rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5960

# **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.

This paper is a product of the Human Development Economics Unit, 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://econ.worldbank.org. The authors may be contacted at majwad@worldbank.org or mazam1@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
<mark>what was previously found in the literature, but still of comparable magnitude with a more robust econometric method. Furthermore, the synthetic control method allows for time-variant effects which previously used methods in the literature were unable to estimate. We show that the cost of default is heavily frontloaded.</mark> <mark>More importantly, to our knowledge, this is the first paper to systematically assess some of the key social costs of sovereign default in a large panel. We find significant effects of sovereign default on poverty, nutrition, electricity and health outcomes, indicating that the most vulnerable segments of the population are heavily impacted by sovereign defaults and the decade that succeeds them. Whether the effects on poverty are predictably only caused by plummeting growth is a question that will need better data and further inquiry, but our study showed that there may be considerable heterogeneity in results. Additionally, the main association stands: Sovereign defaults impose a great burden on a society, pushing about 30% more people into poverty and depriving the population of access to basic needs like nutrition and energy.</mark> <mark>The social cost results are informative despite data limitations of some of the outcome variables. In particular, using data for the 8 countries where we observe output collapses after default and where annual data on the income distribution is available, we find that on average, lower income segments of the population bear the brunt of the default cost. This result is consistent with the results from the less granular poverty data. In other words, just like all roads lead to Rome, all variables here point to a decline in standards of living of those whose standards of living were already sub-standard.</mark> <mark>The link between social outcomes and sovereign default has been under-researched. A notable exception is Andreasen et al. (2019), as well as Jeon and Kabukcuoglu (2018). Both works develop a theoretical model to predict sovereign default based on the level of inequality. In their models, as inequality increases, sovereign default becomes more likely. We contribute to this strand of the literature by studying the flip side of that relationship. Our study shows that social outcomes (such as poverty or infant mortality) are importantly suffering after the default event.</mark> <mark>Yet, our paper is only a starting point to answer important questions. Many research questions are left unanswered mainly due to data availability. For instance, the interaction with other types of crises and how that affects social costs. As discussed above, inflation is a driver for poverty increase and it often occurs during default crises. It would be useful to understand</mark> 32
Among positive outliers, the most easily-identifiable clusters are in west-central India and northeastern China. Smaller clusters are visible in western Zambia, southern Nigeria, southeastern Brazil, central Bolivia and northern Colombia. Negative outlier clusters are visible in southeastern Australia, Indonesia, southern Philippines, southern Thailand, central Vietnam, southwestern and far western China, Sri Lanka, central Islamic Republic of Iran, the Nile Delta, northeastern Tanzania, northern Madagascar, central and southern Mali, western Spain, Uruguay, Cuba, the Dominican Republic, western República Bolivariana de Venezuela, the western coastal areas of Central America and Mexico, and California.

Table 4 provides a summary view, aggregated by states and provinces for administrative units that have more than 10 observations (GADM 2022). The table displays top-20 outliers in descending order. Among the countries with positive outliers, India is represented by 6 areas; five countries have 2 areas (China, Myanmar, Nigeria, Brazil, Bolivia); and four countries have 1 (Azerbaijan, Pakistan, Paraguay, Italy). Among countries with negative outliers, Indonesia has 7 areas; four countries have 2 areas (Mali, Portugal, Cuba, Uruguay); and five countries have 1 (China, Japan, the Arab Republic of Egypt, Guinea, the República Bolivariana de Venezuela). In summary, India and Indonesia dominate the extreme outlier countries at opposite ends of the outlier distribution and, with the exception of China, countries have states/provinces clustered by positive or negative status. Of course, this is not true for the entire set of states/provinces, which number in the hundreds. Full results are available from the authors on request.

