rafmacalaba/datause-encoder-data huggingface dataset:
## 2.2 Estimating the relationship between management practices and performance

To test the relationship between establishments’ management scores and performance metrics, we run the following OLS regression: Where y�,�,� is the outcome variable—the performance of establishment , i , operating in sector, j , and country, c . In our analysis performance is measured either as an establishment’s labor productivity—log of sales per worker, or total factor productivity—the portion of output that is not explained by the amount of inputs used (which, due to data constraints, can be calculated for manufacturing establishments only). MGT�,�,� is the establishment’s management practices score, and the coefficient β��� describes the relationship between management and performance—a positive coefficient indicates that a higher management score is associated with improved performance. X�,�,� represents a vector of establishment‐ level characteristics that we control for; the variables included in the vector X�,�,� are those that may be related to both management practices and to productivity, and so we include them to disentangle their effects from the impact of management, per se. These are establishment size (measured in terms of the number of employees); exporting status; multinational status; whether the establishment is part of a multi‐establishment firm; the establishment’s legal status (sole proprietorship, limited liability company, etc.); the top manager’s years of experience in the sector; and the gender of the top manager. D� and D� represent country and sector dummies respectively and, finally, ε�,�,� is the error term.

# 3. Data and descriptive statistics

## 3.1 World Bank Enterprise Survey data

Our data are from a recent wave of the World Bank Enterprise Surveys (WBES) that were implemented in seven Latin American economies in 2017—Argentina, Bolivia, Colombia, Ecuador, Paraguay, Peru and Uruguay. The WBES are establishment‐level surveys of a representative sample of the non‐agricultural, non‐extractive, formal private economy. The surveys are carried out via face‐to‐face interviews with owners or top managers of establishments in the manufacturing, services, construction, and transport storage and communications sectors. The surveys, which do not cover informal or micro firms, are carried out using a standardized methodology that ensures representativeness at the country level, and comparability across economies and over time. The WBES covers a wide range of topics including access to finance, corruption, crime, infrastructure, as well as measures of establishments’ performance. In recent surveys, several questions have been added to the survey instrument to capture the managerial 7
# **1. Introduction**

Coastal resources in Tanzania have come under increasing pressure over the past three decades, which has led to a significant decline in the biodiversity and productivity of marine fisheries. The Tanzania State of the Coast report (2003) concludes that the inshore fisheries of mainland Tanzania and Zanzibar are over exploited and that shallow reefs are degraded. In a comprehensive review of fisheries studies in Tanzania, Jiddawi and Ohman (2002) report that the average size of lobsters and octopus has decreased substantially and the most abundant sea cucumber species are the low valued species due to overexploitation of these resources. Other investigations, such as interviews with older fishermen, confirm the trend in the decline of fisheries resources in coastal areas (MANREC, 2005).

The coastal areas of mainland Tanzania are home to approximately 25 percent of the country’s population (about 8 million people), and approximately 1 million people live in the islands of Zanzibar. Most of the coastal population depend on these coastal resources— particularly fisheries—for food and income generation. The poor tend to be the most dependent on these resources and are therefore the most severely affected when the coastal environment is degraded or their access to these resources is limited.

Marine Protected Areas (MPAs) are one tool for managing coastal and marine resources that have been increasingly used in Tanzania and elsewhere.<sup>1</sup> This has followed growing consensus among scientists and fisheries managers that conventional measures, such as taxes, quotas, season length and gear restriction management, have failed to prevent depletion of fishery stocks and a new approach is needed (Smith and Wilen, 2003; Pezzey and others, 2000). Conventional fishery management measures have proved particularly difficult to implement in reef fisheries, due to the diversity of species exploited and few resources available for enforcement in developing nations where many of the reefs are located. MPAs have been proposed as a promising alternative for fisheries management, partly because they are perceived as a simpler mechanism to enforce and data requirements for effective management are lower (Pezzey and others, 2000). Interest in MPAs has also been sparked by growing concern about the environmental damages of fishing and desire to preserve areas in

> 1 We use the term MPA broadly to include any marine managed area, such as marine parks, reserves, sanctuaries, conservation areas, as well as collaborative management arrangements.

