rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Differences between Natives and Non-Natives in Rural and Urban Areas

||(1)|(2)|(3)|
|---|---|---|---|
||Difference Rural<br>|Difference Urban<br>|(2)_−_(1)<br>|
||(SE)|(SE)|(SE)|
|Precarious housing|0.225***|0.041***|-0.184***|
||(0.028)|(0.009)|(0.025)|
|Ownership land & house|0.050***|-0.048***|-0.098***|
||(0.016)|(0.006)|(0.017)|
|Years of education [24+)|-1.224***|-0.375***|0.849***|
||(0.145)|(0.101)|(0.140)|
|Use computer [14+)|-0.060***|0.006|0.065***|
||(0.012)|(0.009)|(0.012)|
|Legally married [18+)|-0.166***|-0.058***|0.108***|
||(0.019)|(0.007)|(0.017)|
|Economically active [18-65]|-0.062***|0.004|0.066***|
||(0.016)|(0.005)|(0.015)|
|Formal employment [18-65]|-0.024*|-0.035***|-0.011|
||(0.014)|(0.006)|(0.014)|
|Unemployment [18-65]|0.014***|0.013***|-0.001|
||(0.003)|(0.002)|(0.003)|
|Health insurance|-0.178***|-0.085***|0.092***|
||(0.020)|(0.010)|(0.017)|
|Number of disabilities|0.088***|0.033***|-0.055***|
||(0.012)|(0.004)|(0.012)|
|Teen pregnancy [14-19]|0.058***|0.002|-0.056***|
||(0.015)|(0.005)|(0.014)|

_Source_ : REDATAM INDEC Census Argentina 2010. _Notes_ : SE clustered at department level in parenthesis. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

# **3.3 The role of education in explaining differences in outcomes**

Given that the level of education is an important determinant of socioeconomic outcomes (see, for example, Angrist and Krueger, 1991; Lleras-Muney, 2005), we study whether the differences in these outcomes disappear once we control for the level of education (we consider the education of the parents in the case of minors). Table 5 shows that that is not the case for most outcomes. The only two differences that disappear, or turn around, are in using computers and being economically active. The differences for all the other variables are reduced by controlling for education but they remain statistically significant. On average, the difference in outcomes between Natives and non-Natives is reduced by 43% after we control for the of education. This that the differences in years suggests outcomes between Natives and non-Natives are not only due to differences in years of 11
Policy Research Working Paper 10579

# **Abstract**

Using three rounds of data from the Business Pulse Survey in South Asia, this paper studies the differential effects of the COVID-19 shock on informal firms. It also captures heterogeneity within informal firms based on the degree and motivation of informality. The findings suggest that the severity of the impact of the COVID-19 shock and the recovery speed are strongly associated with the degree of informality. Firms’ external attributes, such as size, sector, age, and gender of the owner, do not explain the depth of the impact. Internal characteristics such as poor management capabilities and education of the manager and owners are strong predictors of vulnerability among informal firms.

In particular, necessity firms experience a larger drop in sales relative to the parasitic type of informal firms. To add to this, the adjustment response (for example, the use of digital platforms) of informal firms is smaller, which perpetuates the gap between formal and informal firms. Within informal firms, the parasitic type typically have a smaller adjustment response. These findings have implications for policies to support the private sector in the presence of informality, including considerations pertaining to targeting, modality of support, and the instruments required for designing more impactful programs during shocks.

This paper is a product of the International Finance Corporation and the Office of the Chief Economist, South 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 agrover1@ifc.org and mpereiralopez@ifc.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
several OECD donor countries – Finland, Japan, Norway, and Sweden – reduced their aid flows substantially after they suffered domestic banking crises in the 1990s. Although Roodman does not provide further analysis, there are reasons to believe that banking crises could have direct effects on aid beyond their effects on incomes.<sup>2</sup> Bank rescues and recapitalizations place massive new fiscal demands on the public sector; even if the government is eventually able to recoup many of the costs of these rescues through asset sales, the short-term effect is to worsen sharply the government’s cash flow. In their comprehensive analysis of systemic banking crises, Laeven and Valencia (2008) estimate that fiscal costs of banking crisis management, even net of recoveries from asset sales, average more than 13 percent of GDP. For the advanced economies, the fiscal cost has typically been less than that – perhaps 3 to 5 percent of GDP (in gross terms) – but Finland is estimated to have spent 12.8 percent of GDP to resolve its banking crisis in the early 1990s. With these additional costs added to the usual cyclical revenue shortfalls from recession, donors may find it more difficult to continue giving aid during and after those crises than they would in a normal downturn of the same magnitude.

