rafmacalaba/data-use-mentions-extended huggingface dataset:
Finally, we implement a test inspired by Benford’s law to detect electoral manipulation in villages won by the incumbent (see Mebane, 2006, 2011). In Appendix Figure A.2, we plot the distribution of the first, second, third, and last digits of candidate vote tallies separately for villages won and villages lost by the incumbent. Using a Kolmogorov-Smirnov test, we cannot reject the null of equal distributions across the two types of villages for any of the four digit distributions—the p-values from these tests are reported at the bottom of each panel. Nonetheless, panels (c) and (d) of Appendix Figure A.2 show significant heaping of candidate vote tallies at zero, plausibly as a result of rounding. Thus, in Appendix Table A.1, we also show that the number of candidate vote tallies with a trailing zero is not significantly associated with turnovers (column 13). Overall, we find no evidence of manipulation of election results.

# **4 Impacts on Bureaucratic Organization and Information Flows**

We now present our estimates of the effects of turnover in village elections. In this section, we highlight three main shifts. First, turnover reshapes village bureaucracies, improves staff morale, and increases effort exerted by bureaucrats, as reflected in more frequent interactions with citizens (Section 4.1). Second, turnover reduces the presence of bureaucrats with nepotistic ties (Section 4.2). Third, turnover improves information flows between officials and citizens. This improves the bureaucracy’s understanding of citizens’ preferences and leads to stronger alignment regarding policy priorities (Section 4.3).

## **4.1 Organizational Changes**

**Leader Turnover.** Our main specification examines the impact of an incumbent’s electoral defeat in the most recent election on village-level and individual-level outcomes. Using our survey data, we first verify that these electoral results induce a leadership change, as expected. Specifically, we show that an incumbent’s
harmonized social sustainability indicators and other indicators of interest for our analysis. The consolidation exercise consists first of selecting a reference period. In order to maximize the number of variables and countries, we refer to a “most recent” period comprising 2016 to 2020, instead of a single baseline year. A single baseline year would require a degree of artificial alignment by updating data from a previous year or simply losing information that is not reported for the year of reference. Once we had processed all the indicators from the different sources within the 2016-20 period, we compiled them into two single data sets according to the source type (national vs regional and global). We next combined both data sets by country name into a single Stata file. Online Annex B contains a readme.file with instructions for creating the final version of the data set, and all do.files are available upon request. The final version of the database contains 71 harmonized social sustainability variables: 23 in the social inclusion component, 18 in the resilience component, 20 covering social cohesion, and 10 for process legitimacy. In addition, we include 21 indicators covering per capita GDP, growth rates, monetary and multidimensional poverty, inequality (of incomes through the Gini coefficient), the World Bank’s Human Capital Index, fertility rate, equality of opportunity and shared prosperity, among others (see list online Annex A), in order to analyze their linkages with social sustainability indicators.

The resulting Social Sustainability Global Database (SSGD) encompasses 193 countries and 37 territories across seven world regions, accounting for 98.7 percent of the global population (see Annex 1 for the list of countries and territories). Table 1 lists all the variables in the SSGD that correspond to each of the social sustainability components (for exact definitions, see the codebook reported in online Annex A), as well as the number of countries for which a value for each variable is observed (Column “Obs”).

8
(TFPR), conflate true firm efficiency with firm price effects that capture input costs, product quality, and market power (Cusolito & Maloney, 2018). Hence, our finding that firm shocks result in a decline in firm productivity does not necessarily imply a decline in firm efficiency, which might appear at odds with the evidence on the shocks also leading to occupational restructuring towards high-skilled workers, but may simply reflect a decline in firm market power. The decline in firm profits in Table 5 corroborates this interpretation.

# **V. Mediating Role of Informality and Lack of Competition**

The long-run severity of crisis-induced employment and earnings losses may vary with local labor market characteristics. For developing countries, a first-order issue is informality. A high degree of informality may lead to substantial misallocation of resources and hamper employment growth, but it may also provide de facto flexibility for firms and workers to cope with adverse shocks and more alternatives for displaced workers. We examine this issue exploiting the wide heterogeneity in local conditions across Brazil’s vast territory and using a widely-used definition of informality. We estimate a modified version of Equation (5) replacing worker categories by indicator variables for higher than median (high) versus lower than median (low) informality rate in the worker’s 2007 employing firm’s municipality. The results in Table 8 point to a significantly smaller decline in formal worker employment due to GFC firm shocks in Brazilian localities with a higher informality rate. In highly informal localities, even formal firms are likely to employ many informal workers and it is for those workers (rather than for formal ones that we capture in our RAIS employer-employee database) that the firm adjusts the months worked and the wages when facing a negative shock. This finding supports the idea that informality provides de facto flexibility for firms and workers to cope with negative demand shocks, in line with the findings on the effects of trade liberalization in Brazil by Dix-Carneiro & Kovak (2019); Dix-Carneiro et al. (2021) and Ulyssea &
|-4.8|
|Uruguay|-0.5|1.2|-5.0|-4.5|-6.0|-2.1|-6.5|-0.9|
|Venezuela|0.0|-2.7|2.9|7.6|3.0|2.3|3.0|-0.4|
|Mean|-0.6|-1.0|2.8|3.4|1.6|1.4|1.0|0.4|

Source: own computations based on SEDLAC (CEDLAS and World Bank).

