rafmacalaba/datause-encoder-data huggingface dataset:
Unfortunately, this is the best data available in terms of coverage, which could be driven by structural bias affecting the availability of survey data during crisis times – Surveys are not necessarily known to be a priority during macroeconomic crises.

If we then take a more strictly distributional view and ask how the income of households at different levels of the income curve is affected during output collapses associated to a default, our cases point to a regressive relationship. The cumulative drop in the level of national income going to the bottom 10 percent<sup>31</sup> of the distribution is a whopping 9 percent when measured from peak to trough during a t-1 to t+2 window around default.<sup>32</sup> For the bottom half of the distribution (including bottom 10 percent), it drops roughly 6.4 percent. But, on the flip side, households in the top 10 percent gained roughly 8.7 percent – all despite an average cumulative GDP p.c. collapse of roughly 8.5 percent.

These summary statistics are based on 8 cases for which we have data and which present GDP downturns along with the default, since we are trying to better ascertain how regressive an output collapse happening concurrently with a default is.<sup>33</sup> Some heterogeneity remains. In two cases, Russia 1998 and Argentina 2014, the share of income going to the bottom 10 percent increased. For all others, it decreased. In cases like Greece 2011 and Argentina 2001, the share of income received by the least fortunate plunged by far more than overall GDP. In Greece’s case, the bottom 10 percent’s share of income fell by 44% while the output collapse amounted to nearly 18%; for Argentina the bottom 10 percent lost roughly 86% of their share of income while the output collapse amounted to 16.7%.

# **V.** **<mark>Conclusion</mark>**

<mark>This paper uses an augmented synthetic control method to show that sovereign defaults are very costly. The associated drop in GDP p.c. is large and long lived. The results are larger than</mark>

> 31 <mark>Measured as the share of income captured by the bottom 10 percent of the income distribution times real GDP in constant 2011 USD.</mark>

> 32 <mark>Peak is defined as the highest GDP p.c. figure present at either t-1 or t, while trough is defined as the bottom GDP p.c. figure present between t, if the peak is at t-1 and the default happens in the early months of year t, and t+2. Consequently, peak-to-trough windows can be as short as 1 year and as long as 3 years.</mark>

> 33 <mark>In practice, this means we ignore the cases of El Salvador 2017, Dominican Republic 2005, and Uruguay 2003 since the countries’ GDP p.c. continued to grow during the default period.</mark>

31
Table 18. Indonesian Exports, ICT use, and Log of Skills Measures. High-educated workers. 2011-2015

||||||N|on-routine<br>|skill measure<br>|s|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||An|alytical|||Interp|ersonal|||M|anual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.000618||0.000596|0.000472|0.000543||0.000527|0.000435|-0.000452||-0.000439|-0.000377|
||(0.000296)**||(0.000295)**|(0.000314)|(0.000255)**||(0.000255)**|(0.000270)|(0.000236)*||(0.000236)*|(0.000251)|
|Share of hhlds with computer||-0.279|-0.279|-0.294||-0.213|-0.212|-0.223||0.170|0.170|0.177|
|||(0.113)**|(0.114)**|(0.116)**||(0.100)**|(0.101)**|(0.103)**||(0.110)|(0.110)|(0.112)|
|IDN exports per worker *||||0.00295||||0.00218||||-0.00145|
|Share of hhlds with computer||||(0.00147)**||||(0.00137)||||(0.00139)|
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|
|R-squared|0.491|0.493|0.492|0.492|0.330|0.333|0.331|0.331|0.781|0.782|0.781|0.781|
|||||Routine skil|l measures||||||||
|||M|anual|||Co|gnitive||||||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|||||
|IDN exports per worker|0.000138||0.000161|0.000176|-0.000330||-0.000319|-0.000285|||||
||(0.000461)||(0.000459)|(0.000462)|(0.000237)||(0.000238)|(0.000253)|||||
|Share of hhlds with computer||0.282|0.281|0.282||0.147|0.147|0.151|||||
|||(0.201)|(0.201)|(0.203)||(0.117)|(0.116)|(0.117)|||||
|IDN exports per worker *||||-0.000337||||-0.000796|||||
|Share of hhlds with computer||||(0.00185)||||(0.00133)|||||
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|||||
|R-squared|0.942|0.942|0.942|0.942|0.773|0.774|0.773|0.773|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, Philippines STEP survey 2015-2016, and US Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. High-educated workers: workers with at least complete secondary education. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and province linear time trends. Robust standard errors between parentheses clustered by provinceindustry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

37
ownership of land in Uzbekistan, though privatization plans have also been announced by the government.

