rafmacalaba/data-use-mentions-extended huggingface dataset:
# 2.2. What are the characteristics of disruptions-prone firms?

Country-level factors are important for the individual firm. The empirical analysis of our firm-level data thus introduces country-specific controls common to all firms in corresponding economy alongside general country-level fixed effects. Country-level data on economic and demographic variables is sourced from the WDI and WGI databases. Climate data to measure natural risks has been extracted from the ERA5 climate re-analysis model (Copernicus Climate Change Service, 2018).<sup>1</sup> While the impact of infrastructure disruptions is typically transmitted across wide networks, they are not felt by all firms in the same way. This section focuses on statistical properties of firm-level variables do identify correlates of disruptions to shed light on exposure and resilience factors. All firm-specific information is retrieved from the _Enterprise Surveys (ES)._ All specifications include two-digit ISIC (revision 3.1) sector fixed effects to capture production technology-induced exposure and other sector-specific factors. Standard errors are clustered at the regional level to account for regressor correlation with region fixed effects and uncertainty about regressors’ marginal effects being region-specific.

Table 2 summarizes the main empirical results. Specifically, it shows regressions of the full set of variables on i) the total annual duration of blackouts, ii) total annual duration of water supply disruptions or iii) a subjective index (1-4) of the severity of transport problems in overall business operation. Those three variables are directly or indirectly provided by the ES, while no similar information is given on telecommunication disruptions, unfortunately. Note that our global climate variables cover a selected set of geo coordinates, specifically up to three of the economically most important cities of each country. Thus, to reliably identify impacts of climate risks at the firm level (in particular extreme precipitation and storms), we restrict attention to firms in those cities, covering approximately a quarter of the full sample. Conclusions concerning non-climatic variables remain qualitatively unchanged in the full sample.

According to ISIC factor estimates, disruptions of **electricity** supply are
# 3. Sectoral data

In this section, the various types of socioeconomic and asset data used to represent human and economic activity for the exposure analysis are described.

- 3.1. Detection of human settlements and different types of agricultural production with high-resolution land use data

The exposure of various types of agricultural production and urban areas is estimated based on land use data with a very high spatial resolution. This dataset was developed by the Japanese Aerospace Exploration Agency and is openly and freely available online (JAXA EORC, 2018).

The land use map provides a fine-grained overview of land use for all of Vietnam for 2015, for northern regions and for central and south Vietnam in 2017. Developed specifically for Vietnam, the map distinguishes between the nine predominant land use categories in the country, which are explained in detail in Table 3.1. The raster dataset has a resolution of 15 x 15 meters in the northern region and 10 x 10 meters in the central and southern regions (figure 3.1).

The dataset was created using an algorithm combining satellite and other data of various resolutions. The algorithm is fed with 24 different satellite bands, measuring surface reflectance in the visible and infrared spectra, topographical data from the Shuttle Radar Topography Mission (SRTM), and road network data from the OpenStreetMap project. It also exploits temporal variation in satellite imagery within a year, which enables classification based on land cover change, such as a change from crop cover to bare soil after harvest (Hashimoto at al. 2014).

**Table 3.1: Description of categories in land use data**

|_Category_|_Definition_|
|---|---|
|Water|Oceans, seas, lakes, reservoirs, and rivers. They can be either fresh or saltwater bodies.|
|Urban|Land covered by buildings and other man-made structures.|
|Paddy|A flooded parcel of arable land used for growing semiaquatic crops, most notably rice.|
|Crop|Lands covered with
0.040)**|(0.290)|
|Total Rainfall in 2015||0.000<br>(0.000)**|
|Constant|0.217|6.409|
||(0.029)***|(0.172)***|
|Mean of dependent variable|0.212|6.676|
|Observations|3122|2387|
|Fstat|4.122|3.535|
|Prob > Fstat|0.000|0.001|
|Adjusted R-squared|0.027|0.027|

_Source_ : Authors based on HIES 2016.

17
?|||||?|
|Growth rate crises||||?|||||||||
|Financial crises|||||||?||||?||
|**Crisis episode**|**×**|**×**|**?**|**?**|**?**|**?**|**?**|**×**|**×**|**×**|**×**<br>**?**|**×**|

_Note: (? ) denotes crisis periods; ?: crisis episode, ×: non-crisis episode Source: Author_ Indicators are derived from the following sources.

