rafmacalaba/datause-encoder-data huggingface dataset:
than regarding urbanization as an evil to be stopped by slowing down migration, blocking the expansion of low income settlements and biasing development in favor of rural areas, they call for improved efficiency, equity and participation in the way urbanization and the growth of cities is managed.<sup>3</sup> While this view in many ways parallels the approach advocated by the World Bank’s _World Development Report 1979_ – but not widely followed in intervening years by national and city governments on the ground – there are distinctive new aspects to the urbanization process which are highly relevant to today’s urban research agenda, different from what were prevailing conditions 30 years ago.

To help establish the universe of today’s potential urbanization research issues, Linn (2010) looks in a broad sweep at the new, the old and the forgotten issues by creating a simple framework shown in Figure 1 to help organize the manifold issues and their relationships. He distinguishes and briefly highlights urban issues under six separate categories:

- Challenges/opportunities<sup>4</sup>

- Instruments

- Analytical and planning tools

- Data and benchmarks

- Institutions

- Regions/countries/cities

Among the new challenges – i.e., issues now in the forefront of urban analysis or policy, but not so 30 years ago – the focus on poverty and inequality<sup>5</sup> , on women, on crime and insecurity, on governance and on the political economy of urban issues stand out. Among the old challenges still regarded relevant today, are slums, urban land management, housing finance and the role of the investment climate. The

> 3 See for example the World Bank’s WDR 2009 (World Bank, 2009a) and the Bank’s new urban strategy (World Bank, 2009b)

> 4 Throughout the remainder of this section we will use the term “challenge” to represent both challenges and opportunities.

> 5 In fairness, poverty was a concern 30 years ago, but the approach to the issue was much less comprehensive and in‐depth compared with today.

4
- Jones, K. W. & Lewis, D. J. (2015), ‘Estimating the counterfactual impact of conservation programs on land cover outcomes: the role of matching and panel regression techniques’, PloS one 10.

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47
# **1. Introduction**

Small retail stores in developing countries are a common form of self-employment, and the main source of food and many other goods for poor urban consumers. These stores face very different supply chains across the products they sell. Multinational suppliers of non-perishable products like Coca-Cola, Unilever, and Avon have famously perfected efficient supply chains that result in store-door delivery to even the most remote regions of the planet (Hammond and Prahalad, 2004). In contrast, the fresh fruit and vegetables they sell are part of what are typically less efficient supply chains, that often end in centralized wholesale markets, necessitating substantial travel time and costs for retail store vendors, and increased food costs for the urban poor (Ruel et al, 1998; FAO 2010). This is the case in Bogota, Colombia, where the typical store owner gets up at 4:30am, spends over two hours daily traveling to, around, and back from the central market of Corabastos, and spends an average of 30 percent of daily profits on transportation costs.

Improving the efficiency of their integration into supply chains could have significant benefits in terms of overall welfare, given their sizeable employment share, but also potentially lower prices for consumers. One potential solution would be for store owners to work together and form buyer cooperatives, allowing them to source products in bulk from the market or even directly from the farmers, and to arrange to have the products delivered to their stores. But the large number of small firms, each working long hours, and with limited and low-trust connections to one another results in a coordination failure that prevents this from occurring. The social enterprise _Agruppa_ was formed with the goal of using new technologies to solve this coordination issue. Their model is to use mobile phones to create virtual buyer groups, agglomerate daily orders from store owners, bulk buy from farmers, and then deliver the produce directly to stores. The aims were to reduce the amount of time and cost store owners spend traveling to the central market, improving their incomes and work-life balance as a result, lowering the cost of fresh produce purchased by stores, and, in turn, reducing the prices paid by consumers.

We conducted a randomized experiment to test the extent to which this innovation was able to achieve these aims. We began with a baseline survey of 1,620 firms in 63 market blocks, in which firm owners were asked about their potential interest in using the new service, with 70 percent expressing interest. These blocks were then grouped into matched pairs, with one block in each pair randomized to treatment and the other to control. Agruppa then phased in their service at the 2
- 39 -

The explicit consideration of educational background reveals that C8te d'Ivoire is not only characterized by wide earnings disparities across locations but also within the same location (Table 15). In urban areas, the mean earnings of a person with post-secondary VTE is more than six times that of a person without VTE and with no more than primary general education. There appears to be a great premium to obtaining education past the primary level, although the relative contribution of secondary VTE vs. secondary general education is not directly obvious from the table. In other cities, graduates of secondary VTE earn significantly more than those with only secondary general education, but the reverse is true in Abidjan. This is again consistent with a tighter labor market in other cities. The multivariate earnings analysis below will have to bring out more clearly the contribution to earnings of each level and type of education.

