rafmacalaba/datause-encoder-data huggingface dataset:
There are two sets of coping strategies in the survey data. One set implies the general household coping strategies used in the past 14 days, and the other indicates the household actions in to the COVID-19 shocks. We use the specifically response first set of coping strategies in the main analysis and cross check the gender differences in COVID-19 specific household actions in the sensitivity analysis.

As shown in the summary statistics displayed in Table A.1 in the Appendix, overall, the most commonly used coping strategy in Kenya is to reduce food consumption. About 38 percent of the households reduced meals when hit by COVID-19. Apart from that, households also take loans and rely on family transfers to cope. Only about four percent of the households have received any help from governments or NGOs.

We first perform descriptive analysis by estimating the probability of using various coping strategies by male-headed and female-headed households in each survey wave. Afterwards, we use linear probability models pooling all survey waves to formally test the gender differences in coping strategies. Specifically, the model takes the following form.

Where on the right hand side, _Copehct_ is a dummy, indicating whether the household _h_ in county _c_ , interviewed in wave _t_ , has used the specific coping strategy in the past 14 days. On the right hand side, there is a constant _α_ , as well as a dummy, _Female Headht_ , indicating whether the head of household _h_ in wave _t_ is female (= 1) or male (= 0). Moreover, several household characteristics might determine the household coping strategies, including the level of wealth, local social norms and institution. In light of this, a set of household controls, **X** _ht_ , is included in the regression model. These include the household head age, household size, the child-adult ratio, a dummy indicating whether the household is located in urban or rural areas, and a dummy indicating the status whether the household’s has a dirt poverty by examining dwelling space floor or not. Lastly, county fixed effects ( _δc_ ) and survey wave fixed effects ( _θt_ ) are included in the regression to control for all time-invariant characteristics specific to each county as well as all macro shocks at different interview waves, which might affect the choice of coping strategies directly. These might include, for example, different social norms 5
10 Poland increased their **share** in CEE-5 exports of manufactures, with Poland registering the largest increase.'<sup>2</sup> Poland took Hungary's position as the largest exporter of agricultural producs: its share rose from _35_ **percent** in 1984 to 41 **percent** in 1989, while Hungary's share fell from 37 percent to _35_ percent. The FCSK Increased its **dominance** as the largest exporter of raw materials and increased its **share** in **all** product categories except manufactures. Romaia's share declined steeply in **all** product categories except ores and nonferrous metals. So did Bulgaria's share, with the exception of raw materials.

TabLe 3: **Ratios of CEE-S Export Groith Rates to OECD Import Growth Rates by Produt Cateories, 1981-83 to 1990-91**

|1981-83|1984-89|1990-91|1981-83|1984-89|<br>1990-91|
|---|---|---|---|---|---|
|CEE-5||iBult|aria|||
|Foods<br>and Feeds<br>2.35*<br>Mineral<br>Fuels<br>0.74*<br>Ores<br>and Non-Ferrous<br>Netals 1.46*<br>Raw Materials<br>1.31*<br>Manufactures<br>7.12*<br>Total Exports<br>2.31*|<br>1.42<br> <br>-0.30*<br> <br>1.19<br> <br>0.70<br> <br>0.80<br> <br>0.88|<br>1.14<br> <br>-0.98<br> <br>-4.07*<br> <br>-0.77*<br> <br>2.72<br>1.87|2.15*<br>0.20*<br> <br>2.26*<br> <br>1.03^<br>9.28*<br>2.46*|0.82<br>1.67*<br>1.02*<br>1.68<br>0.56<br>0.43|.73<br> <br>(-3.89)<br> <br>-15.98*<br>4.44*<br>2.30<br>1.54|
|Czechoslovakia||Hurm|a|||
|Foods and Feeds<br>"<br>Mineral Fuels<br>V|1.47<br>0.04*|0.85<br>(-0.73)|1.40*<br>-1.74*|1.31<br>-1.97*|1.42<br>0.60|
|Ores and Non-Ferrous Metals<br>0.44*<br>Raw Materials<br>1.00*<br>Manufactures<br>4.18*|1.11<br>0.66<br>0.63|-17.20*<br>3.25*<br>3.80|0.73*<br>-0.31*<br>5.12*|1.51<br>0.97<br>0.86|-0.51*<br>-4.53*<br>2.97|
|Total Exports<br>1.48*|0.76|2.92|1.31*|1.09|2.37|
|Poland||Roman|ia|||
|Foods and Feeds<br>2.68*|1.88|1.26|5.14*|0.32|0.91|
|Mineral Fuets<br>0.53*|-0.09*|1.14|0.10*|-1.16*|(-4.13)|
|Ores and Non-Ferrous Metals<br>**1.83'**|0.98|-6.95*|-1.31*|2.42|11.61*|
|RawMaterials<br>2.00'|0.62|-6.11*|2.42'|0.44|11.39*|
|Manufactures<br>13.52*|1.23|4.10|4.36*|0.63|(-1.11)|
|Total Exports<br>3.64*|1.12|3.09|1.56*|0.67|(-2.56)|

