rafmacalaba/data-use-mentions-extended huggingface dataset:
, Tunisia, Chile, Philippines, Morocco, and South Africa) in science.

However, the country’s progress did not stop there. In 2003, Jordan improved its TIMSS science score to 475, from 450 in 1999—an increase of 25 points, or 0.25 standard deviations. This was a significant increase, equivalent to about an entire year of learning. In 2007, Jordan continued to improve its TIMSS student scores, surpassing several countries that had had a similar or slightly higher performance in 1999. In fact, between 1999 and 2007, no other country has improved as greatly in assessments of the science knowledge of its students.

Discovery Schools, moreover, appear academically superior to other public schools (see Figure 2). Students attending these schools had, on average, higher scores in all three subject areas of the PISA 2006 assessment than did those in regular public schools.

7
# **Hotter Planet, Hotter Factories**

**Uneven Impacts of Climate Change on Productivity**<sup>∗</sup>

Woubet Kassa<sup>†</sup> Andinet Woldemichael<sup>‡</sup>

**Key Words** : Climate Change, Firms, Labor Productivity, Temperature Changes **JEL Codes** : D24, J22, J24, J81, O14, O12, Q54 Q56, L60

- © 2024 The World Bank and International Monetary Fund

> ∗ The authors extend their sincere thanks to A. Patrick Behrer, Marco Marini, and Jim Tebrake for their thoughtful feedback. We also thank Cesar Calderon and Andrew Dabalen for their insights and comments. The study was enriched by feedback from attendees at various academic forums including the IMF STA Innovation Talk, the ASSA 2024 Annual Meeting of the American Economic Association, the Southern Economic Association, the Association of Environmental and Resource Economists and the Center for the Study of African Economies, University of Oxford. We are grateful to Somik V. Lall for his thorough review and valuable comments. Special thanks to Joshua Seth Wimpey for access and support with the WBES data. The views expressed in this paper are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

> † The World Bank. Email: wkassa1@worldbank.org

> ‡ International Monetary Fund. Email: awoldemichael@imf.org
on the presence of conflict, is associated with greater observed sovereignty. To explore further the idea that the effect of regional inequality may be contingent on other factors, in Column 4 we present results from a regression that includes an interaction term between regional inequality and ethnic distinctiveness and another variable interacting inequality and regional income. Regional distinctiveness, _pace_ our hypothesis 2, now definitely turns negative and is statistically significant way, but its interaction with regional inequality is positive. Controlling for a given level of ethnic distinctiveness, higher regional inequality is associated with greater observed regional sovereignty. This supports the elite-capture hypothesis regarding how higher regional income inequality gets translated into demand for sovereignty.<sup>44</sup> Finally, another interesting new result is that the interaction between regional income and inequality is negative, which is consistent with richer regions dampening their demands for more autonomy as the presumed need for greater within-regional redistribution increases. With less sovereignty, the burden of redistribution falls to the center rather than the region.<sup>45</sup>

# **5. Conclusion**

The analysis in this paper has pointed to several important relationships not previously shown in cross-country studies of decentralization and sovereignty demand. Using disaggregated data with regions as units of observation and controlling for country-specific fixed effects, we find that richer, more populous and resource-endowed regions are more likely to enjoy higher degrees of sovereignty. This is consistent with our hypotheses. Richer, larger regions are better able to provide public goods to their citizens: either because they are richer, or, controlling for their wealth, because they are more populous and thus per capita costs of provision are less. They

> 44 However the increase in the Gini has to be more than 30 points to overturn the negative coefficient associated with ethnic distinctiveness.

> 45 This hypothesis is consistent with the main predictions of a model by Beramendi (2009).

30
events. That is, the privatized banks might be measured as having negative effects on performance simply because they were not involved in domestic M&As or foreign acquisitions.

We test the effects of governance on bank performance using data from Argentina in the 1990s. We include the static, selection, and dynamic effects of all the major different types of governance that are relevant in Argentina –domestic ownership, foreign ownership, and state ownership – in the same model. The dynamic events analyzed include domestic M&As, foreign acquisitions, privatizations, and state restructurings, where the latter refer to events in which state-owned banks were restructured without privatization.

