rafmacalaba/data-use-mentions-extended huggingface dataset:
not acquired, thus most of the exports since have been resource intensive. There was a severe worsening in the distribution of income, official consumer surveys for CPI construction reveal that from 1969 to 1987 the richest quintile increased its share of consumption from 44.5% to 54.9%. Also, the fast liberalization, as expected cause firms to be more efficient, that is in 1989 the surviving industrial sector was more efficient, but smaller. On the positive end, a financial sector was developed during the regime, which now has more controls after the 1982 crisis. Also, different exports grew significantly diminishing the historical dependence in the copper exports. While in 1974 non copper exports accounted for only 32.7% of the value of total exports, in 1989 this share was 22.6%. Finally, although the fashion and timing of the trade liberalization is very arguable, it left a country outward looking ready to base it growth in the export sector.

# **Export-Driven Growth and the "Social Debt" 1990** - **1995**

With the return of democracy in 1990, the export-driven growth model did not change. However, the government recognized that during the previous regime there was very little spending in social programs, it recognized a "social debt". The period is characterized by very strong growth, with an average GDP growth of 9.6%. Export not only grew at a similar rate, but were diversified in its components and their destinations. By 1996 non copper exports amounted for 60% of the value of the country's total exports.

The most important reform of the period was agreed with the military regime and it consisted of granting the Central Bank independence from the executive power, thus guaranteeing independent monetary policy. The Bank informs agents by the end of the year its goals for next year's inflation, and it has gained complete credibility by reaching the goal each year, reducing inflation each year. The foreign exchange have been liberalized
_Ecological Economics_ . 47, 95-103.

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Dangi, R. 2013. Progress Update on REDD+ Readiness. A Presentation Delivered in REDD+ Stakeholder Forum Meeting Held in 18 June 2013 in Kathmandu.

Dissanayake, S. T. P. Jha, B. Adhikari, R. Bista, R. A. Bluffstone, H. Luintel, P. Martinsson, N. Paudel, E. Somanathan and M. Toman. 2015. Community Managed Forest Groups and Preferences for REDD+ Contract Attributes: A Choice Experiment Survey of Communities in Nepal, Policy Research Working Paper 7326, June 2015, The World Bank, Washington, DC.

Dissanayake, S. T. M. and Ando, A. W. 2014. Valuing Grassland Restoration: Proximity to Substitutes and Trade-offs among Conservation Attributes. _Land Economics_ , 90(2), 237-259.

Drigo, R, R. Bailis, A. Ghilardi and O. Masera. 2014. Tier 1 Report: Pan-Tropical Demand and Sustainability, Yale-UNAM Project Report _Geospatial Analysis and Modeling of Nonrenewable Biomas: WISDOM and Beyond_ , supported by the Global Alliance for Clean Cookstoves 36
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# **Figure A2.6  Percentage of total household expenditures spent on different forms of energy by rural households**

<!-- Start of picture text --> Gasoline and diesel<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data. _Note_ : The expenditure shares are averaged across all households, including those reporting zero expenditures.

Table A2.1 shows all possible combinations of energy sources used by households in Ghana. Ghana was selected for presentation in this paper because it had no fuel subsidies for households during the survey period and one-third of the population lived in households using LPG. In urban areas, 25 percent of households used only electricity or electricity in combination with LPG, against 8 percent in rural areas.
Note: This figure reports the mean percent of fixed assets for manufacturing firms financed through internal funds for noninnovative firms; innovative firms (i.e., innovation dummy equals 1); firms that adopt a new product, service or process (i.e., adoption dummy equals 1); firms that undertake invention proxied by whether a firm spend on research and development.

