rafmacalaba/datause-encoder-data huggingface dataset:
data on unemployment stock data (ugam) from DIOC in conjunction with the unemploy ~~-~~ ment rates from the OECD Shor ~~t-~~ Term Labour Market Statistics, and the replacement rate of the national unemployment insurance scheme (3) from the OECD fiscal database. This allows computing wg,qn, the average income per active hour, and recalibrating ds~. as Wg Au (Qsam/Qsam) Validation of the calibration strategy. A key variable required in the analysis of the equilibrium outcomes is the share of total labor wage bill going to each type of worker in the labor force. These wage bill shares are generally computed by (1) using individual wages and total labor stocks from detailed labor force surveys; (2) using individual hours worked and total labor stocks from detailed labor force surveys; or (3) using total labor stocks and normalized wages, with one group of workers as the base group. Card and Lemieux (2001) and Manacorda et al (2012) present an application of these different approaches. The desired labor force surveys are not publicly available in such micro ~~-~~ format for most countrries; the available harmonised surveys for EU countries provide wage brackets, not the exact wages of individuals.

As a validation exercise, we thus compare our simulated wage bill shares with existing estimates available for 13 countries in the year 2010.'° The share of total wage bill going to a particular age group is the product of the size of that group and the normalized wage going to a given individual in that group, divided by the total wage bill of the entire economy. Results are depicted in Figure 4. The regression of observed wage bills on simulated shares shows a slope of 1.026 and an intercept of 0.003 (R ~~-~~ squared of 0.918), which is almost identical to the 45° line. This shows that the imputation strategy used to construct wage rate proxies (which involves a constant age premium for all countries, and a country ~~-s~~ pecific immigrant premium for all skil ~~l~~ -experience cells) is very consistent with labor force survey data.

# 4 Equilibrium wage effects

In this section, we use the benchmark labor market model to quantify the relative and aggregate wage effects of the soci ~~o-~~ demographic changes (education and ageing) and mi ~~-~~ gration that have taken place between 2000 and 2010, for non ~~-~~ migrant natives by skill and age group. Remember the model is calibrated to match the wage structure observed in the year 2010. We simulate counterfactual wage structures in 2010 as if soci ~~o-~~ demographic changes had not happened, and express the wage effect as a percentage of the counter ~~-~~ factual level. In other words, the wage effects are computed as (wsAM — welAu) /we,M> where we, vr is the equilibrium wage rate obtained in the counterfactual without ageing, <u>~ 10This group includes Austria, Belgium,</u> Switzerland, Spain, Ireland, Hungary, Greece, the United Kingdom, Italy, Norway, Poland, Portugal and the United States.

19
# **1 Introduction**

The world is urbanizing rapidly, especially in developing countries, where, by 2020, more than half the population is projected to be urban (United Nations, 2011). This is welcomed as a most positive development by some observers, as exemplified in “ _The Triumph of the City: How Our Greatest Invention Makes Us Richer, Smarter, Greener, Healthier and Happier_ ” (Glazer, 2011). The view goes back to longstanding theories of economic development, finding their roots in the work of Arthur Lewis and Simon Kuznets. As people shift out of agriculture to more remunerative activities off the farm and outside the rural areas, a positive virtuous economic dynamic is set in motion, with new opportunities being generated, by attracting poor rural workers who gain directly, and by positively affecting the rural areas indirectly, through remittances and increased demand for their goods, fostering economic growth and reducing poverty.

Others view these developments with more suspicion and see them as a forebear of new sources of poverty. They point to congestion effects hindering growth and the negative externalities from geographically concentrated poverty (such as violence) as well as the irreversibility of urban migration. High migration costs may prevent the poor from returning, locking them in their new informal settings. A larger share of the poor appears indeed to be living in urban areas nowadays, coined the “urbanization of poverty”, even though overall poverty has come down substantially (Ravallion, Chen, and Sangraula, 2007; Chen and Ravallion, 2010).

This paper takes a different view and seeks to shift the dialogue beyond the oft stale dichotomy between rural and urban development communities, drawing attention to the nature of the urbanization process. In particular, not only is the developing world poised to urbanize rapidly (with the urban population expanding by about another one billion people between 2010 and 2025 to 3.6 billion), most of this urbanization is also projected to concentrate in large cities. Only 140 million of the new urban population is expected to reside in secondary towns of 500,000 people or less, while 664 million are projected to join cities of one million or more (UN 2011 World Urbanization Prospects Report).

2
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The country has achieved one of the highest rates of GSM coverage in Africa. Over 90 percent of Kenya’s population lives within range of a GSM signal. This is one of the highest rates in Africa. It is likely that another seven percent could be profitably served by private operators. Only about one percent of the population would not be commercially viable to serve and would probably require some degree of public subsidy. Furthermore, about 30 percent subscribe to the service with a further 2 percent of the population being added each year (figure 11).