2
WPS4078

# Informality Trends and Cycles*

Norman V. Loayza The World Bank Jamele Rigolini The World Bank

### **Abstract**

This paper studies the trends and cycles of informal employment. It first presents a theoretical model where the size of informal employment is determined by the relative costs and benefits of informality and the distribution of workers’ skills. In the long run, informal employment varies with the trends in these variables, and in the short run it reacts to accommodate transient shocks and to close the gap that separates it from its trend level. The paper then uses an error-correction framework to examine empirically informality’s long- and short-run relationships. For this purpose, it uses country-level data at annual frequency for a sample of developed and developing countries, with the share of self-employment in the labor force as the proxy for informal employment. The paper finds that, in the long run, informality is larger in countries that have lower GDP per capita and impose more costs to formal firms, in the form of more rigid business regulations, less valuable police and judicial services, and weaker monitoring of informality. In the short run, informal employment is found to be counter-cyclical for the majority of countries, with the degree of counter-cyclicality being lower in countries with larger informal employment and better police and judicial services. Moreover, informal employment follows a stable, trend-reverting process. These results are robust to changes in the sample and to the influence of outliers, even when only developing countries are considered in the analysis.

## World Bank Policy Research Working Paper 4078, December 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*We would like to thank Koichi Kume for excellent research assistance. We would also like to thank Omar Arias, Bill Maloney, and Jaime Saavedra for very useful comments and discussions.
Other studies found supporting evidence that gender role expectations make business accomplishments harder for women, especially through lower access to business capital and fewer social networks (Gupta et al. 2019; Mitra and Basit 2019). In the CBFW (2021) report, half of the women entrepreneurs surveyed reported that family members and friends encouraged them to devote more effort to domestic and care work in their homes. Where women do run their own businesses, they often align with societal expectations that these should be small-scale and entail minimal risk (Adom, 2015; Croson and Gneezy, 2009).

In Madagascar, Nordman and Vaillant (2014) investigated whether genderdifferentiated allocations of time within the household, based on gender norms, cause women to allocate their time sub-optimally to their businesses. They found that particularly for homebased businesses, domestic chores negatively affect business efficiency. Another notable exception is a recent work by Anambane and Adom (2019) which explores the role of culture and gender stereotypes on growth of women operated businesses in Ghana. Using qualitative data from 20 women entrepreneurs from the Upper East region of the country, the authors find that entry into entrepreneurship as well as the kinds of businesses pursued are affected by gender stereotypes. Gender stereotyping influences the size and sectors of women’s businesses, with women operating small businesses in low value-added sectors like retail and services. The authors explain this as a cultural expectation of women showing modesty and humility in all endeavors, including business. It should be noted, however, that entry into other sectors like manufacturing may likely be affected by underlying skill sets and financial capabilities.

In addition to the effects of gender and social norms, there is a growing body of economics literature on the role of personal initiative on business outcomes (Dal Bo et al., 2013; Callen et al., 2015), a subject that has been explored for much longer in the field of industrial psychology. Burger (2010) defines personality traits as characteristics originating from within a person that define their individuality. Zhao (2022) found personality - income 6
his/her household. These include the size of the household, and the entrepreneur’s gender, age, level of schooling and position in the household (i.e. head or not).

The treatments are all captured as indicator (dummy) variables. The first is the receipt of at least one loan at some undetermined date after the business was established ( _New Credit)._ Since the ENAMIN provides information about the source of the corresponding loans, we are able to break this out into the receipt of loans from formal credit providers, namely banks or non-bank financial institutions ( _Formal Credit_ ), and loans from informal sources, including family, friends, clients and suppliers ( _Informal Credit_ ). Our second treatment is the receipt of some type of training during the year preceding the survey ( _Training_ ) by either the owner of the firm or any of its workers. The third is the payment of at least some taxes as one of the regular expenditures made by the micro-firm ( _Taxes_ ). Finally, our last treatment is membership in either the union or business association related to the firm’s activity or sector ( _Guild_ ).