**_Panel B: Interquartile difference in the financing of fixed assets through internal funds, manufacturing_**

<!-- Start of picture text --> p25 p75 Difference<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.11*** -0.10***<br>-0.2 -0.15***<br>Innovation score Adoption Invention<br><!-- End of picture text --> Source: Own elaboration using data from the World Bank – Enterprise Surveys. Note: This figure reports the mean percent of fixed assets financed through internal funds for innovative firms. The blue bar denotes the mean percent of fixed assets financed through internal funds for firms in the bottom 25 percent of the distribution of the innovation score and adoption score. The red bars represent the percentage of fixed assets financed through internal funds for firms in the 75 percent of the distribution of the innovation score and adoption score. The gray bars characterize the interquartile differences for the three groups of firms. *** indicates statistical significance at the 1 percent level of the test of the interquartile difference in means.

Table 3 reports the estimates of the association of firm financial structure and firm innovation. The dependent variable is a firm innovation dummy. Across estimations, the percentage of fixed assets funded by banks is strongly associated with the probability of firm innovation. Comparing estimations
the interaction terms remain positively and significantly correlated with financial inclusion in all regressions. Hence, our findings are unlikely to be confounded by other cultural factors.

# **5.2. Alternative dependent variable**

Beyond the simple ownership of bank accounts, Demirgüç-Kunt and Klapper (2013) highlight the importance of considering their usage. The authors explain that 10 percent of account owners in developing economies maintained an inactive account, i.e., “they make neither withdrawals from nor deposits into their account”. Moreover, Aterido et al. (2011) show that the use of financial services also varies significantly between men and women by considering nine Sub-Saharan countries. As suggested by Duflo (2012), women are relatively less inclined than men to use the account if it was easy for their husbands to get the money out. Thus, one may argue that having an account does allow for women’s empowerment, whereas using the account to achieve development goals may be more relevant for accessing economic opportunities. This is particularly relevant in India, where despite the massive growth in ownership, 43 percent of accounts remain inactive, the highest rate in the world according to the 2021 Global Findex report.

Thus, we consider an alternative measure of financial inclusion to capture the frequency of account use. We focus on withdrawals as suggested by Allen et al. (2016), because such operations are actively initiated by account owners while deposits might be initiated by others (for example, employers or governments). Account holders answer the following question: “In a typical month, about how many times is money taken out of your personal account(s)?” Respondents are asked (categorically) if they conducted: ( _i_ ) 0 withdrawals, ( _ii_ ) 1–2 withdrawals, ( _iii_ ) 3–5 withdrawals, or ( _iv_ ) 6 or more withdrawals on average per month. In line with Allen et al. (2016), we qualified the frequent use of an account using a dummy
rainfall patterns. While our two outcomes are both measures of agricultural productivity, many papers that use weather data to investigate non-agricultural outcomes justify the use of weather in identification because of its relationship to agricultural productivity and productivity’s relationship with the non-agricultural outcome of interest (Jayachandran, 2006; Deschˆene and Greenstone, 2007; Corno et al., 2020). Therefore, we believe our conclusions, and the best practices built on those conclusions, have broad applicability to the use of remote sensing weather data in economics.

# **5.2 Future Work**

There are a number of extensions to this work which are currently ongoing. Some of these relate to data and analysis defined in our pre-analysis plan while others are exploratory in that they are not pre-specified.

In terms of pre-specified elements, we are presently working to incorporate data from Mali as well as additional weather metrics. LSMS-ISA data from Mali comes from two rounds but those rounds are not panel data, thus we could not use it as part of the current analysis which includes household fixed effects in four of six model specification. We are also working to incorporate indices of weather variables (e.g. evapotranspiration, water requirement satisfaction index, Palmer drought severity index, standardized precipitation evapotranspiration index). These additional weather metrics are part of the pre-analysis plan but cannot be generated for all remote sensing products. Because of this, and to maintain blinding of the products, we excluded these indices from the current analysis. Additional details on the complete scope of our pre-specified analyses can be found at OSF and in Michler et al. (2021).