**Figure 11. Kenya has made tremendous progress in expanding GSM coverage**

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# **Challenges**

Prices for ICT services in Kenya remain relatively high. Charges for fixed-line, mobile, and international calling, and for Internet access, are significantly higher in Kenya than in comparable African countries (table 7). The recent award of a fourth mobile license is beginning to exert some downward 20
# **I. Introduction**

In the past few years, several major developing countries (including Indonesia, Korea, Mexico, and Thailand) have suffered crises of confidence that led to sudden, large-scale reversals in the flows of capital that those countries had been receiving. These reversals have had serious adverse effects on, and were in many instances accelerated by, the health of the banking systems in those economies. As a result, policymakers have become increasingly concerned with the link between stability in external financing and stability in the banking sector. This link is particularly important for Central and Eastern Europe (CEE).<sup>1</sup> Banks in the region intermediate a disproportionately large part of the region's capital inflows. In addition, the region is in the midst of completing a difficult process of transition away from central planning and, thus, still lacks the full array of institutions necessary to underpin efficient financial sectors (for example, well-defined property rights; transparent accounting, auditing, and reporting standards; effective court systems; adequate collateral registration and recovery mechanisms and so on).

The literature on the link between capital _inflows_ and banking stability is extensive. This literature focuses heavily on the bank lending booms that frequently accompany large-scale capital inflows, and on the large credit losses that banks often experience as the result of these booms. (World Bank 1997; Hausmann and Rojas-Suarez 1996; and Sundararajan and Balifto 1991). However, the literature on the link between capital _outflows_ (or capital inflow _reversals)_ and banking stability is surprisingly scant, and virtually nonexistent with reference to CEE transitional economies.

This paper analyzes the relationship between capital inflow reversals and banking stability in CEE countries. Following this introductory section, Section II describes the main features of the region's observed capital inflows since the early 1990s, with special emphasis on their potential reversibility and on the role banks have played in channeling those inflows. Section III then presents a conceptual

1. CEE countries comprise Albania, Bulgaria, Croatia, the Czech Republic, Hungary, the former Yugoslav Republic of Macedonia, Poland, Romania, the Slovak Republic, and Slovenia.

1
The OLS regression of firm size on tax inspection is likely to suffer from reverse causality and omitted variable bias. In particular, firms might reduce their size to prevent facing more stringent tax inspection. To address this issue we turn to an instrumental variable strategy. A valid instrument predicts firm size and only impacts tax inspection through its effect on firm size. A vast literature finds that industries vary in their optimal scales and that there exists a structural technological demand for labor at the industry level. Our identification strategy follows the intuition of Rajan and Zingales [1998]: if we consider the US as an undistorted market, then US firms achieve their optimal unconstrained size, which depends on the structural scale parameter of their industry and idiosyncratic shocks. This suggests using the average size of firms in industries in the US as an instrument for the average size of firms in the same industry in lower-income countries. The first stage is estimated with the following regression: Where Rank size _ict_ is the average firm-size rank of ISIC3 industry _i_ , in country _c_ , at time _t_ , and Rank size _i_ , _US_ ,01 is the rank by firm-size, of industry _i_ , in the US census in 2001. _γct_ are country-year fixed effects. The US industry ranking is drawn relative to the set of industries present in a given country-year, and we weight the regression results by the number of observations in a given ISIC3-country-year cell.<sup>16</sup> The slope-coefficient _α_ 1 measures the increase in the size-ranking of an industry in its country, when moving along the ranking of industry size in the US. Table A1 shows that the results are robust to using average number of workers per industry rather than industry ranking based on number of workers. We implement the first stage with a rank-rank specification for two reasons. First, by using an industry‘s ranking in terms of average workers, the coefficient _β_ only depends on the joint distribution of average size in the WBES country and aver-

> 16This allows for ISIC3-country-year cells measured with greater precision to carry more weight in the estimates. Omitting weights does not change qualitatively the results, which remain significant.

11
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# **References**

- **Abbott, Philip C** , “Export restrictions as stabilization responses to food crisis,” _American Journal of Agricultural Economics_ , 2012, _94_ (2), 428–434.

- **Aksoy, Ataman and Bernard Hoekman** , _Food prices and rural poverty_ , Centre for Economic Policy Research, 2010.

- **Anderson, Kym and Ernesto Valenzuela** , “Estimates of global distortions to agricultural incentives, 1955 to 2007,” _World Bank, Washington, DC_ , 2008.

- **Baffes, John** , “Some further evidence on the law of one price: The law of one price still holds,” _American Journal of Agricultural Economics_ , 1991, _73_ (4), 1264–1273.