This direct banking-crisis effect may be particularly relevant for aggregate aid flows in the current crisis, for two reasons. First, two of the largest donors (in absolute terms), the United States and United Kingdom, were the major economies whose banking sectors were hit by systemic crises in 2007. Second, the banking sectors in other major donor countries have also suffered, even if few are likely to be classified as having entered systemic crises. Two other new papers try to estimate the impact of donor-country financial-sector troubles on aid provision. Mendoza, Jones, and Vergara (2009) use 1967-2007 data for the U.S. only and show that stock market volatility, which they call “a proxy for financial volatility and economic uncertainty,” is associated with reduced ODA. The other new study is more directly parallel to this one: Frot (2009) uses data for a panel of donors for the 1986-96 period to estimate how financial crises affect aid flows. He finds that a banking crisis in a donor country decreases aid by 13 percent (when estimated as a level effect) or that aid falls by 5 percent per year after the onset of a crisis (when estimated in trends). Frot also estimates the

> 2 Devarajan questions this analysis, pointing out that since 1960 the only sustained decline in aid came in the 1990s, and not necessarily for economic reasons (Devarajan, S.  (2008).  "Will the financial crisis reduce foreign aid?" _Africa Can . . . End Poverty (A blog by Shanta Devarajan, World Bank Chief Economist for Africa)_ , October 22.).  He cites results from Paxton and Knack (2008) in arguing that “aid is motivated largely by non-economic factors.”

4
47 For those reporting LPG purchase, the proportion of households using LPG was considerably higher in urban areas and increased steadily in both rural and urban areas with quintile level, with more than four fifths of urban households in the top quintile reporting LPG purchase. The amount purchased per household and per capita increased with quintile level, and was higher in urban than in rural areas in each quintile (Table A2.15). Cylinder sizes of 2.3, 5, and 12.5 kg were available and offered flexibility to households in different circumstances. The coefficient of variation was 0.07 with 95 percent of prices falling within 85–115 percent of the national average price, suggesting that unit prices did not vary much with the cylinder size or by location.

Table A2.15: Number of households purchasing LPG and average quantity purchased per month by purchasing households in Sri Lanka

|**Quintile**|**Number of pu**<br>**Rural**|**rchasing househ**<br>**Urban**|**olds (percent)**<br>**Total**|**Kg purchas**<br>**Rural**|**ed per household**<br>**Urban**|**(per capita)**<br>**Total**|
|---|---|---|---|---|---|---|
|**1**|10,774 (1.5)|5,270 (13)|16,044 (2.1)|5.9 (0.9)|8.6 (1.1)|6.8 (1.0)|
|**2**|41,490 (5.4)|16,833 (27)|58,323 (7.0)|6.0 (1.2)|8.0 (1.4)|5.6 (1.2)|
|**3**|106,465 (14)|41,016 (39)|147,481 (17)|6.1 (1.3)|8.3 (1.6)|6.7 (1.4)|
|**4**|204,554 (25)|89,543 (59)|294,097 (31)|6.1 (1.5)|8.9 (2.0)|7.0 (1.6)|
|**5**|439,585 (54)|221,908 (83)|661,493 (61)|7.2 (2.2)|9.8 (2.9)|8.1 (2.4)|
|**All**|802,868 (21)|374,570 (60)|1,177,438 (26)|6.7 (1.8)|9.3 (2.4)|7.5 (2.0)|

_Source:_ Authors’ calculations. _Note_ : See notes for Table A2.3.

The share of total household expenditure allocated to LPG averaged across all households increased, and that for user households decreased, with quintile level (Table A2.16).

