rafmacalaba/data-use-mentions-extended huggingface dataset:
evidence that tariffs on renewable energy technologies and subsidies for fossil fuels do more to limit technology transfer of clean technologies than patent protection.<sup>30</sup> A recent study finds that eliminating tariff and non-tariff barriers in the top 18 developing countries ranked by GHG emissions would increase imports by 63 percent for energy-efficient lighting, 23 percent for wind power generation, 14 percent for solar power generation, and 4.6 percent for clean coal technology.<sup>31</sup> In a study of electric power plants in India, Khanna and Zilberman (2001) find that removing import barriers to higher-quality coal would increase the adoption of more energy-efficient technology and potentially decrease carbon emissions.

The **Clean Development Mechanism** (CDM) of the Kyoto Protocol is an explicit mechanism to boost technology transfer and diffusion, as it allows high-income countries to develop or finance GHG emissions reduction projects in developing countries in exchange for emission reduction credits. However, high-income country investments tend to be small when compared to FDI. Based on an analysis of GHG mitigation technology transfers, Dechezleprêtre et al. (2008) show that international transfers have taken place in less than half of the CDM projects, typically combining transfer of equipment with knowledge and operating skills. They also find that most technology transfers concerned end-of-pipe destruction of GHGs in chemical, agricultural and waste management industries (highly specialized, with

> 29 See Lanjouw and Mody (1996) and Popp (2006) who both find that access to foreign available knowledge is necessary but not sufficient for local use, with foreign knowledge typically serving as a blueprint and further adaptive R&D required to fit the technology to local markets.

> 30 See Copenhagen Economics (2009) and Barton (2007) as cited by Hall and Helmers (2010).

> 31 See Table 3.2, World Bank (2008); the assessment is based on first-round approximations rather than full general equilibrium effects.

24
<!-- Start of picture text --> (g) Republic of Korea: Standard Deviation of (h) Republic of Korea: Standard Deviation of<br>Real GDP Growth, % points GDP growth, % points<br>4<br>2<br>3.5<br>East Asian Crisis 1.8<br>3 Global<br>Crisis 1.6<br>2.5<br>1.4<br>2 1.2<br>1.5 1<br>1 0.8<br>0.5 0.6<br>0.4<br>0<br>0.2<br>0<br>1972-1985 1986-2008 2009-2011(iii)<br>(i) France: Standard Deviation of Real GDP (j) Germany: Standard Deviation of<br>Growth, % points Real GDP Growth, % points<br>1 2.5<br>0.9 Global Global<br>Crisis<br>0.8 2 Crisis<br>0.7<br>0.6 1.5<br>0.5<br>0.4 1<br>0.3<br>0.2 0.5<br>0.1<br>0 0<br>1972 Q1 1974 Q1 1976 Q1 1978 Q1 1980 Q1 1982 Q1 1984 Q1 1986 Q1 1988 Q1 1990 Q1 1992 Q1 1994 Q1 1996 Q1 1998 Q1 2000 Q1 2002 Q1 2004 Q1 2006 Q1 2008 Q1 2010 Q1<br>1981 Q4 1983 Q2 1984 Q4 1986 Q2 1987 Q4 1989 Q2 1990 Q4 1992 Q2 1993 Q4 1995 Q2 1996 Q4 1998 Q2 1999 Q4 2001 Q2 2002 Q4 2004 Q2 2005 Q4 2007 Q2 2008 Q4 2010 Q2 1993 Q1 1994 Q2 1995 Q3 1996 Q4 1998 Q1 1999 Q2 2000 Q3 2001 Q4 2003 Q1 2004 Q2 2005 Q3 2006 Q4 2008 Q1 2009 Q2 2010 Q3<br
inhalation of respirable particles whose diameter is less than 10 microns (PM10), and recent attention has focused particularly on fine particles (PM2.5).

