rafmacalaba/datause-encoder-data huggingface dataset:
term analysis (Petkova et al., 2013).<sup>6</sup> However, some recent examples that look at air pollution in Africa exist. Roy (2016) focuses on the health effects of air pollution and some economic costs, emphasizing the simultaneous challenges the continent faces: economic development and pollution control. Kirchberger (2016) looks at the spatial links between pollution (and other disamenities) and living standards in Sub-Saharan Africa, while Bishop (2017) develops a broader approach by concentrating not only on pollution but also on the health and economic effects of climate change in general. However, to the best of our knowledge, there is no study that focuses on the links between pollution and productivity in Africa.

The contribution of our paper is twofold. First, it extends the literature, particularly the work of Reyes et al. (2017), to focus on Africa, looking at how BE variables affect firm productivity. We follow the same approach by allowing the BE factors to affect firm growth differently for African firms than for those in the rest of the world, and then offer quantitative assessment of key factors for improving African firm growth. We offer diagnoses of hindering business environment (BE) factors for firm performance in Africa with a focus on issues related to agglomeration (or urban factors). Second, it contributes to the pollution literature by focusing on air pollution as another possible determinant of firm productivity and exploring whether its relationship with African firm performance is different, compared to the rest of the world. Although the link between pollution and productivity is well established (see Kahn et al. (2017) for a survey on this topic), this paper focuses on Africa and how air pollution is correlated to worker and firm productivity in that region.

Following Reyes et. al (2017), we use the World Bank Enterprise Survey data set. We distinguish three different classifications for business environment (BE): Basic BE, Refined BE, and Agglomeration Environment. Basic BE is likely essential for facilitating firm growth. It consists of Basic Protection (corruption, basic property rights protection, and basic physical safety), infrastructure (power supply and modern telecom), human capital, and access to finance (such as bank finance and trade credit). Refined BE covers other BE elements that have been frequently mentioned in the literature, but perhaps less frequently mentioned than Basic BE. It includes topics such as entry and exit, labor regulations, tax burdens. The Agglomeration Environment is

> 6 Petkova et al. (2013) provide an overview of available studies.

4
the other two, we use the cooperative-level price and catch data to demonstrate empirical support for the model’s three implications. The difference in price and catch between the cooperative with strong property rights and the other two cooperatives is large, and the cooperative with property rights disincentivizes effort more aggressively than the other two when growth rates are likely to be small. But the magnitudes of the estimates also indicate that the difference in management aggressiveness across the cooperatives shrinks in economically meaningful ways when resource scale is large and growth rates are high.

Given the small number of cooperatives in the analysis, one should be cautious in extrapolating these findings to other settings. Instead, we view our results as improving the general understanding of when and how cooperative-based property rights can be effective. Our work complements the rich literature on community-based resource management institutions. Ostrom (1990) reviews case studies of institutions and derives a set of principles that differentiate those that are successful. We examine the role of some key principles from the Ostrom framework, such as clearly defined boundaries and effective rule enforcement. However, rather than making binary assessments of “success,” we empirically quantify the influence of property rights on economic outcomes. Gutiérrez et al. (2011) consider case studies of fishing cooperatives in particular and find predictors of success, including the existence of quotas, enforcement institutions, long-term planning, and resource mobility. Recent economics literature examines the decision-making of villages or other local user groups regarding other resources (e.g., Edmonds [2002] and Foster and Rosenzweig [2003] on forests). In contrast to these studies, we examine the short-term dynamics of resource management, illuminating the mechanisms that institutions may use to achieve management goals. We do so in the context of fisheries, which are characterized by important spatial externalities and environmental fluctuations not relevant to some other natural resources.

The theoretical literature on optimal fisheries management strategies considers these challenges. For instance, Costello and Kaffine (2010) and White and Costello (2011) examine the implications of spatial externalities in area-based property rights, driven by movement of species across large ranges. Reed (1975), Parma and Deriso (1990), Costello et al. (2001), and Carson et al. (2009) look at how management may respond to temporary or permanent environmental changes. Our paper examines similar issues but introduces an important complication arising from cooperative leaders’ need to ensure returns high enough to retain members. More importantly, our focus is on empirically testing our theoretical model and presenting quantitative evidence on cooperative decision-making.

4
based on rainfall during three separate phases of the Kharif, or monsoon season, and is inexpensive enough to be accessible to farmers of modest income (one policy covering all three phases of the Kharif costs around Rs. 150-250, equivalent to US$3-5). The product is sold to farmers by BASIX, a microfinance institution, and rainfall risk is underwritten by the insurance firm ICICI Lombard and their reinsurers.