Table A2.16: Shares of total household expenditure on LPG for all households and for purchasing households in Sri Lanka (%)

|**Quintile**|**Rural**|**All households**<br>**Urban**|**Total**|**U**<br>**Rural**|**ser households**<br>**Urban**|**Total**|
|---|---|---|---|---|---|---|
|**1**|0.1|0.5|0.1|3.6|4.2|3.8|
|**2**|0.2|0.9|0.2|2.9|3.6|3.1|
|**3**|0.3|1.2|0.4|2.5|3.1|2.6|
|**4**|0.5|1.6|0.7|2.0|2.7|2.2|
|**5**|0.8|1.6|1.0|1.5|1.9|1.6|
|**All**|0.4|1.4|0.5|1.8|2.3|2.0|

_Source:_ Authors’ calculations.

# **Albania (LSMS 2008)**

The data are taken from the Living Standards Measurement Survey (LSMS) 2008 administered by the Institute of Statistics (INSTAT) in the Republic of Albania. The fieldwork for the survey was carried out between June 2008 and August 2008.

The value of all non-purchased items was about 17 percent of total purchased expenditure, while non-purchased food accounted for about 83 percent of all non-purchased items. The former ratio tended to decline at the upper quintile levels. The latter was
8

# **_2.1 Trade measures are costly also to consumers_**

Figure 2 exposes the fact that domestic support measures only accrue to the producer side (triangle adc), while market price supports (induced by tariffs and export subsidies) distort the consumer and producer side of the market (triangles cdf and hij). If the elasticities of demand and supply were equal, that means the welfare cost of a given tariff or export subsidy would be twice as great as that from a domestic subsidy applied at the same rate.<sup>11</sup> Consistent with Huff and Hertel (2000), we specify the total distortion to production resulting from border measures and domestic subsidies as creating a welfare triangle of losses on the production side, and partition this total between the two on the assumption that the two policies are reduced (or increased) in parallel.

For the OECD countries, the $90 billion in domestic support to producers represents 13.5 percent of the value of their agricultural output at market prices. The rates of market price support are 17.0 percent from OECD import tariffs and 3.3 percent from export subsidies in agricultural and food production (Table 2). Using these rates and assuming unitary<sup>12</sup> elasticities of demand and supply, the total welfare cost of agriculture protection in OECD countries is calculated at $87 billion, from which 8 percent is induced by domestic support, 11 percent by export subsidies, and 81 percent by import tariffs. Using the same process for non-OECD countries, we calculate the cost of protection to producers at $42 billion, from which barely 1 percent is attributed to domestic support and export subsidies so almost all is attributed to market access measures. These back-of-the-envelope calculations yield a world total cost of protection in agriculture of $129 billion, of which 6 percent is attributed to domestic support, 8 percent to export subsidies and 86 percent to market price support (Table 3).

# **_2.2 Variations in rates of assistance add to welfare costs_**

> 11 As well, as pointed out in note a of Table 1, over half of all domestic support globally is at least somewhat decoupled from production. Even though we believe that this decoupling reduces the extent to which domestic subsidies distort production, this is taken into account to only a limited degree in global CGE models. (In the GTAP model, decoupling is incorporated to the extent that payments to land distort output less than other factor payments given that land is specific to the agricultural sector in the model.)Thus, if anything, CGE models should overstate the contribution of domestic support measures to the total welfare cost of protection. Our BOTE analysis also ignores decoupling and similarly overstates the contribution of domestic support to the overall cost of protection.

> 12 The choice of unit elasticities is quite arbitrary. For our interest in the relative importance of different barriers, it is only the relative magnitude of supply and demand elasticities that is relevant.
# Table S1. Effect of GDP per capita on Income Inequality

||(1)|(2)|(3)|
|---|---|---|---|
|Gini Variable is:|Gini<br>Brueckner et al. (2|015)<br>Net Gini<br>Solt (2015)|Market Gini<br>Solt (2015)|
|||Panel A: Dependent Variable is Giniit||
|ln(yit)|-0.09**<br>(0.04)|-0.04**<br>(0.02)|-0.06*<br>(0.03)|
|Hansen J, p-value|0.47|0.40|0.40|
|Country Fixed Effects|Yes|Yes|Yes|
|Time Fixed Effects|Yes|Yes|Yes|
|||Panel B: Dependent Variable is Giniit*ln(yit-1)||
|ln(yit)|-0.54<br>(0.36)|-0.15<br>(0.15)|-0.35<br>(0.23)|
|Hansen J, p-value|0.65|0.37|0.58|
|Country Fixed Effects|Yes|Yes|Yes|
|Time Fixed Effects|Yes|Yes|Yes|
|||Panel C: First Stage for ln(yit)||
|OPS|2.64**|2.64**|2.64**|
||(1.15)|(1.15)|(1.15)|
|TWWI|0.50***<br>(0.09)|0.50***<br>(0.09)|0.50***<br>(0.09)|
|Country Fixed Effects|Yes|Yes|Yes|
|Time Fixed Effects|Yes|Yes|Yes|