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||(1)|(2)|
|---|---|---|
|FA3|-3.238**|-6.042|
||(1.254)|(6.308)|
|Famine Control|-6.224**|-7.078**|
||(3.070)|(3.337)|
|FA3×Famine Control||4.019|
|||(8.863)|
|Constant|9.609***|10.292***|
||(2.553)|(2.782)|
|Obs.|53|53|
|_R_<sup>2</sup>|0.116|0.103|

Table 21: The Effect of FA3 and Famile Control (1959-1961) on Retaking the Power During 1971 and 1979.

purged from power by the military in the process. The county Core Leading Group stayed in power until late in 1970, when the county Party Committees were reestablished and military cadres gradually retreated from the county leadership. That is, the county Party Committees attempted to regain their leadership around the end of 1970. Consistent with our model, we expect that those counties that either kept a close connection with the provincial leaders or had strong grassroot support were likely to regain their power from the military-led Core Leading Group faster after 1971.

In Table 21, we attempt to directly test the effect of the connection to higher level government and the grassroot support on the ability of a faction to regain power from the military Core Leading Group. The dependent variables in Table 21 are measures of speed to regain power by the original faction in the county between 1971 and 1979. For simplicity, we use the OLS specifications, and the dependent variable is “years to regain power during the period.” We use FA3 and Famine Control during the Great Chinese Famine (1959-1961) to respectively proxy the connection to the provincial leaders and grassroot support. In Columns (1)-(2), the negative OLS coefficient estimates of FA3 and Famine Control mean that FA3-affiliated counties and counties with strong grassroot support regain power faster. Interestingly, in Column (2) we find that the interaction term FA3×Famine Control is positive, suggesting that counties that already had strong connections to the higher-level government benefits less from pro-local policies in their speed of regaining power; however, the coefficient estimate is not statistically significant.

The results from Table 21 provide direct evidence that connections to strong factions and grassroot support both expedited the local cadres’ regaining of power after the purge; in addition, grassroot support is not as effective for the strong FA3 faction as for the weak YRD faction in expediting their regaining power. Both are consistent with the mechanisms of political survival described in our model presented in Section 2.

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no studies in the economic literature that are specifically focused on the distinction between bonding and bridging. Bartolini et al. (2008) consider, besides generalized trust, people’s participation in social organizations, whether of the Olson or Putnam variety, thus following Knack and Keefer (1997). The impact on life satisfaction of the former type of organizations emerges as negative and of little significance, while the impact of the latter emerges as positive and highly significant.

Other studies focus on the _impact of specific indicators of social capital on life satisfaction_ , but their findings are not necessarily in line with the results discussed above. _Family ties_ are found to be positively, significantly, and sizeably correlated with life satisfaction (Alesina and Giuliano 2007), which is consistent with the recurrent result of the positive association between marriage and life satisfaction. However, this same result is not easily reconciled with the negative correlation found between family ties and generalized trust, which is positively associated with life satisfaction.

A similar but less clear issue emerges from _religiosity_ . This variable appears to be positively correlated with life satisfaction in several studies (Dolan et al. 2008; Clark and Lelkes 2009), but it also depends on the indicators of religiosity used (Helliwell 2008), and (according to psychologists) on the indicators of self-reported well-being used (Lewis 2005). In particular, if the life satisfaction scale is of the ‘Cantril ladder’ type, as in the Gallup Poll dataset, then the importance of religion does not show a significant correlation with life satisfaction (Helliwell 2009).

The conclusions from this brief review of the literature (summarized in Table 1) are thus the following. _First_ , social capital cannot be taken as a homogeneous concept. In fact, some exploratory and partial analyses show that social capital can be captured by specific measurable indicators, which fit nicely with the definitions of bonding and bridging as two dimensions of social capital, and which seem to relate negatively to each other in some important instances. Therefore, further analyses on the relevance of this distinction should especially investigate the relationship between these two dimensions of social capital. _Second_ , the distinction between bonding and bridging indicators of social capital is important because of their distinct role in growth and life satisfaction. The literature on social capital and growth has shown that specific individual indicators or composite indicators often relate positively with economic growth if they are of the bridging type, but negatively if they are of the bonding type. Similar studies on the role of bonding and bridging indicators for life satisfaction are still scarce.

