rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Simulating the Impact of the 2009 Financial Crisis on Welfare in Latvia**<sup>*****</sup>

Mohamed Ihsan Ajwad<sup>†</sup> Francisco Haimovich<sup>‡</sup> Mehtabul Azam<sup>§</sup>

**Abstract**

This note details simulations of the distributional impacts of the 2009 financial crisis on households in Latvia. It uses household survey data collected prior to the crisis and simulates the impact of the growth slowdown. The simulations show that Latvia experienced a sharp rise in poverty, widening of the poverty gap, and a rise in income inequality due to the economic contraction in 2009. The 18 percent contraction in gross domestic product (affecting mainly trade hotels and restaurants, construction, and manufacturing) likely led the poverty head count to increase from 14.4 percent in 2008 to 20.2 percent in 2009. The poverty gap, which measures the national poverty deficit, was simulated to increase from 5.9 percent in 2008 to 8.3 percent in 2009. The analysis finds that the results are robust to most assumptions except post-layoff incomes, which substantially mitigated household welfare. The authors also simulate the impact of Latvia’s Emergency Social Safety Net components and find that the Safety Net likely mitigated crisis impacts for many beneficiaries. The simulations measure only direct short-run impacts; hence, they do not take into account general equilibrium effects. Post-crisis income data from a different data source suggest that poverty rates increased by 8.0 percentage points between 2008 and 2009. As a result, the authors suggest that their ex-ante simulation performs reasonably well and is a useful tool to identify vulnerable groups during the early stages of a crisis.

## **JEL Classification:** I31, I38, C15, H23

**Keywords:** Simulation, poverty, crisis, social protection policies, Latvia **Sector Board:** Social Protection (SOCPT)

* This note was written in
the impact on the overall market including migrants, not just on the native community. Overall the results suggest that although there are some winners and some losers of the population shock, the more vulnerable groups of the labor force bear a disproportionate burden of the displacement crisis.

This paper is divided into six sections. Section 2 gives a description of the migration trend in Brazil, focusing on the influx of Venezuelan refugees and migrants and a discussion on the overall economy of Roraima. Section 3 provides a brief survey of the past literature, while Section 4 discusses the time-series data and the main variables used. Section 5 discusses the identification strategy and Section 6 presents the results along with robustness check and falsification analysis, while Section 7 concludes with some policy implications and discusses venues for further research.

# **2 Venezuelan Migration in Brazil and the Economy of Roraima**

The diaspora population in Brazil has been increasing rapidly from 2016 and the blue bar in Figure 1 shows that the total migrants and asylum seeker population increased from about 0.7 million in 2016 to about 1.4 million in July 2020. From being one of the lowest diaspora populations in 2016, among the Latin American countries, Venezuelans in Brazil (shown in brown) quickly became the largest migrant population by 2019. Brazil also has significant number of migrants<sup>4</sup> from Bolivia and Haiti, followed by Colombia, Argentina and China as the figure shows. During the period of July 2017 to October 2020, Brazil received about 126,256 migrants and 30,000 asylum

> 4Regular migrants and asylum seekers

3
data would face two limitations. First, we only three countries: Brazil, Vietnam, and Senegal. In addition, the data for Brazil covers only one region, whereas the data from Vietnam and Senegal cover the entire country. To overcome these limitations, we focus on the region level to conduct aggregate cross-sectional analysis. Because all three surveys are stratified by region, regional aggregates of our technology measures are representative. Using the sampling weights, we compute the average level, cross-firm variance, and within-firm variance at the regional level for all technology measures (e.g., EXT, MOST, and GAP of ABF, GBF, and SSBF).

Another issue we must confront is that the regional-level data on GDP per capita or labor productivity is unavailable for both Senegal and Vietnam. We overcome this challenge by taking advantage of the fact that our sample of companies is representative at the regional level, and use firm-level information on value added per worker to estimate regional labor 68
the year following the disaster.<sup>6</sup> In order to get a better sense of the magnitude of the changes in economic activity we additionally derive Fonden’s heavy rainfall benefit cost ratios. This calculation is preformed in four steps. First, we restrict the sample to 790 municipalities that received Fonden funds between 2004 and 2011 and for whom we have complete information on the resources allocated by the program. Second, we proxy municipal GDP before the introduction of Fonden, by multiplying UNDP estimates of municipal GDP per capita in 2000 by municipal population. Third, we calculate the dollar value of the economic activity generated by FONDEN by multiplying municipal GDP by the product of the elasticity of light to GDP and our IV estimate of the impact of Fonden, that is, we multiply the level of GDP by the additional growth created by Fonden. Fourth, we sum across municipalities to derive the total benefit and cost of the program.<sup>7</sup> To account for the uncertainty in estimating the elasticity of light to GDP, and of estimating the impact of Fonden on night lights, the third step of the calculation is preformed using coefficients drawn from two normal distributions. In each case the mean of the distribution is set to the point estimate, and the standard deviation is set to the standard error. Steps three to four are then repeated 5,000 times using random draws of coefficients described on step three. We then

> 6Note that the counter-factual is not no reconstruction response at all, but rather non-Fonden discretionary reconstruction response.

