rafmacalaba/data-use-mentions-extended huggingface dataset:
For countries outside Europe and Central Asia, we use the GDIM dataset, which provides estimates of intergenerational educational mobility for four birth cohorts (individuals born in the 1950s, the 1960s, the 1970s, and the 1980s) across 114 countries (van der Weide and others 2023). It draws on microdata from social and household surveys, which contain retrospective information on parental education. Similarly to the LiTS, the GDIM relies on a correspondence between categorical information on attained levels of education and the equivalent number of years of schooling.

Our empirical analysis includes variables from other sources. GDP per capita and population come from the World Development Indicators <u>(World Bank 2023). Historical GDP per capita and</u> population (used to estimate GDP per capita and population at the decade of birth of the different cohorts) are from the 2020 edition of the Maddison Project Database (Maddison Project Database <u>2020; Bolt and van Zanden 2020). Historical infant mortality rates from 1950 onward are estimated</u> by the United Nations’ World Population Prospects Population Division (UNDESA 2022); rates for 1930–50 are from various historical sources.

Our cross-country panel of educational mobility and economic outcomes includes 1,419 countryyear observations in the period 2000-2020, covering 108 countries. However, we have 10 or more non-missing country-year observations only for 68 countries.<sup>14</sup> This sub-panel of 1,244 countryyear observations is the primary sample of our empirical analysis. <u>Table 1</u> presents the descriptive statistics of the main variables of our analysis for the cross-country sample.

# **6. Educational mobility across countries and generations**

This section describes the patterns of intergenerational educational mobility observed across countries and generations, using the enlarged sample of 115 countries for which we have estimates of mobility. We start our descriptive analysis by looking into the patterns of the upward mobility gap, the new oriented mobility measure that we apply in this work.

>
12

# **<u>References</u>**

- Akin, J. S., W. H. Dow, et al. (2004). "Did the distribution of health insurance in China continue to grow less equitable in the nineties? Results from a longitudinal survey." Social Science <u>& Medicine</u> **58** (2): 293-304.

- Baldwin, M., L. Zeager, et al. (1994). "Gender differences in wage losses from impairments." <u>Journal of Human Resources</u> **29** : 865-887.

- Baldwin, M. L., L. A. Zeager, et al. (1994). "Gender Differences in Wage Losses from Impairments: Estimates from the Survey of Income and Program Participation." Journal <u>of Human Resources</u> **29** (3): 865-87.

- Behrman, J. R. and A. B. Deolalikar (1988). Health and Nutrition. Handbook of development <u>economics. Volume 1. Handbooks in Economics, vol. 9. H. Chenery and T. N. e.</u> Srinivasan. Amsterdam; New York and Oxford, Elsevier Science **:** 631-711.

- Bound, J. (1991). "Self-reported versus objective measures of health in retirement models." <u>Journal of Human Resources</u> **26** : 106-138.

- Bound, J., M. Schoenbaum, et al. (1999). "The dynamic effects of health on the labor force transitions of older workers." Labor Economics **6** (179-202).

- Contoyannis, P. and N. Rice (2001). "The impact of health on wages: Evidence from the British Household Panel Survey." Empirical Economics **26**
more expensive and suggest sluggish investment growth will continue (Kose and Ohnsorge 2023).

Against this backdrop, this paper investigates the question of whether public investment, as a fiscal policy tool, can crowd-in private investment. We utilize data from a large panel of 109 EMDEs and employ an instrumental variable approach to identify the relationship between these two types of investment. The empirical setting is guided by a tractable model, where we show that the complementarity (substitutability) between public and private investment is sufficient to crowd-in (-out) private investment. Consequently, the parameter of interest we estimate captures, simultaneously, the structural relationship between public and private investment and the crowding-in (-out) of private investment by public investment. We extend the analysis to low-income countries (LICs) and Sub-Saharan Africa (SSA), two subgroups of countries where infrastructure needs are large given low levels of development and growing populations. Furthermore, we examine whether the effect of public investment on private investment varies by the level of corruption and financial development to understand the potential implications of factors that may impede or enhance the effectiveness of public investment in crowding-in private investment.