Note: * denotes negative growth rates in OECD imports; (-) denotes a negative growth rate of CEE-5 exports.

Source: Se; Table 1.

The regional composition of CEE-5 aggregate exports to OECD economies remained relatively stable throughout the 1980s. **The shares** going to the **EC-10** (68 percent) and the EFTA (18 percent) declined slightly between the two periods, while the shares going to other OECD counteies increased slightly. These changes were the result of the redirection of exports away from Europe by Hungary and Romania.

12 Poland's share rose from 21 to 27 perceat between 1984 and 1989, whilo Hungary's share increased from 21 to 24 percent. The FCSK, which remained the second largest exporter of manufactures in the CEE-S region in the 1984-89 period, registered a fall from 26 to 25 percent.
Inequality, the Price of Nontradables, and the Real Exchange Rate: Theory and Cross-Country Evidence* By Hong-Ghi Min Poverty Reduction Group The World Bank JEL Classification Numbers: D31, F41 Keywords: Inequality, Price of Nontradables, Real Exchange Rate Author's E-Mail Address: hmin@worldbank.org

* I am grateful to Richard Adams Jr., Robert Cull, and David Dollar for their very helpful comments but all remaining errors are my own.
<!-- Start of picture text --> Access to Electricity Network Access to Impoved Water Sources<br>(% of population) (% of population)<br>100 110<br>87<br>85<br>82 100<br>80<br>93<br>90 89<br>60 60 85<br>80 80<br>80<br>40<br>70<br>64<br>20<br>20<br>15 60<br>0 50<br>Namibia Lower Middle SSA Middle Income Upper Middle World Namibia Lower Middle SSA Middle Income Upper Middle World<br>Income Income Income Income<br>Teledensity<br>(total telephone subscribers/1000 people)<br>800 Road Density in Terms of Population<br>(road-km/1000 people)<br>25<br>635 21.3<br>20<br>501<br>468<br>15<br>400<br>301<br>10 9.2<br>7 6.7<br>182<br>4.9<br>5<br>99 3.3<br>0 0<br>Namibia Lower Middle SSA Middle Income Upper Middle World Namibia Lower Middle SSA Middle Income Upper Middle World<br>Income Income Income Income<br><!-- End of picture text --> 49
with the exception of the quadratic specification for health care expenditures. Table 24, Table 25 and the graphical representation of ECs in Tables 13 and 14 report the results of this robustness exercise. We find that the predicted shapes of the Engel curves, using total expenditure per capita instrumented by income per capita and the control function approach, are consistent with the Engel curves from our baseline model, using income per capita, with the exception of the quadratic specification for health care expenditures.

Overall, the Engel Curves for the expenditure shares of food, education, school materials and the perceived constraint on health services seem to be consistent across different estimation approaches, taking into account the potential endogeneity issues previously discussed.

In the next section, we will compare the results of the EC baseline model with the results from the RDD approach. The baseline model is used as an additional counterfactual and is informative of household behavior under an income expansion without conditionalities. In the interpretation of the comparison, we will focus on the signs of the estimated parameters (more than their magnitude). The results of the IV procedure (using non-labor income as an instrument) and the estimations with total expenditure as an explanatory variable (section 8.7 of appendix) are kept as robustness check for the baseline model.