With respect to exploratory analyses, at present we have four research questions we are seeking to answer. The first is the degree to which results vary by agro-ecological zone. The analysis in this paper focused on differences across country, which is the level at which data are collected and organized in the LSMS-ISA. Economics researchers is frequently conducted using nationally representative data or regional data from within a single country. However
a flush to pit<br>latrine?|1 if the answer is no, 0 if the<br>answer isyes|Available|
||Does the household use a bottle/sachet<br>water for drinking?|1 if the answer is no, 0 if the<br>answer isyes|Not Available|
|**Work Status**|Does the head of the household/main<br>income-earner have a contract with his/her<br>employer?|1 if the answer is no, 0 if the<br>answer is yes)|Not available. We use information<br>on whether respondent has a formal<br>contract as aproxy|
||What is the occupation of the household<br>head/main income-earner of the family?|Validation question|Available|
|**Illness/Disability**|Does anyone in your household suffer from<br>illness or disability?|1 if yes, 0 if no|Available|
|**Total possible score**||10|9|

_Source_ : Authors’ calculations based on administrative data from NaCSA (2020) and CIMS(2020). _Note_ : LPMT variables and threshold were calibrated by analyzing the overlap the LPMT score and poverty level in the SLIHS (2018) household survey data.

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_Brazilian spatial labor dynamics Page 27_

|Dependent Variable:<br>Delta ln Employment|2SLS.Est.|2SLS.SE|Spatial GMM<br>Est.<br>(Cutoff = 0.5)|Spatial GMM<br>SE<br>(Cutoff = 0.5)|
|---|---|---|---|---|
|Intercept|-.95503801|.15016687|-.9338026|.21124536|
|ln Wage in 1991|.19092101|.0156507|.18901078|.02148466|
|ln 5 to 15 over 15 to 55 yrs Ratio|<br>.1983383|.03620152|.19401434|.05055868|
|Proportion of Natives in 1991|.28972116|.04454679|.3009288|.06903289|
|Delta ln Market Potential|.30840242|.03091629|.30340591|.05130368|
|ln Teacher Qualification in 1991|-.00807114|.00235408|-.00812438|.00351447|
|Homicides in 1991|-6.9250641|4.325995|-8.436305|11.13018|
|Employment in Farming in 1991|-.46696147|.16523983|-.50415081|.25446269|
|Bank Dummy|.00072828|.00950343|.00397452|.01257344|
|ln Population in 1991|-.02531787|.00636568|-.02616172|.00975625|
|Mean Temperature|-.00678719|.00265857|-.00660142|.00409379|
|Total Rainfall|-.00005046|.00001317|-.00004882|.00002093|
|Rainfall - 1o Princ. Component|.00971466|.00196289|.00973935|.00324289|
|Rainfall - 3o Princ. Component|.02245587|.00465159|.02228627|.00712442|
|Employment rate in 1991|.15742175|.14161712|.17703837|.2183038|
|crit. fn. test of overid
# decades.

In the context of international migration from Nepal, I investigate the effect of three types of shocks using a panel of 452 villages across the country and observed in years 2001, 2008 and 2010. The first is the shock to farm income in the origin. Deviation of rainfall from its historic mean generates an exogenous variation in farm income both across the country for a given year and between different time periods for the same village. The second shock is a measure of conflict in origin which creates an income loss as well as utility loss, more so for the richer households. The ten-year long Maoist insurgency, which killed over 13,000 people, generates a variation in conflict intensity experienced by different parts of the country. In addition, the dramatic change in the trajectory of the insurgency gives rise to inter-temporal variation in conflict intensity experienced by a given village. In addition to the two ‘push’ shocks that affect the origin, I investigate the effect of an increase in the demand for low-skilled workers from abroad. I use the growth in the construction and manufacturing sectors in various destination countries to proxy for these ‘pull’ shocks from the destination countries. Here as well, different destinations countries that are plausibly in quite different macroeconomic cycles generates the variation that identifies my estimates. Differential effects of the global recession of 2008, further generates variation in growth rates across different destination countries.