- Liquidity type banking crises (demand deposits to total assets ratio): IFS line 24 divided by the sum of IFS line 24-27;

- Solvency type banking crises (capital account to total assets ratio): IFS line 27A divided by the sum of IFS line 24-27;

- Balance of payments crises (year-on-year growth rates of gross international reserves): total reserves minus gold (IFS line 1L.d);

- Currency crises (end of period nominal exchange rates against the US dollar): IFS line ae;

- Debt crises (external debt outstanding to exports ratio): external debt outstanding data is obtained from CEIC data base, countries’ publications and GDF, and outstanding is divided by IFS line 70;

- Growth rate crises (year-on-year real growth rates): Data is obtained from CEIC data base, countries’ publications and interpolated WDI (World Bank’s World Development Indicators) data if quarterly data is unavailable;

- Financial cris es (capital flights): IFS line 78 BC (capital account), line 78 BJ (financial account), line 78 CA (net errors and omissions) minus line 78 BD-E (FDI), line 78
## 2.2 Estimating the relationship between management practices and performance

To test the relationship between establishments’ management scores and performance metrics, we run the following OLS regression: Where y�,�,� is the outcome variable—the performance of establishment , i , operating in sector, j , and country, c . In our analysis performance is measured either as an establishment’s labor productivity—log of sales per worker, or total factor productivity—the portion of output that is not explained by the amount of inputs used (which, due to data constraints, can be calculated for manufacturing establishments only). MGT�,�,� is the establishment’s management practices score, and the coefficient β��� describes the relationship between management and performance—a positive coefficient indicates that a higher management score is associated with improved performance. X�,�,� represents a vector of establishment‐ level characteristics that we control for; the variables included in the vector X�,�,� are those that may be related to both management practices and to productivity, and so we include them to disentangle their effects from the impact of management, per se. These are establishment size (measured in terms of the number of employees); exporting status; multinational status; whether the establishment is part of a multi‐establishment firm; the establishment’s legal status (sole proprietorship, limited liability company, etc.); the top manager’s years of experience in the sector; and the gender of the top manager. D� and D� represent country and sector dummies respectively and, finally, ε�,�,� is the error term.

# 3. Data and descriptive statistics

## 3.1 World Bank Enterprise Survey data

Our data are from a recent wave of the World Bank Enterprise Surveys (WBES) that were implemented in seven Latin American economies in 2017—Argentina, Bolivia, Colombia, Ecuador, Paraguay, Peru and Uruguay. The
This matter has yet to be resolved. As Al Harberger allegedly mentioned: _"I know of no one who has canceled his vacation because the government has increased its bond issue, but I know people who have canceled their vacation because the government increased taxes "._ Yet the empirical evidence seems to tilt in favor of the Ricardo/Barro approach. A survey of empirical studies on the deficit-interest rate _'found that only four of twentyone studies provided evidence supporting a positive, statistically significant impact of deficits on interest rates.... another ten studies found no evidence_ .... _the remaining seven studies produced mixed results that also sometimes indicated no link"_ (Bradly 1986).

Bradley (1986) links interest rate changes to government expenditures: _"In this article we demonstrate that a crucial difference exists between the economic impacts of increased government spending and the economic impacts of increasing the bond stock Using a simple dynamic model, we find that increases in the stock of government bonds, by themselves, do not force interest rates. When the debt increase is caused by a rise in spending, however, we find that, after a lag, both interest rates ...... rise. "_ Barro (1990) draws a distinction between temporary and permanent variations in government purchases, arguing that the interest rate and outputs are mostly affected by temporary changes in government expenditures.

The issue concerning the impact of government deficit and fiscal policy on interest rates is far from being resolved. Most of the empirical evidence is inconclusive and pertains mostly to the US. Nevertheless, it seems safe to conclude that, at least for Latin America, an increment in the primary surplus would tend to reduce interest rates due to its contribution to reducing country risk. Also, a reduction of government expenditures, for example, through privatization of SOEs or reduction in bloated bureaucracies, would help to (a) increase the rate of income growth (allocation effect) and (b) reduce the interest rate
informal to formal jobs in several developing countries like India, Vietnam, and China (McCaig, 2011; McCaig and Pavcnik, 2018; Artuc et al., 2019; and Erten and Leight, 2017). On the other hand, trade liberalization measures slowed the pace of poverty reduction and triggered a persistent declining trend in wages and employment in few developing countries like India and Brazil (Topalova, 2010; Dix-Carneiro, 2017; and Artuc et al., 2019).

Despite this burgeoning literature shedding light on the heterogeneous effects of trade shocks across regions, evidence on local labor market effects from “positive demand shocks” is only nascent, and transitional dynamics driving labor market effects from shocks have not been widely studied.

