rafmacalaba/data-use-mentions-extended huggingface dataset:
indicators documented in Table 1.<sup>3</sup> As of 2007, Turkey’s trade-weighted applied tariff on manufacturing products was only 1.0 percent, and its simple average applied MFN tariff was only 4.8 percent. More comprehensive and economically meaningful indicators such as the trade tariff restrictiveness index (TTRI) or the overall trade restrictiveness index (OTRI) were also quite low for Turkey during this period.<sup>4</sup> Nevertheless, there are two key indicators for Turkey from Table 1 that point to a slightly more nuanced story. The first is that Turkey’s simple average tariff binding – or the rate beyond which Turkey is _legally_ committed not to raise its MFN tariff at the WTO – for its manufacturing products was 16.9 percent; this was much higher than its MFN applied rate of 4.8 percent. The implication is that, for the products over which Turkey had made WTO binding commitments, there remained substantial “overhang” or “water” in the tariff bindings – i.e., Turkey could legally raise its average applied MFN tariff rate by more than 12 percentage points. The second is that Turkey had legally bound at the WTO only a very small share – i.e., 42.8 percent – of even its manufacturing products’ tariff lines. The implication is that Turkey could legally raise its applied MFN tariffs by any amount without WTO legal obligation for more than half of its import product lines.

On the other hand, what the relatively poor indicators for Turkey’s tariff binding overhang and low tariff binding product coverage do not capture is that Turkey has made substantial trade policy commitments outside of the WTO system through its customs union with the European Union.<sup>5</sup> First, two-way trade between Turkey and the European Union is effectively duty free. Second, Turkey has sequentially adopted many of the other free trade agreements that the EU has negotiated with third countries, thus also extending
**Figure 3: Predicted Discoveries by Industry**

<!-- Start of picture text --> 0 5000 10000 15000 20000 25000<br>gdppc<br>2<br>1.5<br>1<br>predicted number of events .5<br>0<br><!-- End of picture text --> Source: Author’s calculations If discovery activity was driven by the process of structural transformation, the curves for some sectors would peak earlier than those in more capital-intensive sectors. However, we see that this is not the case. While the estimated curves are not uniform, each reaches its peak early in the development process (GDP per capita of $3000 - $6000) and then declines.

One could argue that export data grouped by industry do not clearly indicate the level of technological complexity nor the stage of the production process that countries are involved in, which would be necessary for a fair evaluation of the factor-endowment hypothesis. Export data for manufacturing industries in some cases may simply capture the labor-intensive nature of the assembly processes that are performed in developing countries due to the fragmentation of the production process (Lall 1998, Jones 2000).

28
**Country-level vaccination rate (at least one dose).** We include an estimate of the proportion of the country’s population that received at least one dose of a COVID-19 vaccine. Country level data was obtained at the time of data collection from Our World in Data (2024), which collected data from public official sources. The effect of the intervention could be smaller in countries with higher vaccination rate because the remaining unvaccinated sample may be particularly reluctant to receive the vaccine. It might also reflect the level of deployment of the vaccine as well as the general acceptance of the vaccine.

**Intervention start date.** We included the start month of the data collection. The dates were centered so that the starting month of the first intervention (i.e., January 2021) was set to month 0, and so on until month 16, corresponding to the start of the last intervention (May 2022). The intervention start date might also reflect the available knowledge about the virus and availability of the vaccine at the time of the intervention.

**GDP per capita PPP.** We also explore country wealth using gross domestic product (GDP) converted to international dollars using purchasing power parity (PPP) rates from the World Bank (2024). An international dollar possesses equivalent purchasing power relative to GDP as the U.S. dollar does within the United States. The GDP per capita has been shown to correlate with a large number of social and health outcomes across countries (Bloom & Canning, 2000), which may influence the effectiveness of the behaviorally informed messages targeting vaccine intentions.  When including this variable in the study-level moderator analysis, we center the data by subtracting the median GDP per capita PPP of the sample countries from the GDP per capita PPP of each country.

**Trust in healthcare workers.** We explore whether trust in healthcare worders (e.g., doctors and nurses) before the onset of the pandemic moderates impacts of message
rates for those answers: 80, 40, and 10% respectively. Column (2) shows the resulting formal employment rate imputation. In column (3) we show average outcomes calculated using individual-level administrative data; wages refer to individuals working in the formal sector and the formal employment rate is the percentage of SCP graduates who work formally. Formal workers include the following: employed and self-employed in Colombia, Peru, and Ecuador; and employed in Brazil. Columns 4-6 report validation outcomes using household survey data for different age groups; wages refer both to formal and informal employees.

66
2 regarding functional forms and error structares. The model is also unique in that it is estimated with micro data from a developing country.