We also run some “nonrobustness” checks with various exclusions from our performance model. These allow us to examine the consequences of not specifying all of the important different types of governance or changes in governance in the same empirical model. Thus, the nonrobustness checks allow 2
.385|-0.144|0.073|0.330++|0.103|
|<br>Medianvalueand below|(0.382)|(0.233)|(0.323)|(0.202)|(0.137)|(0.370)|(0.208)|(0.304)|(0.177)|(0.098)|(0.310)|(0.187)|(0.236)|(0.158)|(0.088)|

Notes: See Table 1. Long-differenced estimations consider changes in the location and productivity of organized-sector manufacturing activity in 106 non- nodal districts located within 0-50 km of GQ for the time period starting from 2000 to 2009 from the Annual Survey of Industries (ASI). Panel (a) repeats the base estimation for this group. In panels (B)-{L). the base effect is interacted with indicator variables for above or below median values for indicated district traits. Estimation controls for unreported main effects of district traits. District level access to infrastructure, literacy, access to banking, population density is extracted from 2001 census, while distance from nodal cities to district is computed from district edge using India's district level GIS maps. Lastly, initial TFP is plant-level TFP for the year 2000-01 computed following LP-Sivadasan methodology on ASI data. misallocation matrices are computed using the methodology proposed in Duranton et al. (2015a).
Pfaff, A.S.P., Robalino, J., Reis, E.J., Walker, R., Perz, S., Laurance, W., Bohrer, C., Aldrich, S., Arima, E., Caldas, M., et al. (2018). Roads & SDGs, tradeoffs and synergies: Learning from Brazil’s Amazon in distinguishing frontiers. Economics: The Open-Access, Open-Assessment E-Journal _12_ , 1–26.

Raballand, G., Macchi, P., and Petracco, C. (2010). Rural road investment efficiency : lessons from Burkina Faso, Cameroon, and Uganda (The World Bank).

Raptopoulos, A. (2013). No roads? There’s a drone for that.

Roberts, P., KC, S., and Rastogi, C. (2006). Rural Access Index: A Key Development Indicator (Washington DC, USA: World Bank).

Stevens, F.R., Gaughan, A.E., Linard, C., and Tatem, A.J. (2015). Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLOS ONE _10_ , e0107042.

USAID Global Health Supply Chain Program-Procurement and Supply Management (2017). Unmanned Aerial Vehicles Landscape Analysis : Applications in the development context (Washington DC, USA: Chemonics International Inc).

World Bank (2016). Measuring rural access : using new technologies (The World Bank).

World Bank (2018). Road Costs Knowledge System (ROCKS) – Doing Business Update (Washington DC: World Bank).

# **Annex 1: OSM roads classification**

Below is a list of OSM road labels and classification in primary, secondary, tertiary, and track. All other types of roads are in “other”.

|‘highway’ tagin OSM|Meta‐Classification|
|---|---|
|unsurfaced<
Political and institutional arguments have focused on community organization, including state versus communal security/policing arrangements.

Many developing countries are afflicted by high levels of communal and inter-communal conflict that does not take the form of civil war, but may yet be the source of significant destruction of livelihoods and material property. In Indonesia, for example, only Aceh would fall under Collier-Hoeffler’s definition of civil war.<sup>2</sup> The measurement of local conflict incidence for the purposes of more systematic evaluation of variance at low levels of geographic specification within countries has remained limited. Our contribution is meant as a starting point toward a better mapping and understanding of local conflicts within Indonesia, and aims to test some hypotheses about potential explanatory factors.

The paper proceeds in four sections. Section 2 sets out issues concerning the definition and empirical measurement of local conflict and lays out hypotheses about the factors associated with conflict. Section 3 describes quantitative and qualitative evidence on conflict in Indonesia. Section 4 estimates an empirical model to investigate which factors are associated with conflict based on the Indonesian village census. Section 5 concludes.

# **2. Conceptual Framework**

Conflict is a complex phenomenon that takes on a very wide range of forms. Major instances of civil war entail significant casualties and can set development back by decades. At the other end of the spectrum, conflict is often a productive aspect of everyday interactions that

> 2 The Collier-Hoeffler dataset focuses on civil war, defined as occurring when an identifiable rebel organization challenges the government militarily and the violence results in more than 1,000 combat related deaths, with at least 5 percent on each side (World Bank 2003a).

