rafmacalaba/data-use-mentions-extended huggingface dataset:
Subsequently, we perform further tests in Table A2 (Appendix) to ensure the robustness of our results against different temperature measures. We conduct these tests by employing (i) log of temperature (Column 1); (ii) temperature data at 0.5<sup>◦</sup> resolution from the Climate Research Unit of the University of East Anglia (CRU) (Column 2); (iii) the number of days when temperature exceeds 28<sup>o</sup> C (Column 3); (iv) excluding regions where temperature exceeds this level (Column 4); and (v) employing temperature shock, defined as the difference between actual temperature and long-term temperature being greater (less) than 2 (-2) standard deviations (Column 5). The outcomes of these tests show little deviation from our baseline specification. We also conduct alternative measurements of temperature bins for Figure 2 by dividing the temperature distribution within each region into deciles, with the 60<sup>th</sup> percentile serving as the baseline group. The results presented in Figure A1 (Appendix A) indicate that the effects of extreme weather remain consistent with those observed in Figure 2.

Finally, we examine alternative chronic poverty measures. Specifically, we define chronic poverty using the poverty line of $2.15/day (both number of incidences and proportion of incidence), and we also measure the average poverty rate using various window lengths (4year and 5-year windows). For this test, we employ the fixed-effects model as outlined in Equation (2). The results shown in Table A3 (Appendix A) reaffirm our previous findings.

# **5. Conclusion**

We offer an updated and comprehensive review of recent studies on the effects of climate change on poverty and inequality, paying special attention to studies’ data sources as well as empirical methods. Our findings suggest that while studies generally find negative impacts of climate change on poverty, especially for poorer countries
there are substantial downstream benefits from innovations in road freight services, both dampening cost increases and raising sales revenues of business users. The additional finding that increased intensity of competition in road freight services is significantly associated with the provision of innovative services suggests that easing any remaining barriers to competition in upstream business sectors should be a priority.

# **_2.4.1 Air Transport Indicators_**

# _<u>International Civil Aviation Organization (ICAO) Statistics Digests</u>_

The “Digests of Statistics” contains financial data and summary traffic data for international airports, monthly and yearly statistics for airports open to international traffic, financial data for commercial air carriers, data on fleet and personnel for commercial air carriers, revenue traffic performed for city-pair, annual data of civil aircraft on register, traffic data for commercial airlines, and statistics on the flow of traffic carried from point to point. The digests are available on a commercial basis as well as electronically.

27 ____________________________________________________________________________________________________________
for labor with the use of CC yields, these results suggest that if we suspect a high degree of correlation between the measurement error of labor with the true value of labor, it may be better to estimate the yield-input relationship using a predicted measure of CC yields that does not include SR yields among the list of predictors. On the other hand, if _λ_ is not high (as it seems to be the case with quantity of seeds per hectare), it is better to include SR yields among the list of predictors for CC yields. In any case, it is always better to use the ML yields rather than SR yields. Finally, the right panel of Figure 4 shows the results from the alternative regressions of ML yields which are computed with ML-based imputed measures of CC harvest that are in turn standardized by plot area (as opposed to imputing ML yields directly). We can see that the coefficients associated with the key inputs are not estimated correctly - especially for plot area. This confirms that deriving ML-based imputed CC yields, as opposed to CC harvests, is the most appropriate.

To further confirm that the use of ML yields de-biases the estimation of the relationships between yields and inputs, we estimate regression, as presented in Table 3, using the test sample and both SR and ML yields. The results are presented in Table 9. First, inputs do not correlate with overestimation of ML(SR,W) as much as they do with SR. In fact, none of the coefficients are significant. And although the coefficient for quantity of seeds per hectare was positive but not significant under the use of SR yields in the test sample, it is now reversed and is no longer significant. Second, the adjusted _R_<sup>2</sup> decreases considerably which indicates that the covariates are associated to a lesser extent with the difference in values between CC and ML yields.