In a previous paper, we analyzed variations in average IAP levels across Bangladeshi households (Dasgupta, et al., 2004). We found that common variations in fuel use, cooking locations, construction materials and ventilation characteristics lead to large differences in IAP. Non-fuel characteristics are so influential that some households using “dirty” biomass fuels have PM10 concentrations comparable to those in households using clean fuels such as liquid natural gas. Under adverse conditions, on the other hand, Bangladeshi households using dirty fuels can experience 24-hour average PM10 concentrations as high as 800 ug/m<sup>3</sup> .

Such concentrations are far higher than outdoor PM10 levels considered dangerous for public health in industrial societies (Galassi et al., 2000). In those societies, however, use of clean fuels is so pervasive that attention focuses on outdoor pollution. In biofuel-using Bangladeshi households, particularly in rural areas, the calculus is often reversed: Indoor air pollution (IAP) may be much worse than outdoor pollution, and health risks may be severe for household members who are exposed to IAP for long periods during the day.

In this paper, we use our survey data to estimate the incidence of IAP exposure for family members by age-sex group, with a particular focus on young children. We investigate the two major sources of differential exposure: individuals’ time spent in different locations (cooking areas, living areas and outside), and hourly fluctuations in pollution from cooking. We also assess the effect of parents’ income and education on average household pollution levels.

The remainder of the paper is organized as follows. In Sections 2-5 we study the sources of variation in individuals’ exposure to pollution within households. The four sections analyze individuals’ daily location patterns, their interaction with daily cycles in pollution from cooking, the 4
This project addresses these gaps and sheds light on these questions by gathering original data on Moroccans’ attitudes, experiences, and perceptions around unpaid labor, care work, and paid work outside the home. How do Moroccans’ attitudes and perceived norms around various aspects of unpaid labor and care work within the household relate to one another and to their actual behavior? To what extent do attitudes and perceived norms around household roles hinder the emergence of more gender-equal distributions of labor?

# **3 Data**

## **3.1 Survey Sample**

The original data discussed in this paper come from a survey of 1,038 Moroccan respondents recruited through Qualtrics from January 25 to March 3, 2023. Qualtrics maintains access to a pre-recruited pool of Moroccan respondents who complete surveys in exchange for a small financial incentive.<sup>2</sup> Demographic quotas based on gender, age, and geographic location were employed to ensure a baseline level of respondent diversity. Appendix Table 1 summarizes the demographics of the sample and compares it to that of a national probability face to face survey (F2F) conducted in 2022 by the Arab Barometer. The online sample disproportionately surveys individuals who are currently employed (58% versus 38% in the F2F survey), while under-representing students, the unemployed, housewives, and retired individuals. Online respondents are also more likely to self-identify as only somewhat religious or not religious, rather than religious. Despite higher employment rates, socio-economically, the online sample respondents identify themselves as struggling a bit more, on average, than respondents in the F2F survey, in response to a question about whether their household makes enough money to meet

> 2. In one sense, all respondents in this survey are therefore “employed.” However, completing surveys for compensation likely takes up no more than a small portion of respondents’ time and can be completed from home. While housewives and the unemployed are underrepresented in the sample, around 19% of female respondents self-identified as primarily housewives,
want.

Given the lack of gold standard gauge station data in many countries, where does this disparity in how EO products measure the weather leave researchers who want to incorporate this type of data into economic contexts? As we cannot know the truth of what weather occurred where, we are left in a second-best world where more work may be required to demonstrate the accuracy of our scientific findings. Based on the results in this paper, we recommend that researchers carefully choose which remotely sensed EO source to use in their analysis. More justification needs to be given in papers for the choice of one EO data set over another. In EO product documentation there is often a list of known issues, such as over-estimation of rainfall events or difficulty in capturing precipitation in rugged terrain. In the same way aseconomists already document and justify choices in terms of variable selection, we also need to document and justify our selection of EO product. Additionally, researchers may need to demonstrate that key results are not a function of their choice of EO product. Economists already verify the robustness of results to changes in data and specification. Checks for robustness to alternative EO weather products may also be called for, particularly if weather is a key component of the story or plays a role in causal identification.