When fit to Uruguayan data, the model yields several basic findings. First, corporate income is very sensitive to output demand and the cost of doLlar credit. Second, fluctuations in corporate income have a cLear direct effect on the rate of net worth expansion. Third, firms absorb most short run fluctuations in net worth via adjustments in assets, not debts. Finally, the interest elasticity of corporate demand for peso debt is very small. Inter alia, these findings imply that rapid changes in the exchange rate have large effects on corporate sector leverage and liquidity.

The remainder of the paper has two major sections; or. Ievelop the model, and one to report an application to Uruguayan data. There L's also a brief summary section.

# 1I. A General Model of Corporate Income and Portfolio Adjustment

# A. The Basic Model

Our model can be summarized as follows. Each firm's net income is a function of miacroeconomic variables (like output demand and interest rates) and firm-specific factors (like physical asset stocks, currency exposure, and overall indebtedness). Once earned, some portion of this income is retained by each firm, depending upon dividend policy and past earnings performance. Retained earnings increment net worth, and are distributed among specific asse;-s and liabilities according to, once again, macroeconomic and firmspecifiu; variables. These increments to assets and liabilities set the stage for next period's adjustment behavior.
firms. However, it is important to remember that the data collected in the business environment surveys provides information only about the constraints facing _existing_ enterprises. It is not a useful source for addressing the constraints facing potential firms.

Before discussing our framework in more detail, it is useful to address the question of why the importance of the business environment for economic performance cannot be readily estimated directly using firm-level indicators.

_Why can’t the importance of the business environment be estimated directly using firm-level indicators?_ A commonly-employed approach to using firm-level data to estimate the impact of variations in the business environment on firm performance is to estimate a regression in which firm performance is the dependent variable and with a measure of the business environment as reported by the firm used as a regressor along with various covariates. For example, firm-level data can be used to estimate a total factor productivity equation in which output appears on the left-hand side and inputs and other covariates appear on the right-hand-side along with one or more business environment measures. There are three reasons why such an attempt to estimate the effect of variations in the business environment at the level of the firm on productivity (or growth) is likely to be unsuccessful.

First, the many dimensions of the business environment are likely to be correlated, which makes it very difficult to tease out their separate effects on performance. Inclusion in the regression of single measures of the business environment reported by the firm is likely to result in endogeneity/omitted variable bias, whereas including many measures and controls will typically lead to very imprecisely estimated coefficients (the “curse of dimensionality” problem). This is the same problem faced by attempts to uncover the institutional determinants of growth in cross-country studies.<sup>3</sup>

> 3  See, e.g., Easterly (2009), Dethier (2008, 2010), or Durlauf et al. (2005) for discussions of the macro literature.

4
0.005)|(0.005)|(0.003)|(0.005)|(0.005)|(0.004)|(0.003)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Demographic|No|Yes|Yes|Yes|No|Yes|Yes|Yes|
|Education|No|No|Yes|Yes|No|No|Yes|Yes|
|Employment|No|No|No|Yes|No|No|No|Yes|
|Panel A Adj.R<sup>2</sup>|0.398|0.410|0.457|0.801|0.319|0.329|0.382|0.760|
|Panel B Adj.R<sup>2</sup>|0.232|0.254|0.352|0.789|0.183|0.201|0.303|0.750|
|Nr. of countries|16|16|16|16|16|16|16|16|
|Nr. of hh|119276|119276|119276|119276|119276|119276|119276|119276|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors are in parentheses. WorldPop 250m is used for the DOU and DB method. The dependent variable is a share of household workers employed in a respective sector. The baseline category is “Urban center” in all specifications in Panel A, and “Cores” in all specifications in Panel B. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in
**<u>Table 2. Types of disabilities included in definition of “person with a disability”</u>**

||_Type of_<br>_survey_|_Visual_<br>_disability_|_Hearing_<br>_disability_|_Speech_<br>_disability_|_Physical_<br>_disability_|_Mental_<br>_disability_|
|---|---|---|---|---|---|---|
|Burundi 2000|MICS2||||X||
|Cambodia 1999|SES|X|X|X|X|X|
|Cambodia 2000|DHS||||X||
|Indonesia 2003|SES|X|X|X|X|X|
|Jamaica 1998|LSMS||||X|X|
|Jamaica 2000|LSMS|X|X|X|X|X|
|Mongolia 2000|MICS2|X|X||||
|Mozambique 1996|NHS|X|X|X|X|X|
|Myanmar 2000|MICS2|X|X||||
|Romania 1995|LSMS|X|X|X|X|X|
|Sierra Leone 2000|MICS2|X|X|X|X||

_Note_ : See Annex Tables more precise wording and disaggregations.