# **4.2.4 Application on the nationally representative survey**

To assess the external
manufacturing sector to sectors involving high-skill activities, and, at the same time, caused service employment to rise, largely in informal, small retail businesses.

# **3 Data and Empirical Strategy**

## **3.1 Data Description**

We combine geo-coded survey data with spatial data on internet and road networks.<sup>1</sup>

### **3.1.1 Demographic and Health Surveys (DHS)**

The DHS are cross-sectional surveys that have been conducted in the majority of developing and middle-income countries since the 1980s. They are representative at the national and subnational levels. They provide information on demographic characteristics and socioeconomic status (e.g., age, gender, education, occupation) of all household members and recent surveys have been georeferenced. Recent studies have used DHS to examine the economic and social impacts of infrastructure in developing countries (Okoye et al., 2019; Hjort and Poulsen, 2019; Moneke, 2020; Canning et al., 2020; Herrera Dappe and Lebrand, 2021; Lebrand, 2022; Abbasi et al., 2022). We rely on data from 56 surveys conducted in 20 Sub-Saharan African countries. The total sample size is 806,378, with 552,022 women and 254,356 men. Grid cells, 20 kilometers by 20 kilometers in area, are constructed around each location to account for any time-invariant factor that might affect our infrastructure location and outcome variables. We group those who are employed into two categories: high-skilled workers and low-skilled workers. We also differentiate between agricultural workers and non-agricultural workers. Skilled workers include employed workers in the following occupation categories: professional, managerial, skilled manual labor, services, or agriculture. In our sample, 67.7 percent of respondents claimed to be employed, while 32.3 reported being

> 1Table (A1) in the Online Appendix provides details on the countries analyzed and years in which the surveys were conducted.

11
Policy Research Working Paper 8786

# **Abstract**

Growth fluctuations exhibit substantial synchronization across countries, which has been viewed as reflecting a global business cycle driven by shocks with worldwide reach, or spillovers resulting from local real and/or financial linkages between countries. This paper brings these two perspectives together by analyzing international growth fluctuations in a setting that allows for both global shocks and spatial dependence. Using annual data for 117 countries over 1970–2016, the paper finds that the cross-country dependence of aggregate growth is the combined result of global shocks summarized by a latent common factor and spatial effects accruing through the growth of nearby countries—with proximity measured by bilateral trade linkages or geographic distance. The latent global factor shows a strong positive correlation with worldwide TFP growth. Countries’ exposure to global shocks rises with their openness to trade and the degree of commodity specialization of their economies. Despite its simplicity, the empirical model fits the data well, especially for advanced countries. Ignoring the cross-country dependence of growth, by omitting spatial effects or common shocks (or both) from the analysis, leads to a marked deterioration of the empirical model’s in-sample explanatory power and out-of-sample forecasting performance.

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

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do
- Persico, N., Postlewaite, A., and Silverman, D. (2004). The Eect of Adolescent Experience on Labor Market Outcomes: The Case of Height. Journal of Political Economy, 112(5):10191053.

- Persson, P. and Rossin-Slater, M. (2018). Family Ruptures, Stress, and the Mental Health of the Next Generation: Reply. American Economic Review, 108(4-5):12561263.

- Pivovarova, M. and Swee, E. L. (2015). Quantifying the microeconomic eects of war using panel data: Evidence from nepal. World Development, 66:308 321.

- Quintana-Domeque, C. and Ródenas-Serrano, P. (2017). The hidden costs of terrorism: The eects on health at birth. Journal of Health Economics, 56:4760.

- Ra, S. and Singh, B. (2005). Measuring the economic costs of conict. Working Paper Series 2, Asian Development Bank,.

- Rutstein, S. O. and Johnson, K. (2004). The dhs wealth index. DHS Comparative Reports 6, Calverton, Maryland: ORC Macro.

- Satyanarayana, K., Prasanna Krishna, T., and S Narasinga Rao, B. (1986). Eect of early childhood undernutrition and child labor on growth and adult nutritional status of rural indian boys around hyderabad. Human nutrition. Clinical nutrition, 40:1319.