Note: The method of estimation is two-stage least squares. Huber robust standard errors (shown in parentheses) are clustered at the country level. OPS is the interaction between the natural logarithm of the international oil price and countries' net-export GDP shares of oil. TWWI is trade-weighted world income. These instruments were used in Brueckner et al. (2015) for estimating the effect of GDP per capita on the Gini.

45
satellite feature set that performed best for each geolocation method (Table 7). Henceforth in this section, each model is referenced by the name of the geolocation method.

Table 7: Testing scenarios used for model training

|**Geolocation method**|**Area threshold**|**Satellite features**|**Out of sample MCC**|
|---|---|---|---|
|Boundary points|0|Optical only|0.21|
|Centroid|0.05|Optical and SAR|0.24|
|Convex hull|0.2|Optical only|0.21|
|Corner|0.05|Optical only|0.23|
|Hull mean|0|Optical only|0.25|
|Plot points|0|Optical only|0.24|
|Plot mean|0.05|Optical only|0.26|

Each model was then used to estimate the probability that each 10-meter pixel in Malawi was maize (a 0 to 1 continuous variable) during the 2018/19 rainy season. The pixel-level maize probabilities were converted into a binary classification using a threshold. Pixels with a maize probability above 0.6 were classified as maize, and otherwise are classified as non-maize. Absent objective data on which to empirically calibrate the classification threshold value, we selected a threshold higher than the typical value (0.5) in order to reduce the overclassification of pixels in maize resulting from the overrepresentation of maize plots in our training data set. Data set users can select a threshold value that suits their use case.

We used these maizeland maps in conjunction with a cropland mask (showing seasonal cropland coverage) trained on crowdsourced land cover labels (see Appendix) over Malawi to estimate which pixels were cropped with maize in a particular season. Specifically, we first used the cropland mask to remove all pixels in Malawi that were not cropped. We then used each of our trained maize classification models to identify cropped pixels where maize was present. The process resulted in seven different maizeland maps, one for each geolocation method. We calculated the sum of maize pixel areas in the country separately for each geolocation method (Table 8).

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18

# **(f) Survey of former employees**

In order to know what happened to the former employees of RFFSA and to check if the resources allocated to training were effective in maintaining the standard of living, RFFSA's management commissioned a detailed monitoring in each region, based on field interviews. RFFSA retained the Rio de Janeiro Catholic University (PUC) to conduct these interviews. This survey began in January 1998, in Sao Paulo. Eventually, it covered 5,334 former employees who left the company between January 1995 and October 1997 and was distributed over 600 municipalities. The reply percentage was over 77% (including 90% of men) which is high but not complete because 1,217 workers moved without a trace. Only seven percent of the workers covered by the sample refused to cooperate The forst survey showed that the average worker interviewed is 43 years old. The education level is quite heterogeneous and higher than the Brazilian average, with only 1 % "analphabet" and 4% with a post-graduate degree. The majority (22%) had a high-school diploma. Almost half (2,301) work on their own and 20% have opened their own business. Only 18% are employees and 4% are civil servants. About 13 % are employed illegally. Most found the training good but irrelevant to their new job. About 44% of these employees have been in their new job for an average of 13.2 months. Overall, they all earn less than when they were working for the railways.

A more detailed survey conducted among 675 formers employee in September 1998 was also quite revealing. Only 10% where unemployed but they count most of the older workers and/or the less educated. Also the salary structure had changed quite a bit. In particular, the survey shows that while 53% of the workers are earning less than when they were at RFFSA, 27 % and making a better living. In general, the dispersion of wages increased for that sample.