> 7Since UNDP GDP estimates are in 2005 PPP USD we convert Fonden reconstruction expenditures to the same units.

10
.92|4.31|3.74|
|**Plot characteristics:**||||||
|Years of possession|19.05|4.94|5.06|4.67|3.21|
|Good soil quality|0.45|0.46|0.48|0.44|0.33|
|Medium soil quality|0.37|0.37|0.37|0.38|0.41|
|Flat land|0.72|0.71|0.72|0.64|0.74|
|Gently sloped land|0.24|0.24|0.24|0.25|0.26|
|Irrigated land|0.06|0.05|0.05|0.02|0.00|
|**Crop choice:**||||||
|Teff|0.29|0.35|0.34|0.44|0.38|
|Wheat/barely|0.21|0.22|0.22|0.24|0.28|
|Sorghum/maize|0.33|0.25|0.25|0.21|0.28|
|Pulses|0.12|0.14|0.15|0.05|0.05|
|Oilseeds|0.05|0.04|0.04|0.05|0.00|
|Number of observations|2630|1135|955|135|39|

_Source:_ Own computation from AU/WB/UG land tenure survey. _Note:_ Only plots with cereals, pulses and oilseeds included.

27
considered accurate and authoritative, but which has not been hitherto systematically analysed.<sup>2</sup> While various aspects of the TPR could be strengthened, (Keesing, 1998), There is little dispute over the accuracy of the information they contain. For the countries for which only a GATT period TPR was available (i.e. before 1995) however, it has been necessary to update the TPR information based on recent country notifications, e.g. regarding tariffs, which contain URA commitments. In others, such as antidumping measures or LDC problems, different and more complete data exist in the WTO and have been used to supplement TPR based infornation. In still others, for example market access issues, additional outside sources have been used, mainly from the OECD.

The group of developing countries on which the study is based includes thirteen developing economies from Latin America and the Caribbean, thirteen from Asia and the Pacific, eleven from Sub-Sahara Africa and five from Europe, Middle-East and North Africa. A detailed list of the countries can be found in the Appendix Table A- 1. They account for the bulk of the trade of developing members of the WTO (see below). The main limitation of the group of developing countries on which the study is primarily based is that it contains only four least developed countries (Bangladesh, Benin, Uganda and Zambia) out of a total of 29 which are members of the WTO. On the other hand, the study has used information regarding the challenges least developed countries face in integrating into the multilateral system developed in the context of the assessments of trade-related technical assistance needs prepared for 38 LDCs in 1997-1998 (WT/COMTD/IFI-38, 1997-1998). Also, the analysis is limited to merchandise trade only, excluding services, partly in order to make the scope of the project more manageable and partly because the TPRs did not cover services before the establishment of the WTO.

> [By the end of 1998, TPRs had been prepared for
# **3. Data and Main Variables**

Our main data source is the World Bank’s Women, Business, and the Law database<sup>4</sup> and the World Development Indicators (WDI). This is complemented with other data sources mentioned below. Our main specifications as well as the IV specification take advantage of the panel nature of the data by including both country and year fixed effects. The sample is an unbalanced panel of up to 95 countries for which data are available.<sup>5</sup> The time period covered is 1990 to 2012. The equation we estimate is as follows: Subscripts i,t denote the country and year, respectively. Y is the dependent variable, X is our main explanatory variable for domestic violence legislation. Z is the vector of various controls that vary across countries and years. YFE and vi denote year fixed effects and country fixed effects respectively; uit is the error term.

Ideally, the dependent variable would be adult mortality rate due to domestic violence, however the data for this variable are scarce and unreliable due to gross underreporting. Thus, we use the overall mortality rate, which biases our results downward especially if domestic violence has a low prevalence. However, domestic violence legislation may have far-ranging effects on women’s empowerment and thereby mortality beyond just domestic violence, giving some support

> 4 http://wbl.worldbank.org

> 5 List of countries: Algeria, Angola, Argentina, Armenia, Azerbaijan, Bangladesh, Belarus, Benin, Bolivia, Botswana, Burkina Faso , Burundi, Cambodia, Cameroon, Chad, Chile, Colombia, Congo, Dem. Rep., Congo, Rep., Costa Rica, Côte d'Ivoire, Denmark, Djibouti, Dominican Republic, Ecuador, Egypt, Arab Rep., El Salvador, Equatorial Guinea, Ethiopia, Fiji, Gabon, Georgia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, Indonesia, Iran, Islamic Rep
Policy Research Working Paper 9231