Despite the fact that all 11 surveys have an impairment definition of disability, non comparable definitions remain an issue in any effort to compile data across countries. Table 2 summarizes the items covered in each survey that define a person as having a disability. Clearly the definitions are non-comparable, even across surveys within the same country. Six of the surveys use an “extensive” definition that includes visual, hearing, speech and physical disability. But even in this group of six surveys, the definition of each type of impairment varies. For example, in Cambodia 1999 the physical disability category contains a detailed list of potential cases—“
Little is known about what determines higher education programs’ quality—namely, the program practices and inputs that contribute to good student outcomes. The rich data collected by the WBSCPS provides a unique opportunity to make inroads into this issue for a specific type of higher-education program, SCPs. We collected program-level data on quality determinants; HEI, student body, and program characteristics; and aggregate outcomes for 2,103 SCPs in five countries in LAC. We complemented this novel dataset with individual-level information on academic and labor market outcomes from Brazil and Ecuador. We document a large variation in program quality determinants and outcomes and exploit it to identify the practices and inputs associated with better outcomes after controlling for student, program, and HEI characteristics.

We find that outcomes are generally associated with quality determinants from multiple categories. While the specific outcome predictors vary by outcome and across analyses, two practices are positively associated to _all_ labor market outcomes based on individual-level data from Brazil and Ecuador—teaching numerical competencies and providing labor market information—and one of these—teaching numerical competencies—is positively associated with labor market outcomes in the survey countries based on program-level data. Besides their intrinsic importance, numerical competencies may proxy for related skills such as logical reasoning, problem solving, and critical thinking and may remediate students’ cognitive deficiencies at entry. By providing labor market information, programs take a first step towards placing their graduates and break with the LAC tradition of not assisting graduates in their job search. Further, we find that program quality determinants account for a substantial share (15-60 percent depending on the regression and outcome) of the explained variation in academic and labor market outcomes. Taken together, these findings suggest that the adoption of the quality determinants identified as outcome predictors—shrinking the gap in quality determinants—might also shrink the gap in outcomes.

35
# 1. Introduction

There is a large and consistent performance gap observed between boys and girls in the Middle East and North Africa (MENA) region. On top of having the second largest proportion of children in learning poverty (i.e., lacking basic skills in reading toward the end of primary school<sup>1</sup> ), MENA has the largest gender gap in learning poverty among regions with available data (Gregory et al. 2021; World Bank 2021).<sup>2</sup> This gender gap in student performance, however, varies across MENA countries. The Kingdom of Saudi Arabia (KSA) is among the countries with the largest gender gaps in student achievement in the world. Boys in Saudi Arabia consistently and significantly underperform compared to girls across different grades and subjects. For example, in the Trends in International Mathematics and Science Study (TIMSS) 2019, grade 4 boys in Saudi Arabia scored below girls by approximately 26 points in mathematics and 60 points in science (Mullis et al. 2020). A similar gender gap exists in grade 8, where boys underperform girls by approximately 17 points in mathematics and 47 points in science. Furthermore, in grade 4, 53 percent of boys failed to achieve minimum proficiency in mathematics compared to 45 percent of girls. Similarly, in grade 8, 58 percent of boys did not achieve the lowest international benchmark (400 points) to be considered as minimally proficient in mathematics, compared to 49 percent of girls. Data from the Programme for International Student Assessment (PISA) 2018 also show that almost twice as many 15year-old boys (65 percent) failed to achieve minimum proficiency in reading, compared to 38 percent of girls (OECD 2019).

Although the magnitude of the gender gap in Saudi Arabia is among the largest in the world, significant differences in achievement between boys and girls are also observed in many other countries (Mullis et al. 2017; 2020). A large body of research has explored the
MULTILINGUAL CHILD ASSESSMENT The average age of children in the study was 54.42 months (range 24–83 months) ( **Table 1** ). About one-quarter (27%) of caregivers were illiterate. Maternal and household characteristics were similar to those observed in the representative 2014 Kenya Demographic and Health Survey sample for the study area.

# **Psychometric Properties of the Instruments**

The internal consistency of the vocabulary measures ranged from  = 0.57 to 0.90: Cronbach’s alphas were lowest for the expressive and English receptive tests and highest for the MDAT language test ( **Supplemental Table 1** ). The internal consistency of the receptive vocabulary assessments was higher for Luo (  = 0.78) and Swahili (  = 0.76) than for English (  = 0.57). The IRR of the receptive vocabulary tests was  = 1 for Luo,  = 0.89 for Swahili, and  = 0.95 for English. The internal consistency of the expressive vocabulary test was  = 0.67, while the IRR was  = 0.95. The internal consistency of the MDAT fine motor and language tests was  = 0.94 and  = 0.90, respectively. IRR of the total score for each measure was  = 0.93 for fine motor and  = 0.86 for language.

