rafmacalaba/datause-encoder-data huggingface dataset:
income shock is 39.74 percent in Donetsk when _β_ = 0 _._ 90 – about a third greater than the case with _β_ = 0 _._ 97.

# **5 Discussion**

The economic impact estimates provided in this paper differ from more conventional metrics like GDP effects in several dimensions. In addition to including non-pecuniary aspects of welfare, they also reflect the agents’ expectations about the future in a rational expectations framework (while this assumes that agents do not make systematic errors, they are not required to have perfect foresight). These differences, combined with the missing GDP impact estimates for the conflict between 2014 and 2019, make it difficult to benchmark our estimates.<sup>9</sup> An important feature of the analysis in this paper is its ability to infer lower bound estimates of the economic impact from migration flows into only a subset of destinations. For example, when flows abroad can not be measured accurately (as in the case of the war in Eastern Ukraine), flows within the country can provide sufficient information to conduct the assessment. This feature follows from the equalization of expected maximized utility associated with choices, conditional on choosing them, regardless of whether displacement to other destinations is welfare-enhancing ex post or not.

This property also allows the exclusion of some regions when moving costs might decline significantly due to humanitarian concerns (e.g., border openings for refugee outflows), potentially threatening our “war is not good for some destinations” assumption. Although having such destinations in the sample set, Φ, does not break this assumption as long as they do not tilt the sign of the average across all regions, they can be excluded from the analysis to remove any doubts.

A related concern in this case could be that some IDPs who register in non-conflict areas may be using these locations as stops en route to living abroad (i.e., transit migration), potentially decreasing the effectiveness of exclusion of foreign regions. To alleviate such concerns, we reproduce our estimations by (i) excluding Ukrainian regions on the country’s western border (which could provide a natural gateway into Europe), in addition to (ii), excluding regions with airports serving more than 100,000 passengers annually plus regions in

> 9Regional GDP numbers produced by the Ukrainian Statistics Office (UkrStat) excluded the non-government controlled areas (NGCAs) in Donetsk and Luhansk oblasts starting from 2014. World Bank, 2021 uses nightlight emission levels as crude proxies for economic activity in the Donbas region. Estimates show a 7.2 percent reduction in Luhansk GCA, 42.9 percent reduction in Luhansk NGCA, 20.2 reduction in Donetsk GCA, and 28.1 percent reduction in Donetsk NGCA.

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processes, have or are currently in the process of getting ISO and other internationally recognized quality certification, and providing its workers with training. The training results are especially significant, with treated firms providing a higher proportion of its skilled and unskilled workers with training, both in-service and externally, and financing training using the SENCE tax incentive.

Panel B also indicates that program participation is associated positively with higher mean values of several final outcomes. Relative to the matched control group, treated firms have significantly higher levels of sales, share of exports in sales, employment, and compensation per worker; however, no significant differences were found for labor productivity. These results are especially striking when compared to the unmatched group means of the treatment and control groups reported in Table 4. There, the treated firms had higher means of employment and exports, but lower mean values of sales, wages and labor productivity.

How do we interpret these sign reversals? They suggest that the confounding effects of unobserved heterogeneity on treatment effects <u>estimated in levels</u> can be overcome in part through improved matching of treated and control firms based on their propensity scores. Recall the results in Table 5 from estimating a Cox proportional hazards model to calculate propensity scores for the treatment and control groups. To the extent that unobserved firm heterogeneity is correlated with these observed pre-treatment attributes, matching based on the propensity score can control for some part of this unobserved productivity differences. It is also worth noting that the propensity score index gives weight to pre-treatment sales and sales growth, in effect mimicking the difference-indifference (DID) comparisons of pre- and post-treatment outcomes discussed next.

19
|**Table 3.6: Percentage of children behind according to age, 1999 **|
|---|

||**7**<br>**years**<br>**old**|**8**<br>**years**<br>**old**|**9**<br>**years**<br>**old**|**10**<br>**years**<br>**old**|**11**<br>**years**<br>**old**|**12**<br>**years**<br>**old**|**13**<br>**years**<br>**old**|**14**<br>**years**<br>**old**|
|---|---|---|---|---|---|---|---|---|
|**Paraíba**|34.1|57.4|71.8|73.3|79.0|88.2|83.5|88.3|
|**Northeast**|28.7|57.0|68.1|75.4|79.3|84.8|86.3|88.9|
|**Brazil**|19.5|39.9|45.3|53.5|58.9|67.7|69.9|75.8|