The relevance of public investment to spur private investment in EMDEs is critical. Yet, systematic evidence of the crowding-in effect of public investment on private investment in EMDEs remains scarce. The existing literature has focused on a limited number of lowincome countries (Eden and Kraay 2014), high-income OECD countries (Boehm 2020) or U.S states, European countries, and Argentine provinces (Izquierdo et al. 2019). Expanding this to a large set of EMDEs will provide systematic and stronger evidence of the crowding-in effect of private investment in these countries, which can help drive policy strategies to address the weak outlook for investment growth.

Importantly, the existing literature studies the implication of the crowding-in effect of public investment under perfect capital markets. Capital market imperfections and distortions are, however, ubiquitous in developing countries. For example, access to financial markets, which is important for the growth of
absenteeism) and appropriately track them periodically over time. By comparison, Croatia’s average score on practices relating to target setting and incentives is relatively closer to that in the United States firms. Incentives practices relate to how managers and non-managers are awarded bonuses and promotion, for instance. The finding for Croatia regarding the weaknesses of these sub-indices is similar to that in Russian manufacturing, that is, monitoring aspect of structured management is weaker. By comparison, in the United States and Mexico

> 5 Apart from data availability, it makes sense to compare Croatia with Russia due to comparable per capita GDP in recent years and also the fact that both countries are transition economies in the same region. Comparison with Mexico is revealing in the sense that Mexico has a lower per capita GDP than Croatia, but has a strong manufacturing base, and thus offers another perspective for comparison (lower bound among emerging economies), aside from the US, which is the benchmark frontier economy (upper bound).

> 6 For comparator countries, the sample frame varies significantly. For example, MOPS in the United States used the sample frame of the Annual survey of manufacturing, comprising of around 50,000 plants in each wave for the years 2010 and 2015. Nearly 74,000 responses for the two years were received, that is, an average response rate of 74 percent. Likewise, in Mexico the survey was conducted for nearly 25,000 firms in manufacturing and services, with a response rate of 96 percent. The survey in Russia is perhaps closest to that in Croatia where 727 firms were interviewed face to face across five broad regions, with response rate similar to that in Russia (about 17 percent). Although the survey in Croatia covered both manufacturing and services, in this paper we present comparisons with manufacturing only. It is also worth reminding that although much of the lagging regions in Russia is not surveyed, the survey also skips bigger cities and agglomerations such as Moscow, Moscow Oblast, Saint Petersburg and Leningrad Oblast.
to females in ELA only communities.

**Table A10** Impact of Treatments on Intimate Partner Violence (Restricted to those who report sometimes or never condom use at baseline)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|IPV often index|||||
|treatment x post|-0.155|-0.034|0.000|760|
||(0.223)|(0.218)|||
|treatment x post x Goal|-0.195<br>(0.362)||||
|IPV in last year index|||||
|treatment x post|-0.333|-0.462*|-0.000|760|
||(0.240)|(0.265)|||
|treatment x post x Goal|0.277<br>(0.378)||||

_Notes._ This table presents coefficient estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 1. See notes for Table 2 for details on table structure, control variables, and outcome definitions. Indices are centered around females in ELA only communities who report sometimes or never using a condom and were not assigned to the _Goal_ treatment, at baseline and endline separately. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Female baseline and endline data, balanced
prices (values divided by quantities) reported<sup>in the Susenas database (for a given</sup> reference group). Either of these detailed food<sup>price series can be used to construct</sup> an inflation rate for food using expenditure<sup>shares for items within the food basket</sup> based on a sample of poor consumers.9 However for non-food prices only CPI prices exist - there is no Susenas equivalent. So, this gives<sup>a combination of six possible</sup> inflation rates in the poverty line: three methods<sup>of choosing weights to combine</sup> food and non-food inflation and two estimates<sup>of food price inflation.</sup> Since it is well known from consumer theory that<sup>a fixed weights (Laspeyres)</sup> price index _overestimates_ the change in welfare<sup>from a given change in prices (since</sup> the fixed weights do not allow for substitution<sup>effects due to relative price changes)</sup> we begin by using two Laspeyres indices, with<sup>the only issue being the weight on</sup> food and non-food inflation (both from the CPI).