- **and Bruce Gardner** , “The transmission of world commodity prices to domestic markets

- under policy reforms in developing countries,” _Policy Reform_ , 2003, _6_ (3), 159–180.

- **Barrett, Christopher B** , “Market analysis methods: are our enriched toolkits well suited to enlivened markets?,” _American Journal of Agricultural Economics_ , 1996, _78_ (3), 825–829.

- **Becker-Reshef, Inbal, Chris Justice, Mark Sullivan, Eric Vermote, Compton Tucker, Assaf Anyamba, Jen Small, Ed Pak, Ed Masuoka, and Jeff Schmaltz** , “Monitoring global croplands with coarse resolution earth observations: The Global Agriculture Monitoring (GLAM) project,” _Remote Sensing_ , 2010, _2_ (6), 1589–1609.

- **Brenton, Paul, Alberto Portugal-Perez, and Julie Régolo** , “Food prices, road infrastructure, and market integration in Central and Eastern Africa,” _World Bank Policy Research Working Paper 7003_ , 2014.

**Brown, Molly E** , _Food security, food prices and climate variability_ , Routledge, 2014.

- **Brückner, Markus and Antonio Ciccone** , “International commodity prices, growth and the outbreak of civil war in sub-saharan Africa,” _The Economic Journal_ , 2010, _120_ (544), 519–534.

- **and** , “Rain and the democratic window of opportunity,” _Econometrica_ , 2011, _79_ (3),

- 923–947.

- **Burgess, Robin and Dave Donaldson** , “Can openness mitigate the effects of weather shocks? Evidence from India’s famine era,” _The American Economic Review_ , 2010, pp. 449–453.

- **Burke, Marshall, Solomon M. Hsiang, and Edward Miguel** , “Climate and Conflict,” _Annual Review of Economics_ , 2015.

- **Burke, William J and Robert J Myers** , “Spatial equilibrium and price transmission between Southern African maize markets connected by informal trade,” _Food Policy_ , 2014, _49_ , 59–70.

- **Cuesta, José, Aira Htenas, and Sailesh Tiwari** , “Monitoring global and national food price crises,” _Food Policy_ , 2014, _49_ , 84–94.

20
information from outside homelands is sufficient. Hence we are unable to pinpoint the underlying micro-mechanisms driving our results.

The remainder of this paper is organized as follows. Section 2 details the historical development of South Africa thereby providing evidence for the quasi-random location of the homelands. Section 3 discusses the related literature and introduces the theoretical thought experiment that serves as a framework for the empirical analysis presented in Section 4. The heterogeneous responses to a positive population shock are discussed in Section 5, and section 6 concludes.

# **2 Historical Background**

Around two-thirds of South Africa’s total population live in urban areas, making it one of the most urbanized countries in Africa. In the second half of the 20th century, urbanization in South Africa was shaped by the apartheid policy of the National Party government (1948-1994). Apartheid - literally meaning “apart-ness” - was by its very nature a spatial concept (Christopher, 2001). The government aimed to completely separate the black and non-black populations.<sup>3</sup> Policies ranged from installing two town hall bathrooms to segregating city quarters and creating native reserves, the so-called homelands (or ‘bantustans’) that were to become independent states for the black population.

Segregation and mobility restrictions imposed on the black population had a long tradition in South Africa dating back to at least the 18th century (Lapping, 1986). The

> 3 We use the same terminology for racial categories as the census, namely ‘Black’ or ‘African’, ‘Colored’, ‘Asian/Indian’, and ‘White’, where the last three categories make up to the ‘Non-black’ category.