A basic research question for the study of micro-insurance markets is estimating the crosssectional determinants of household insurance takeup, and identifying the impediments to trade that prevent remaining households from participating. After describing the insurance product, we present empirical evidence on the determinants of insurance participation, based on a household survey implemented by ICRISAT and the World Bank in late 2004. We first evaluate takeup patterns against a simple neoclassical benchmark, which predicts that insurance participation is increasing in risk aversion and the variance of risk, and decreasing in basis risk between insurance payouts and the risk to be insured. We find some evidence consistent with the basis risk prediction; namely, households that historically plant a high share of castor and groundnut, the two crops for which contracts are designed, are more likely to purchase insurance. Takeup rates are also higher among wealthy households, and lower among households identified as credit constrained. These findings are consistent with an extension of the “benchmark” model to include borrowing constraints.

Other evidence is more difficult to reconcile with the benchmark model. First, among the quantitatively most significant determinants of insurance takeup are variables measuring the household’s degree of familiarity with the insurance vendor, such as whether the household is an existing BASIX customer. Participation is also higher among households that are members of the town Gran Panchayat (local council), and those that are connected to other village networks, especially when a larger number of other members or the household’s primary network also buy insurance. Second, risk-averse households are somewhat _less_ likely to take up rainfall insurance, 2
- _The relevance of search costs_ : In October 2009, three months after the wage subsidy offer was made, we asked those eligible for the wage subsidies a series of questions related to search. At the time of the survey, 29 percent of those eligible had hired an employee, indicating they were able to find someone fairly quickly. Another 43 percent indicated that they had no intention of hiring an employee.<sup>12</sup> Among the remaining 29 percent of owners who indicated they were still searching and expected to hire an employee, only 38 percent (representing 11 percent of the sample) indicated that a reason for not hiring was that they had not found the right employee. The majority indicated instead that at present the additional employee would not be profitable given the subsidy level. These data suggest that search costs are not a main constraint for the majority of owners in our sample. This conclusion is supported by data from a survey of owners of small and medium sized businesses conducted around the same time.<sup>13</sup> In April 2009, among 160 owners with five or fewer employees, the median owner said it would take seven days to locate an employee if s/he wanted to hire one; the mean search period was 14 days. Moreover, these owners said they could hire a worker for a median of 400 LKR per day, or 2,000 LKR per week, just above the average wage the employees hired under our subsidy program report being paid.  That search costs are important for only a small percentage of owners of small firms is consistent with long-held views that labor markets work relatively well among informal firms. This is perhaps not surprising given that most workers are hired to do relatively routine, physical tasks. Our owners say that the sex of the worker and physical strength of the worker are the two most important characteristics of workers they consider hiring, with education the least important of the characteristics listed. Search tends to be local, with a majority of workers hired being previously known to owners and living within one kilometer of the business.<sup>14</sup> We read these responses together with the response reported earlier just under a quarter of the owners eligible for the wage subsidy report not being able

> 12 These owners said in response to later questions that their business would not support or benefit from an additional employee. Only 11 percent of them indicated that the inability to find the right employee was a reason for not hiring.

> 13 The SME survey was conducted in urban areas throughout Sri Lanka, but a majority of respondents come from the same urban areas in which we conducted the experiment. The survey is described in Appendix 4.

> 14 The local nature of search holds both in these data and in data from a separate survey of wage workers. In the wage worker survey, among 171 workers in firms with 4 or fewer employees, 61 percent say they knew the owner before beginning work, and 45 percent say they live within one kilometer of the business. The wage worker surveys is described in Appendix 4.

26
# **Figure 18 Public ownership: size, scope and extent of control over public enterprises**