**_Age–grade distortion._** Another serious problem in Brazil compared to other countries is the age–grade distortion. Although Paraíba is not the worst in the Northeast, it is far worse than the Northeast and national average. Thirty-four percent of the 7-year-olds in Paraíba are not in the appropriate grade, but for 10-year-olds the figure increases to 73.3 percent, compared to 53.5 percent for Brazil as a whole, and for 14-year-olds it is 88 percent, compared to 76 percent for Brazil as a whole (Table 3.6). Finally, the promotion rate, which measures the number of students who pass from one grade to the next, was 64 percent in Paraíba for grade 1-4 in 1999, 1 percentage points lower than the national average of 74 percent (Table 3.5). Moreover, the age-grade distortion is exacerbated in rural areas.  Hence, over-aged primary school students are a key challenge for the education system.

**<u>Table 3.7: Average Graduation Age from Primary and Secondary School, 1999</u>**

|||Total<br>|**P**<br>|**araíba**<br>|Public<br>|||Private<br>|<br>|
|---|---|---|---|---|---|---|---|---|---|
||Total|Male|Female|Total|Male|Female|Total|Male|Female|
|**Primary**|16|16|16|17|17|17|15|15|14|
|**Secondary**|19|19|19|20|20|20|18|18|18|
||||**N**|**ortheast**||||||
|||Total|||Public|||Private||
||Total|Male|Female|Total|Male|Female|Total|Male|Female|
|**Primary**|16|17|16|17|17|17|15|15|14|
|**Secondary**|20|19|20|20|20|20|18|18|18|
|||||**Brazil**||||||
|||Total|||Public|||Private||
||Total|Male|Female|Total|Male|Female|Total|Male|Female|
|**Primary**|15|15|15|16|16|15|14|14|14|
|**Secondary**|19|19|19|19|19|19|18|18|18|
|_Source:_MEC/INEP.||||||||||

**_Graduation._** Students graduate later in Paraíba and the Northeast, than in the nation. In Paraíba and the Northeast, students are on average one year older when graduating from primary school than in Brazil as a whole (Table 3.7). The average graduation age for primary and secondary education is high in public schools (17 and 20 years of age respectively) and two years higher than in private schools.

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3 up to a half of total in Finland. In Austria, a quarter of total exports are in environmentally sensitive p' ;)Cucts. The lowest shares among the seven countries are in Japan and the US - about one tenth uf their total exports.

Overall export performance among industrial countries is diverse (Annex Table 2). Given the endowments of natural resources, competitiveness is influenced by many other factors such as productivity growth, and macro-economic stability. These are likely to matter more fo,r total export performance than increases in environmental expenditures. Finland and Austria with high environmental standards and shares of sensitive goods in total exports have increased or maintained their world shares both in total and manufactures exports over the past decade. The fact that Germany, Sweden, Norway and the United States have lost world markets in manufactures is unlikely to be due to excessive environmental expenditures. Germai.y and Norway increased their shares in total world trade.

Table 1: **Share** of environmentally sensitive goods in total merchandise exports and imports In 1970 and 1990

|Regiona/Counaies|Exports:<br>1970|1990|**Inports:**<br>1970|1990|
|---|---|---|---|---|
|**World**|22|18|(percent)<br>21|IS|
|**InbW**|23|**18**|21|**18**|
|**AuVts**|**28**|25|19|14|
|**Finland**|54|47|22|22|
|Norway|47|26|22|25|
|Sweden|35|31|26|23|
|**Genmany**|21|**1s**|23|20|
|**Japan**|25|1 1|14|17|
|Us|16|14|22|**l1**|
|**Devlopjig**|**18**|19|**nG.**|**nU.**|

Source: Derived **from Unitd** Nationa COMTRADE **datAbass.**

**Trad1e** flows **in** sensitive goods. The share of environmentally sensitive goods in total exports declined in all the sample countries, and in industrial countries as a whole (Table 1). In
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# **6.1 Silencing Political Opposition through Forced Displacement**

Forcibly displaced individuals are likely to oppose the governments they hold responsible for the crises underlying their flight. Their exit, however, removes them from the electorate, depleting opposition support and diminishing the collective will to demand change from within. In Hirschman’s framework, exit and voice are substitutes. When the most dissatisfied citizens leave, those who remain face higher costs of political engagement and weaker incentives to mobilize against incumbents.