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**Table 1: Data Sets Used in this Report**

|**Data Title**|**Year(s)**|**Type**|**Source**|
|---|---|---|---|
|**Demographic and Health Survey**|2011|Household<br>Survey|CSA|
|**Urban Bi-Annual Employment and**|2009, 2010, 2011,|Household|CSA|
|**Unemployment Survey**|2012|Survey||
|**Large and Medium Scale Manufacturing**<br>**and Electrical Industries Firm Survey**|1999-2010|Firm Census|CSA|
|**Welfare and Monitoring Survey**|2011|Household<br>Survey|CSA|
|**Household Income and Consumption**<br>**Survey**|2011|Household<br>Survey|CSA|

Notes: XXXX-YYYY: Refers to panel data; XXXX, YYYY: Refers to cross-sectional data.

Data on firms, industries<sup>4</sup> and employment within the report are taken from different data sources. All of the data used in the report are taken from surveys undertaken by the Ethiopian Central Statistical Agency (CSA). The _Large and Medium Scale Manufacturing and Electrical Industries Survey_ (LMMIS) covers all establishments with 10 or more employees that use power-driven machinery, often manufacturing firms. It is undertaken in over a 100 of the largest towns and cities in Ethiopia, which relate to the urban administrations.<sup>5</sup> The _Urban Bi-Annual Employment and Unemployment Surveys_ (UBAEUS) were conducted first in 2003, 2004 and 2006. After 2009, they have been conducted annually. The survey provides information on the economic characteristics of the population aged 10 years and over, including their activity status, employment, and unemployment situation. It also covers detailed sociodemographic background variables such as age, gender, educational status, training and marital status. The unit of analysis is the household and the individual, and the survey identifies regions, zones and woredas. All surveys, except for the LMMIS which is a census, are representative at the woreda level. Figure 2 shows graphically the distribution of employment across the country – and the proportion accounted for by formal manufacturing firms.

The report also supplements the analysis on firms and employment with data drawn from the _Demographic and Health Survey (DHS),_ the _Welfare and Monitoring Survey (WMS)_ and the _Household Income and Consumption Survey (HICE)._ All of these are sample surveys with the household identified as the unit of analysis. They provide information on a rich array of variables, including social indicators (health, education, poverty) and economic indicators (access to services, assets, consumption).

> 4 Industry refers to the ISIC 2-digit industry classification for manufacturing. 5 Informal in Ethiopia is less widespread than elsewhere in Sub-Saharan Africa. In 2011, 37 percent of those employed in Ethiopian cities were in the informal sector—this is low, versus average rates of 60 percent or higher elsewhere. Also, informal employment is more widespread in cities other than Addis Ababa.

4
|`Period 3 dummy`|`0.075`|
|---|---|
||`(0.49)`|
|`Period 4 dummy`|`0.379`|
||`(2.33)**`|
|`Constant`|`-1.754`|
||`(0.24)`|
|`Observations`|`179`|
|`Absolute value of z statistics in p`|`arentheses`|
|`* significant at 10%; ** at 5%; ***`|`at 1%`|

# **Discoveries:**

||`Discoveries`|
|---|---|
|`Gini`|`-2.874`|
||`(14.96)***`|
|`Net exportgrowth`|`-0.009`|
||`(1.44)`|
|`ln(historical discoveries)`|`0.175`|
||`(2.95)***`|
|`ln(GDPper capita)`|`2.239`|
||`(3.55)***`|
|`ln(GDPper capita)`<sup>`2`</sup>|`-0.135`|
||`(3.73)***`|
|`ln(population)`|`0.038`|
||`(1.70)* `|
|`commodity group 3`|`-0.478`|
||`(3.00)***`|
|`commodity group 4`|`-0.461`|
||`(2.85)***`|
|`commodity group 5`|`0.277`|
||`(1.91)* `|
|`commodity group 6`|`0.068`|
||`(0.45)`|
|`commodity group 7`|`0.501`|
||`(3.71)***`|
|`commodity group 8`|`0.458`|
||`(3.44)***`|
|`commodity group 9`|`0.327`|
||`(2.43)**`|
|`commodity group 10`|`0.735`|
||`(5.82)***`|
|`Constant`|`-6.947`|
||`(2.50)**`|
|`Observations`|`391`|

``` Absolute value of z statistics in parentheses
* significant at 10%; ** at 5%; * at 1%
```