# **a. Size of the public enterprise sector**

<!-- Start of picture text --> 6.0<br>5.0<br>4.0 OECD average<br>3.0<br>2.0<br>1.0<br>0.0<br>b. Scope of the public sector<br>6.0<br>5.0<br>OECD average<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>c. Control of public enterprise by legislative bodies<br>5.0<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5 OECD average<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>AustraliaAustriaJapanIcelandSwedenDenmarkUnited StatesIreland CanadaGreeceMexicoTurkeyKoreaBelgiumFranceNetherlandsCzech RepublicGermanySpainNorwayNew ZealandSwitzerlandUnited KingdomFinlandLuxembourgPolandBrazilSlovak RepublicBulgaria ItalyPortugalRomaniaHungary<br>United KingdomSlovak RepublicBelgiumJapanKoreaNew ZealandIceland DenmarkUnited StatesIreland AustraliaCanadaNetherlandsBrazilRomaniaGreeceMexicoGermanyAustriaFinlandHungaryLuxembourgSpain SwedenCzech RepublicBulgaria PortugalSwitzerlandFrance ItalyNorwayTurkeyPoland<br>Japan Slovak Republic Brazil Australia United States New Zealand Switzerland Luxembourg United Kingdom Portugal Canada Denmark Spain Ireland Sweden Korea Iceland Netherlands Hungary Germany Czech Republic Finland Belgium Bulgaria Mexico Italy Greece Austria Norway France Turkey Poland Romania<br><!-- End of picture text --> _Source:_ see Figure 3. _Note_ : Other MICs are Brazil, Mexico, Turkey. Values refer to 2006 for Bulgaria and Romania, 2004 for Brazil, and 2003 for all other countries. For full data set see appendix I.

24
mostly involved in supporting agriculture and manufacturing in 1990 toward responding to demands fuelled by oil revenues. Since 1990, all sectors have increased their real output, with the strongest increase for services; however, for oil, output has declined strongly in recent years even though it remains higher than in 1990.

As implied by the increased GDP share of oil at current prices, the sector has been a prime driver of economic growth, especially in the 1990s, inter alia making a dramatically increased contribution to the public treasury. Oil revenue has also allowed the government to adopt a relaxed attitude towards raising domestic resources. In addition, by strengthening the exchange rate of the Yemeni rial, oil has had a negative impact on the growth of sectors whose outputs are relatively tradable (agriculture and industry) relative to the less tradable service sectors. The poor performance of the agricultural sector has meant that the country is now more dependent on food imports.

Figure 2.1: GDP share by sector at current prices, 1990-2009 (Percent) <!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Agriculture Qat Oil and Gas Manufacturing Services<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Central Statistical Organization of the Government of Yemen.

Government current consumption has remained close to 14 percent of GDP over the entire period 1990-2009. The reduction in the public deficit since 1996 led to a reduction in the spending share devoted to interest payments, from 10 percent in the early 1990s to 8
- 18a-.

Table 4. The table lists the major three and four-digit SITC labor intensive products exported by developing countries (ranked in terms of their 1986 export values)-to developed countries and also shows the share of each item in total trade of the group. This information is given for selected years from 1965 to 1986 to identify items of increasing and declining importance 16/ Table 4 suggests that the dynamic elements in developing country labor intensive manufactures are not confined to a few items, but have a fairly broad base. Footweat and transistors each increased their share by more than four percentage points, while the share of toys and sporting goods more than doubled. Several electronic equipment products (i.e., statistical machines, telecommunications equipment, electronic power machinery, office machines and radio broadcast receivers) had their share of labor intensive products increase by one to two percentage points, while leather clothing, motor vehicle parts, other electrical machinery also recorded a similar increase.

# V. Trends in Labor Intensive Production

The key point that emerged from the preceding analysis is that labor intensity was a useful predictor of developing country exports, although there was variation in national supply of, and demand for, these goods. Items identified as labor intensive in 1965 registered a superior developing country

- 16/ Pearls and precious stones (SITC 667) experience the steepest decline in Table 4 as the share of these items in labor intensive exports went from 10.2 per cent in 1965 to 3.6 per cent in 1986. Protectionism in developed countries has not played a role in this decline as tariffs are generally low on these items and there are few reported nontariff barriers. Other items whose share declined by two percentage points or more include: preserved fruit (SITC 053); woven cotton fabrics (SITC 652); shaped wood (SITC 243); textile products nes (SITC 656); floor covers (SITC 657); leather (SITC 611); plywood (SITC 631.2); and preserved vegetables (SITC 055).
# Abbreviations

|ALCO|Asset and LiabilityCommittee|
|---|---|
|ALM|Asset and LiabilityManagement|
|ANCAP|_Administración Nacional de Combustibles, Alcoholesy Portland_|
|BS|Balance Sheet|
|BSE|_Banco de Seguros del Estado_|
|CB|Central Bank|
|DB|Danmarks Nationalbank|
|DMO|Debt Management Office|
|EFA|Exchange Fund Account|
|EU|European Union|
|EUR|Euro|
|FEES|_Fondo de Estabilización Económicay Social_|
|FX|Foreign exchange|
|GDP|Gross Domestic Product|
|IMF|International MonetaryFund|
|IMSN|_Índice Medio de Salarios Nominales_|
|IT|Information Technology|
|JPY|Japanese Yen|
|MOF|Ministryof Finance|
|OECD|Organization for Economic Cooperation and Development|
|SALM|Sovereign Asset and LiabilityManagement|
|SOE|State‐Owned Enterprise|
|SWF|Sovereign Wealth Fund|
|TSA|TreasurySingle Account|
|USD|United States Dollar|
|UTE|_Administración Nacional de Usinasy Trasmisiones Eléctricas_|
|WB|World Bank|

4
43 Cuddington, John (1986), "Capital Flight: Estimates, Issues and Explanations," Princeton Studies in International Finance No.58.