As measures of firm performance we work primarily with “profits” as reported in the ENAMIN. This is calculated as revenues minus declared costs not including owner’s labor and is perhaps better seen as a measure of income. In fact, as a robustness check, we also use the net income from self-employment reported by the owner of the firms when interviewed in the ENEU. In logs, the correlation of the two variables is 0.59. We construct the variables measuring firm growth and survival, by exploiting the panel nature of the ENEU. The survey follows the owners of the firms covered by the ENAMIN for four quarters after the initial interview so we can calculate the log difference between initial and final income over a period of one year as a measure of growth. Survival is captured by a dummy variable indicating whether individuals that initially reported being self-employed (with the detailed firm characteristics covered in the ENAMIN) were still self employed a year later or rather transitioned to being salaried workers, unemployed or out of the labor market. Since the nature of the rotating panel implies that only 20% of the sample linked to the ENAMIN can be observed a year later, the samples in which we explore dynamic outcomes are reduced to about one-fifth of the original ENAMIN sample.

The variables employed in the paper are described in Appendix I with summary statistics presented in Table 1. Within the group of micro-firms on which we focus – those with at most 6 workers – the average number of workers (besides the owner) is _0.6_ , and two-thirds of the firms are owner-only. However, despite their small size the sampled 12
# **Who Is Bargaining?**

Although households, especially in rural areas in developing countries, often include multiple adults involving several generations, the theoretical models usually only include two decision-makers. And while these two decision makers would not have to be a husband and wife, often there are elements of the model which implicitly or explicitly assume that this is the case. The same is true in empirical studies, which often look only at bargaining between two individuals, usually husband and wife. Other dimensions of household structure are simply absorbed by control variables for household characteristics. Thus, the literature often implicitly assumes that within multi-member households, the only bargaining is between the husband and wife; other members are assumed to be passive or unimportant to the bargaining process.

Frequently, households that are _not_ headed by a couple are dropped from the sample. This is expedient in that it allows for analysis of bargaining between the husband and wife, but it may critically assume away many other important facets of women’s bargaining power. Men and women who never marry or are divorced might be expected to be different from those who are married and to have different access to resources. For example, in an analysis of improved maize technology adoption in Ghana, Doss and Morris (2000) find that while women farmers are less likely to adopt improve improved varieties of maize and fertilizer, the gender differences in adoption are explained by gender-linked differences in access to complementary inputs.

However, in female-headed households, women are less likely to adopt improved varieties, even after controlling for these other factors. Thus, women farmers in male-headed households appear to be able to bargain within their households to obtain some of the unobservable factors that are needed to adopt these technologies. Those in female-headed households are not able to 8
14 -

-

Table 5: School type effects on achievement and social class differentiation

|<br>|Mathema<br>|tics<br>|Portug<br>|uese<br>|
|---|---|---|---|---|
|Independent<br>variable|Coeff.|t-stat|Coeff.|t-stat|
|Fixed|||||
|Female|-1.48***<br>|<br>-7.06<br> <br>|0.58**<br>|3.02<br> <br>|
|Age|-0.37***|<br>-5.84|-0.48**<br>|<br>-8.24<br>|
|Family size|0.05|0.27<br>|0.05<br>|1.11<br> <br>|
|Working hours|-0.03***|<br>-4.65|-0.02**|<br>-3.62|
|Mean achievement||||<br>|
|Intercept|13.92***|<br>22.29<br>|16.81***<br>|<br>43.71<br> <br>|
|Average SES|4.71***|<br>4.74|3.82***<br>|<br>6.42<br>|
|Federal technical|9.28***|<br>5.20<br>|3.68**<br>|3.44<br>|
|SEtNAI|-1.52|-0.63|-0.84|-0.58<br>|
|Teacher training|-1.37|-1.06<br>|-0.09<br>|-0.12<br>|
|Private|3.20*|2.12|-0.20|-0.22|
|SES achievement gap|||||
|Base|0.11|0.54<br>|0.21<br>|1.26<br>|
|Federal technical|0.68<br>|1.31<br>|-0.25<br>|-0.57<br>|
|SENAI|-0.69|-1.07<br>|-0.37<br>|-0.70<br>|
|Teacher training|-0.35|-0.80<br>|-0.28<br>|-0.74<br>|
|Private|0.02|0.06|0.22|0.72|

