rafmacalaba/datause-encoder-data huggingface dataset:
level. They find that the poor are most likely to reduce savings and least likely to engage in behavioral change. Decerf, Ferreira, Mahler and Sterck (2020) provide global mortality and poverty estimates and estimate that the average number of additional years spent in poverty due to COVID-19 will be about 15 times greater than the number of lives lost. Busso, Camacho, Messina and Montenegro (2020) focus on social assistance to households in Latin America and find a substantial coverage gap in the 2nd and 3rd quintiles. Similarly, Lustig, Pabon, Sanz and Younger (2020) report evidence on the impact of lockdowns and expanded social assistance in Argentina, Brazil, Colombia, and Mexico. They conclude that impacts were worst for households in the middle of the ex-ante per capita income distribution.

At least two studies examine real-time private sector data to document impacts. Abay, Tafere and Woldemichael (2020) use Google Search data to estimate the demand for various services in 182 countries and find substantial contraction in demand for services such as retail trade, restaurants, and hotels. Meanwhile, Sampi and Jooste (2020) use Google Mobility data for nowcasting economic activity in the Latin America & the Caribbean region and find that it predicts falls in industrial production.

Several related papers consider the ability of different workers to work from home in a wide set of countries. Garrote Sanchez et al. (2020) examine the EU and find that jobs most at risk account for 30 percent of all EU employment and tend to be filled by lessskilled workers. Gottlieb, Grobovˇsek, Poschke and Saltiel (2020) find that only about 20 percent of urban workers can work from home in poorer countries, versus 37 percent in rich countries. Hatayama, Viollaz and Winkler (2020), using a different sample of 53 surveys, confirm that more developed countries have a greater share of jobs amenable to working from home. Delaporte and Pe ̃na (2020) study working from home in Latin American and Caribbean countries and estimate that the share of workers that can work from home varies from 7 percent in Guatemala to 16 percent in the Bahamas. Another set of global studies considers policy responses to the pandemic and their consequences. Alon, Kim, Lagakos and VanVuren (2020) develop a macroeconomic model and conclude that blanket lockdowns are 8
9 Table 2 Female Participation by Selected Sample Characteristics (percent)

|Characteristic|Argentina<br>1985|Chil<br>1987|Colombia<br>1988|Costa Rica<br>1989|Ecuador<br>1987|Guatemala<br>1989|Panara<br>1989|Peru<br>1990|Uruguay<br>1989|Venezuela<br>1987|
|---|---|---|---|---|---|---|---|---|---|---|
|Education<br>|||||||||||
|Less than Primary<br>|21.7<br>|na|11.0|16.9|44.0|21.4|10.0|38.6|28.9|31.8|
|Primary<br>|31.4<br>|23.7<br>|20.0|21.6|46.0|22.4|14.1|38.7|34.7|34.4|
|Secondary<br>|32.6<br>|32.5|34.0|30.9|47.0|40.7|33.6|40.0|46.9|62.8|
|University|57.6|60.7|53.0|38.1|49.0|47.2|47.7|63.2|54.1|87.4|
|Marital Status<br>|||||||||||
|Single<br>|55.9<br>|40.8||40.4||33.0||47.3||56.2|
|Married|24.5|13.9||17.7||14.1||33.1||34.3|
|Number of Children<br>|||||||||||
|None<br>|37.2|28.6|25.0|25.2|49.0|22.3|26.7|42.6|39.8||
|One|33.6|23.0|}<br>20.0|24.4|45.0|21.3|23.7|38.2|32.5||
|Two|30.2|18.0|}|23.5|42.0|20.3|20.1|34.0|25.9||
|Household<br>|||||||||||
|Head<br>||37.8|47.0|34.1|65.0|30.3|57.2|57.9|65.8|64.7|
|Not Head||na|21.0|22.7|43.0|19.6|20.4|na|34.2|41.7|
|Residence<br>|||||||||||
|Rual<br>||17.9||19.9||15.6|17.8|||32.7|
|Urban||na||28.9||29.7|29.6|||45.6|