The L2CU study included a survey experiment to further establish the degree of popular support for reform of urban housing policy and domestic migration limits. The objective of the experiment was to examine the differences between four “treatments” in which the wording of a short vignette varied slightly. In each vignette, the introduction and the outcome were the same: “I would like to ask you to imagine you read about this fictional scenario in a newspaper: More than 2,000 workers moving to Tashkent struggle to find housing”. However, for each treatment arm, a different impediment to mobility was mentioned. These impediments are listed in table (5). In each round, respondents were randomly assigned to one of four treatment groups, or the control group. In the control group, no impediment to mobility was mentioned. Participants were then asked a question as to what they think the government response should be. There were four options: i) do nothing, ii) restrict the number of workers allowed to relocate, iii) modify government regulations or procedures to help workers relocate (propiska), iv) directly provide subsidized housing.

**Table 5: Description of Experiment Treatment and Control Arms**

|||Target|
|---|---|---|
|Treatment Group||Sample|
|T1= Registration|due to...official registration difficulties|300|
|T2= Rent|due to...lack of available rental apartments|300|
|T3= High prices|due to...high prices of either renting or buying apartments|300|
|T4= Construction|due to...pace of housing construction|300|
|C= Control|“More than 2000 workers move to Tashkent”|300|
||Total|1500|

The results can be interpreted by comparing the responses of the treatment arms to those of the control group in Table (6). They suggest that respondents become more supportive of removing propiska limitations and providing more subsidized urban housing specifically when registration difficulties or the pace of housing construction are mentioned. Further restricting migration was already unpopular in the control arm and becomes even less popular when either propiska limits or the slow pace of urban construction was mentioned. Column (6) shows that support for propiska reform rose by 6 percentage points when registration difficulties were mentioned. Column (8) shows that support for additional subsidized housing in urban areas rose by a similar amount when the pace 38
Policy Research Working Paper 9065

# **Abstract**

After the tragic factory collapse of Rana Plaza in 2013, both the direct reforms and indirect responses of retailers have plausibly affected workers in the Ready-Made Garment (RMG) sector in Bangladesh. These responses included a minimum wage increase, high profile but voluntary audits, and an increased reluctance to subcontract to smaller factories. This paper estimates the net impact of these responses using six rounds of the Labor Force Survey and a triple difference approach that compares garment workers to non-garment workers, in districts containing the vast majority of export garment factories versus other districts, pre versus post Rana Plaza. As intended by the reforms, we find that increased international scrutiny improved working conditions by 0.80 standard deviations. In contrast with - what the theory of compensating differentials would sug gest, we do not find that workers’ wages were negatively impacted: instead, the post-Rana Plaza responses increased wages by about 10%.

This paper is a product of the Social Protection and Jobs Global Practice. 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 lbossavie@worldbank.org or ycho1@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
exported, fixing of prices and levels of bidding on foreign contacts and the selection in advance of the firm that will submit the lowest bid.

Using extensive time series information on unit values for homogenous goods, this paper first examines the distribution of import prices paid by developing countries whose trade is highly concentrated with a major exporting country (France), and compares these prices with those paid to France by other countries whose imports come from more diversified sources. 3/ Where evidence of "excess" prices are found the paper attempts to quantity the overall level of economic costs involved. In addition, the analysis employs correlation and regression tests to account for (quantify) the influence of other economic and institutional factors such as the degree of market concentration, size of the importing market, or the number of alternative trading contacts on relative prices. Next, the paper attempts to determine if other European countries (e.g., Belgium, Portugal and the United Kingdom) follow similar pricing policies with former colonies). The paper closes with an overall assessment of the findings for developing countries trade and commercial policies and also suggests some lines of related research that appear to have a high priority.

- 3/ There is a potentially important inter-active effect between the influence of market structure on. prices and investment links between French exporting firms and associated enterprises in the developing countries. Given the formal institutional links that exist between exporting and importing firms, the latter could have little incentive (or capacity) to turn to non-French sources for imports even if they were more competitively priced. While an analysis of the precise relation(s) between French firms and their associated companies in developing countries is beyond the scope of the present study, the empirical results of this investigation strongly suggest the matter warrants further analysis.
-6-

(Botswana, 1976), **Zaria** Region, Northern Nigeria (Natlon, Simmons and Hays, 1982), and farm households in Gambia (IL, 1985b). Noreover, some activitie&, such as female-dominated food preparation, do appear consistently to enhance income equality (see Simmons, 1976a; Norman, Simons and Hays, 1982; Botswana, 1976; and Nilimo and Fisseha, 1986). Ultimately, even if the impact of nonfarm earnings on relative income equality is uncertain or negative, access to nonfarn earnlngs does nonetheless improve the absolute incom levels of the poor.