One conclusion which Table 15 does bring out clearly is that acquiring VTE in an informal way, through apprenticeships, is a much less certain road to high earnings. For people with no education, an apprenticeship adds to their earning ability, but once any general education has been obtained, the role of an apprenticeship in enhancing earnings is unclear. The strongest positive effect occurs in rural areas where workers with primary education and an apprenticeship receive more than twice the earnings of those without either.
# **3 Estimation**

Our goal in this paper is to create the most comprehensive map of estimates of labor mobility costs possible. Artuç, Chaudhuri and McLaren (2010) derive estimators of these mobility costs based on panel data. The panels are used to build measures of gross employment flows across sectors that, together with utility compensating differentials, identify _C_ , _ν_ , and _η_<sup>_i_</sup> . In many countries, where the needed panel data is not available, this approach cannot be implemented. For many of these countries, there is availability of time series of sector-level employment and wages, for example in the UNIDO database. In this section, we derive estimators of the mobility costs based on this readily available data.

## **3.1 The UNIDO Data**

It is convenient to begin with a description of the data, because the type of data available imposes some restrictions on the estimation algorithm. In the analysis, we use INDSTAT4, the UNIDO Industrial Statistics Database for the period 1990-2008. The dataset provides information on number of establishments, number of employees, wages, output, value added, and gross fixed capital formation. For our purposes, we need information on employment and wages for the estimation and also on value added for the simulations (see section 5 below). From these data, we build series for the wage streams _wt_<sup>_i_andforthelaborallocations</sup><sup>_Li_</sup> _t_<sup>,forsector</sup><sup>_i_attime</sup><sup>_t_.Fortheestimation,weaggregate</sup> the data into eight major sectors, namely Metals & Minerals, Chemicals & Petroleum Products, Machinery, Food & Beverages, Wood Products, Textiles & Clothing, Miscellaneous Equipments, Motor Vehicles. The UNIDO data has a good coverage of the manufacturing sector but does not cover the non-manufacturing sector. To overcome this limitation, we use national account data to construct measures of labor allocations in the non-manufacturing sector, which we label as sector 1. Note that we do not observe wages for the non-manufacturing sector. In the end, our data comprises time series of employment allocations for both non-manufactures and manufactures, _L_<sup>1</sup> _t_ and _L_<sup>_i_</sup> _t_<sup>,andwagesformanufactures,</sup><sup>_w_</sup> _t_<sup>_i_.Wages(utilities)inthethenon-manufacturingsectorwill</sup> be calibrated from the data.

7
# **1. Introduction**

Improving the productivity of smallholder farmers is widely considered one of the most effective avenues for reducing poverty and food insecurity, and thus has been a longstanding goal in many African countries (Byerlee et al., 2007). The evidence concerning (i) agriculture contributing up to 69 percent of rural household income in Africa (Davis et al., 2017), and (ii) higher rates of expected poverty reduction associated with agricultural vis‐à‐vis nonagricultural growth (Dorosh and Thurlow, 2016) helps sustain the policy focus on achieving this goal at the national level. Similarly, at the international level, doubling productivity and incomes of smallholders have been identified as a key target within the Sustainable Development Goal (SDG) #2 of Ending Hunger.

Accurate measures of production and productivity are, therefore, essential to (i) tracking progress towards the relevant SDG targets; answering fundamental questions on the role of agriculture in household and individual welfare (Darko et al., 2018); and understanding which production factors have the most important role in determining productivity. Ongoing debates about the relationship between (land) productivity and (i) fertilizer use (Harou et al., 2017), (ii) plot/farm size (Bevis and Barrett, 2017; Desiere and Jolliffe, 2018; Gourlay et al., 2017), or (iii) soil quality (Berazneva et al., 2018) reflect the substantial knowledge gaps and the need to improve the accuracy and precision of recommendations for raising productivity in particular locations.

The most common way to assess outcomes related to economic productivity of smallholder farmers, including land productivity (e.g. crop yields), is by using information collected through in‐person interviews for household and farm surveys. For example, the household surveys supported by the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS‐ISA) initiative measure plot areas with handheld GPS units and solicit farmer‐ reported information on crop production and input use, among other topics, at the plot level. This information, together with multi‐topic household survey data, have informed a burgeoning field of development research on Africa over the last decade.