Potentially the most important recommendation resulting from our findings is the need to recognize, as Stephen Jay Gould wrote, that science is a social phenomenon, not the collection and 22
0.5 percent in Mali and 0.3 percent in the Comoros.

# Results

The LFPW is estimated using the following regression specification, for worker i, in a survey in country k and year t: �,� where wage� is the hourly wage (in real dollars, at current exchange rates); Large� indicates whether worker i’s employer has more than 100 employees; α<sup>�,�</sup> is a country year fixed effect accounting for productivity differences across countries and years; Xi is a vector of Mincerian worker characteristics (i.e. age, age squared, and dummies for completion of primary, secondary and tertiary education); 3
Prior evidence on the employment effects of employment subsidies primarily comes from high-income countries where labor markets are characterized by very little informal employment. In addition, most studies for high-income countries examine the effect of programs targeted to the unemployed or disadvantaged workers and in many cases, estimates of effects are based on surveys to employers. One of the best-known programs is the one implemented in France in the 1990s, which reduced the employer portion of the payroll tax for the low-wage workers. The impact of the French program was estimated by several papers including Kramarz and Philippon (2001) and Crepon and Desplatz (2003). Kramarz and Philippon (2001) base their evaluation on household survey data and examine the effects of changes in the minimum labor costs - hence capturing the effects of both the changes in the minimum wage and the changes in payroll tax subsidies at the minimum wage level. Their analysis regarding the effects of a decrease in labor costs due to an increase in the payroll tax

> 7 In 2020, Turkey had the 15th highest tax wedge among the 37 OECD member countries, occupying the same position in 2019 (calculated for a single worker). 8 As of 2018, about one third of workers in non-agriculture in Turkey were informally employed according to the Turkish Household Labor Force Survey (HLFS).

4
# **Investing in Human Capital: What Can We Learn from the World Bank’s Portfolio Data?**

Roberta Gatti<sup>1</sup> and Aakash Mohpal<sup>2</sup> JEL: O10, O15, O19 Keywords: Aid allocation, World Bank lending, Human Development, IDA, IBRD Acknowledgements: We thank Aart Kraay, Keith Hansen, Amit Dar, Celine Ferre, Amer Hasan, Mahesh Shrestha, and Michel Welmond for their useful comments, and our colleagues Sadaf Alam, Milagros Delgado, Anton Dobronogov, Stephane Guimbert, Steven Knack, and Tihomir Stucka for useful conversations. This work does not necessarily reflect the views of the World Bank or its Executive Directors. All errors are our own.

> 1 Chief Economist for Human Development, the World Bank Group, rgatti@worldbank.org

> 2 Young Professional, Health, Nutrition and Population, the World Bank Group, amohpal@worldbank.org
-- End of picture text -->

Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for different commodity categories and non-commodity exporters. Chad, Sudan, and Tajikistan are also classified as major agricultural exporters (black portion of the bar not shown). *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

Sources: Authors’ calculations based on WEO (IMF) data. Commodity categories from World Bank.
Given the many channels involved, an evaluation of the exact impact of the migration of skilled labor (the ”brain drain”) for source countries is a very complex task. As we shall advocate in this paper, most of this impact may ultimately be captured through the e¤ect of emigration on the composition of the labor force, that is, on the stock of human capital per worker remaining in the home country. Until recently, empirical attempts in this direction have been hampered by the lack of harmonized international data on migration by origin country and education level. In the absence of such empirical material, the debate has remained almost exclusively theoretical. The early ”brain drain” literature of the 1970s emphasized its negative consequences for those left behind. The main conclusions were that skilled emigration contributes to increased inequality at the international level, with the rich countries getting richer at the expense of the poorer countries. By contrast, more recent contributions ask whether the traditional negative e¤ects of the brain drain stressed in the early literature may be o¤set by possible bene...cial e¤ects arising from remittances, return migration, creation of trade and business networks, and possible incentive e¤ects of migration prospects on human capital formation at home.<sup>1</sup> In particular, a new brain drain theoretical literature has emerged around the idea that migration prospects may well foster human capital formation in developing countries even after actual emigration is netted out; this literature studies the theoretical conditions under which the overall e¤ect of the brain drain may be positive (i.e. brain drain reduces international inequality). During the last two decades, there has been a signi...cant increase in the magnitude of the brain drain. However, as recent theories show, it could be that some developing countries have experienced a social gain from this brain drain.