(See Psacharopoulos and Tzmaatos (1991), Table 5.) Note: Numbers are marginal effects or predicted probabilities of participation, controlling for other variables present in the Logit function.
**Table 2. Characteristics of the poor, vulnerable, middle class, and upper class in Mexico, 2012** **<u>(cont’d)</u>**

||**< $4**<br>**(poor)**|**$4 – 10**<br>**(vulnerable)**|**$10 – 50**<br>**(middle)**|**> $50**<br>**(upper)**|**Total**|
|---|---|---|---|---|---|
|**Food security**||||||
|Food security|34.0%|49.9%|75.4%|95.0%|56.1%|
|Low food insecurity|26.5%|23.4%|14.5%|3.5%|20.6%|
|Moderate food insecurity|21.3%|16.1%|6.5%|0.9%|13.7%|
|Severe food insecurity|18.3%|10.5%|3.6%|0.5%|9.7%|
||_100%_|_100%_|_100%_|_100%_|_100%_|
|**Shock incidence during 2002-2005(by social**|**groups as of 200**|**5)***||||
|Anyshock|30.3%|27.2%|23.7%|16.7%|27.0%|
|Death|9.4%|7.9%|6.9%|5.3%|8.1%|
|Health shock|12.7%|12.1%|12.2%|10.6%|12.3%|
|Business bankruptcyor unemployment|7.6%|9.2%|7.8%|5.3%|8.3%|
|Natural disaster resultingin:||||||
|Loss of dwelling|1.5%|0.6%|0.7%|0.0%|0.9%|
|Loss of crops|5.8%|2.1%|1.5%|0.0%|3.1%|
|Loss of livestock|2.5%|1.2%|1.6%|0.8%|1.7%|
|Loss of dwelling,crops and livestock|8.2%|3.4%|3.2%|0.7%|4.8%|

_Source_ : Author’s estimations based on ENIGH 2012. * Author’s calculations based on data from MxFLS panel database.

The MxFLS data set shows the incidence of self-reported shocks experienced by households between 2002 and 2005, and that may contribute to poverty transitions. As expected, health shocks top the list of livelihood impacts in these surveys, bearing a strong resemblance to other similar studies for Mexico. We cannot rule out the possibility that worse-off households report more impacts as these groups have less means to fight any source of distress by definition, but the occurrence of deaths, health shocks requiring hospitalization, weather shocks and, in general, any kind of shock, are somewhat stable through the different social groups suggesting that the entire population is prone to negative shocks. While not reported here, such stability in the occurrence of shocks remains for households experiencing different patterns of poverty transitions or economic mobility. Yet, those closer to poverty face a higher risk of falling back into poverty (or at least, of experiencing some degree of impoverishment) as a result of shocks. During the reported period half of the vulnerable households were food insecure and a little more than a quarter reported a shock. Another interesting result from Table 2 is that bankruptcy or unemployment was higher than any other economic group among the vulnerable.

In sum, according to the profiling of the economic group comprised by households prone to fall into poverty described in this section, the vulnerable are more likely to reside in urban areas and be engaged in wage activities, most likely in the informal sector (and to a lesser extent in the primary sector), with almost half of them under food insecurity and perhaps more likely vulnerable to economic contractions.

14
According to reports by the SECOFI and PROPICE,<sup>49</sup> the increase in antidumping investigations resulted from other economic events taking place at the time Mexican trade was further liberalized —namely fluctuation of some goods in international markets, recession periods in industrial countries and other events that led to the restructuring of the world trade, forcing some countries to look for new export markets and to compete under unfair trade conditions.<sup>50</sup> Figure 3 illustrates how some major economic and political events occurred simultaneously in Mexico during the peak of antidumping activity.

**Figure 3 - Investigations Initiated in Mexico, 1987-2003**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>DGPCI UPCI<br>Law Regulating Art. 131<br>WTO’s establishment<br>NAFTA’s entry into force<br>Foreign Trade Act<br>Mexico’s accession to the GATT<br>NAFTA negotiations concluded<br>Investigations initiated<br>1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> Source: 1987-1992 and 1994-2002, UPCI; 1986, 1993, 2003, DOF.