Consistent with the theoretical framework, I find that rainfall shocks increases migration to India, but have no effect on migration to non-India destinations. Increase in farm income of US$ 100 due to rainfall increases migration to India by 2.5 percentage points (54 percent increase from its 2010 level) but has no effect on migration elsewhere. Rainfall shocks increases the income of the poor, who rely on agriculture, and enable them to finance low-cost migration to India. However, since rainfall shocks generate income gains that are small in magnitude relative to the costs of migration to other destinations, they do not enable too many households
**Figure 8: Regional variation in female employment in Bangladesh between 2005 and 2013**

<!-- Start of picture text --> (0.13,0.25]<br>(0.05,0.13]<br>(-0.01,0.05]<br>[-0.19,-0.01]<br><!-- End of picture text --> Source: Calculations based on Labor Force Surveys for Bangladesh.

# Regional Wage Dispersion

The local labor market methodology used in many recent studies depends critically on the identification of distinct local labor markets. One “heuristic” measure of the presence of local labor markets is the standard deviation of wages for different types of workers across regions overtime. This measure is “heuristic”—that is, imperfect—because there are many reasons why wages might not equalize across regions.<sup>8</sup> Table 1a shows that the standard deviation of wages in Bangladesh decreases consistently between 2005 and 2016, if assuming that highly integrated markets show lower wage differences among workers over time. Table 1a suggests that local labor markets in Bangladesh have become more integrated over time. In addition, we also compared this dispersion of wages across districts between India and Bangladesh for each year and worker type. Comparing Table 1a (Bangladesh) with Table 1b (India) shows that dispersion of wages across districts is substantially lower in Bangladesh relative to India, even when the means are very similar. This finding holds for every year and type of worker.

> 8 <mark>Wages may differ across regions due to preferences, distribution of capital, or other factors. Even if workers are perfectly mobile, wages will not equalize if there are “preference” shocks. Consider an assignment model, like Eaton-Kortum (2002, 2012), with no transportation costs, where there is a productivity or utility draw. In that model, wages will not equalize. While
Brazil: a regional perspective. International Monetary Fund, Washington DC.

Golgher, A., & Coutinho, R. Z. (2020). Life satisfaction in Brazil: an exploration of theoretical correlates and age, period and cohort variations using the World Values Survey (1991-2014). _Revista Brasileira de Estudos de População_ , 37, 1-27 Gori-Maia, A. (2013). Relative income, inequality and subjective wellbeing: evidence for Brazil. _Social Indicators Research_ , 113(3), 1193-1204.

Graham, C. (2005). The economics of happiness. _World Economics_ , 6(3), 41-55.

Graham, C. (2008). Happiness and health: Lessons—and questions—for public policy. _Health Affairs_ , 27(1), 72-87.

Graham, C., & Nikolova, M. (2015). Bentham or Aristotle in the development process? An empirical investigation of capabilities and subjective well-being. _World Development_ , 68, 163-179.

36
institutions granted prior to the crisis together with the late and not necessarily comprehensive resolution processes, imay have added to the overall uncertainty and loss of confidence in the East Asian countries, aggravating the financial crisis.

The rest of the paper is organized as follows: section 2 provides the motivation and reviews the literature on financial institutions' distress and closure. Section 3 describes the data we use, provides a characterization of our sample of financial institutions, documents various financial data (balance sheet and income data for end1996), and gives an overview of the degree of financial distress among our sample. The next section analyzes the contribution of various factors in explaining financial distress and closure through logit models. Section 5 concludes.

