rafmacalaba/datause-encoder-data huggingface dataset:
average income growth. The bottom panel of Figure 4 illustrates this, but without the log transform required to do the variance decomposition. On the horizontal axis I graph the growth component of the change in poverty, while on the vertical axis I graph growth in average incomes. While the slope of this regression cannot be interpreted as a variance share, it nevertheless is very clear that cross-country differences in the growth component of poverty are overwhelmingly accounted for by cross-country differences in growth. Put differently, it is clear from this graph that if the growth component of poverty reduction is large, it is most likely that growth itself was large, rather than that the sensitivity of poverty to growth was large.<sup>8</sup> Two striking features of Tables 2 and 3 merit further discussion: (a) the share of the variance due to growth is smaller over the short horizons represented in the dataset of all spells, and is larger in the dataset of long spells; and (b) in both datasets, the share of the variation in poverty measures due to growth declines as the poverty measures become more bottom-sensitive, for example when we move from the headcount to the poverty gap to the squared poverty gap. We can understand these properties better with the help of a simple example using the EDEI poverty measure. We can write the discrete proportional change in EDEI as: Recall that EDEI(1) is just average income, and that the sensitivity of EDEI to growth in average incomes is one. As a result, Equation (6) is a way of writing the Datt-Ravallion decomposition for this measure, with the first term corresponding to the growth component and the second to the distribution component. Moreover, the distributional change component of the change in this poverty measure corresponds to the change in

> 8 At first glance this result seems inconsistent with Ravallion (1997), who documents that the sensitivity of poverty to growth varies significantly with initial inequality.  However, using either sample of spells I can replicate the result that the interaction of growth with the initial Gini coefficient is significantly correlated with the change in headcount measures of poverty. Intuitively, the difference between the results here and those in Ravallion (1997) can be understood as follows:  although the interaction of growth with initial inequality is significant in explaining changes in poverty, it does not add much to the explanatory power of the regression in my samples.  Put differently, although there are cross-country differences in the sensitivity of poverty to growth which are significantly correlated with initial inequality, in the data these differences are dominated by the much larger cross-country differences in growth itself.

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<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Agriculture Industry Services<br>5<br>4<br>3<br>2<br>1<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Agriculture Industry Services<br>Gap in consumption per working person per year<br>Gap in consumption per hour worked<br><!-- End of picture text --> Figure 10: Consumption gaps by sector. Figure 10a (top) shows the ratio between consumption based on expenditures per working household member per year between households primarily participating in agriculture and those primarily participating in industry, services and ”unknown” sectors, respectively. Figure 10b (bottom) shows the ratio between consumption per hour of labor supplied by the household for households primarily participating in agriculture vs. in other sectors.

30
<!-- Start of picture text --> Migration Flows by State - In Order of Inter-State Frequencies<br>% frequencies within categories<br>Intra-State Inter-State<br>Uttar Pradesh 09Rajasthan 08Bihar 10West Bengal 19Madhya Pradesh 23Uttarakhand 05Orissa 21Jharkhand 20Chhattisgarh 22Delhi 07Kerala 32Karnataka 29Andhra Pradesh 28Himachal Pradesh 02Punjab 03Jammu & Kashmir 01Tamil Nadu 33Assam 18Haryana 06Maharashtra 27Gujarat 24Nagaland 13Chandigarh 04Pondicherry 34Arunachal Pradesh 12Sikkim 11Tripura 16Meghalaya 17Dadra+Nagar Haveli 26Mizoram 15Manipur 14Daman & Diu 25Goa 30<br>40<br>30<br>% 20<br>10<br>0<br><!-- End of picture text --> Source: Authors’ calculation based on the IHDS

# **C. Socioeconomic Characteristics That Influence Migration**

The decision to migrate for economic reasons is typically driven by the spatial earnings gap, although the ability to migrate is influenced by a range of individual and household characteristics. The evidence on rural-urban migration consistently highlights differences in earning opportunities between rural and urban areas as the predominant factor driving relocation. Much of this evidence on the spatial gap is about differences in wages or rates of pay, although the likelihood of finding employment is also represented. At the same time, internal migrants may have widely varying degrees of education and income levels, as well as varying profiles in terms of caste, religion, family composition, age, and other characteristics.

Analysis based on the IHDS shows that young, married men with lower secondary school education constituted an overwhelming majority of all nonresident household members. In 201112, more than 80 percent of all nonresident household members were male (Figure 11). Of these, 57 percent were married in 2004-05, but the share of married men in the stock of nonresidents declined to 33 percent in 2011-12 (Figure 12). On average, male migrants were around 30 years of age (Figure 13) with nine years of schooling (Figure 14). In Uttar Pradesh, the average years of schooling for migrants was higher compared to the resident household head as well as the state average (Figure 15). The same pattern held true in Bihar (Figure 16).