Compared to the body of methodological research that has shown severe systematic biases in farmer‐reported plot area measures (Carletto et al., Forthcoming) and that have underlined the increasing use of GPS‐based plot area measurement in national household surveys, there is a dearth of evidence on the accuracy of farmer‐reported crop production. It is, however, known that the process of soliciting farmer‐reported production information is mediated by complexities that include (i) the use of non‐standard measurement units, (ii) various conditions and states of crop harvest; (iii) partial/early crop harvests; (iv) potential recall bias, and (v) tendency to round off numbers, among others (Carletto et al., 2015). Recent research highlighted 2
**Table A3: Instrumental Variable Estimates of Employment Determinants Overall, 1983-2002**

|||First St|age||Second Stage|
|---|---|---|---|---|---|
|Variable|Lagged log of<br>low income<br>affiliate<br>employment|Lagged log of<br>high income<br>affiliate<br>employment|Import<br>Share Using<br>1979<br>Weights|Export<br>Share Using<br>1979<br>Weights|Log U.S.<br>Manufacturing<br>Sector<br>Employment|
||(1)|(2)|(3)|(4)|(5)|
|Industry Share of Routine Jobs|0.68***<br>(0.27)|1.31***<br>(0.21)|0.23***<br>(0.040|0.37***<br>(0.04)||
|Internet High|0.003***|0.001***|-0.00002|.0.00007***||
||(0.0001)|(0.00009)|(0.00002)|(0.00002)||
|Fixed line and mobile phone|0.0003***|-0.0003***|0.00009***|0.00002||
|subscribers (per 1,000 people)|(0.0001)|(0.0001)|(0.00001)|(0.00002)||
|Education Low|0.03***|0.009***|0.003***|-0.002***||
||(0.01)|(0.003)|(0.0009)|(0.0009)||
|Education High|-0.02***|-0.02|.0.002***|-0.004||
||(0.005)|(0.004)|(0.001)|(0.0008)||
|Lagged log of low income<br>affiliate employment|||||-0.05**<br>(0.26)|
|Lagged log of high income<br>affiliate employment|||||0.02<br>(0.71)|
|Lagged log of price of|-0.53***|-0.15***|0.31***|0.08***|0.96|
|investment using 1979 weights|(0.09)|-0.07|.(0.01)|(0.01)|(0.92)|
|Lagged total factor<br>productivity level using 1979|0.296***|0.03|0.19***|0.06***|-0.18***|
|weights|(0.05)|(0.04)|(0.008)|(0.004)|(0.07)|
|Lagged export share using<br>1979 weights|||||-0.82<br>(2.86)|
|Lagged import penetration<br>using 1979 weights|||||-0.42***<br>(0.15)|
|Lagged log of real price of|-0.23***|-0.06***|-0.18***|0.08***|0.14***|
|shipments using 1979 weights|(0.03)|(.020)|(0.004)|(0.01)|(0.04)|
|F statistic|18.96|17.31|12.09|19.20||
|Sargan Chi-sq (1) P-Value|||||0.19|
|Number of observations|1,756|1,756|1,756|1,756|1,756|

_Source_ : See Table A1.

_Note_ : Robust standard errors reported in parentheses below coefficient estimates. All specifications include year, education, industry and location fixed effects.
- 41 -

Table 5.7. Base Simulation with Active Government Exchange Rate Policy A/

||1|983|19|84|19|85|
|---|---|---|---|---|---|---|
|A GNP|||3.5|(2.5)|-0.7|(0.3)|
|D|-10.3<br>|(-8.5)<br>|-5.4<br>|(-5.4)<br>|-2.6<br>|(-2.0)<br>|
|I<br>|10.2<br>|(10.2)<br>|9.1<br>|(9.7)<br>|12.2<br>|(12.2)<br>|
|X|13.5<br>|(13.5)<br>|10.1<br>|(10.3)<br>|9.2<br>|(9.3)<br>|
|M|5.9|(5.9)|6.4|(6.4)|(6.6)|(6.6)|
|TB|7.6|<br>(7.6)|3.7|(3.9)|2.6|(2.7)|
|w|||60.2|(62.3)|35.5|(35.6)|
|i|69.0|(70.0)|72.6<br>|(72.1)<br>|72.7<br>|(72.7)<br>|
|e|||33.1|(36.7)|24.3|(22.7)|
|R|4.22|(3.43)|1.97|<br>(3.43)|2.00|<br>(3.43)|
|E|||20.4|(18.7)|9.0|(10.5)|

A/ The numbers in parenthesis are the simulation outcomes reported in Table 5.8, while all footnotes of Table 5.8 apply here also.