The first deflator, we call method I, uses the actual<sup>expenditures of poor</sup> households. This would be the natural deflator<sup>in defining household's real</sup> expenditure, as it uses their actual consumption<sup>shares. In order to do this we</sup> estimated an Engel curve: a,, = ar +,B * ln(e ) + ej Based on the predicted values of the food share<sup>from this regression we created a</sup>

# deflator for each household:

ri _j (i *nr F + (1 9j6) * NFv_

> 9The expenditure shares were taken from the consumers
data. Forty-seven percent of the Afghan people are poor, with urban poverty only slightly lower than rural poverty.

# **3.2 Weather Data**

As discussed earlier, we expect temperature and NDVI deviations to be better measures of weather shock in the country. In addition, we also use variations in precipitation to confirm if this hypothesis is valid in Afghanistan. Below we detail the data sources for each of these measures.

_Precipitation (CHIRPS):_ The daily rainfall data is available at a 5-kilometer resolution

> 10Incorrect locations are characterized by missing household latitude or longitude, point outside the specified district, etc.

> 11Afghanistan has 14 poverty lines for rural and urban areas separately in eight regions across the country: North, Northeast, West, West-Central (only rural), South (only rural), Southwest, East, Central regions.

7
households each month, the household-level data is not publicly available. However, the WFP publicly shares each month the governorate-level averages and confidence intervals of all key variables collected<sup>13</sup> , and these monthly governorate-level estimates are used in the empirical analysis to analyze changes in food assistance and food access following the 2017 IPC announcement.<sup>14</sup> We further merge the mVAM data with data on violence using data from the Armed Conflict Location & Event Data Project (ACLED) on the number of people killed in each governorate each month to be used as control variables.

However, there are important caveats to note about the data. First, it is likely that there is a significant amount of district-level variation, local factors, and household factors that impact both food assistance and food access. However, we are unable to analyze these factors in the present analysis given the data is publicly available at only the governorate level.

Second, the survey can only be representative of the mobile phone-using population following the beginning of the conflict. Although it is difficult to identify exactly how access to mobile phones have changed since the start of the conflict, all evidence suggests that access has remained

> 8See Appendix 2 for the number of responses by governorate. 9The WFP is unable to observe whether a phone number exists or not in the random digit dialing procedure, and thus we are unable to identify the share of active phone numbers that do not respond to the survey.

> 10The FCS is a weighted count of the number of days a household consumed particular food groups in the past week. A higher FCS is associated with better food access.

> 11The rCSI is a weighted count of the number of times the household has relied on food coping strategies in the past week. A higher rCSI is associated with a worse food access.

> 12See Appendix 3 for the variable definitions used in the survey, and for an example of one of the monthly questionnaires.

> 13They also share the estimates and
Table 2: Which surveys can be used to monitor the SDG labor market indicators?

||LSMS‐ISA|LSMS‐type surveys|LFS|DHS|
|---|---|---|---|---|
|Number of surveys reviewed|8|6|11|1 (harmonized across countries)|
|Disaggregation by:|||||
|Age|100%|100%|100%|100% (population aged 15 to 49)|
|Sex|100%|100%|100%|100% (questions differ by sex)|
|Employment (previous definition)<sup>1</sup>|88%|100%|100%|100%|
|Unemployment|75%|50%|100%|0%|
|Inactivity|75%|50%|100%|0%|
|Domestic and care work (hours)|13%|50%|36%|0%|
|Sector|100%|83%|100%|0%|
|Occupation|100%|67%|100%|100%|
|Formal/Informal employment|75%|67%|82%|0%|
|Hours worked by occupation|100%|67%|100%|0%|
|Hourly earnings of employees|88%|100%|82%|0%|
|Income by occupation<sup>2</sup>|±|±|0%|0%|
|NEET<sup>3</sup>|75%|57%|92%|0%|