4
|Dependent variable: Forecast revi|sion of real consumption|
|---|---|
|Constant|-1.967 **|
||(0.98)|
|Stringency index|-0.207 ***<br>(0.02)|
|COVID-19 infections|-0.042 ***<br>(0.02)|
|Adjusted R-squared|0.574|
|No. of observation|63|
|No. of countries|21|
|Sample periods|2020Q1-Q3|

Note: Results obtained from pooled panel regression. Forecast revision of real consumption is the deviation between pre-pandemic forecast (January 2020) and the latest forecast (November 2020) from Oxford Economics; stringency index is Oxford Covid-19 government response stringency index; and covid19 is new “true” covid-19 cases per capita estimated by Imperial College London and is assumed to proxy voluntary social distancing. Standard errors in parentheses. ∗∗∗p < 0.01, ∗∗p < 0.05, ∗p < 0.1.

Table 1 shows that both the stringency index and COVID-19 cases economic activities can have a significant adverse impact.<sup>9</sup> The historical shocks to consumption obtained from the regression are broadly consistent with results from the behavioral SIR model literature (Bethune and Korinek 2020; Eichenbaum et al. 2020).

# Quantifying global growth impacts

Using parameters from equation (3) and the SIR model’s epidemiological projections, future consumption shocks are extrapolated to consider the impact of both voluntary and mandatory social distancing on private consumption. The expected evolution of the stringency of pandemic-control measures is based on projected COVID-19 cases per capita obtained from the SVIR model combined with economist judgement (Figure 2.C). Consumption shocks are projected for the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and EM7 excluding China (India, Brazil, Mexico, the Russia
one of the poorest countries in Europe in terms of statehood and demographics, and GDP per capita. Despite stable economic growth in the last 10 years, job creation in Kosovo is at a low level. Between 2012 and 2018 about 44,000 new jobs were generated and the employment rate increased by 3.4 percentage points, from 26.6 percent to 29.4 percent (World Bank, 2020). Over the same time, the increase in the employment rate averaged 8 percentage points, from 44.4 percent to 52.5 percent in the Western Balkan region. While commerce and construction have been the main drivers of employment growth over the past decade, these sectors are characterized by low productivity and low wages. Still, employment

> 4 Our work also relates to a literature using text mining techniques due to the increased availability and scale of job portal data. For instance, a literature around Wowczko (2015) uses clustering methods to identify key skill requirements in job titles and descriptions of online vacancies. Despite their usefulness, the results are fully inductive and thereby often result in high error rates, which are especially problematic when findings are used for policy recommendation. We circumvent these challenges, by following a dictionary-based approach with pre-defined and validated key words, key sentences and dynamic regular expressions.

> 5 Appendix A is a technical appendix providing the detailed methodology for the analysis of platform data. Appendix B presents in more detail the skills taxonomy and the methodology for the textual analysis on skills. Additional graphs are provided in Appendix C. A comparison of STEP survey data with job platform data is provided in Appendix D. Instructions to replicate the analysis are provided in Appendix E.

- 4 -
10318 Washington, D.C. : World Bank Group.

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World Bank (2019). Aspiring Indonesia: Expanding the Middle Class. Washington, D.C.: The World Bank.

World Bank (2021a). The Gradual Rise and Rapid Decline of the Middle Class in Latin America and the Caribbean. Washington, D.C.: The World Bank.

39
garment makers in Ghana, a not implausible pattern for some industries in the relatively traditional countries in our sample. On the supply side, differences in labor supply responses (from both the owner/manager and the business employees) could also be driving some of the effects we document. During the pandemic, mobility restrictions and closures of schools and daycare facilities have disproportionately increased the need for women to allocate time to housework and childcare relative to men (De Paz Nieves et al., 2021), and this increased demand for caregivers in the home during the pandemic could have resulted in time-constraints for female business owners and fewer employees available to operate regularly. Our BPS-WBES data, however, are not well suited to examine these underlying supply and demand channels (whose importance likely varies across countries). Still, when we examine the differentiated impact of the pandemic on firms with a disproportionate share of women employees (above the regional average), we find a statistically significant effect for the likelihood of reporting supply disruptions, the negative shock to sales, the availability of cash, and the likelihood of falling into arrears, which suggests that supply factors and the high fraction of women employees among women-led firms could partly drive the patterns we document.<sup>24</sup> Deep diving into this channel, however, is outside the scope of this paper.