31
Das, J., S. Dercon, J. Habyarimana, P. Krishnan, K. Muralidharan, and V. Sundararaman. 2013. “When Can School Inputs Improve Test Scores?” American Economic Journal: Applied Economics 5 (2): 29–57.

Davis, E. A., F. J. Janssen, and J. H. Van Driel. 2016. “Teachers and Science Curriculum Materials: Where We Are and Where We Need to Go.” Studies in Science Education 52 (2): 127–60.

De Hoyos, N. R., A. J. Ganimian, and P. Holland. 2017. “Teaching with the Test: Experimental Evidence on Diagnostic Feedback and Capacity Building for Public Schools in Argentina.” Policy Research Working Paper No. 8261, World Bank, Washington, DC.

De Hoyos, R., P. A. Holland, and S. Troiano. 2015. “Understanding the Trends in Learning Outcomes in Argentina, 2000 to 2012.” Policy Research Working Paper No. 7518, World Bank, Washington, DC.

De Philippis, M. 2016. “STEM Graduates and Secondary School Curriculum: Does Early Exposure to Science Matter?” CEP Discussion Paper No 1443, Center for Economic Performance, London School of Economics and Political Science.

DiNIECE. 2004. Programme for International Student Assessment. Informe Nacional República Argentina. Buenos Aires: DiNIECE. http://repositorio.educacion.gov.ar/dspace/handle/123456789/55289.

DiNIECE. 2015. Anuario Estadístico Educativo. Relevamientos anuales 2007–2014. Buenos Aires: DiNIECE. http://portales.educacion.gov.ar/diniece/.

Duflo, E., R. Hanna, and S. Ryan. 2012. “Incentives Work: Getting Teachers to Come to School.” American Economic Review 102 (4): 1241–78.

Duflo, E., P. Dupas, and M. Kremer. 2011
S. Yadav. 2017. “How can we better capture food away from home? lessons from india linking person-level meal and householdlevel food data.” _Food Policy_ 72: 81–93.

- Gibson, J. 2002. “Why does the engel method work? food demand, economies of size and household survey methods.” _Oxford Bulletin of Economics and Statistics_ 64(4): 341–359.

- Gibson, J., and B. Kim. 2007. “Measurement error in recall surveys and the relationship between household size and food demand.” _American Journal of Agricultural Economics_ 89(2): 473–489.

25
data sets, and exploring exciting institutional options as the apply in and across many different geographic locations at a global, regional, national and city level.

In conclusion, it may help highlight some commonalities among the very diverse research areas on which each author conducted the scoping exercise. These areas of common interest emerge:

- All authors focused their recommendations on issues of relevance to the practitioners’ audience, rather than to an academic audience. This is important, since there is a great gap between academic research and practical application in the area of urban development. As a result many of the key findings of academic research have not found their way into practical application (e.g., the market oriented regulation of land and housing markets, the principle of subsidiarity, etc.).

- Related to the first commonality is a second one: All authors stressed the need to focus on specific country and city contexts and on the specifics of the interventions, rather than pursuing general theories and testing them against cross‐country data sets. Still, all probably would subscribe to Alm’s statement, which stands somewhat in contrast of what otherwise is his pessimistic attitude towards the relevance of theory: “This does not mean, I believe, that our theories‐‐‐in this domain and elsewhere—are not useful: they provide a necessary framework in which we can think about these issues, and they often allow us to identify specific policies that are clearly inconsistent with our theories and whose implementation would clearly lead to inefficiencies and inequities. However, practical implementation requires that we also incorporate the specifics of any country’s institutional structure in devising and implementing specific policies.” (Alm, 2010, p. 38)

- All authors called for improvements in data: data on urban assets, regulatory practices, urban finances, the capacities of urban institutions, or the nature of aid activities.

- They further stressed the need for a better understanding of the role that institutions play and of the political economy of urban
- 20 -

export performance in terms of market shares over the next two decades (19651986) and also accounted for a steadily increasing share of nonfuel exports. As such, updating the NBER indices of labor intensity into the 1980s could provide insights concerning likely changes in developing countries exports in the 1990s and beyond.