We explore this idea by examining the effects of forced migration on electoral turnout and support for the political opposition. Presidential turnout is defined as the total votes in each election divided by the electoral turnout of 2000, the earliest available before the 2013 oil price shock. We keep the electoral turnout fixed as it may be affected by displacement flows; hence, we only measure changes in the numerator. Opposition support is defined as the ratio of opposition votes (votes for candidates besides Ch ́avez or Maduro) to the electoral turnout of 2000. The year 2000 is a natural baseline because forced displacement was negligible at this time, when Venezuela was benefiting from surging oil prices. Importantly, these results are not mechanically driven by population loss, as the estimates are unchanged when using contemporaneous electoral censuses, which are available for the whole period of study (Table E.14). In our view, however, fixing the denominator in 2000 both simplifies interpretation and yields more conservative estimates, as it provides a lower bound on the effects.

Table 2 presents these results, revealing consistent negative effects of forced displacement in both electoral outcomes. The OLS estimates are again smaller in magnitude than the 2SLS estimates, consistent with the pattern documented in the development results. The Kleibergen-Paap Wald F-statistic is approximately 15, above the conventional threshold of 10, though weaker than in the development specifications, reflecting the smaller sample of five presidential election years. The 2SLS estimates in Panel B indicate reductions of 2.81 percentage points for turnout and 1.51 percentage points for opposition support per 33
The household survey data used for analysis (EHCVM 2020) contains information on household aggregated consumption expenditures in nominal terms and the monetary value of household assets. It provides extensive household and individual welfare indicators used in estimating objective and subjective poverty measurements and labor market outcomes. The aim of this paper is to empirically estimate the impact of the Covid-19 pandemic on household expenditure, asset value and ownership, poverty status, and labor market outcomes in the Comoros. To achieve the above, the paper analyzes the impact of the virus curtailment measures at both the household and individual levels. The household-level analysis explores total per capita household expenditure, asset accumulation, and poverty. We construct the log of household per capita consumption expenditures from the estimated consumption expenditure for a given household. We extend our analysis by constructing monetary and non-monetary outcome measures for household asset accumulation. The monetary measure captures the log value of total assets owned by the household. The non-monetary household welfare metric includes the total count of assets owned by a household, the different types of assets, and the total count of livestock ownership.<sup>7</sup> Our last household measure considers poverty status using both objective and subjective measures. The objective poverty status is a binary variable that takes the value 1 if a household is below the national poverty line and 0 otherwise.<sup>8</sup> The subjective poverty measures are three separate binary variables taking the value 1 if a household self-reports as “living averagely well”, “living in difficulty”, or “living rich” according to their socio-economic standards, respectively, and 0 otherwise. The binary subjective measures come from a categorical subjective measure of poverty. The motivation for creating binary subjective

> 7 Assets include chair, table, bed, mattress, cupboard, carpet, iron, stove, gas cylinder, oven, food processor, fruit press, refrigerator, freezer, fan, radio, TV, DVD, Satellite dish, washing machine, dryer, vacuum cleaner, air conditioner, lawnmower, generator, car, motorcycle, bicycle, camera, camcorder, stereo, landline phone, cell phone, tablet, desktop computer, laptop, printer/fax, video camera, boat, hunting rifle, guitar, piano, building/house, unbuilt land, solar panel. Livestock includes cattle, sheep, goats, rabbits, chickens, guinea fowl, duck, turkey, pigeon, geese and other poultry.

> 8 The estimated poverty line used in this analysis is the 2020 national poverty line of 497,957 Comorian francs per person per annum.

13
returns. Financial institutions can help eliminate the agency problem by monitoring investors and ensuring that credit is used in productive activities rather than on private consumption (Adu, Marbuah, and Mensah 2013).

The IMF has developed several indexes that can be used to measure financial development in an economy (see Svirydzenka 2016 and annex A for details). Its overall Financial Development (FD) Index is divided into financial institution indexes and financial market indexes. The financial institution indexes (the Financial Institutions Efficiency [FIE] Index, the Financial Institutions Depth [FID] Index, and the Financial Institutions Access [FIA] Index) measure the level and progress of development of financial institutions in terms of access, depth, and efficiency. The financial market indexes (the Financial Markets Efficiency [FMEI] Index, the Financial Markets Depth [FMDI] Index, and the Financial Markets Access Index [FMAI]) measure the level and progress of development in the financial markets along the same parameters.