# **2. Breakdown of Investigations by Country and Sector**

Mexico has filed proceedings against 49 countries,<sup>51</sup> three of which account for 54% of all the investigations. The United States comes in first place (27% of all initiated cases), followed by China (18.3%), and Brazil (10%) (see Table 5 below). This ranking changes when the analysis

> 49 According to the PROPICE, there were three decisive factors that account for antidumping activity in Mexico in the 1991-1994 period: (i) the effect of the international economic cycle on the raw material markets and the expansion in the related industries of surplus exports at very low prices during the world recessive economic cycle lasting from 1990 to 1993; (ii) the increasing role played by socialist countries in international trade; (iii) the almost uninterrupted reduction (from 1988 to the end of 1993) of the real exchange rate, when domestic competitiveness dropped and import incentives rose.

> 50 SECOFI, (1997). _Informe de Labores de la UPCI_ , p. 19.

20
32%; Millicom (marketed as Tigo), 23%; Zantel, 7%; and TTCL, 1%.<sup>42</sup> As documented in Worley, Vodacom Tanzania is a subsidiary of South Africa-based Vodacom (Pty) Ltd, which owns 65% of the company. The remainder is held by Tanzanian companies Planetel Communications (16%) and Caspian Construction (19%). Regarding MIC Tanzania (also known as TIGO), Luxemburg-based Millicom International Cellular (MIC) owned 84% of the company until it assumed 100% ownership in early 2006 when it bought out other minority shareholders. The Indian company Airtel bought the shares of the Zain group in Celtel Tanzania. Celtel Tanzania Ltd was 60% owned by Celtel International and 40% owned by the Tanzanian government. Regarding Zanzibar Telecommunications Corporaton (Zantel), Etisalat of the UAE acquired a 34%. stake, with the government retaining 18%. The other shareholders are Kintbury Investment of the Channel Islands (24%) and MEECO International of Tanzania (24%). Finally, TTCL is 65% owned by the government of Tanzania and 35% owned by Airtel.43

**This implies the following shares for the sectors of our model: Tanzania, 29%; European Union 24.7%; SADC, 24.1%; and Rest of the World, 22.2%.**

# **Rwanda**

Rwandatel has an estimated 11 percent of the fixed plus mobile market and MTN Rwanda has 89 percent. Rwandatel is 99 percent owned by a consortium of US investors, with the remaining 1 percent Rwanda owned. MTN Rwanda is a South African company with headquarters in Johannesburg.

Based on these data, the ownership shares of communications sector in Rwanda are: **USA= 10.9%; SADC= 35.6%; Rwanda= 53.5%.**

# **Uganda**

The Uganda Communications Commission (2012) reported the active mobile and fixed line service suppliers in 2011-2012, along with their assets. Based on their assets, the telecom companies and their market shares in 2011 were the following: Airtel, 15.4%; MTN Uganda,

> 42 Tanzanian Communications Regulatory Authority, “Quarterly Telecom Statistics,” September 2013. Available at: <u>http://www.tcra.go.tz/images/documents/telecommunication/telecomStatsSept13.pdf. In addition, Benson had a</u> miniscule share of 0.002%.

> 43 See: http://www.telegeography.com/products/commsupdate/articles/2013/02/13/tanzanian-govt-considers-plan-totake-100-ownership-of-ttcl/

106
their θ s are higher than those of firms with higher variable trade costs, even though both types of firms serve the same market. Thus, once we allow private characteristics of individual firms to generate variation in variable and fixed trade costs, lowering trade costs also increases the export intensity of individual firms.