# **2. Motivation and review of the empirical iterature**

The East Asia financial crisis has spawned a large literature on explaining its causes, onset and evolution.' Whether sudden shifts in market expectations and confidence were the primary source of the financial turmoil has been hotly debated. Proponents of the "sudden shift" view argue that, while perhaps macroeconomic and other fundamentals had worsened in the mid-1990s, the extent and depth of the crisis cannot be attributed to a deterioration in fundamentals, but rather to the panic reaction of domestic and foreign investors (Radelet and Sachs 1998; Stiglitz 1999). Others argue that the crisis reflected structural and policy distortions in the region-including weak macroeconomic and micro-economic policies-and that fundamental imbalances triggered the crisis (Corsetti, Pesenti, and Roubini 1998).

Studies attempting to empirically identify the causes and origins of the East Asian financial crisis (and other crises) have mainly focused on the macro-economic factors that can help predict banking and currency crises.<sup>2</sup> Such early warning systems based on macro variables are important tools for timely detection of systemic crises; however, they do not allow one to analyze the importance of micro-economic weaknesses contributing to the occurrence of the crisis. In particular, they are unlikely to be able Ito discriminate between the view that distressed
locations.

Finally, the two types of infrastructure have greater effects in combination, on employment and job creation overall and on migration of the labor force.

# **4.3 Heterogeneous Complementary Effects of Roads and Internet on Employment**

This section investigates heterogeneities in the complementary effects of being both closer to roadways and having access to internet. Our findings also provide some insights regarding the channels through which infrastructure investment influences employment.

## **4.3.1 Economic Development**

We study how the employment impacts of roads and fast internet vary across levels of economic development. Different professional choices among countries with different levels of economic development could cause mixed outcomes. Developing economies may have a greater share of the labor force working in agriculture, for example; and developed countries, by contrast, may have a greater share of people working in the service sector. Mindful of the potential for such differences in the labor forces of countries at various stages of development, we categorize development levels based on the 2019 GDP classification of countries. Using purchasing power parity (PPP)-adjusted GDP per capita, we categorize countries as less developed or more developed. A country is considered less developed if its PPP-adjusted GDP per capita was below USD 3,400 in 2019.<sup>17</sup> The effects of road expansion on employment differ by a country’s level of economic development. In areas with access to the internet, increasing the proximity to the road network by 1 kilometer does not statistically increase the probability of having offered their labor force during the 12 months preceding the interview in more developed. In less-developed countries, the corresponding effect is 1.2 per-

> 17The results of the regression of equation (2), distinguishing between countries of different development levels are reported in columns 3, 4, 7, 8 11, and 12 of Table (A8) in the Online Appendix.

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closely monitored with GPS software, tracking enumerators inside and outside areas with mobile phone coverage. The HFS also leveraged innovations in questionnaire design which permitted reducing the number of consumption items asked to the respondents while still obtaining unbiased poverty estimates through

> 2 See, UNOCHA: https://www.unocha.org/south‐sudan  &  UNHCR: http://data.unhcr.org/SouthSudan/regional.php

> 3 Not only has insecurity made fieldwork dangerous, but much of the South Sudanese population lives in isolated and hard to access areas. More than 85 percent of the 12 million South Sudanese reside in sparsely populated rural areas connected by a mere 200 km of paved roadways – about 2 percent of all roads – spanning an area of 650,000 square kilometers, approximately the size of France (Pape et al. 2017; African Development Bank 2013). The poor state of infrastructure combined with the size of the country means nationally representative surveys are expensive and time‐consuming.

> 4 The Greater Upper Nile region was where the opposition forces, the SPLM‐IO, kept their stronghold and were thus contested in the fighting. In Appendix Figure 11 this region corresponds to the non‐HFS states, where the number of conflict events in non‐ HFS covered states is much greater throughout 2014. During the year 2015 the conflict lost some of its intensity. Especially in the HFS states, where although conflict events continued to be recorded most of the violence remained concentrated, particularly in a few select areas which were relatively close to the border with the non‐HFS states (Figure 12).

> 5 UNDP Human development index, available at: http://hdr.undp.org/en/composite/HDI; and World Development Indicators.

> 6 For a comprehensive review of issues in data collection in fragile and conflict situations see Mneimneh et al., (2016).

2