# **Abstract**

This paper analyzes the Indian National Sample Survey data spanning 1987/88–2011/12 to uncover patterns of transition into and out of different classes of the consumption distribution. At the aggregate level, income growth has accelerated, accompanied by accelerating poverty decline. Underlying these trends is a process of mobility, with 40–60 percent of the population transitioning between consumption classes and increasing mobility over time. Yet, the majority of those who escape poverty remain vulnerable. Most of those who are poor were also poor in the preceding period and, thus, are likely to be chronically poor. The characteristics of upwardly mobile households contrast with those of the poor; these households are also far less likely to experience downward mobility. The paper also finds that states exhibit heterogenous mobility patterns.

This paper is a product of the Development Data 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/prwp. The authors may be contacted at hdang@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
|Bel & Holst<br>(2018)|Mexico City|2003-2007|Pollution levels, average<br>daily wind speed and<br>average daily wind direction|<br>Difference-in-<br>differences and quantile<br>regression|Expanding bus rapid<br>transit (BRT) services<br>reduce emissions|
|---|---|---|---|---|---|
|Silva et al.<br>(2012)|Brazil||Strike event, air pollution<br>and weather|Regression<br>discontinuity|Decreased public transit<br>use led to more air|
|Anderson<br>(2014)|Los Angeles|2003|Total flow (vehicles per<br>hour), freeway speed data<br>(average vehicle speed for<br>each detector), average<br>spacing between loop<br>detectors|Regression<br>discontinuity|pollution and traffic<br>congestion.|
|Bauernschuster<br>et al. (2017)|Germany|2002-2011|Strike, traffic (number of<br>passing vehicles on<br>freeway), traffic congestion,<br>accidents, pollution (hourly<br>atmospheric data), weather<br>(daily), holidays|Difference-in-<br>differences||
|Rivers et al. (in<br>press)|Canada|1974-2011|Air pollution, weather and<br>work actions (public transit<br>strikes)|Regression<br>discontinuity|no evidence of air quality<br>improvement from the<br>expansion of public transit|
|Beaudoin &<br>Lin-Lawell<br>(2017)<br>**_Restrictions on D_**|United States<br>**_riving and Veh_**|1991-2011<br>**_icle Purchas_*
2 Thirdly, systematic 3D analyses of horizontal and vertical settlement extent are often derived from between a few hundred to a thousand selected city regions (Jedwab et al., 2021b; Lall et al., 2021; Li et al., 2022; Zhou et al., 2022). The resulting empirical evidence therefore tends to favor the characteristics of urban areas, and large cities among them, focusing on developed economies rather than developing economies where most of the data needs are.

To address these limitations, this research focuses on several major improvements. Based on a unique combination of area-wide information provided by the World Settlement Footprint 3D (WSF3D) raster layer (Esch et al., 2022), with the exceptional precision of the Emporis point database of high-rise buildings (Emporis, 2022), a new dataset (WSF3Dv2) is generated with detailed information on the area, height, and volume of the building stock worldwide today. Since the data does not rely on local administrative data, the low data capacity of less developed economies does not constrain our analysis. At the same time, our data is most useful where alternative sources of data are limited, which is the case of poorer economies.

This research derives information on building area and height with a geometric resolution of 12m*12m with global coverage. Biases in terms of income status (e.g., from over-relying on data from developed economies), geographic region (e.g., driven by selection of sample regions) or settlement type (e.g., megacities) are avoided and completeness, representativeness, and general validity of the analyses and findings are assured. To meet requirements of German data security regulations (Palacios-Lopez et al., 2021) and for an open dissemination of the novel WSF3Dv2 data and derived results, all outcomes are finally provided in the form of zonal statistics (building area, building height, and building volume) aggregated to a grid of 90m*90m.

We then use the data to investigate the
# 5 Discussion and conclusion

In this research, we present a unique dataset merging firm-level financial statements with weather data to explore how climate change uncertainty affects firm investment behavior. Using financial records from 64 countries from the Orbis database and daily surface temperature data on a 1x1 grid from BEKREARTH, we created a comprehensive grid-level dataset. This dataset integrates firms’ fixed asset growth with temperature uncertainty.

A key objective of this study is to provide grid-level evidence on the impact of temperature uncertainty on firms’ fixed asset growth. Our findings showed a strong negative association between temperature uncertainty and firm growth.

Several factors could explain this significant negative relationship. First, temperature uncertainty introduces additional complexity into firms’ operational and strategic planning, making it harder to forecast costs and returns on investment accurately. Firms might reduce or delay capital investments as they become uncertain about the economic viability of long-term projects, especially when faced with unpredictable climate conditions.