We ...rst summarize in Section 2 the data on the magnitude of the brain drain, and
care in Kenya by Nguhiu, Barasa, and Chuma (2017).<sup>12</sup> Restricting our analysis to the sample of women who received any antenatal care, we proceed by comparing in more detail the quality of care received by women of different socioeconomic backgrounds. Figure 1 shows the estimated wealth gradient for each component of antenatal care separately. Of the 13 items reported in the household survey, an average of 53 percent was performed. A one standard deviation increase in wealth is associated with a 4‐percentage‐point increase in the completeness of the antenatal care exam. On average, women in the lowest decile reported that 48 percent of the items were performed, compared with women in the highest quintile, who reported that

> 10 At the time, the conversion rate was about CDF 920 per US$1.

> 11 The selection of procedures is based on Nguhiu, Barasa, and Chuma (2017), although we do not have data on prescription of intestinal parasite drugs. Their definition of coverage is conditioned on a minimum of four consultations; we define coverage as any consultation.

> 12 When we condition coverage on at least four consultations, as in Nguhiu, Barasa, and Chuma (2017), the concentration index is 0.187.

9
- [19] Harvey, A.C. (1989). _Forecasting, Structural Time Series Models and the Kalman Filter_ . Cambridge University Press.

- [20] IEA (2016). Extended World Energy Balances. IEA World Energy Statistics and Balances (database). DOI: http://dx.doi.org/10.1787/data-00513en (Accessed on 23 June 2016).

- [21] Inglesi, R. (2010). Aggregate Electricity Demand in South Africa: Conditional Forecasts to 2030. _Applied Energy_ , 87(1), 197-204.

- [22] Jamasb, T. (2006). Between the State and Market: Electricity Sector Reform in Developing Countries. _Utilities Policy_ , 14(1), 14-30.

- [23] Johansen, S. (1995). _Likelihood-Based Inference in Cointegrated Vector Autoregressive Models_ . Oxford University Press.

- [24] Joseph, K. L. (2010). The Politics of Power: Electricity Reform in India. _Energy Policy_ , 38(1), 503-511.

- [25] Joutz, F. L., Maddala, G. S., and Trost, R. P. (1995). An Integrated Bayesian Vector Auto Regression and Error Correction Model for Forecasting Electricity Consumption and Prices. _Journal of Forecasting_ , 14(3), 287-310.

- [26] Luger, R. (2004). Exact Tests of Equal Forecast Accuracy with an Application to the Term Structure of Interest Rates. _Bank of Canada Working Paper 2004-2._

- [27] Lütkepohl, H. (2005). _New Introduction to Multiple Time Series Analysis_ . Springer.

- [28] Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E., and Winkler, R. (1982). The Accuracy of Extrapolation (Time Series) Methods:
# **4. Empirical Analysis**

# **_1. Effect of business-as-usual approach on labor reporting_**

To understand the effect of survey approach on labor reporting, we estimate the following equation separately for the samples of men and women aged 18-64: 1 + γ (1) iih 1iih iih yy = ∝ + ββ TT + εε Above, _i_ and _h_ represent individual and household, respectively; _y_ represents individuals’ labor outcomes (as presented in Table 2); and _α_ and _ɛ_ represent constant and error terms, respectively. 1 is a binary variable identifying the adults in the IHS4 sample, with the individuals in the IHPS constituting the comparison category. _D_ is a vector of individual and household attributes TT presented in Table 1 earlier to capture any remaining unobserved heterogeneity that may also jointly determine both the dependent variable and the likelihood of being selected for one survey versus the other.