Correlations among the baseline child development assessments ranged from _r_ = 0.32 to 0.56, and all correlations were statistically significant at the _p_ < 0.001 level ( **Supplemental Table 2** ). The three age-normalized receptive vocabulary scores were all moderately correlated with each other, the expressive vocabulary score, and the MDAT scores, while the expressive vocabulary score was also moderately correlated with both MDAT scores. The MDAT tests had the strongest correlation ( _r_ = 0.56) with each other; among the vocabulary assessments, they had the strongest correlations with Luo
Policy Research Working Paper 5682

# **Abstract**

This paper offers an empirical evaluation of the output contribution of infrastructure. Drawing from a large data set on infrastructure stocks covering 88 countries and spanning the years 1960–2000, and using a panel time-series approach, the paper estimates a long-run aggregate production function relating GDP to human capital, physical capital, and a synthetic measure of infrastructure given by the first principal component of infrastructure endowments in transport, power, and telecommunications. Tests of the cointegration rank allowing it to vary across countries reveal a common rank with a single cointegrating vector, which is taken to represent the long-run production function. Estimation of its parameters is performed using the pooled mean group estimator, which allows for unrestricted short-run parameter heterogeneity across countries while imposing the (testable) restriction of long-run parameter homogeneity. The long-run elasticity of output with respect to the synthetic infrastructure index ranges between 0.07 and 0.10. The estimates are highly significant, both statistically and economically, and robust to alternative dynamic specifications and infrastructure measures. There is little evidence of longrun parameter heterogeneity across countries, whether heterogeneity is unconditional, or conditional on their level of development, population size, or infrastructure endowments.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at ccaldero@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
23.0|22.2|22.3|24.7|25.8|25.8|25.9|27.1|28.2|
|Portugal|36.4|37.6|37.4|37.4|36.8|37.5|37.3|36.9|36.5|37.1|38.0|37.6|41.4|41.2|
|Spain|38.9|39.1|38.6|38.8|39.0|39.1|39.0|38.0|38.3|39.7|40.0|40.6|40.7|40.7|
|EU‐28<sup>*</sup>|42.9|42.7|42.7|42.8|40.7|40.6|40.4|40.9|40.5|40.4|40.9|41.1|41.0|41.0|

*: Simple average. Source: Joint European Commission‐OECD project, Tax & benefits indicators database.

Recent ECB research notes that wages and prices have begun to be more flexible in these countries, responding to underlying conditions.<sup>78</sup> However, when comparing levels and ranking across countries, Figures 15a‐c show that GPS still have very restrictive labor markets‐ their ranking remains very poor, while the tax wedge remains high.

# **Lesson 11: The short term affects the long term.**

_<u>Hysteresis effects are real and can be large; “stabilization” policies have short and long‐run effects. Labor and product market reforms are effective when they reduce the length of time that resources remain unemployed or underused.</u>_ _<u>Large losses
of new households would match the natural changes in the overall cross-sectional population. In this case, the only loss of representativeness for the follow-up rounds would be from new households being formed by members not living in a particular country at the time of the round 1 survey.<sup>3</sup> This paper is primarily based on the experience of calculating the round 2 weights in the LSMS-ISA surveys from Tanzania and Uganda, and the round 3 weights in Uganda. The methodology described in this note builds upon published documentation from established panel surveys, such as the Panel Study of Income Dynamics [PSID], conducted since 1968 by the Institute for Social Research at the University of Michigan, and the British Household Panel Survey [BHPS], conducted since 1991 by the Institute for Social and Economic Research at the University of Essex ( <mark>Gouskova et al, 2008, Taylor, 2010)</mark> . Both the PSID and the BHPS are nationally-representative panel surveys in the USA and the UK respectively.

# **Calculating Weights for Round 2 of a Panel Survey**

The methodology for calculating weights for a panel survey is developed in the following eight steps:

- 1) Begin with the base weights (i.e. those calculated for round 1 of the survey) for round 2, or the ‘shadow weights’ for subsequent rounds (see section on the calculation of panel weights subsequent to round 2  for details on the calculation of shadow weights);

- 2) incorporate the probability of sub-selection of a round 1 unit into the round 2 sample; 3) incorporate the probability of sub-selection into tracking (if applicable); 4) derive fair-share factors for all household composition changes; 5) pool the weights in (1), (2) and (3) together;

> 2 It is also possible that the survey loses representativeness through changes in the population resulting from inmigration, although this is not believed to be a major threat in the LSMS-ISA survey context.

>
Does Rainfall Matter for Economic Growth? Evidence from Global Sub-National Data (1990-2014) Richard Damania<sup>1</sup> Sébastien Desbureaux<sup>1</sup> Esha Zaveri<sup>1</sup>

**Keywords:** rainfall, economic growth **_JEL_ codes:** O13, Q25, Q54

> 1 Water Global Practice, World Bank, 1818 H St. NW, DC 20433. *Author Emails: rdamania@worldbank.org. <u>ezaveri@worldbank.org, sdesbureaux@worldbank.org</u> The authors thanks Jason Russ for comments on an earlier draft.