Using detailed data from Bangladesh, we extend the existing literature by estimating not only the short run effects but also dissipation and long-run general equilibrium effects linked to neoclassical 31
3 share of countries with private capital in main fixed lines operators increased from 9% to 53% in developing countries and from 17% to 82% in developed countries between 1990 and 2003. This information is summarized in table 1.<sup>7</sup>

**<u>Table 1: Evolution of reform implementation from 1990 to 2003</u>**

||**% of sample by**|**country group**|**Number**|**of Observatio**|**ns**|
|---|---|---|---|---|---|
||**Developing**|**Developed**|**Developing**|**Developed**|**Total**|
|**Privatization/competition**||||||
|Number of Countries with Private Capital as of 1990|9%|17%|129|35|164|
|Number of Countries with Private Capital as of 2003|53%|82%|129|34|163|
|Average Year of Privatization*|1997|1997|57|23|80|
|**Regulation**||||||
|Number of Countries with IRA as of 1990|5%|12%|153|51|204|
|Number of Countries with IRA as of 2003|65%|57%|153|51|204|
|Average Year of Establishment of IRA*|1998|1997|93|23|116|

Source: Own calculations using ITU data. * Average among countries that reformed between 1991 and 2003.

The regulatory environment has also changed significantly but tracking changes in regulatory systems is much more complex than tracking the extent of private sector participation. One of the most directly observable policies aimed to change regulatory systems may be establishment of an independent
Krugman, Paul & Elizondo, Raul Livas, 1996. "Trade policy and the Third World metropolis," Journal of Development Economics, Elsevier, vol. 49(1), pages 137-150, April.

Lall, S., M. Lebrand, and M. E. Soppelsa. 2021. “The Evolution of City Form: Evidence from Satellite Data.” Policy Research Working Paper 9618, World Bank, Washington, DC.

Lenzu, Simone, David Rivers, and Joris Tielens. 2019. "Financial Shocks and Productivity: Pricing response and the TFPR-TFPQ bifurcation." Available at SSRN 3442156.

Maré, D.C. and G. Graham. 2013. Agglomeration elasticity and firm heterogeneity. Journal of Urban Economics, 75: 44-56 Marschak, Jacob and William H. Andrews. 1944. “Random Simultaneous Equations and the Theory of Production.” Econometrica 12 (3-4):143–205 McMillen, D. P. 2003. “Employment subcenters in Chicago: Past, present, and future.” Economic Perspectives, Federal Reserve Bank of Chicago, 27 (2): 2–14.

Mendershausen, H., 1938, On the significance of Professor Douglas's production function, Econometrica 6, no. 2, 143-153.

Moretti, E. 2004. “Human Capital Externalities in Cities.” In Handbook of Regional and Urban Economics, Vol. 4., edited by J. V. Henderson and J-F Thisse, 2243–91. Amsterdam: North-Holland.

Nakamura, S., B. Paliwal, and N. Yoshida. 2018. “Overview of the Trends of Monetary and Non-Monetary Poverty and Urbanization in Sub-Saharan Africa.” Paper presented at the 2018 Jobs and Development Conference, World Bank, Washington, DC.

Quintero, L. E., and M. Roberts. 2018. “Explaining Spatial Variations in Productivity: Evidence from Latin America and the Caribbean.” Policy Research Working Paper 8560, World Bank, Washington, DC. Redding, Stephen and
Policy Research Working Paper 9481

# **Abstract**

The paper formalizes and tests the hypothesis that greater exposure to big shocks induces stronger societal responses for adaptation and protection from future big shocks. Support for this hypothesis is found in various strands of the literature and in new empirical tests using cross-country data on deaths due to COVID-19 and deaths during World War 2. Countries with higher death rates in the war saw lower death rates during the COVID-19 pandemic. The tests are robust to a wide range of model specifications and alternative assumptions.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. 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/prwp. The authors may be contacted at mlokshin@worldbank.org, vkolchin@worldbank.org, and mr1185@georgetown.edu.

_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
hypothesis of validity of the instruments. The number of degrees of freedom of the difference Sargan test statistic is given by the number of additional restrictions in the system estimator, which equals the difference between the nunber of degrees of freedom of the system estimator and that of the difference estimator.

The Hausman statistics tests the difference between the coefficients of the GMM system estimates and the corresponding GMM first-differenced estimates, that is the estimates without the additional levels equations. The Hausman test statistic is a Wald test of the hypothesis that the distance between the coefficients is zero, and the degrees of freedom is given by the number of additional level equations.