Moreover, temperature uncertainty may also raise firms’ operating costs, particularly in sectors dependent on stable climatic conditions, such as manufacturing, energy, or logistics. For instance, unpredictable fluctuations in temperature could affect energy consumption, maintenance costs, or supply chain efficiency, all of which contribute to a more cautious approach to capital expenditures. Firms may allocate fewer resources to expanding fixed assets if they expect higher variability in operational expenses due to environmental uncertainties.

We also documented significant heterogeneity in the impact of temperature uncertainty on firm growth across industries. Industries with higher levels of irreversible assets, as measured by the ratio of tangible fixed assets to total assets, tend to experience a more pronounced negative effect from temperature uncertainty. This result aligns with the pediction by ROT (Myers 1977, Baldwin 1982), which suggests that firms with more irreversible investments are more likely to delay or reduce their investment activities in response to uncertainty. To be more specific, firms in those sectors with high proportion of irreversible assets typically face high sunk costs, making it difficult to adjust or reverse investments once they are committed. As a
Similar mechanisms are likely at play in determining firm exposure as well. Larger, more productive firms may be more able and more likely to choose to locate in areas at lower risk from climate hazards. Although the presence of many large firms in hot states like Gujarat underline that there are other important determinants of firm location choice than climate risk. Like in the case of households, the consequences of this sorting are likely to be magnified by the direct impact of climate hazards on firm (and worker) productivity.

Our results have several important limitations. Crucially we do not directly model how exposure to climate hazards will change in the future with additional climate change. To the extent that locations that have been highly exposed will remain relatively more exposed, the overall message of our results would remain the same. The magnitudes of the difference in exposure between wealthy and non-wealthy areas is likely to change even in this case, however. If climate change changes relative risk profiles on the other hand – increasing risk in currently low-risk areas – our results would not be a good guide to the future exposure to climate driven risks.

Our results also rely on estimated wealth levels from the RWI. These estimates are based on observable features of a location and the modelled relationship between those features and measured wealth in locations for which wealth measurements exist. But these measurements do not exist for most of the locations in our sample and so our results rely on these estimated measures of wealth. These could be over- or under-estimates of true wealth. If there is systematic bias in the estimation of wealth in the RWI, it could bias our results. The direction of this bias is not clear and depends on the nature of any mis-estimation in the RWI.

Our results have several important implications for policy-makers. The first is the critical relationship between poverty alleviation and building resilience to climate change. We have shown that the poor are disproportionately exposed to climate hazards. This implies that polices to increase resilience to these climate hazards that are otherwise distributionally neutral will naturally
Steckel, Jan C., Ira I. Dorband, Lorenzo Montrone, Hauke Ward, Leonard Missbach, Fabian Hafner, Michael Jakob and Sebastian Renner (2021). Distributional impacts of carbon pricing in developing Asia. Nature Sustainability 4:1005–1014.

- Timilsina, Govinda R. (2022). Carbon Taxes. Journal of Economic Literature, Vol. 60 (4), pp. 1456–1502.

- Timilsina, Govinda R.; Sebsibie,Samuel. Distributional Effects of Carbon Tax in Ethiopia : A Computable General Equilibrium Analysis (English). Policy Research working paper ; no. WPS10476 Washington,D.C.:World Bank Group.

- <u>http://documents.worldbank.org/curated/en/099313006082325882/IDU0da75974707627 04e92081db0a02effa4a553</u>

- Timilsina, Govinda and Sheoli Pargal (2020). Economics of Energy Subsidy Reforms in Bangladesh.  Energy Policy 142, https://doi.org/10.1016/j.enpol.2020.111539.

- Timilsina, Govinda, Jun Pang and Yuanhua Chang (2022). Economic Impacts of Meeting China’s NDC through Carbon Taxes with Alternative Schemes for Recycling Tax Revenues. World Bank Policy Research Working Paper #10087, World Bank, Washington, DC.

- Timilsina, Govinda, Yazid Dissou, Mike Toman and Dirk Heine (2021). Carbon Tax in an Economy with Informality: A Computable General Equilibrium Analysis for Côte d’Ivoire”. World Bank Policy Research Working Paper, WPS 9710. World Bank Washington, DC.

- Williams, Roberton C., III, Hal Gordon, Dallas Burtraw, Jared C. Carbone, and Richard D. Morgenstern (2015). The Initial Incidence of a Carbon Tax across Income Groups. National Tax Journal 68 (1):195-213.

- World Bank (2022a). World Development Data Indicator Database. <u>WWW.worldbank. World</u> Bank, Washington, DC.

- World Bank (2022b