* p < .05, ** p < .01, *** p < .001

20. SES-achievement gap. Although the SES-achievement relationship was significantly different from zero for both mathematics and Portuguese (Table 2), neither school type nor sector explained this relationship.<sup>However, for</sup> mathematics, the direction of the effect was positive for federal technical schools, suggesting that they increased social class<sup>differentiation,</sup> while it was negative for both SENAI and teacher training schools,<sup>suggesting that they</sup> may have ameliorated the effect. For Portuguese, effects of all three types of schools were negative, but statistically insignificant. Private schools increased social class differentiation, but weakly and statistically nonsignificantly.
Policy Research Working Paper 5004

# **Abstract**

This paper describes an approach to forecasting future climate at the local level using historical weather station and satellite data and future projections of climate data from global climate models (GCMs) that is easily understandable by policymakers and planners. It describes an approach to synthesize the myriad climate projections, often with conflicting messages, into an easily-interpreted set of graphical displays that summarizes the basic implications of the ensemble of available climate models. The method described in the paper can be applied to publicly-available data for any country and for any number of climate models. It does not depend on geographic scale and can be applied at the subnational, national, or regional level. The paper illustrates the results for future climate for Ethiopia using future climate scenarios projects by 8 global climate models. The graphical displays of nine possible future climate regimes (average temperature, precipitation and their seasonal distribution) for each grid-cell about 50km X 50 km). It also provides the probability associated with each of the nine-climate regimes.

This paper—a product of the Environment Department and Development Research Group of the World Bank—is part of a larger study on the Economics of Adaptation to Climate change in the Environment Department. The study is a partnership of the World Bank which is leading the technical aspects of the study, and the Governments of the United Kingdom, the Netherlands and Switzerland who are funding the study. Additional information and papers from this study can be found at http://WorldBank.org/Environment/EACC. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kpandey@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
- Build on the outcomes of actions outlined above (microeconomic data) to develop suitable panel data to test these models.

- Develop suitable econometric techniques to address the challenges raised by new economic geography models.

# **2. Data Development**

## _<u>Transport</u>_

Country-level roads length statistics need to be disaggregated at the regional / state level, and should also distinguish between different types of road. An example of the standard road statistics data that should be aimed can be found in Fan and Chan-Kang (2005) for China. They provide length of roads for 6 different classes, from expressways to substandard types, with more than 20 years coverage and disaggregated numbers at the regional level. Similarly disaggregated figures for alternative transport modes (railways, airports, ports, etc.) may complete the picture. Simultaneously, household / firm survey data should be generated on access to service (roads, railroads, etc.), assessment of cost / quality, and ownership of different types of vehicles.

## _<u>Energy</u>_

Electricity is mostly proxied for by country-level electricity generation capacity. However, this is often a rather bad proxy to the availability and quality of electric services (see examples in Straub, 2007). Instead, we should aim at connection data at the household level and then aggregate these to get average connections rates at the village and district level. Similar data should be aimed at for gas connections. Additionally, both household and firm surveys infrastructure modules should include questions aimed at capturing:

- The quality of services (number of outages, appliances or machines failures).

- The cost of services (share of household budgets and of firms’ total costs).

- The institutional nature of service providers and regulatory arrangements.

- When applicable, investments in alternative supply mechanisms (generators, pumps) and their costs.

## _<u>Telecommunications</u>_

Connection data on use and ownership of fixed telephone lines / mobile telephone lines / internet connections, should be collected at the household /firm level and then aggregated to get average connections rates at the village and district level. Distance to the closest available service point should be considered when no service is available in rural communities. As in the case of energy, additional information on the nature of the services (public phones, shared private phones, etc.), their quality, their cost and their institutional nature should complete the picture.

## _<u>Water and Sanitation</u>_

Water and sewerage connection data should be collected at the household / firm level and then aggregated to get average connections rates at the village and district level. Distance to the closest available service point should be considered when no service is available in rural communities. Standard information on the nature of the services, 39