# **(g) RFFSA's Remaining Staff Training Program**

With the privatization of RFFSA's operational system, the company underwent a substantial modification of its core business. To cope with the changes and face the company's new attributions, it was crucial to retrain RFFSA's remaining staff. Retraining of the company's employees began in March 1998 and will end in December 1999 This retraining program has been managed by a consulting firm (De Consult) since December 1997. In view of the lack of a clear definition by the federal administration as to what will be RFFSA's corporate objectives or what the country's public transportation policy is, De Consult is concentrating its retraining program on tasks that are now under the responsibility of RFFSA, which are:

- Administration of the company's indebtedness and receivables;

- Sale of assets; and
Table 4.5: Activity when Not Attending School, 2001 <u>(percent)</u>

|Age|Employed|Unemployed<sup>27</sup>|Inactive|
|---|---|---|---|
|15–19|20.1|24.0|55.9|
|20–24|34.6|27.8|37.6|
|15–24|29.3|26.4|44.3|
|25–34|53.4|23.2|23.4|
|35–44|67.2|12.6|20.2|

_Source:_ Own calculations based on ECVH 2001.

Table 4.6: Unemployment Rates, 2001 (percent)

|Age|Male|Female|Total|
|---|---|---|---|
|15–19|46.4|62.9|54.3|
|20–24|38.3|51.4|44.5|
|15–24|40.6|54.8|47.4|
|25–34|24.1|34.5|29.1|
|35–44|11.4|20.2|15.6|

_Source:_ Own calculations based on ECVH 2001.

Unemployment and premature entry to the labor market have a number of negative effects such as lower self-esteem, depreciation of skills, as well as negative effects on earnings potential, lower schooling and human capital stock, and lack of role models for children and youth. In addition, lost tax revenues for the state, reduced household income, and crime and violence (see Section 6) may result from unemployment.

A larger proportion of male youth attend school than females at the same age. Even though this appears to contrast with men as breadwinners it is explained by many girls working in their household, some of whom are not allowed to attend school. With a female unemployment rate 35 percent higher than the male unemployment rate, males are unemployed to a much lesser extent than females (Table 4.6). However, getting a job in Haiti is far from a clear path to fortune or even to a decent standard of living. The quality of jobs is generally low because wages are low and work is often extremely tiring. In rural areas the abundant supply of labor outside harvest season reduces salaries and wages. It is clearly not enough to simply create jobs; it is also important to improve the conditions for those who work.

In Haiti, only 29.3 percent of youth who do not attend school are employed (Table 4.5). For youth who find employment, Figure 4.3 shows the distribution of workers by sector. Two main sectors absorb youth: agriculture and service. Agriculture is by far the main sector for the age cohort 15–19 and absorbs a decreasing proportion of the labor force with increasing age. Public sector and other formal sector jobs are the hardest to obtain because they require education and connections and are therefore practically inaccessible for the majority of youth, especially for those from poor households.

> 27 This is not the unemployment rate because those inactive are included (Table 4.6 shows unemployment rates).

17
as well. However, the author finds no relation between Federal Reserve actions and systemic risk during the first part of the COVID-19 crisis. An international study of systemic risk during the COVID-19 crisis suggests other moderating effects. Duan, El Ghoul, Guedhami, Li, and Li (2021) finds that the increased systemic risk caused by the crisis is moderated by bank regulation, such as deposit insurance; ownership structure, such as foreign and government ownership, and informal institutions, such as culture and trust.

In the interest of brevity, we contrast one large comparable policy program from each crisis – the TARP bailout of banks during the GFC and the PPP bailout of small businesses during COVID-19.<sup>23</sup> Both bailouts were targeted toward where the largest problems were – bank distress in the GFC and the distress of small businesses and their employees during COVID-19. However, key differences are that the PPP was a much larger and more widely applied stimulus and occurred much earlier in its crisis than TARP.

# **5.1 TARP**

The TARP bank bailout passed Congress on its second try in October 2008 as part of Emergency Economic Stabilization Act of 2008 (EESA). Its main goals were to save the real economy and financial system via purchasing up to $700 billion of “troubled assets” for which markets were frozen or asset values were massively depressed. This would allow markets to stabilize, avoid further losses, and encourage banks to increase lending to improve the real economy.

TARP was later changed to a direct bailout of banks. Its main component, the Capital Purchase Program (CPP), invested in the preferred equity of selected financial institutions to enhance their capital ratios. This included an initial total of $125 billion to nine large institutions

> 23 Details of initial policy responses to COVID-19 in the U.S. and around the world may be found in International Monetary Fund (2020).

30