Source: Based on LSMS‐ISA, LFS and DHS data. Table B in Appendix links each indicator to the SDG labor market indicators; Table D in Appendix shows the coverage of the SDG labor market indicators by type of survey. Note:<sup>1</
# **4 Empirical Strategy**

## **Push shocks**

Ordinary Least Squares estimates of the effect of shocks on migration in a cross-sectional data are fraught with problems. For instance, places with fertile land that is responsive to rainfall might also have more entrepreneurial people who are more likely to migrate. Similarly, places with extremely unequal land ownership might invite conflict as well as force people to migrate due to lack of economic opportunities. These characteristics, which are not always observable, will bias the OLS estimates obtained from cross-sectional data. Panel data solve this problem if such characteristics are time invariant by comparing changes in migration rates with changes in shocks within the same village over different periods.

Given the structure of my migration data, I estimate the following model to study the impact of ‘push’ shocks on migration: where _Mit_ is the migration rate in village _i_ observed in time _t_ , _γt_ represents the survey year fixed effects which absorbs any national trends common to all villages, _μi_ is the village fixed effect which absorbs all time invariant characteristics of the village, _Xit_ is the shock that affects village _i_ at time _t_ and _εit_ represents the error term. I allow the error term to be correlated between different villages within a district as well as over time. _Xit_ are either conflict intensity in the district as defined above, or normalized rainfall in very recent past (one or two years ago). The identifying variation for this specification comes from variation within village over time as well as variation across villages in a given year.

Further, my measure of rainfall shock is in standard deviation units relative to the average historic rainfall in the village and is likely to be uncorrelated with any village specific trends in any of the related observed and unobserved variables. More importantly, individuals cannot predict rainfall shocks and engage in anticipatory migration. Hence, the variation in rainfall measure is likely to be uncorrelated with the error term.

Conflict intensity could, however, be driven by village or region specific trends. This
, and the expected years of schooling, according to the Human Capital Index data, for both boys and girls in Burkina Faso is seven years. Moreover, the country is undergoing political turmoil and a series of military coups<sup>4</sup> , which could have a severe economic impact.

Although the overall educational attainment remains low, the gender gap in both gross primary and secondary school enrollment has reversed in recent years. Enrollment for boys and girls in primary school is approximately equal (92.2 percent for boys vs. 93 percent for girls), and enrollment for girls in secondary school is slightly higher at 39.4 percent for boys vs. 41.8 percent for girls (UNESCO 2020). These gaps are smaller than the Sub-Saharan African average of a 4-5 pp gender gap in favor of boys for both primary and

> 3 Changes in labor dynamics have been largest in the North, where labor force participation grew from 67 to 87 percent over the same period.

> 4 “Burkina Faso soldiers announce military takeover, Kaboré ‘suspended’” France24. <u>https://www.france24.com/en/live-news/20220124-burkina-faso-president-kabore-held-by-mutinying-soldierssources-tell-france-24; “The old junta leader makes way for the new in Burkina Faso's second coup of the year” NPR. https://www.npr.org/2022/10/02/1126478594/burkina-faso-coup.</u>

4
organizations’ role looms large. In addition, experts from other advanced countries can assist the reform efforts. However, it would be best to have local expert groups that have knowledge of reform program and local institutional context.

# **_Conclusion: Leadership and Change_**

Perhaps the most important component of PB success was effective leadership throughout the change. After the demise of the first reform, the Korean government developed a vision and strategy, based on several case studies commissioned to external public research institutes. Top administrators understood that PB reform would require not only administrative capacity but also analytical capacity within leading and line ministries. Thus they assigned these research institutes an important role in supplementing and developing this capacity. Over time, ministries have built up in-house capacity and knowledge regarding PB processes. Because bureaucratic turnover is low, this information is transferable to from administration to administration.