We thus notice that this change in exchange rate policies of the government has relatively minor effects. There is a slight decrease in the aggregate growth of the economy, and there is an decrease in the reserves of the Central Bank, as the exchange rate devalues slightly less under the -ew regime than it did under the pure float. Finally, the utility of the domestic consumer in the floating rate case was 461.8, while under the new jegime it was 463.0, so "leaning against the wind" seems here to be a Pareto improvement, primarily because the reduction in reserves adds to the consumer's utility.
# **Health Facility Surveys:**

## **An Introduction**

### Magnus Lindelow

The World Bank, 1818 H St. NW, Washington, D.C. 20433, USA Centre for Study of African Economies, St Anthony's College, Oxford OX2 6JF, UK mlindelow(worldbank.org

### **Adam Wagstaff**

The World Bank, 1818 H St. NW, Washington, DC, 20433, USA The University of Sussex, University of Sussex, Brighton, BN 1 6HG, UK awagstaff(worldbank.org Background paper prepared for World Bank Workshop on Health Facility Surveys, 12 December 2001.
these and results from other studies, Weil (2005) finds that 9.5% to 29.5% of the variation in the log of income can be explained by the variation in health outcomes.

# _Investment_

Healthier people live longer, and may save more for retirement. Thus, savings can rise as the average level of health improves. This in turn can affect investment. A healthier labor force can attract more foreign direct investment, and health conditions also directly affect certain industries, especially tourism (Bloom and Canning 2003). Alsan, Bloom, and Canning (2006) showed that one year increase in life expectancy is associated with 9% increases in gross FDI flows to the lower- and middle-income countries.

# **_IV.  Testing for the Income-Health Causality_**

To further explore the relationship between income and health we conducted two tests described in this section. First, we examine how income levels relate to people’s health using cross-country data from 1965 to 2004. A “Granger causality” test is used to identify the direction of causality. Secondly, we exploit the natural experiment arising from the sharp decline of employment in China following the privatization and divestiture of state owned enterprises in 1997. With this data, we can test for causality in China between income levels caused by enterprise reform and health during 1996 to 2004.

# **Cross-Country Test**

Data on life expectancy, literacy, per capita GDP, and the GDP by sector (agriculture, industry, and service) was obtained from the World Development Indicators database for all the available countries from 1960 to 2004.<sup>40</sup> Literacy data are available for 1990 and 2004 only. We selected GDP per capita (constant 2000 US $) and relied on the data base assembled by Maddison (2006) because it provides a continuous series from

> 40 The data for 2005 include too many missing observations to be useful and therefore, are excluded from the analysis.

19
Vousden, Neil. 1990. The Economics of Trade Protection. Cambridge: Cambridge University Press.

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120
is significantly high compared with North and Latin America with less than 30 percent. About 50 percent of African countries are regulating mobile-to-fixed access with price controls. The database also indicates that 4 out of 26 countries answered that domestic (local and long-distance calls and interconnection) tariffs are determined by “the state.” This does _not_ mean that only a small fraction of countries in the region control telecommunications prices. Rather, prices are actually regulated and determined by “independent” or “autonomous” regulatory authorities in most countries. Regulatory autonomy does not directly mean liberalized prices, but it is generally expected that more market-friendly and diverse pricing would be promoted under autonomous regulators.

<u>Table 12. Price Control Practices by Region, 2005</u>

||Mobile s<br>|ervices<br>||Accessf<br>|rom fixed to <br>|mobile<br>|Domestic tariffsettin<br> <br>|g<br>|
|---|---|---|---|---|---|---|---|---|
||No. of|With price|Share|No. of|With price|Share|No. of<br>By|Share|
||sample|controls|(%)|sample|controls|(%)|sample<br>government|(%)|
|World|66|29|43.9|93|54|58.1|97<br>33|34.0|
|Africa|11|5|45.5|23|11|47.8|26<br>4|15.4|
|Americas|7|2|28.6|10|6|60.0|10<br>7|70.0|
|Asia|24|13|54.2|26|18|69.2|28<br>12|42.9|
|Europe|23|8|34.8|33|19|57.6|31<br>10|32.3|
|Oceania|1|1|100.0|1|0|0.0|2<br>0|0.0|

Note that the sample data are unbalanced and that the figures may not be consistent because of possible multiple answers in the survey.

Source: ITU Tariff Policies Database.

According to another ITU database, Regulators Profile, only half of the African countries exclusively delegate the (maximum) price regulation to autonomous regulatory authorities (Table 13).<sup>33</sup> There are only two countries, Liberia and Mozambique, where no ceiling is imposed on telecommunications prices. In the sample countries the above econometric analysis examined, 11 out of 18 countries are curbing prices through exclusively autonomous regulators. The rest of the countries have some price controls by line ministries, which are possibly deemed relatively vulnerable to politically motivated intervention. In terms of interconnection rates, more autonomous regulators seem to have exclusive responsibility for network access supervision (Table 14).

> 33 The figures in the table are subject to available data and may not reflect the latest regulatory framework of each country.

36