5
# **Table 8. Use of Capital and Timing of Structural Reforms**

Dependent variable: log capital stock ratio, ln(K/Y)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|
|**Pillar 1**|0.0655***<br>(2.66)|0.0582***<br>(2.22)|0.0711***<br>(2.79)|0.054***<br>(2.20)|
|**Pillar 2**|0.0000537***<br>(4.19)|0.0000504***<br>(3.79)|0.0000554***<br>(4.24)|0.00005***<br>(3.88)|
|**Pillar 3**|0.000393|0.000530*|0.000651*|0.00044|
||(1.12)|(1.29)|(1.63)|(1.28)|
|**Pillar 4**|0.0313|-0.0175|0.0294|0.023|
||(0.61)|(-0.34)|(0.56)|(0.46)|
|**Pillars 5 and 6**|0.0254|0.0624|0.0347|00267|
||(0.28)|(0.65)|(0.37)|(0.30)|
|**Pillar 7**|||||
|Tax rate|-0.200***|-0.214***|-0.197***|-0.158***|
||(-3.11)|(-3.27)|(-3.06)|(-2.43)|
|Regulatory quality reform|<br>0.00429|<br>0.00220|<br>0.00246|<br>0.009**|
||(0.84)|(0.40)|(0.46)|(1.69)|
|Regulatory quality reform, slack|-0.0155***<br>(-2.53)|-0.0137**<br>(-1.84)|-0.0166***<br>(-2.34)|-0.0101*<br>(-1.76)|
|Regulatory quality (t-1)||||0.074***<br>(2.74)|
|Tariffs|-0.00354***||-0.00386**|<br>-0.0034***|
||(-2.26)||(-1.89)|(-2.24)|
|Openness||-0.000521*<br>(-1.47)|||
|**Pillar 8**|||||
|Women (%)|0.0137*|0.0181**|0.0123|0.0160***|
||(1.55)|(1.79)|(1.27)|(1.85)|
|CBR working time|0.0579|0.0432|0.0925|0.0512|
||(0.50)|(0.34)|(0.78)|(0.45)|
|**Pillar 9**|0.0394*|0.0227|0.0427*|0.179|
||(1.58)|(0.76)|(1.54)|(0.70)|
|**Pillar 11**|-0.000996|-0.000993|-0.00118|-0.0016|
||(-0.46)|(-0.41)|(-0.52)|(-0.75)|
|**Pillar 12**|<br>0.0105|<br>0.0139|<br>0.0121|<br>0.0157**|
||(1.22)|(1.11)|(1.01)|(1.81)|
|EU dummy|0.859**|0.901**|0.833*|0.776*|
||(1.73)|(1.91)|(1.56)|(1.55)|
|Year effect|<br>No|<br>Yes|<br>Yes|<br>No|
|Time trend|Yes|No|No|Yes|
|Constant|Yes|Yes|Yes|Yes|
|Observations|257|259|257|257|
|Wald Test|685.0|650.0|703.7|719.24|
|Wald Test p-value|0|0|0|0|

_Note: t_ statistics in parentheses; * p<0.2, ** p<0.1, *** p<0.05 21
Policy Research Working Paper 8568

# **Abstract**

This paper proposes a model-based approach to estimate income boundaries for identifying the middle class in Kazakhstan over 2003–15. The approach exploits the subjective evaluation of Kazakhstan households about their social status, relating self-declared social class membership to income. Income data come from the Kazakhstan Household Budget Survey, which also includes a specific module on quality of life and perceived social status. As social status is intrinsically an ordinal response, the paper estimates a proportional odds model with income as the key explanatory variable. Although other factors influence the self-perception of being in the middle class, income is by far the most important determinant. Benchmarking on 2013, the estimated middle class lower bound is $14 at 2011 purchasing power parity and the upper bound is $52. The Kazakhstan middle class has increased massively in size and income concentration. The increase is essentially due to a growth effect rather than a redistributive cause.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/research. The authors may be contacted at grazia.pittau@uniroma1.it and roberto.zelli@uniroma1.it.

_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
the location being studied (Fuente et al. 2016). Furthermore, no previous studies have obtained such information from both utility providers (water and electricity) for the same location. Finally, we found no studies in the literature that matched household socioeconomic data to billing records obtained from both water and electricity utility companies.

In short, hardly any studies have simultaneously analyzed the incidence of residential water and electricity subsidies. Of the few that do, none have used customer billing records from utilities to determine household water and electricity use. In this study we match households’ customer identification (ID) numbers—obtained from our household survey of socioeconomic conditions— with the billing records of the water and electricity utility companies in Addis Ababa, Ethiopia.<sup>5</sup> Also, we estimate the total average cost of both services using financial data collected from the electricity and water utility companies serving the Addis Ababa population.

# **_<mark>Comparison of water versus electricity subsidies in the residential sector</mark>_**

<mark>Most studies on subsidy incidence and the cost recovery of water and electricity public services report that utilities use IBTs to calculate household bills. Foster and Yepes (2006) look at both the water and electricity sectors and present an analysis of cost recovery by these utilities for several large cities in Latin America. Utilities in most of those cities use IBT structures. Of the 17 water utilities, 15 use IBTs, and 8 of 14 electricity utilities use IBTs. Komives et al. (2006) review previous studies on water and electricity subsidy incidence in the residential sector in developing countries. Their case studies come from 21 different countries. For 12 cases in the water sector, these authors find that seven utilities used IBTs, one used geographically defined tariffs with IBTs, one used a uniform volumetric tariff, and three used means-tested discounts. For the electricity sector, four of 12 cases</mark>

> 5 We were able to nearly match all our observations of electricity private connections to the electricity utility database. In addition, we were able to match 70% of our observations of private water connections to the water utility database; for the remaining 30%, we relied on customer self-reports and back-calculations.

7