# IV. CHARACTISTICS OF RURAL NO1ARM ENTERPRISZS

# A. Al"g

Whether they use employment or capital investment as the yardstick, studies of African rural nonfarm activity overwhelmingly conclude that nonfarm enterprises are small (see Page, 1979; Chuta and Liedholm, 1979; Anderson and Leiserson, 1980; Page and Steel, 1984; Liedhols and Head, 1986). In countries such as Burkina Faso, Ghana, Kenya, Sierra Leone and Zambia, one-person enterprises constitute about half of all rural nonfarm enterprises; and well over 95 percent employ five people or less (Wilcock and Chuta, 1982; Thcmi and Yankson, 1985; Freeman and Norcliffe, 1985; Williams and NcClintock, 1981; Liedholm and Chuta, 1976; Milimo and Fisseha, 1986).

The large-scale firms that do operate in rural areas are frequently limited to trading establishments--either distributors or wholesalers of major agricultural commodities--and resource-based menufacturers who take advantage of weight-reducing production processes or specialize in the preliminary processing of perishable commodities. Studies in Ghana (Andrae, 1981), Kenya (Freeman and Norcliffe, 1985), and Zaire (Congo, 1968) clearly point to the concentration of large-scale rural industries in sawmilling, mining and food processing.
During the first stage, the equation (4b) is estimated using Stata’s command for mixed-effects maximum likelihood regression. In the second stage, the squared residuals and their squares sum from equation (4a) may be regressed on Xij and Zj : The estimated coefficients of equations (4a), (5a), (5b) and (5c) may then be used to estimate the expected 2 2 2 mean and the expected idiosyncratic σeij , covariate σuoj , and the total σuoj+eij of a household’s consumption based on the observed characteristics of both the household and the community.

Assuming that consumption is log-normally distributed, one can estimate the probability of consumption falling below the poverty line z and using a threshold for this probability (for example 50 percent) to define who is vulnerable to poverty.

The probability of consumption falling below the poverty line z is estimated by assuming that consumption is log-normally distributed, where X and Z are household and community level characteristics i.e.

Where ∅ denotes the cumulative density of the standard normal distribution function; lnz, the log of poverty line; lncij⌃ the expected mean of per capita (log) consumption.

Expression (6) can be used to derive an estimate of the vulnerability to poverty from covariate or 2 2 community level shocks by replacing (σu0j+ eij) by σu0j , while an estimate of the vulnerability to poverty 2 2 from idiosyncratic shocks by using σeij in place of (σu0j+ eij) .

The last step to identify a vulnerable household involves a selection of a threshold for the probability of being poor. Following previous empirical literature (Gunther and Hartgen, 2009; Skoufias, Vinha & Beyene, 2021) households are considered vulnerable if they have a probability of 50 percent or higher to fall below the poverty line at least once in the next two years. This is equivalent to a 29 percent or higher probability in any given year. This threshold is chosen in the current study.

# 3. Data

## _Uganda National Household Survey 2019/20_

The data used in this paper is based on the Uganda National Household Survey (UNHS) conducted in 2019/20 by Uganda Bureau of Statistics (UBOS). This survey is the seventh in the series of household surveys conducted by UBOS. Previous Uganda National Household Surveys were conducted in 1999/2000, 2002/2003, 2005/2006, 2009/2010, 2012/2013 and 2016/2017. UNHS is the only survey used to monitor national poverty numbers in the country. The main objective of the survey is to collect high quality and timely data on demographic, social and economic characteristics of the household population; to inform as well as monitor international and national development frameworks.

6
# **<mark>Introduction</mark>**

<mark>Female-owned microenterprises have been promoted in development policy circles as a powerful engine of economic growth, poverty alleviation, and a pathway to the reduction of gender inequality (Agyapong, 2010; Fox and Sohnesen, 2012; Kepha et al., 2013; Muzondi, 2014). At the same time, research has documented gender gaps in microenterprise business performance and investment, which can serve to reproduce gender inequality and hinder economic growth. In a number of experiments that attempt to reduce capital constraints faced by microentrepreneurs, women appear less likely than men to invest available capital in their business (De Mel et al., 2009; Fafchamps et al., 2014; Berge et al., 2015). Recent research has increasingly pointed toward intrahousehold dynamics as an important mediating factor in women’s business development, potentially influencing women’s business management and investment decisions (Bernhard et al., 2017).</mark> <mark>Building on this research, this paper investigates how women’s business decisions are influenced by their relationships, particularly those between spouses. Based on an in-depth qualitative study of women microentrepreneurs in urban Ghana, we explore the intertwined structure of intrahousehold relationship and economic resource management practices, and the implications of that structure for women’s business. We find that women’s efforts to arrange their relationships to best meet their short and long-term needs often influence the decisions they make about their business in several ways. In particular, business decisions are influenced by: 1) a desire to maintain pressure on their partner to take responsibility for certain expenses; 2) attempts to fulfill normative expectations regarding daily provision of needs for the family; and 3) a need to prepare for long-term security.</mark> This paper, therefore, begins t <mark>o address a gap in the development economics literature on how and why intrahousehold economic management</mark> 2
nevertheless necessary to take into account characteristics such as firms’ size, global orientation or sector specific activities. The firms’ characteristics in the model are defined as:

- Firm size: measured here as the natural logarithm of the number of employees.

- Export orientation: used as a proxy of how globally oriented the firm is. Being open to global competition leads to a higher probability of being innovative, by introducing new products or processes to survive the increased competition (this variable is not available in the Colombian survey).

- Sector dummies: to control for sector specific activities.

- Firm age (for Chile): defined as 2001 minus start up year (natural log)

- Capital location (for Colombia): this variable defines whether the firm is located in the federal district of Bogota.

- (ii) **Human capital:** Human Capital normally refers to the “knowledge” embedded in the employees or workers of a firm. A comprehensive definition includes both “general” knowledge, in terms of formal education, and “firm-specific” knowledge acquired through work experience. Normally the education level of employees is the most commonly used proxy for human capital. This type of data is available in the Colombia survey. Unfortunately, in case of Chile, the survey does not ask any question about the educational level of the employees but it asks how important, in a scale from 0 to 4,  the production knowledge of  managers, supervisors and  workers is in fostering innovation capacity in the firm. Therefore our variable will be defined as a dummy, with value 1 if the response to the question was 3 or 4 on the scale range, and 0 otherwise. There are two observations regarding this variable for the Chilean survey that are worth noting: the first is that the question asked in the survey refers to knowledge of the production methods and not knowledge in terms of educational level, therefore is more linked to a “firm-specific” type of knowledge, of the same type that could be proxied by the number of years of work in the firm or in firms working in a related field; the second observation is more relevant, and it refers to the nature of the variable. This variable is in fact a purely perceptional proxy for human capital, since we implicitly assume that the firms’ high rating of production knowledge of managers, supervisors or workers represents the actual embedded knowledge of these groups. This is quite a strong logical jump and therefore the results coming from this variable should be interpreted with caution. This observation applies to most of the variables of the Chilean survey that have a qualitative and perceptional nature.

Other explanatory variables that affect the educational level of workers are those regarding training and re-education. Normally, data on the quantity and frequency of training could be a proxy of how updated is the level of technical knowledge inside the firm, but unfortunately this kind of data is not available in our survey. What is available in fact is the type of training (organizational, innovation and development, etc.) for Colombian firms and a qualitative type of data regarding 10
||**ESPS**|||**PSF**||
|---|---|---|---|---|---|
|**Respondent**|**Number and list of items**|**Recall period**|**Respondent**|**Number and list of items**|**Recall period**|
||subproducts; Vegetable<br>(olive, cotton, sesame) and<br>peanut oil; Other oils<br>(palm,...); Tomato<br>concentrate; Fresh<br>tomatoes; Vegetables and<br>tubers; Condiments and<br>seasonings; Fresh fish;<br>Smoked and dried fish; Red<br>meat; Poultry; Sugar;<br>Coffee; Tea; Cola; Non-<br>alcoholic beverages (water,<br>coke, sprite...); Local fruit<br>juices; Alcoholic beverages;<br>Bread (wheat, millet);<br>Cakes and cookies; Milk<br>(fresh and concentrate);<br>Other dairy products;<br>Fruits; Meals and other<br>foods consumed outside.|amount over the<br>12 last months.||the act of purchasing may<br>be delegated). A total of 25<br>food items are<br>distinguished: Breads; Rice;<br>Millet and sorghum; Other<br>cereals; Oils; Fish; Meat;<br>Vegetables; Fruits; Sugar;<br>Potatoes; Cassava; Other<br>tubers; Milk; Butter; Eggs;<br>Salt; Coffee; Tea; Drinks;<br>Other food products;<br>Prepared meals; Food<br>consumed outside;<br>Beverages consumed<br>outside; Meals taken<br>outside;|from every day to<br>every year.<br>Respondent is asked<br>about the usual<br>expenditure.|

4