- Schmidt, I., Jørgensen, M. H., and Michaelsen, K. F. (1995). Height of conscripts in europe: is postneonatal mortality a predictor? Annals of human biology, 22(1):5767.

- Singh, S. (2004). Impact of long-term political conict on population health in nepal. CMAJ, 171(12):14991501.

- Strauss, J.
5

# **Introduction**

Among the many shifts of emphasis that have been evident in global health over the last 25 years or so, two stand out. One is the concern over equity: there has been a growing realization that the poor continue to lag behind the better off in receipt of key health interventions and health outcomes, and that international goals couched in terms of population averages could perfectly possibly be met without faster progress among the poor. The other is a concern over out-of-pocket health spending: getting people the health interventions they need is one part of the overall goal of any health system; the other is to ensure that families do not end up impoverished or otherwise suffer financial hardship by paying large sums of money out-of-pocket to ensure family members get the services they need. Neither of these concerns was reflected in the Millennium Development Goals (MDGs), which focused on population averages and made no mention of out-of-pocket expenses. By contrast, both are reflected in the Sustainable Development Goals (SDGs): the SDG equity commitment to ‘leave no one behind’ calls for data that are disaggregated by inter alia living standards; and the SDG commitment to universal health coverage (UHC) explicitly includes a commitment to ‘financial risk protection’.

This paper provides an overview of an international database on Health Equity and Financial Protection Indicators (HEFPI). The data set provides data on the delivery of health service interventions, health outcomes, and ‘financial protection’ in health, at both the population level and for subpopulations defined by household living standards. The data are computed from well-known household surveys that have been conducted by, or in partnership with, national governments, such as the Demographic and Health Survey (DHS) and the Living Standards Measurement Study (LSMS). None of our data comes from official reports by national governments, in part because such data do not lend themselves to disaggregation by household living standards, and in part because of concerns about accuracy, especially where governments do not face incentives to report
.018*|0.001|
||(0.010)|(0.012)|(0.009)|(0.003)|
|Observations|3,960|3,960|3,960|3,960|
|_Controls in allpanels_|||||
|Wave FE|Yes|Yes|Yes|Yes|
|HH FE|Yes|Yes|Yes|Yes|
|Department_×_wave|Yes|Yes|Yes|Yes|
|Geographic sampling_×_wave|Yes|Yes|Yes|Yes|
|Pre-migration controls_×_wave|Yes|Yes|Yes|Yes|

_Notes:_ The table presents the estimates of the specification described in equation (1). Panel A presents results using data from the baseline and wave I, panel B shows results using data from the baseline and wave II, and panel C presents results stacking all the data together (baseline, wave I, and wave II). Department corresponds to the five departments in which the sample was collected and geographic sampling corresponds to the four geographic levels at which the sample is representative, including three main cities and a fourth group that accounts for nine smaller urban centers with prevalent migration from Venezuela. Pre-migration control variables include: (i) individual controls for the head of household (gender, age, and education); (ii) labor history for the head of household (probability of being employed, type of job, probability of having a written contract, and the time gap between the last job and the migration episode); (iii) household characteristics (number of children, household size, access to public services, owning dwelling, and having a smartphone); and (iv) networks prior to migration episode (had family and friends in Colombia, knew of job opportunities before migrating, and migrated for health-related reasons). Standard errors clustered at the household level in parentheses. *** p _<_ 0.01
# **Annex 4: River Basin Organization Survey**

Dear Survey Respondent: This survey is part of a research project which tries to assess in which way the creation of Riverbasin Organizations leads to decentralization of water resources management to other – lower - levels of decision making. The research project also tries to assess in which way the creation of Riverbasin Organizations leads to improved water resources management results.

The specific information (in the box below) regarding each individual basin will be kept in confidentiality not to allow identification of the Riverbasin Organization.

The results of the research effort will be made publicly available and hopefully help in the worldwide effort to bring about sustainable integrated water resources management.

If you find you have not enough space to fill out the questionnaire, you can expand the sections in this Word document or provide annexing sheets.