We also introduce multiplicative dummies to assess differences across firms along certain criteria. If we define 4,, to be a firm-specific dummy variable, then introducing this variable as a multiplicative dummy changes equation (14) as follows If the multiplicative dummy is an exogenous variable and xt, 2<sup>is a valid instrument for</sup> the endogenous variable xi,, then _4,ixit 2_ is a valid instrument for _A,txi,_ . In estimating the investment model in equation (13) we treat the weakly exogenous variables as endogenous variables and potential multiplicative dummies as exogenous variables. If we interact the weakly exogenous variables with the multiplicative dummies we use the aforementioned appropriate transformations of these interacted variables as instruments.

# **5 Data**

To explore the impact of financial reforms on financial constraints of firms we need a measure of financial liberalization and firn-level data. We construct an index of domestic financial liberalization of the banking sector based upon country reports from various sources. The problem of constructing such an index is that financial liberalization often takes place in various ways.

We construct the financial liberalization variable as follows. We collect data on the implementation of reform packages related to six different measures. The liberalization 17
# Annex A Methodological annex

## Measures of trade costs

Conceptually, trade costs may be defined as the excess cost of an internationally traded good compared with a similar good traded domestically. By construction, trade costs can therefore move without any change in external costs of trading, simply as a result of changes in domestic trading costs. To measure trade costs, two main approaches have been developed in the literature: direct and indirect approaches (Chen and Novy 2012).

Direct approaches rely on observable data that serve as a proxy for individual components. For instance, measures of costs faced at the border are often based on counting the average number of days that is needed for a good to cross the border, while transport costs are often inferred from the cost of ocean and air shipping (Hummels et al. 2007). Policy barriers such as tariffs and nontariff measures are directly available from a range of statistical sources. Direct approaches suffer from a series of limitations, including the fact the underlying variables are only partially observable and may not be easily converted to plausible ad-valorem tariff equivalents, which makes it difficult to compare them but also to aggregate them into a summary measure of trade costs (Anderson and van Wincoop 2004). Therefore, trade cost estimates taken from such measures tend to be only partial.

Indirect approaches aim to circumvent these difficulties. These infer trade impediments top-down, from measures of trade flows and aggregate value added. Under this approach, trade costs correspond to the difference between the trade flows that would be expected in a hypothetical “frictionless” world and what is observed in the data and are computed relative to domestic trade costs. Measures built through the indirect approach can be tracked over time and include all observed and unobserved factors that explain why trading with another country is more costly than trading domestically. Novy (2013) developed a micro-founded measure of aggregate bilateral trade costs using a theoretical gravity equation for the trade cost parameters that capture the barriers to international trade. The resulting solution expresses the trade cost parameters
spouses at marriage, far more women than men experience the death of a spouse at some point in their lives; by the same token, significantly more elderly women are widows than men are widowers. The last section found that households headed by widowed women have significantly lower consumption than other households even when controlling for an extensive set of household and individual characteristics, including age. Household level data on consumption or income does not allow us to construct corresponding measures of economic welfare for specific individuals within the household.

Here I examine how, relative to other marital statuses, widowhood (including prior widowhood of currently married women) is correlated with indicators of women‘s own personal welfare and that of their dependents ─ whether they are household heads or living in male headed households. It is worth noting that I focus only on some aspects of well-being, and (for lack of data) neglect what may be important dimensions of welfare associated with widowhood ─ including bereavement, emotional loss and distress, changes in social and economic status, lifestyle and identity, and frequently, rejection and accusations of having caused the death. Widowhood rites and cleansing rituals are extremely widespread in African cultures, although I have not found references to such customary practices specific to Mali (Sossou 2002).

Demographic and Health Surveys are available for Mali for 1996 and 2006. These surveys have some drawbacks for an analysis of gender and vulnerability ─ namely, a primary focus on health and reproduction; far greater and more comprehensive coverage of women than of men; the collection of detailed information on women that is restricted to individuals aged 15 to 49; and no income or consumption expenditure data. However, the substantial attraction of the surveys is that they contain information on individuals, including a number of individual level welfare indicators, something that household surveys typically do not. Here, I use the 2006 DHS for Mali.

The 2006 survey identifies current marital status for all women aged 15 to 49, and uniquely, also whether a currently married woman was previously widowed or
br>GNB GIN SEN GHACIVMRT GAB MRT GAB<br>7 7.5 8 8.5 9 9.5 7 7.5 8 8.5 9 9.5<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.4117 R2 = 0.2405<br>Poverty rate in urban areas Poverty rate in rural areas<br>Poverty rate in urban areas Poverty rate in rural areas<br>Poverty rate in urban areas Poverty rate in rural areas<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: GDP per capita is measured in PPP (constant 2017 international $). For the DOU and DB methods, WorldPop 250m is used. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method, and the categories “Core” and “Suburb” for the DB method. Poverty is measured using the $2.15 poverty line.

33