Egypt’s FD Index rose over the last two decades, increasing from 0.18 in 1994 to 0.30 in 2016, with slight fluctuations between 2001 and 2010; it peaked at 0.37 in 2006 (figure 1). This trend seems to have depended more on financial market development than financial institution development. <mark>In contrast,</mark> the financial market development indexes closely followed the trend of the financial development index (figure 2). These findings suggest that financial markets in Egypt contributed more to financial development than financial institutions did.

**Figure 1 Financial institution indexes in Egypt, 1980–2016**

<!-- Start of picture text --> 0.9<br>0.8<br>0.7<br>0.6<br>0.5 FD<br>FIA<br>0.4<br>FID<br>0.3<br>FIE<br>0.2<br>FII<br>0.1<br>0<br>Financial development indexes<br><!-- End of picture text -->

# _Source_ : Data from IMF (2017).

_Note_ : FD: Financial Development Index; FIA: Financial Institutions Access Index; FID: Financial Institutions Depth Index; FIE: Financial Institutions Efficiency Index; FII: Financial Institutions Index.

9
SUMMar The responsiveness of exports to changes in the incentive structure has long interested policy makers. But the empirical literature has provided little guidance on when or how exporters will respond to new incentive structures. Research in this area has produced a variety of supply elasticity estinates that vary dramatically across countries and time periods, and few hints on how to reconcile the diverse results.

Paul Krugman and Richard Baldwin have recently argued that the emirical literature fails because - there are sunk costs associated with breaking into foreign markets including upgrading product quality, packaging, and the establishment of marketing channels. Hence the currcnt-period export supply function depends upon the number and type of producers that were exporting in previous periods. Further, startup costs mean that transitory policy changes or macro shocks can lead to permanent changes in market structure, and thus that trade flows may not be reversed when a stimlus is removed. That is, sunk entry or exit costs produce "hysteresis' in trade flows. Finally, wlhen future market conditions are uncertain, sum,. costs make pattrns of entry and exit dependent upon the stochastic processes that govern variables like dhe exchange rate.

Taking the Baldwin/Krugman perspective as a point of departure, this paper develops an econometric model of a plant's decision to export. The model is fit u mwiro data for a large group of anufacturing plants in Colcmbia from 1981-1989, and used to direcdy examine the determinants of a plant's export decision for consistency with the theory.

The econometric results reject the hypothesis that sunk costs are zero. They also reveal that the re-entry costs of plants that have been out of the export market for a year are substantially less than the costs of a first-time exporter. Beyond a one year absence, however, the re-entry costs are not significantly different than those faced by a new exporter. This is consistent with the view that an important source of sunk entry costs for Colombian exporters is the need to accmulate information on demand sources, information that is likely to depreciate upon exit from the market.

While the results indicate that sunk costs are a significant source of export market persistence, both observed and unobserved plant characteristics also contibute to an individual plant's export behavior. For example, plants that are large, old, and owned by corporations are all more likely to export.

A number of policy implications emerge. For example, it appears especially difficult to break into foreign markets during periods of world recession. Also, although only 25 percent of the plants in the Colombian panel exported during the sample period, the results imply that sufficiently favorable macro conditions and/or reductions m sunk costs could make exporting profitable for a much larger proportion of plants. Finaly, and most generaly, the estimates imply that countries undertaldng export promotion policies should distinguish measures aimed at exanding the export volume of exisitag exporters from policies aimed at promoting the entry of new exporters.
# Brazil within Brazil:

Testing the Poverty Map Methodology in Minas Gerais<sup>*</sup> Chris Ebers (Vrije University, Amsterdam) Peter Lanjouw (World Bank) Phillippe George Leite (World Bank) JEL: I32, C31, C42 Key words: Small Area Statistics, Poverty, Inequality, Survey Methods, Heterogeneity

> ***** We thank seminar participants at the World Bank Applied Micro Seminar for useful comments and suggestions.  We are particularly indebted to Wasmalia Bivar, Elisa Caillaux, Antonio Dias, Mauricio Lila, Viviane Quintaes and Debora de Souza at the Instituto Brasileiro de Geografia e Estatística (IBGE) Rio de Janeiro, for making available the unit record census data for Minas Gerais for the purpose of this validation study, and for numerous comments and suggestions during the course of this study.  We are also most grateful to Kathy Lindert for supporting this work and to DECRS for providing financial support (RF-P107113-RESE-BBRSB).  The views in this paper are the authors’ and should not be interpreted to reflect those of the World Bank or affiliated institutions.