Drawing on U.S. Census Bureau data, Table 5 provides details on trends in labor intensity ratios for broad two-digit U.S. Standard Classification (SIC) industry groups over 1963-1982 for years in which census data were available, and also gives similar information for selected three and four-digit textile and apparel products. To assist in evaluating the relative importance of each industry in total U.S. manufacturing, statistics on the 1982 value of shipments are shown along with industry employment information for 1963, 1972 and 1982.

For most industries listed in Table 5 the 1963-1982 labor intensity ratios fluctuated in a narrow range, but there are important exceptions. Petroleum and coal products (SIC 29) experienced more than a 50 per cent rise in factor intensity ratios (from 215 to 337) while the ratios for tobacco products (SIC 21) rose from 192 to 359 points. These developments indicate both sectors were becoming relatively highly capital intensive. A similar increase in capital intensity is evident in the ratios for both food products and paper although the magnitude of change is smaller. Since these industries' ratios exceeded the United States average throughout the 1963-1982 period the rising trend implies they were becoming even less suitable for export by developing countries. However, the ratios for primary metals (SIC 33) fell from about 15 points above the United States average to 10 points below average during 1963-1982, a development which suggests an increase in developing countries' comparative advantage.
Policy Research Working Paper 7682

# **Abstract**

This paper assesses the equilibrium value of the Argentine peso exchange rate based on the country’s economic fundamentals and compares it with the official exchange rate value. The paper estimates a behavioral equilibrium exchange rate model that allows for movements in the equilibrium real effective exchange rate based on changing economic fundamentals, using monthly data from 1980 to 2015. The analysis identifies four key fundamentals driving the equilibrium exchange rate in Argentina: terms of trade, productivity differentials, foreign currency reserves, and trade openness. Based on these fundamentals, before the exchange rate reunification that took place at the end of 2015, the Argentine peso was overvalued by 39 percent. The results are robust to alternative estimation approaches.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice 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 authors may be contacted at acoppola@worldbank.org, andresa.lagerborg@eui.eu, and zmustafaoglu@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 not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
can significantly increase the signal-to-noise ratio. However, going beyond equal-weights can also serve another goal, namely improving the perceived credibility and legitimacy of multidimensional poverty indicators. Yet, these methods come with their own issues, which limit the impact they may yield on the indicators’ perceived credibility. There will always be doubts about the relationship between the preference data and “true” wellbeing (see footnote 64). Furthermore, applying preference information in another context than the one in which it is obtained is bound to raise questions on its external validity.

Finally, although the dimension-by-dimension approach to policy making is unlikely to change overnight, it might still be worth conducting policy analysis in a multidimensional way. Indeed, some of its insights may be productively used. And even if it is not immediately the case, these insights could raise awareness of the potential gains of an integrated approach to policy making.

# **5 Research papers**

In this section, we outline a list of seven research papers based on the opportunities

1. Empirical implications of going beyond the dichotomous counting methodology Dichotomizing non-binary variables “wastes” well-being relevant information. The solutions listed in section 3.2 provide ready-to-use conceptual improvements on the dichotomous counting methodology underlying mainstream multidimensional poverty measures. They will thus help improve the targeting of policies across households. One open question is the empirical significance of these solutions. By how much do they affect inter-household and cross-region well-being comparisons? How different are the households they identify as poor when compared to those identified under the counting methodology? This could be investigated both for the World Bank’s MPM as well as for national multidimensional poverty measures.

2. Harnessing separate surveys through hybrid well-being indices Hybrid well-being indices, which are part-synthetic and part-objective, are one way of avoiding “wasting” part of the data available in separate surveys, like LSMS and DHS. Sections 3.3 and 3.4.1 list solutions for the construction of hybrid multidimensional indices. They may thus help improve cross-regions well-being comparisons as well as
1), 5-32.

- Bricongne, J-C., Turrini, A., & Pontuch, P. (2019). Assessing House Prices: Insights from ‘Houselev’, A Dataset of Price Level Estimates. _European Economy Discussion Papers 101_ .

- Bricongne, J-C., Meunier, B., & Pouget, S. (2021). Web Scraping Housing Prices in Real-time: the Covid19 Crisis in the UK, _Banque de France Working Paper,_ No. 827.

- Brueckner, J. K., Rabe, C., & Harris, S. (2018). Backyarding: Theory and Evidence for South Africa. _Policy Research Working Paper No. 8636_ .  Washington, D.C.: World Bank.

- Campbell, D., Morris, D., Gallin, J., & Martin, R. (2009). What Moves Housing Markets: A Variance Decomposition of the Rent-Price Ratio. _Journal of Urban Economics_ 66, 90-102.

