following graduation. They include the following: _Employed at least one month after graduation_ is a dummy that takes the value of one if the individual was employed for at least one month during that period.

_Percent of time employed after graduation_ is the number of months the individual was employed during that 12-month period divided by 12 56
are 5 and 25 percent in Cameroon, 1 and 7 percent in Ghana, and 1 and 17 percent in Rwanda. Likewise, state-owned establishments are less than 1 percent of manufacturing establishments. However, they contribute a small fraction to manufacturing employment; about 1 percent in Burkina Faso, Cameroon, and Ghana, and 4 percent in Rwanda.

> 12 National Institute of Statistics of Rwanda (NISR). The Establishment Census, 2014. June 2015.

8
change depending upon the scenarios. However, forecasting the load and energy is beyond the scope of the study; they are exogenous to the electricity planning model used.

# **4.3. Economic and Technical Characteristics of Power Generation Technologies**

The data on the economic and technical characteristics of power generation technologies are presented in Tables 4a to 4e. Economic data include costs (capital costs, O&M costs, fuel costs/prices) and fuel prices (subsidized/local and unsubsidized/international). Technical data include heat rates, natural gas availability constraints, and availability factors for renewable energy technologies.

**<u>Table 1. Peak load and energy demand forecasts (Annual Average, %)</u>**

|**Economy**||**Peak load**||**E**|**nergy deman**|**d**|
|---|---|---|---|---|---|---|
||2020-25|2025-30|2030-35|2020-25|2025-30|2030-35|
|Algeria|4.0%|3.8%|4.0%|4.2%|4.0%|4.0%|
|Bahrain|3.1%|3.4%|3.9%|3.9%|3.7%|3.9%|
|Egypt, Arab<br>Rep.|5.7%|5.4%|5.5%|6.2%|5.5%|5.5%|
|Iraq|1.2%|0.6%|3.9%|4.3%|3.8%|3.9%|
|Jordan|5.0%|4.8%|5.9%|5.3%|