# **3.  Data**

The empirical part of this study uses the firm-level World Bank Investment Climate Survey (ICS) data from the manufacturing sectors of seven Sub-Saharan African countries: Benin, Ethiopia, Kenya, Madagascar, Senegal, Tanzania, and Uganda.<sup>9</sup> These seven countries were chosen on the basis of availability of detailed information on firm-level export destinations in ICS. The ICS data are collected to prepare Investment Climate Assessments (ICAs), diagnostic reports intended to serve as the basis for policy reforms that will help improve these countries’ business environments with the support of the World Bank.<sup>10</sup> The data are collected through firm surveys that include a common set of questions for all countries surveyed, supplemented by country-specific questions to help each country assess its investment climate. The sample is selected by a stratified random sampling method controlling for size, sub-sector, and geographic distribution, based on the company registration records or manufacturing census information available from the government. More than 80 countries have been surveyed since the program started in 2002. The sample size varies, ranging from about 100 firms for some small African countries such as Lesotho to more than 1,000 for countries such as India, China, and Brazil.

The World Bank ICS data are comprehensive in covering firms’ business performance (production, sales, raw material purchases); access to and conditions of factor markets (labor, capital); business environments surrounding the firms (administrative barriers, infrastructure problems, informal transactions such as bribes); and participation in various government-sponsored business incentive programs such as tax exemption schemes. The ICS survey data are unique in allowing researchers to link firm-level microeconomics, such as productivity, employment, investment, and supplier relationship, with the institutional aspects of private sector development each industry faces.

According to the ICS data, a relatively limited number of firms in Sub-Saharan Africa actually export their products. Figure 1 shows percentage shares of exporters in the total sample of manufacturing firms ( _export participation rate_ ) in the seven Sub-Saharan African countries in this study, as well as average export values in the total sales (average _export intensity_ ), shown separately for destination markets. Among 2,039 manufacturing firms in the ICS data of the seven African

> 9 The World Bank ICS has been renamed as the World Bank Enterprise Survey.  Visit <u>www.enterprisesurvey.org</u> for more information.

> 10 ICA reports for Sub-Saharan African countries are available from the World Bank Africa Regional Program on Enterprise Development (RPED) website http://www.worldbank.org/rped).

10
information on court cases against firms to inform consumers (see MacLeod, 2006). However, as our focus is to study the _interdependence_ between alternative enforcement institutions, we abstract from these issues.

# **3 Reputation and Legal enforcement as Endogenous Institutions**

We now let consumers invest in their own connectedness to increase the probability with which they are informed about the firm they are trading with. Similarly, we let firms choose how much to bribe court officials to decrease the probability of having to reimburse consumers. To be sure, a more general model would conceivably allow for consumers and firms influencing both variables _Q_ and Φ. Nevertheless, what we want to capture here is the fact that consumers have a comparative advantage in investing in connectedness, while firms have an advantage in bribing.

We begin by describing the consumers’ maximization problem. Let _mc_ ( _i_ ) denote consumer _i_ ’s investment in her own connectedness. Then individual connectedness is equal to _q_ ( _mc_ ( _i_ )), where _q_<sup>_′_</sup> _>_ 0, _q_<sup>_′′_</sup> _<_ 0, and to exclude corner solutions we assume that _q_ satisfies the Inada conditions _q_<sup>_′_</sup> (0) = _∞_ , _q_<sup>_′_</sup> ( _∞_ ) = 0.<sup>10</sup> In deciding how much to invest, consumers take the price _p_<sup>_NM_</sup> , average connectedness _Q_ , and judicial efficiency Φ as given, so that for constant values of _θ, Q,_ Φ the consumers’ maximization problem is: Notice that each consumer faces the same maximization problem (4). Thus, _ex post qi_ = _Q_ , and we can use the first order conditions of the maximization problem to characterize the average level of connectedness _Q_ : Proposition 2 _For each_ ( _θ,_ Φ) _there exists a unique level of connectedness Q resulting from the consumers’ maximization problem (4). Moreover, ∂Q/∂θ <_ 0 _, and ∂Q/∂_ Φ _<_ 0 _._ Intuitively, consumers invest in connectedness to be informed about bad firms in

> 10In a previous version we considered the case where _qi_ also depends on _average_ investment 1 _Mc_ = 0<sup>_mc_(</sup><sup>_i_)</sup><sup>_di_,capturingtheideathatindividualconnectednessincreasesproportionallymore</sup> if other consumers also invest in their own connectedness. This adds significant algebraic burden while conveying similar results.