# **Export Booms:**

||`Export Booms`|
|---|---|
|`Gini`|`-3.052`|

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<u>Table 4: Main Regression Results from OLS and 2SLS</u>

|<br>Variable|<br>(1)<br>OLS<br>License|<br>(2)<br>2SLS<br>License|<br>(3)<br>OLS<br>Payroll|(4)<br>2SLS<br>Payroll|
|---|---|---|---|---|
|Payroll registry|0.0738**<br>(0.0298)|-0.0964<br>(0.206)|||
|License|||0.107***<br>(0.0377)|0.686**<br>(0.331)|
|Log distance to SUNAT|-0.0751***<br>(0.0190)|-0.0794***<br>(0.0198)|||
|Firms in E-Payroll / firms in SUNAT|||0.019***<br>(0.005)|0.0238***<br>(0.006)|
|Log product per worker|0.0304*|0.0383**|0.0491***|0.0267|
||(0.0157)|(0.0185)|(0.0176)|(0.0234)|
|Log number of workers|0.0672***<br>(0.0167)|0.0835***<br>(0.0258)|0.0891***<br>(0.0185)|0.0522*<br>(0.0293)|
|Incorporated business|0.154***|0.211***|0.310***|0.205***|
||(0.0316)|(0.0755)|(0.0344)|(0.0711)|
|Age 5-8|0.0355|0.0352|-0.0112|-0.0326|
||(0.0370)|(0.0373)|(0.0417)|(0.0480)|
|Age 9-12|0.112***|0.120***|0.0313|-0.0314|
||(0.0389)|(0.0405)|(0.0439)|(0.0605)|
|Age 13-16|0.154***|0.142***|-0.0754|-0.163**|
||(0.0429)|(0.0457)|(0.0486)|(0.0735)|
|Age 17-+|0.201***|0.204***|-0.000107|<br>-0.120|
||(0.0388)|(0.0393)|(0.0443)|(0.0839)|
|Constant|0.302|0.253|-0.736**|-0.936**|
||(0.267)|(0.275)|(0.315)|(0.370)|
|Observations|888|888|888|888|
|R-squared|0.188|0.157|0.267|0.068|
|Weak identification test||19.00||14.24|

_Note:_ Standard errors are in parentheses. Significance: *** p<0.01, ** p<0.05, * p<0.1. Stock-Yogo weak ID test critical values: 16.38 (10% maximal IV size); 8.96 (15% maximal IV size).

The 2SLS results are consistent with business formalization being an easier and less costly step than labor formalization: labor formalization may be a step that many less productive firms may not be willing or able to take, although an SME-specific labor regime exists in Peru. Under labor regulations, firms must comply with an array of requirements, including social security and health insurance payments (12 percent of the corresponding wage bill), granting paid vacations to their workers (a paid month per year), and making bi-annual deposits for severance payments (8.3 percent of the corresponding wage bill per year) in case of worker dismissal.

These results are also consistent with a higher probability of detection for business informality. In practice, labor regulations are more difficult to enforce, as the Ministry of Labor and its
Table 10: Average Job Quality between 2007-2021, by economic sectors