Cumby, _R.E._ and M. Obstfeld (19r18), "Capital Mobility and the Scope for Sterilization: Mexico in the 1970s" in P. Aspe et al., (ods) L iancial Policies and the World Capital Markets, University of Chicago Press: Chicago.

Edwards, Sebastian (1989), Real Exchange Rates, Devaluation, Adiustment: Egchanc. Rate Policy in Developing Countri MIT **Press: Cambridge.** and The Edwards, Sebastian and A. Cox-Edwards (1987), "Monetarism and Liberalization: The Chilean Experiment," Ballinger Publishing Co: Cambridge.

Edwards, S. and S. van Wijnbergen (1987), "Tariffs, The Real **Exchange** Rate and The Terms of Trade: On Two Popular Propositions in International Economics," Oxford Economic Papers, 39, (September), 458-64.

Haque, Nadeem Ul and Peter Montiel (1989), "Capital Mobility in Developing Countries - Some Empirical Tests," International Monetary Fund (mimeo).

Joshi, Vijay "Exchange Rate Regimes in Developing Countries," in M. Fg. Scott and D. Lal (eds) Public Policy and Economic Development: Essays in Honour of Ian Little, Oxford University Press: Oxford, 1990.

Krueger, Anne (1978), Foreian Trade Recimes and Economic Develooment: Liberalization Attempts and Conseauences, Ballinger Publishing Co.: Cambridge.

Lessard, Donald R. and John Williamson (1987), CaRita' Flight and Third World Debt, Institute for International Economics: Washington, DC. Salter, W.E.G. (1959), "Internal and External Balance: The Role of Price and Expenditure Effects," Economic Record, 35, (August).

Swan, T.W. (1963), "Longer Run Problems of the Balance of Payments," in W.M. Corden and H.W. Arndt (eds) The Australian Economy: A Volume of Readings, Cheshire: Melbourne. Warr, Peter G (1984), "Exchange Rate Protection in Indonesia," Bulletin of Indonesian Economic Studiel, 20, (August), 53-89.

Williamson, John (1982), "A Survey of the Literature on the Optimal Peg," Journal of Development Economic$, 11, 39-61. Williamson, John (ed), (1981), Exchange Rate Rules, St Martin's Press: New York.
# **2. Different Methods Used for Sampling Migrants, Families of Migrants, and Other Rare Elements**

Any attempt to carry out a specialized survey of migrants or of migrant-sending households must face the problem that international migration is a relatively rare event in most countries. Bilsborrow et al. (1997) note that in three-quarters of the countries in the world, the proportion of international migrants was at most 6.5 percent in the early 1990s. Even in countries in which international migration is more common, finding a household with a migrant currently abroad or a recently returned migrant can be a rare event. Therefore carrying out a survey of migrant-sending households is essentially a problem of surveying “rare elements” or “rare populations” (Kish, 1965, Kalton and Anderson, 1986). Our application fits well this description: it is estimated that there are approximately 1.4 million Nikkei households in Brazil, relative to an overall population of over 170 million.

Conducting a probabilistic sample of a rare population presents no problem if a full sample frame is available. Representative samples of legal migrants have thus been recently conducted using administrative records on new immigrants. Examples include the New Immigrant Survey (NIS) in the United States, the Longitudinal Survey of Immigrants to Australia (LSIA) and the Longitudinal Immigration Survey (LISNZ) in New Zealand. This is more difficult to carry out for migrant-sending households, as it requires obtaining from migrants and migrant records the contact details for the remaining household unit. The only application we are aware of which does this is the Pacific Island-New Zealand Migration Survey (McKenzie, Gibson and Stillman, 2006), which links new Tongan migrants in New Zealand to their remaining households in Tonga, and surveys the sending households in Tonga.

The much more common situation is one in which no survey frame is available. Three approaches to sampling rare elements have then been most commonly used in practice to survey migrant-sending households or migrants.<sup>2</sup> These are stratified sampling using

> 2 Note that surveys of households with migrants will also want to include some households without migrants for comparison purposes. In many circumstances these households are not rare elements, and a

4

-  -