8
household income for the bottom 40 percent of the income distribution, from 34 percent in 2002 to 45 percent in 2012. For households at the top 60 percent of the income distribution, this share remained constant, close to 50 percent.

**Figure 4: Per capita income and labor earnings in the Russian Federation, 1995-2012** _(at 2011 constant rubles)_

<!-- Start of picture text --> 20,000<br>16,000<br>12,000<br>8,000<br>4,000<br>0<br>Mean Monthly Wages Median Monthly Wages<br>Per capita income<br>1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> _Source:_ RLMS (Rounds VI to XXI) _Notes:_ Mean and median monthly wages are calculated for currently employed wage workers in the 18-60 age group, reporting over 140 worked hours in the last 30 days. Monthly wages were trimmed at the bottom and top 0.25 percent in each round, to remove outliers. Average per capita income is calculated for the entire population using the variable ‘ _tincm_n’,_ divided by the number of household members and adjusted to 2011 constant rubles. Wages and income for rounds VI and VII were divided by 1,000 to account for the nominal revaluation of the ruble in January 1998 (1,000 old rubles = 1 new ruble). All values are expressed in 2011 rubles using WDI consumer price index. The RLMS was not administered in 1997 and 1999.

5
care of by a pesticides tax. It was found that under all plausible externality cost estimates, this resulted in a "win-win" situation since cotton remained (or became more) competitive _aD domestic externality costs were significantly reduced. One major difference between the two approaches was that the efficiency gains within cotton available under IPM were left largely unexploited under the second approach.

In sum, given the basic objective of correcting the externalities generated in the process of cotton cultivation, implementation of an IPM program seems to be the best strategy.

# XI. CONCLUSIONS AND SUGGESTIONS FOR FURTHER RESEARCH

# Summary and Policy Recommendations

In this report we have pointed out the most important externalities arising in the process of pesticide use in cotton cultivation in a theoretical framework. Then, a preliminary quantification has been attempted for some of them. Some of the important points emerging from the analysis are: a) According to the PEG trials, there is an overuse of pesticides to the extent of 20% annually, as compared to the present usage levels, in terms of expenditure on pestieides.

91
I POIcY RESEARCH WORKING PAPER 2783

# Abstract

> Over the past few years, many studies have looked at the macroeconomic, cultural, and institutional determinants

> of corruption. This study complements these crosscountry studies by focusing on microeconomic factors that affect bribes paid in a single sector of the economy.

> Using enterprise-level data on bribes paid to utilities in 21 transition economies in Easter Europe and Central Asia, Clarke and Xu look at how characteristics of the firms paying bribes (such as ownership, profitability, and size) and characteristics of the utilities taking bribes (such as competition and utility capacity) affect the equilibrium

> level of corruption in the sector. On the side of bribe payers, enterprises that are more profitable, enterprises that have greater overdue payment to utilities, and _de nova_ private firms pay higher bribes. On the side of bribe takers, bribes paid to utilities are higher in countries with greater constraints on utility capacity, lower levels of competition in the utility sector, and where utilities are state-owned. Bribes in the utility sector are also correlated with many of the macroeconomic and political factors that previous studies have found to affect the overall level of corruption.

This paper-a product of Regulation and Competition Policv, Development Research Group-is part of a larger effort in the group to understand the economic impact of privatization and competition in network utilities. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Paulina Sintim-Aboagye, room MC3-300, telephone 202-473-7644, fax 202-522-1155, email address **iTH[.n l i..** K .. ..rld.lbm . **rg.** Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gclarke@a worldbank.org or lxu t(a worldbank.org. February 2002. (33 pages)

> _The Policy Research Working Paper Series disseminates the findings of work in progress to encotirage the exchange of ideas about dei'elopmenrt issues. Ain obhectioe of the series is to get the findings out quiickly, even if the presentatioins are less than f(lly polished. The papers carry the names of the authors and shouild be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the auithors. They do not necessarily represent the View' of the WXIorld Bank, its Executive Directors, or the counstries they represent._

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