**Figure 12: Share of married male migrants, 2004-05 and 2011-12**

**Figure 11: Male-female distribution of migrants, 2004-05 and 2011-12**

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PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA

# 3   The extent of  own generation in Africa

In-house generation of electrical power accounts for only around 6 percent of the installed generating capacity in Sub-Saharan Africa (table 1).<sup>11</sup> This is a bit more than the share in the United States (3.7 percent) and about the same level as in the enlarged European Union (7.3 percent).<sup>12</sup> There is a considerable variation in the scope and size of in-house generation across Sub-Saharan Africa. The trade-adjusted share of own generation in countries where it exceeds 1 percent is shown in figure 9.<sup>13</sup> In most Sub-Saharan countries, own generation represents less than 10 percent of the installed capacity. In three countries, however, it accounts for more than 25 percent of installed capacity, and for 10–25 percent in nine others.

**Table 1 Distribution of installed electrical generating capacity, by region, income group, and conflict status, 2006**

|Percentage shares oftotal|
|---|

|||Utilit /|Self-|generation, by sec<br>Manfac-|tor<br>Commerce /|
|---|---|---|---|---|---|
||Total MW|y<br>government|Mining|u<br>turing|<br>services|
|_Geographic areas_||||||
|Central Africa|5,639.29|92.33|5.33|1.52|0.83|
|East Africa|2,731.07|92.07|2.95|3.32|1.66|
|West Africa|14,080.21|82.87|10.62|2.01|6.28|
|South Africa|50,352.16|97.47|0.99|1.41|0.13|
|_Income groups_||||||
|Low Income|26,357.53|88.19|6.82|2.27|3.67|
|Lower-middle-income|3,971.91|94.23|3.27|1.40|1.09|
|Upper-middle-income|42,473.29|97.66|1.05|1.21|0.07|
|_Conflict status_||||||
|Nonconflict|54,652.76|96.43|2.04|1.43|0.18|
|Conflict / postconflict|18,149.97|86.86|6.94|2.14|5.21|
|_Africa total_|72,802.73|94.05|3.26|1.60|1.43|

_Source_ : UDI World Electric Power Plants Data Base (WEPP).

11 The estimates are based on the assumption that 100 percent of installed capacity is functional. In most countries of Sub-Saharan Africa this is not the case, and in many it is as low as 50 percent. Because exact statistics for each country are not available, the size of the observed phenomenon is downward biased. The size of the bias in the share of own-generation capacity in the country's total generation portfolio may vary by half or double.

12 U.S. Department of Energy, <u>http://www.doe.org; EUROSTAT, http://www.eurostat.europa.eu.</u> 13 The results reported in figures 9 and 10 are based on the WEPP dataset and hence underestimate the value of small generators (less than 100 kW). The size of the bias is too small (about 250 kW per country) to make any significant changes in the ratios of capacity to GDP and capacity to gross fixed capital formation.

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capital is 5.3%. We see a similar pattern for each of the components of agricultural capital. Fixed capital and treestock have median growth rates of 5.6 and 5.7%, respectively. While livestock grew in all countries, the growth was slower than for the other two components (median of 3.6, with the distribution lying almost completely to the left of the others). This changing composition of agricultural capital is even more apparent in Figure 3. In 90% of the countries in our sample, the share of livestock in total agricultural capital fell over the period, with treestock and fixed capital becoming increasingly important inputs to agricultural production, depending upon the geographical constraints faced by the country.

We can gain insight on the dynamics of growth in agriculture by examining the changing composition of agricultural capital over time. The patterns of capital accumulation for each component vary over time. In Table 1, we present the average annual growth rates for the sample for each decade. Fixed capital grew at a rapid pace of 7.3% in the 1970s. The slowdown in fixed investment began in the 1980s and continued into the 1990s, though fixed capital was still growing by 3.6%. Livestock grew at a similar pace in the 1970s and 1980s (3.9 and 4.0% respectively), but then experienced slower growth in the 1990s. Treestock achieved a growth rate for the entire period comparable to that of fixed capital, but with a very different pattern over the decades. Treestock grew slower than fixed capital and livestock in the 1970s (3.2%) but achieved nearly double-digit growth rates in the 1980s. While the growth slowed in the 1990s, treestock continued to grow faster than fixed capital and livestock. Thus the decreasing share of livestock in total agricultural capital was due in part to the rapid growth in treestock in the 1980s and 1990s, as well as the slowdown in the accumulation of animal stocks in the 1990s.

In addition to the time dimension, there is much cross-country variability in agricultural capital stocks. For purposes of comparison, we divide the sample into high-income countries versus middle- and lower-income countries (Table 2).<sup>18</sup> High-income countries achieved higher growth rates of fixed capital (6.2% versus 5.5%), while the growth of treestock was considerably faster in middle- and lower-income countries (6.6% versus 4.0%). The growth rate of livestock was only slightly higher in middle- and lower-income countries (3.8% versus 3.3%).

These growth patterns led to interesting changes in the composition of capital for each income group as shown in Table 3 and Figure 4. The shares of each component in total agricultural

> 18 The income classifications are based upon the World Development Indicators of the World Bank in 2000.

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**_<u>________________________________________________________</u>_**

<u>Module 5:</u> (PDF, 626KB) discusses the rationale for and concepts of competition policy in the sector.