The aim is to understand how the business-as-usual survey setting compares to the best-practice approach of conducting individual interviews with self-reporting respondents. For the binary outcome variables, Equation (1) is estimated as a linear probability model with probability weights adjusting for non-response.<sup>15</sup> For weekly hours across activities, and annual hours and earnings in wage and _ganyu_ , OLS regressions are run on the conditional sample of respondents (those who reported working in that activity over the relevant reference period).<sup>16</sup> Standard errors are clustered at the enumeration area level.

Based on equation (1), Tables 3 and 4 summarize the effect of the business-as-usual survey setting on the estimation of different labor statistics (for the full sample, and for men and women separately). On the whole, after controlling for demographic and survey characteristics, we find that the business-as-usual scenario (relative to the individual interviews) underestimates men’s and women’s participation in livestock, managing or working in an NFE, as well as wage employment for men. After controlling for demographic and survey
from several reports of the Mexican Economic Census, covering the universe of business establishments located in Mexico City. Second, I use the microdata of the Mexican Population Census to determine the residence of both formal and informal workers. Third, I use detailed information on the transportation network in Mexico City, which I complement with transportation diaries (origindestination survey data). Additionally, I use the 2015 Intercensal Survey to construct commuting and trade flows at the city level for both sectors. I also use standard household survey data to calibrate some of the parameters of the model.

In the first part of the paper, I document three empirical findings that suggest a negative relationship between the accessibility of jobs and informality.

First, I exploit cross-sectional variation among informal vs. formal workers to show that informal workers spend less time commuting, and work closer to home relative to their formal counterparts. For instance, informal workers spend 40% less time commuting on average. This implies that informal workers are more sensitive to commuting costs than formal workers. These findings are robust to controlling for different sets of fixed effects and individual characteristics.

Second, I compare the residence of informal and formal workers. I document that formal jobs concentrate in the west and center of the city, where most economic activity takes place. By contrast,

> 4See Bartelme et al. (2019) for recent work that studies the effect of optimal policies when agglomeration externalities across sectors or locations.

> 5See Perry et al. (2007) and Ulyssea (2018) who document informality rates in Latin America. In this region, the informal economy varies from 35% in Chile to 80% in Peru.

2
compared to 120-125 ha for those not affected by the conflict. As 2022 winter crops were planted before the Russian invasion in February 2022, it is not surprising that even for VCs that were affected by the conflict during the growing season for 2022 winter crops, the mean winter crop area was reduced by 228 ha only if satellite imagery is used, or by 106 ha if open-source conflict data are used.

<mark>Weather affects agricultural production in ways that are unrelated to conflict. To account for weather variation, we generate data on precipitation and minimum or maximum temperature from the US National Center for Environmental Prediction Global Forecast System, which produces weather forecasts every six hours. For the 2019–23 growing seasons, we use the first six-hour interval of each forecast, extracted u</mark> sing Google Earth Engine scripts <mark>, as a “nowcast” to derive daily aggregated values</mark> at the rayon level.

<mark>The temperature data are then converted into growing degree days (GDDs), using</mark> daily values of _Tmin_ and _Tmax_ <mark>with a sinusoidal distribution as suggested by d'Agostino and Schlenker (2016).</mark> In line with the literature, we then compute the cumulative GDDs as the number of hours with temperatures above 5̊° C (41° F) for summer crops (see figure 4) and 0° C (32° F) for winter crops (see figure 6). To complement GDDs, we also compute cumulative precipitation in each year for summer crops (figure 5) and winter crops (figure 7). The summer growing season in 2019 was very warm; in 2022, rainfall after mid-May remained below average; and 2023 had ample rain in the spring but a dry summer. For winter crops, 2023 combined 9