> 24Results from linear regressions of each outcome on a dummy for whether the firm employs a fraction of women above the regional dummy and fixed effects for size, sector, income level, region, severity of the shock, and timing of the survey.

32
Cost-seeking FDI ( _motive_cost_ ) is negatively correlated with the share of local workers (column 2) as well as the probability of offering supplier assistance (column 4) if this motive has a moderate importance for multinationals.

Resource-seeking FDI ( _motive_res_ ) clearly shows a negative correlation with the share of sales going to the host country if this motive is important (column 3). Moreover, it is also negatively associated with supplier assistance, regardless of the importance of this motive (column 4).

# **4 Which Absorptive Capacities Facilitate FDI Linkages?**

This section focuses on the role of domestic supplier characteristics for FDI linkages. In section 4.1, we present the data, while section 4.2 introduces the empirical model where we relate absorptive capacities with FDI linkages. Section 4.3 examines if there are differences in the extent of FDI linkages between different groups of suppliers, depending on their absorptive capacities. Section 4.4 describes the regression results.

## **4.1 Data**

The focus of sections 4 and 5 is on national suppliers (see section 3.1 for a description of our dataset). The national suppliers’ surveys cover 148 firms in Chile (18), Ghana (26), Kenya (29), Mozambique (36) and Vietnam (39). More than half of the suppliers (88) supply to multinationals in agribusiness, followed by mining (48) and apparel (12). These suppliers produce a variety of inputs across the value chain, as shown in Table 7, ranging from chemicals, to equipment, to food and food processing, to business, technical, and other services, among others.

**Table 7: Distribution of Suppliers by Sector**

|**Sector**|**No. of firms**|**%**|
|---|---|---|
|Apparel accessories|4|2.7%|
|
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Northern Western<br>3. Improved Seed Use by Consumption Quintile,<br>2005/06 – 2013/14<br><!-- End of picture text -->

protocols to generate gross income aggregates across waves. Where inconsistencies were noticed they were communicated to the RIGA team and corrected in the protocols we used. The RIGA aggregates calculate crop and livestock income using information on the amount of goods produced by the household in the last 12 months and the price of these goods reported by the household. Prices are imputed from other sampled households when they are not collected. The measures of crop and livestock income calculated are gross income not netting out costs of land, labor or purchased inputs. Wage income is generated from data collected on wages earned in the last 12 months. The self‐employment income aggregate calculated all self‐employment income earned in the last 12 months and nets out the cost of purchased inputs. Purchased inputs can be quite a large share of the income from self‐ employment activities such as petty trading or handicrafts and netting out the cost of these inputs is important. For more details on how these aggregates are constructed, see Carletto et al. (2007).

8
in the initial years plummeted and saw the proportion of people in the least happy categories soar (Blanchflower and Freeman 1997; Lelkes, 2006a and 2006b ; Angelescu, 2008). By 2011, there is still a considerable gap in subjective well-being between transitional and no transitional countries, even though smaller than after the initial reforms (Lelkes, 2006b).

# **3. Individual and National Level Data**

**The individual-level data used for this study comes from the Life in Transition Survey collected in 2006.** This survey was collected by the European Bank of Reconstruction and Development (EBRD) and the World Bank (WB). Although the LiTs was conducted in 2010 as well, the study focuses on the 2006 round to capture perceptions in a period of economic boom – to avoid results being driven by the impact of the global economic crisis – and given the more comprehensive information on labor histories collected in the 2006 round. Perceptions on economic mobility in 2010 are, however, presented in annexes as well to complement the analysis. It collected information about socio-demographics characteristics and attitudes and values of the population in Eastern Europe, Central Asia and Turkey. It covered 29 countries, with a sample of 1,000 households per country.<sup>4</sup>

**The respondent of the survey is a random member of the household interviewed older than 18 years old.** The survey is divided in two sections. The first one was answered by the household head and contains the household roster and the variables on perceptions of economic mobility. The second part is answered by a randomly selected household member older than 18 years old, and contains information on employment, employment history since 1989 and an extensive set of questions on attitudes and values. We merged the information coming from the two respondents in the household. This implies we assume the answer given by the household head is representative of the household opinion.

**Personal and household characteristics used for the estimations are constructed based on the