||||||||Economic Se|ctors|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
||Agricultur|e Mining|Food|Textiles,|Utilities|Construction|Wholesale|Transport and|Financial and|Social and|Hotels and|Public Ad-|
||||and bev-|wood and|||and retail|communica-|enterprise|personal|Restaurants|ministration|
||||erages|others||||tions|services|services|||
|2007|0.15|0.53|0.41|0.58|0.60|0.63|0.35|0.63|0.70|0.73|0.36|0.85|
|2008|0.15|0.69|0.45|0.38|0.84|0.65|0.34|0.62|0.73|0.75|0.35|0.84|
|2009|0.17|0.79|0.46|0.38|0.71|0.66|0.37|0.66|0.71|0.75|0.35|0.84|
|2011|0.18|0.76|0.47|0.41|0.63|0.66|0.36|0.65|0.75|0.76|0.37|0.86|
|2012|0.18|0.80|0.46|0.40|0.84|0.67|0.37|0.66|0.73|0.76|0.36|0.87|
|2013|0.19|0.81|0.46|0.39|0.90|0.68|0.37|0.67|0.78|0.78|0.37|0.88|
|2014|0.16|0.75|0.42|0.57|0.88|0.65|0.32|0.64|0.72|0.74|0.34|0.86|
|2015|0.18|0.77|0.43|0.40|0.82|0.65|0.35|0.63|0.74|0.73|0.36|0.87|
|2016|0.17|0.58|0.43|0.58|0.92|0.66|0.33|0.64|0.75|0.74|0.36|0.88|
|2017|0.15|0.80|0.41|0.58|0.61|0.66|0.33|0.63|0.74|0.73|0.34|0.88|
|2018|0.16|0.80|0.45|0.39|0.92|0.66|0.35|0.64|0.74|0.76|0.35|0.90|
|2019|0.17|0.75|0.47|0.39|0.90|0.67|0.35|0.65|0.75|0.75|0.34|0.92|
|2020|0.18|0.77|0.41|0.58|0.70|0.65|0.35|0.63|0.75|0.76|0.33|0.90|
|2021|0.20|0.73|0.41|0.38|0.87|0.61|0.32|0.58|0.68|0.73|0.34|0.88|

_Notes:_ Based on data from EH 2007-2021. Estimation using a rescaled score from the first principal component of the PCA.
standard modern open-economy models that generate the desired causation from a more depreciated real exchange rate to a higher household saving rate, the requirement for this outcome to emerge in the context of such models -- i.e., that the exchange rate policy results in a front-loaded productivity boost -- appears to be quite stringent. We have described one mechanism that would indeed generate this result, operating through inflation-induced transactions costs, but the empirical relevance of this mechanism remains to be established, and in any case the result emerges only when the exchange rate policy is transitory.

Finally, the literature on cross-country differences in saving rates has not identified exchange rate policy in general, or a depreciated real exchange rate in particular, as an important factor in explaining such differences. Even in the high-saving, high-growth countries that have provided the main impetus for professional interest in the capitalaccumulation channel, explanations for high saving rates have tended to focus on factors such as demographics, financial-sector policies, mandated saving schemes, and fiscal policies, rather than exchange rate policies.

Though we have not focused on it in this paper, the second link in the capital accumulation channel, from higher saving rates to higher growth rates through higher investment rates, appears to be at least as problematic as the first. There are two major difficulties with this link. The first one is empirical: as indicated above, theory is consistent with reverse causation from growth to saving, so a positive empirical correlation between saving and growth rates is subject to more than one interpretation (Deaton 1999). The possibility of bidirectional causality between saving and growth has led several authors to perform various tests of Granger causality with international data, often with the result that growth typically precedes saving, rather than vice versa.<sup>13</sup> Although Granger tests can hardly be viewed as conclusive, the upshot is that time series studies provide little evidence that the positive cross-country correlation between

> 13 The classic reference is Carroll and Weil (1994). Baharumshah, Thanoon and Rashid (2003) reach similar conclusions using data for East Asian countries. Attanasio, Picci and Scorcu (2000) find that the patterns of Granger causality between saving and growth in a large cross-country sample are not robust to minor changes in specification and econometric technique. Rodrik (2000) found in an international sample of saving transitions that increases in saving rates tended to be followed by only temporary increases in growth rates, while increases in growth rates tended to be associated with permanent increases in saving rates, consistent with the East Asian experience summarized in the last section. He interpreted this as suggesting that saving is insignificant as a causal factor for long-run growth, and tends to be an outcome -- rather than a determinant -- of high growth rates. More recently, Mohan (2006) derived a similar result for a large sample of countries at various income levels.

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