Your collaboration in this effort is highly appreciated.

The survey can be found in: www.worldbank.org/riverbasinmanagement or directly by clicking on: http://wbln0018.worldbank.org/essd/rdvsurveys.nsf/home?OpenView 74
for Social Research.

- Bruil, Arjan, Essen, C ́eline van, Leenders, Wouter, Lejour, Arjan, M ̈ohlmann, Jan, & Rabat ́e, Simon. 2022. Inequality and Redistribution in the Netherlands. _CPB Discussion Paper_ , Mar. Number: 436 Publisher: CPB Netherlands Bureau for Economic Policy Analysis.

- Burdin, Gabriel, Esponda, Fernando, & Vigorito, Andrea. 2014. _Desigualdad y sectores de altos ingresos en Uruguay : un an ́alisis en base a registros tributarios y encuestas de_ .

- _hogares para el per ́ıodo 2009-2011_ Tech. rept.

- Burd ́ın, Gabriel, De Rosa, Mauricio, Vigorito, Andrea, & Vil ́a, Joan. 2022. Falling inequality and the growing capital income share: Reconciling divergent trends in sur.

- vey and tax data. _World Development 152, 105783_

- Burkhauser, Richard V., H ́erault, Nicolas, Jenkins, Stephen P., & Wilkins, Roger. 2016 (Feb.). _What has Been Happening to UK Income Inequality Since the Mid-1990s? Answers_

22
0.174]**|[0.356]|[0.491]|
|Observations|663|663|221|221|

_Source:_ 2010 Yemen RIC Survey; Notes: Sampling weights are applied; Standard errors are in parentheses; *, **, and *** denote significant levels at the 10, 5, and 1% respectively, based on standard errors; The dependent variable for entry is a dummy of a household member’s success in starting nonfarm business. The dependent variable for closure is a dummy of a manager’s decision to close after business started within the past three years.

The unexpected signs of the results are the availability of formal financial institutions and community level of high-school education. Many rural entrepreneurs are not using the formal 13
of 14 percent (Brynjolfsson, Li, and Raymond 2023).

# **_2.3. Does digitalization increase capital accumulation?_**

The literature identifies ICT investment as a key factor behind productivity growth in the United States (Jorgenson, Ho, and Stiroh 2002; Oliner and Sichel 2000). Further work highlights the dominance of IT-oriented sectors in driving the pickup in TFP growth observed in the United States during the 1990s (Jorgenson et al. 2007; Jorgenson, Ho, and Stiroh 2008; Stiroh 2002). A series of studies following similar analytical approaches have found similar results for other advanced economies (Vu 2011). The productivity effects of ICT investments have been linked to the investments in intangible capital that comprises computer-related software and data, R&D, design and other properties of innovation, and company competencies such as marketing, firmspecific training, and organization practices (Corrado, Hulten, and Sichel 2005).<sup>8</sup> In their review of the firm-level econometric evidence from the United States, Brynjolfsson and Hitt (2003) argue that the surge in the aggregate productivity statistics during the 2000s reflect the contributions of intangible capital accumulated in the past. This includes complementary “organizational” investment in business process design, job training, customer service, and the like. In another review, Draca, Sadun, and Van Reenen (2009) find that there is a much larger

> 8 There are several economy-wide and industry-level studies on advanced economies, primarily using a growth-accounting approach, that find a positive impact of intangible capital on productivity growth, such as in the United States (Corrado, Hulten, and Sichel 2009); Japan (Fukao et al. 2009); and the Republic of Korea (Chun and Nadiri 2016). Firm-level studies provide additional supporting evidence (Bontempi and Mairesse 2015; Marrocu. Paci, and Pontis 2012; and Di Ubaldo and Siedschlag 2021).

10
in the R&D and non-R&D sectors is attributed to investment-specific shocks by 56 and 38.4 percent, respectively.