# **5.2 Is there an impact beyond cash transfer?**

Now that we have an idea of the counterfactual effect of a pure cash transfer in the Brazilian context, we can analyze whether the effect of Bolsa Fam ́ılia goes beyond the pure cash transfer. We apply a specification which is largely similar to the previously estimated Engel curves – a variant of a Regression Discontinuity Design with income as one of our forcing variables. On the one hand, income determines the eligibility to the Bolsa Fam ́ılia program and, on the other hand, income is increased as a result of the program and an important determinant of expenditure shares (see Engel curves). By applying an RDD based on income (net of the transfer), we investigate whether there is a continuous relationship between the outcome variables and income; or whether due to the program, we find a jump in the outcome variables at the eligibility threshold while controlling for initial income. If there is a discrete jump in expenditure share for treated households in this relation which goes beyond what would be expected by the above Engel curves, we can reject the hypothesis that BFP only unfolds its impact through the income effect.

To identify the treatment effect, we exploit exogenous variation in treatment created through the eligibility criteria (see section 8.1) of BFP. The eligibility to receive the cash transfer under 38
**Table 3. Exchange rate regimes, December 31, 1997 (or later)**

|**Country**|**Exchange rate**<br>**regime**|**Basket or target; remarks**|
|---|---|---|
|**_West and Central Africa_**|||
|CFA franc zone<br>Gambia, The|Fixed peg<br>Independent float|Euro (formerly pegged to French franc)|
|Ghana|Independent float||
|Guinea|Independent float||
|Liberia|Independent float|Pegged to the U.S. dollar|
|Mauritania|Managed float|Dual exchange rate structure|
|Nigeria<br>Sierra Leone|Managed float<br>Independent float|Pegged to the U.S. dollar|
|**_East and Southern Africa_**|||
|Angola|Fixed peg|Pegged to the U.S. dollar since July 1, 1996|
|Botswana|Fixed peg|Basket of weighted currencies of regional trading partners and SDR|
|Burundi|Fixed peg|Basket of currencies of main trading partners|
|Congo, Dem. R.<br>Eritrea<br>Ethiopia|Independent float<br>Independent float<br>Managed float||
|Kenya|Managed float|U.S. dollar is the principal intervention currency|
|Lesotho|Fixed peg|South African rand|
|Madagascar|Independent float||
|<br>Malawi|<br>Managed float|Exchange rate is managed in a flexible manner with interventions<br>limited to smoothing out of rate fluctuations and considerations of<br>reserves levels|
|Mauritius|Managed float||
|Mozambique|Independent float||
|Namibia|Fixed peg|Pegged to the South African rand|
|Rwanda|Independent float||
|Somalia|Independent float|Dual exchange rate structure. Official rate applies to goods and<br>services and debt-service payments of the government. The U.S.<br>dollar is the principal intervention currency|
|South Africa<br>Sudan|Independent float<br>Managed float|<br>Extemal value determined in the interbank market|
|Tanzania|Independent float||
|Uganda|<br>Independent float|External value determined in the interbank market|
|Zambia|Independent float|Multible exchange rate structure. Market-determined official rate|
|Zimbabwe|Independent float|External value determined in the exchange market. The US dollar is<br>the intervention currency.|

_Source: IMF_ 1998.

the euro appreciates, an effect that will be stronger the smaller is the euro area's share in total trade (see table 4). Countries that peg to a basket of currencies of their main trading partners will not be quite as exposed and vulnerable to fluctuations in the euro. CFA members are among the most exposed and vulnerable to a loss of competitiveness. If the euro appreciates relative to the U.S. dollar, countries with a relatively low share of exports going to the European Union would be the most affected. Among these are Benin (16.9 %), Togo (15.8 %), Senegal (14.7 %), Guinea-Bissau (14.4 %), and Gabon (12.8 %). Cameroon is the only CFA country with more than half its exports going to the European Union (73 percent).

8
Policy Research Working Paper 8281

# **Abstract**

This paper explores the relationship between the regional business environment, lagging regions, and firm performance in four European countries: Italy, Poland, Romania, and Spain. It combines firm-level data from Orbis measuring sales, employment, productivity, and profitability, with indicators from recently completed Subnational Doing Business studies in the four countries to examine if and how the business environment is associated with firm outcomes, and whether this is mediated by being located in a lagging region. The results indicate that firms located in regions with better business environments indeed display better performance, in employment and sales growth, as well as profitability. Moreover, the paper finds evidence that being located in a lagging region aggravates the impact of a poor business environment on firm performance. The results also highlight that it matters to distinguish between regions that are “low income” (relatively poor) and those that are “low growth” (stagnating but not necessarily poor). Firm performance, the business environment, and the relationship between the two are much stronger in the latter than in the former. Overall, the results suggest that policies that improve the business environment in lagging regions would be beneficial to the performance of firms in these regions, in growth, profitability, and investment.