- Campbell, J. Y. & Cocco, J. F. (2007). How do House Prices affect Consumption? Evidence from Micro Data. _Journal of Monetary Economics_ 54 (3), 591–621.

- Can, A. (1992). Specification and Estimation of Hedonic Housing Price Models. _Regional Science and Urban Economics_ 22(3), 453-474.

- Catte, P., Price, R. W. R., Girouard, N., & André, C. (2004). Housing Markets, Wealth and the Business Cycle, _OECD Economics Department Working Paper_ , No. 394. Paris: OECD.

- Ceccato, V., & Wilhelmsson, M. (2020). Do Crime Hot Spots Affect Housing Prices? _Nordic Journal of Criminology_ 21(1), 84-102.

- Čeh, M., Kilibarda, M., Lisec, A., & Bajat, B. (2018). Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices
**Figure 2:** Trends in Non-Farm Employment by Gender

<!-- Start of picture text --> Non-Farm Non-Farm<br>Worker Type Sectors<br>55 61 66 68 55 61 66 68 55 61 66 68 55 61 66 68<br>Male Female Male Female<br>Self-Employed Casual Manufacturing Trade<br>Salaried Construction Services<br>.2 .2<br>.1 .1<br>Fraction of Workers Fraction of Workers<br>0 0<br><!-- End of picture text --> _Notes:_ The above figures shows trends in non-farm employment categories and sectors, conditional on participation in the labour force, and disaggregated by gender. NSS survey rounds 55, 61, 66 and 68 correspond to the 1999-00, 2004-05, 2009-10 and 2011-12 years.

37
/mark> p <mark>ublicly available Sentinel-2 imagery with medium spatial (10-meter pixel size) and temporal (five-day revisit cycle) resolution to identify conflict-induced damage to agricultural fields. We constructed a measure of the area of fields damaged by military actions such as crop burning, heavy vehicle movement, or artillery shelling in nine oblasts (districts) affected by fighting for every two-week period since the start of the war.</mark><sup>4</sup> <mark>During the growing season, field damage is often visible only for a short period, although it may change a field’s level of productivity in the long term, especially if unexploded ordnance is left behind.</mark> To ensure that our measure focuses on agricultural field <mark>s, w</mark> e mask out areas that were not cultivated with agricultural crops in at least one of the pre-war years during 2019–21.<sup>5</sup> <mark>We complement this with a measure that indicates if a VC was o</mark> ccupied, involved in active fighting, or had troop presence <mark>, based on online daily situational reports from local and regional military administrations.</mark><sup>6</sup>

> 4 The work involved <mark>visual inspection by trained university students who, in the training phase and in cases of doubt, were also provided with higher resolution imagery. The presence of field damage was recorded as a zero/one dummy.</mark> Duncan et al. (2023) use a machine learning approach with higher resolution imagery to detect transitory artillery craters and draw out potential longer-term implications in the Ukrainian context. For Cambodia, Lin et al. (2021) discuss the longer-term damage wrought by unexploded ordnance and farmers’ adjustment to the associated dangers. In Ukraine, the oblasts included are <mark>Kyiv, Chernihiv, Sum, Kharkiv, Luhansk, Donetsk, Zaporizja
enforcement, trauma, stigmatization, and individuals with criminal backgrounds while detained, which may drive their impacts on labor market prospects and firm dynamics upon return.

The levels of education of deportees tend to be low. About 71 percent of the deportees in our sample have completed primary education, whereas only 4 percent have secondary education, and 2 percent have completed technical education or university. Deportees have significantly lower levels of education than Salvadorans living in El Salvador and the United States (13 and 14 percent of them, respectively, has at least 14 years of education, Figure 3). On the other hand, Salvadorans living in the United States have higher educational attainment than their counterparts in El Salvador, which is in line with the idea that repatriated individuals were able to improve their knowledge abroad (Figure 3). We also have data on English proficiency for about 45 percent of our sample, which indicate that 51 percent out of the total of returnees do not speak English.<sup>12</sup> The characteristics of deported individuals suggest that any positive impacts of deportees on Salvadoran firms through a knowledge transfer from a developed to a developing country will be hard to find. Instead, given that most repatriated individuals did not get to live and work in the United States and that they returned to El Salvador within a month of arrival in the United States, any impacts of repatriation flows on Salvadoran firms are likely to reflect the experience of being deported and its impacts on deportees.

# **IV Data**

We combine multiple sources of administrative data to construct a municipal panel of individual firm outcomes and aggregated repatriation inflows.

> 12While we do not observe the sector of economic activity of the jobs of deportees who worked abroad, data from the American Community Survey (ACS) show that most Salvadorans in the United States work in construction, restaurants and hotels, business services, commerce and education and health (see Figure 4). In other words, they are more concentrated in skilled services when compared