The three technology shocks jointly explain 92.3 percent and 78.5 percent of the variance of outputs in the R&D sector and the rest of the economy, respectively. Ouyang (2011) argues that technology shocks are important factors in explaining the procyclicality of R&D. Our results confirm this claim since the main sources of output volatility in the two sectors, capital investment-specific and R&D productivity shocks, induce output responses of the same sign. Furthermore, technology shocks, jointly explain a moderate proportion of the variance of hours which is in the order of 46.1 percent and 56.4 percent in the R&D sector and the consumption-good sector, respectively. Table 4 displays variance decompositions when the R&D sector is not modeled as a separate sector and R&D is not treated as investment. In this case, aggregate output correponds to the GDP reported in the NIPA tables while hours correspond to aggregate per capita hours.<sup>30</sup> These results show that under this specification of the model, investment-specific shocks and neutral productivity shocks explain 40.2 and 33.3 percent of the variability of NIPA output while the combined e↵ect of technology shocks is 90.3 percent; this result is not too di↵erent from findings of previous studies that used quarterly data.<sup>31</sup> The combined e↵ect of technology shocks on productivity and hours increases significantly compared to the model where there is a separate R&D sector and R&D is treated as investment than solely as an expense.

In the second set of results (tables 5 and 6), we compute variance decompositions of the

> 30In the model of section 3, the R&D channel is closed when _↵_ 1 = 0.

> 31Altig et al. (2011), find that
4 structural unemployment in many developing countires. This has lead to recommendations for the deregulation of the labor market to attain equilibrium. Cross-sectional studies aimed at identifying the actual and potential coverage of HWs in terms of labor force groups face the statistical problem of selectivity bias. In fact, cross-sectional studies on wage setting are based in sample results that exclude the unemployed, thus creating a specification error which implies that the statistical estimates will be influenced by the relative size of the unobserved population in the sample.<sup>5</sup> As a consequence, given the unavailability of data **or.** actual wages below the legal MW level, statistical estimatcs of the coverage of MWs and of the damages caused to specific groups of the labor force, will be underestimated. We attempt to apply a corrective statistical technique to a cross-sectional analysis performed with Chilean data.

# 1II. The Case

The performance of the labor market has been central to the process of macroeconomic adjustment in the 1970s and 1980s in Chile, as indicated by prevailing unemployment levels and enormous wage fluctuations. Given that the role of the MW has been acknowledged as important, the economic policy has put strong emphasis in keeping them down in rnil terms. However, the evidence reported by available empirical £tudies does not support the negative effects of MWs on employment. For instance, on the basis of aggregate lator demand estimates, Solimano (1983) has indicated the existence of a low potential negative impact of the MW on the economy. On the other hand, Co%rbo (1981), based on the parameter estimates for a production function, has calculated an important negativc impact of the MW on manufacturing employment. With a more aggregate model, Paldam and Riveros (1987) found that, even though the causality between increases in the MW and the average wages is significant, the causality
_Figure 1: Average Annual Exports: Pre and Post-AGOA_ (a) Large Exporters to the US <!-- Start of picture text --> Nigeria<br>Angola<br>South Africa<br>Gabon<br>Congo (ROC)<br>Chad<br>0 5,000 10,000 15,000 20,000<br><!-- End of picture text -->

- (b) Medium to Small Level Exporters to the US

<!-- Start of picture text --> Kenya<br>Lesotho<br>Ghana<br>Mauritius<br>Cameroon<br>Botswana<br>Namibia<br>Ethiopia<br>Liberia<br>Malawi<br>Tanzania<br>Mozambique<br>Uganda<br>Zambia<br>Senegal<br>Sierra Leone<br>Rwanda<br>Togo<br>Seychelles<br>Benin<br>Burkina Faso<br>0 100 200 300 400<br>Average Annual Imports: Pre-AGOA (bill. USD)<br>Average Annual Imports: Post-AGOA (bill. USD)<br><!-- End of picture text --> The second set of data necessary to identify factors that may explain the heterogeneity in impact draws from multiple sources. Macroeconomic data such as incomes, population, size 20