In addition, the Korean government created a sense of urgency among the line ministries and scaled up the reform program as quickly as possible. The Ministry of Planning and Budget stipulated in the budget draft guideline that at least a 10 percent budget cut should be considered for “ineffective” programs. This shocked line ministries into quick action. Another incentive for bureaucrats was public pressure. PB review results were made available to the public, and the media called attention to underperforming organizations. This publicity motivated government officials to honor the new system and, at the same time, helped the reform gain social acceptability among civil society organizations and citizens.

Establishing a dedicated reform team consisting of budget officials and policy researchers from the public institutes allowed reformers to merge expertise on practical and theoretical issues. The team came up with innovative solutions to integrate performance management into the actual budgeting process. The public institutes hosted many workshops and meetings to build consensus on policy direction and coalition among various stakeholders, and at the same time also provided training and consulting services to support line ministries.

Communication with stakeholders is a key element of successful change management. To facilitate additional communication with line ministries
# **Appendix A Variable Definitions**

This appendix consists of four tables (tables A.1–A.5) that define the variables used in the paper.

**Table A.1 Definition of variables used from 2010, 2012 and 2013 rounds of the Ghana Urban Panel Survey**

|Variable|Definition|
|---|---|
|Male|Dummy variable = 1 if male, 0 if female|
|Age|Age in years|
|Education|Years of formal schooling|
|Married|Married = 1, 0 otherwise|
|Self-employed|Dummy variable = 1 if primary work activity is self-employment, 0 if<br>primary work activity is wage employment, out of labor force, or<br>unemployed|
|Wage-employed|Dummy variable = 1 if primary work activity is wage-employment, 0 if<br>primary work activity is self-employment, out of labor force, or<br>unemployed|
|Dependency ratio|Number of household members younger than 15 or older than 64 divided<br>by household size divided by household size|
|Log hh assets|Log of household assets|
|Accra|Dummy variable taking value 1 if the individual lives in Accra, 0 otherwise|
||Dummy variable taking value 1 if the individual lives in Cape|
|Cape Coast|Coast/Takoradi,0 otherwise|

|**Table A.2 Definition of baseline variables**<br>Variable<br>Definition|
|---|
|Self-employed<br>Dummy variable = 1 if primary work activity is self-employment, 0 if<br>primary work activity is wage employment, out of labor force, or<br>unemployed|
|Wage-employed<br>Dummy variable = 1 if primary work activity is wage employment, 0<br>if primary work activity is self-employment, out of labor force, or<br>unemployed|
|Manufacturing<br>Dummy variable=1 if respondent works in the
# 2.4. PIRLS (2016)

The newest round of PIRLS includes 50 countries. It was designed to be comparable to prior rounds. Inclusion of PIRLS is straightforward and we use the same parameters as used in prior databases using the microdata. Overall, through the inclusion of recent rounds of PASEC, SACMEQ, and PIRLS as well as the new inclusion of EGRA, we are able to improve the quality of, and include recent data for, over 75 countries since Altinok, Angrist and Patrinos (2018).

# 3. Methodology

# 3.1. Linking Methodology

We build on a literature linking learning assessments starting with Barro and Lee in 2001. Our methodological approach in this paper extends and improves on the one detailed in Altinok, Angrist and Patrinos (2018) to build the most current database covering the largest number of countries to date. Our Harmonized Learning Outcomes (HLO) database includes 164 countries/territories from 2000-2017.

Various methodologies can be used to make assessments comparable. We do not attempt to equate scores. Rather, we link scores to achieve comparability (Kolen and Brennan, 2014; AERA, APA, NCME, 1999; Holland and Dorans, 2006; Linn, 1993; Mislevy, 1992). This distinction is important, since we do not claim tests or test scores are equivalent across tests. Rather, we aim to compare scores on a similar scale.

We do not link using Item Response Theory (IRT) – the technique used to generate scores for each respective international and regional assessment. IRT models the probability a given pupil answers a given test item correctly as a function of pupil and item-specific characteristics. While this methodology is used within each of the international and regional tests we use, to use it across ISATs and RSATs would require overlap in test items. This is not true for a significant enough set of tests and time intervals to create a globally comparable panel dataset. Moreover, even when there is overlap, for IRT to be reliable there must be large