This paper is a product of the World Bank Social, Urban, Rural and Resilience Global Practice Group, the Finance, Competitiveness and Innovation Global Practice Group, and the European Commission Joint Research Centre. 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 tfarole@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
**Managers, Investors, and Crises: Mutual Fund Strategies in Emerging Markets**

Graciela Kaminsky* Richard Lyons Sergio Schmukler

**JEL: F3, GI, G2.**

Keywords: mutual funds; managers; investors; trading strategies; emerging markets; momentum; feedback trading; crisis; contagion.

Respective affiliations are George Washington University, **UC** Berkeley and **NBER,** and the World Bank. Correspondence to Sergio Schmukler, The World Bank, **1818** H Street NW, Washington, **D.C.,** 20433, Tel: _202-458-4167,_ sschmukler@worldbank.org. We thank the following for valuable comments: Jeff Frankel, Mike Gavin, George Hoguet, Andrew Karolyi, Federico Sturzenegger, and participants at the World Bank/Universidad Torcuato Di Tella conference on Integration and Contagion (June **1999),** the Cancun Meeting of the Econometric Society, the Latin American Economic Association, and the **IDS-U.** of Sussex. For help with data we thank the World Bank (East Asia and Pacific Region), Erik Sirri from the **SEC,** Konstantinos Tsatsaronis from the BIS, and Ian Wilson from Emerging Market Funds Research. For excellent research assistance we thank Jon Tong, Sergio Kurlat, Cicilia Harun, Jose Pineda, and Allen Cheung. (The efforts of Sergio Kurlat and especially Jon Tong were prodigious.) For financial support we thank the **NSF** and the World Bank (Latin American Regional Studies Program and Research Support Budget).
**Table 1: Share of firms that decrease, increase and are stagnant at the country level**

||**Change**<br>|**in employ **<br>|**ment**<br>|
|---|---|---|---|
|**Industry**|**Decreased**|**Stagnant**|**Increase**|
|Other Manufacturing|25%|31%|44%|
|Food|23%|41%|36%|
|Electronics(31-32)|23%|31%|46%|
|Construction|23%|24%|52%|
|Services of Motor Vehicles|17%|32%|51%|
|Hotels & Restaurants|21%|32%|47%|
|Transport(60-64)|15%|32%|53%|
|Real Estate Renting& Business Activities(70-74)|21%|12%|67%|
|Textiles and Garments|36%|21%|43%|
|Chemicals,Plastics & Rubber|18%|28%|54%|
|Non Metallic Mineral Products and Basic Metals|19%|33%|48%|
|Fabricated Metal Products and Machineryand Equipment|19%|26%|55%|
|Wholesale and Retail|18%|33%|48%|

_Source: INTERIM ES, Own calculations_ _Industries with fewer than 30 observations excluded; growth rate over three years; unweighted_

**South Asia’s employment levels are very dynamic.** The growth performance of firms in the sample was impressive, and suggests that companies are very dynamic. This finding that in many industries the median employment growth is greater than zero is striking. Evidence from developed economies such as the US as well as from Europe (incl. catching-up Eastern Europe) states not only that the median growth rate is nil, but also that most firms do not grow (e.g. Schreyer, 2000; Hoelzl and Friesenbichler, 2008).

# **Employment growth at the country level**

**The median firm is typically stable in size. Average growth rates differ from country to country.** The median firm did not grow in most countries, with the exceptions of Bhutan (8%), India (5%) and Bangladesh (1%). Firms that expanded their employment number by far outweighed firms that decreased in size. While firms of most sampled countries increased their employment between seven and nine percent on an annual average, companies in Pakistan and Sri Lanka expanded by 3% and 2%, respectively. The notable exception is Bhutan, where firms increased by an annual average of 38% (see figure 2).<sup>7</sup>

> 7 The result for Bhutan confirms the rapid expansion of the private sector which has been documented in the most recent Investment Climate Assessment on the country (The World Bank, 2010), which found that the median firm in Bhutan increased 25 percent in employment between 2006 and 2008. Large firms of 100 workers or more reported the fastest increases employment.

8
24 companies. If some industries are inherently riskier than others and could not be funded privately, SBA funded businesses would generally be located in different industries than privately funded small businesses. However, the distribution of SBA funde!! businesses in particular industries is almost identical to that of non-SBA funded small businesses. A 1982 study by Armington and Odle' also looks at the employment growth of small businesses and determines that small businesses generally have larger shares of slower-growing industries and appear to have higher g.-awth rates in regions of the country with overall low growth rates. Small businesses also generate a proportionate share of jobs, not an unusually high number of jobs. Similarly, the credit market imperfections argument works if good credit risks are being denied credit. However, the high default rates of SBA loans suggests that these firms are in fact not good credit risks. Gale's study also shows a limited impact on credit allocation, in the order of a 25% larger share of credit allocated to small businesses due to SBA programs, despite a healthy subsidy.

# Tax-exemnt

The fifth category of government credit involvement has grown rapidly in recent years. Little evidence exists on the credit allocation effects of tax-exempt state and local financing. These funds are used to finance home mortgages, industrial development, and student loans prinarily. By Gale's analysis this tax-exempt group draws 8.3% of the overall credit allocation currently. Its share would be around 6.5% without the tax subsidy.

# Export Financing

`;Catherine Armington and Marjorie Odle, "Small Business-How Many Jobs?" Brookings Review, Winter 1982, pp. 14-17.
# **Table A11:  Economist Intelligence Unit (EIU)**

**Data Provider Description Website Data Source Type Respondents Frequency Coverage Public Access Description**

Economist Intelligence Unit Commercial business information provider headquartered in London, United Kingdom www.eiu.com Country Risk Service, Country Forecasts Expert Assessment Network of over 500 correspondents, reviewed for consistency by panels of regional experts Quarterly since 1997 Global sample of countries Commercially available We use data on the components of these two data sources made available to us by the Economist Intelligence Unit. We use data from December of each year with exception of 1996 in which we draw data from first quarter of 1997.

||2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|
|**Voice and Accountability**|||||||||
|Orderly transfers|X|X|X|X|X|X|X|X|
|Vested interests|X|X|X|X|X|X|X|X|
|Accountablity of Public Officials|X|X|X|X|X|X|X|X|
|Human Rights|X|X|X|X|X|X|X|X|
|Freedom of association|X|X|X|X|X|X|X|X|
|**Political Stability**|||||||||
|Armed conflict|X|X|X|X|X|X|X|X|
|Violent demonstrations|X|X|X|X|X|X|X|X|
|Social Unrest|X|X|X|X|X|X|X|X|
|International tensions / terrorist threat|X|X|X|X|X|X|X|X|
|**Government Effectiveness**|||||||||
|Quality of bureaucracy / institutional effectiveness|X|X|X|X|X|X|X|X|
|Excessive bureacucracy / red tape|X|X|X|X|X|X|X|X|
|**Regulatory Quality**|||||||||
|Unfair competitive practices|X|X|X|X|X|X|X|X|
|Price controls|X|X|X|X|X|X|X|X|
|Discriminatory tariffs|X|X|X|X|X|X|X|X|
|Excessive protections|X|X|X|X|X|X|X|X|
|Discriminatory taxes|X|X|X|X|X|X|X|X|
|**Rule of Law**|||||||||
|Violent crime|X|X|X|X|X|X|X|X|
|Organized crime|X|X|X|X|X|X|X|X|
|Fairness of judicial process|X|X|X|X|X|X|X|X|
|Enforceability of contracts|X|X|X|X|X|X|X|X|
|Speediness of judicial process|X|X|X|X|X|X|X|X|
|Confiscation/expropriation|X|X|X|X|X|X|X|X|
|Intellectual property rights protection|X|X|X|X|X|X|X|X|
|Private property protection|X|X|X|X|X|X|X|X|
|**Control of Corruption**|||||||||
|Corruption among public officials|X|X|X|X|X|X|X|X|
|**Country coverage**|**152**|**127**|**125**|**120**|**120**|**120**|**120**|**120**|
|**Year of Publication**|49<br>**2006**|**2005**|**2004**|**2003**|**2002**|**2000**|**1998**|**1996**|