<u>Module 6:</u> (PDF, 747KB) provides insight into the concept of universal services and discusses universality approaches and funding mechanisms.

<u>The Appendices:</u> (PDF, 1.0MB) include a World Trade Organization Regulatory Reference Paper and information on the economics of telecommunication prices and costs.

_<u>World Economic Forum</u>_<sup>_iii_</sup> _<u>(WEF) Report and The NRI</u>_ Published since 2001, the _Global Information Technology Report_ assesses the ability of a country to take advantage of technology to improve productivity. The Networked Readiness Index (NRI) measures "the degree of preparation of a nation or community to participate in and benefit from ICT developments".

The Global Information Technology Report 2004-2005 is the fourth in a series assessing the state of the networked readiness of 104 economies. It is an update of the previous Reports, capturing new insights and best practices and gleaning policy lessons from various country experiences. The Report remains the most comprehensive and authoritative international assessment of the preparedness of countries to capture the benefits of participating in the Networked World.

The NRI is composed of three component indexes which assess:

- the environment for ICT offered by a given country or community

- the readiness of the community's key stakeholders - individuals, business and governments

- the usage of ICT among these stakeholders

_<u>Organization for Economic Cooperation and Development (OECD) Report on Measuring the Information Economy (2002)</u>_ _Measuring the Information Economy 2002_ includes over 80 indicators based on official surveys, providing a comprehensive international comparison of countries' performance in the information economy. It addresses issues such as international differences in the quality and price of the ICT infrastructure, diffusion of Internet technologies in larger and smaller firms, relative size of cross-border electronic transactions, and barriers to Internet commerce. For the first time, data annexes with time series for the ICT sector are made available in the electronic version. Indicators include number of websites, number of internet hosts per 1000 inhabitants, number of businesses with internet access, internet penetration by activity, businesses using internet for purchasing and selling.

_<u>McConnell International Report: Global E-Readiness</u>_ “E-Readiness” measures the capacity of nations to participate in the global economy, and represents multiple levels of ICT development by benchmarking progress through indicators of Connectivity, E-Leadership, Information Security, Human Capital, and E-Business Climate. The 2001 report analyzes 53 countries, covering two-thirds of the world’s population and the biggest potential markets. The assessment was developed in collaboration with experts from public, 36 __________________________________________________________________________________________________________
housing subsidies. The phasing out of subsidies over this period may have reduced urban income inequality.

The various estimates of income discussed so far are for disposable income, which includes various private and public transfers as well as factor incomes (derived from productive activities). In fact, taxes and subsidies have done nothing to remedy factor income inequality, although the degree of fiscal regressivity (in which the effect on income is disproportionally greater on poorer than richer people) has fallen as reforms have progressed (Khan and Riskin 2007). In 2007, the urban Gini for income after the deduction of direct taxes was only 1 percentage point lower than its pretax counterpart (Xu and Yue 2013).

A rich entrepreneurial class emerged remarkably rapidly in China. There were above-normal profits to be earned, and the combination of a semimarketized economy, weak legal system, and ill-defined or insecure property rights provided room for corruption, cronyism, and rent seeking. Because not all income derived from such activities is detectable in the NBS or CHIP surveys, incomes at the top of the income distribution are likely to be understated. An ingenious attempt to measure this effect claimed to find much “grey income” in the highest income group (Wang and Woo 2010)<sup>2</sup> .

During the period of central planning, there was almost no personal wealth in China. Economic reform brought not only rapid accumulation but also considerable 6
# **Introduction**

A country’s statistical capacity is increasingly recognized as crucial for its development, for richer and poorer countries alike. Stronger statistical capacity results in better measurement of economic and social activities and outputs, which facilitates timely decisions by policy makers. Stronger statistical capacity can also contribute to better governance and accountability, where citizens are better informed about government activities and can be more engaged in the monitoring process. Indeed, the Sustainable Development Goals (SDGs) call for more capacitybuilding support to developing countries to help significantly increase the availability of highquality, timely, and reliable data (United Nations, 2023; SDG number 17.18). In a recent global flagship report, the World Bank highlights the strong, positive relationships between a country’s statistical capacity and the independence of its national statistical office (NSO) and its freedom of the press (World Bank, 2021).

Some practical examples can further illustrate the consequences of statistical capacity— stronger and weaker—in action. For the past decades, the U.S. Bureau of Labor Statistics has kept close track of the latest (un)employment trends in the economy and publicly releases these statistics every month. Data on employment trends keep all stakeholders well-informed, and typically feed into prompt actions by both the (interest-rate-setting) Federal Reserve Board and private investors in the stock market. Given the prominent role of the U.S. economy in the world, these actions typically reverberate overnight to other economies and stock markets around the world.

On the other hand, Roseth _et al._ (2019) observe that inaccuracies in the measurement of the municipal population resulted in approximately US$92 million in mis-targeted government budget transfer in El Salvador between 2000 and 2007. Weak statistical capacity not only causes inefficiencies but could lead to corruption and consequential damages to the economy. A notable case occurred when three state-backed Mozambican firms borrowed US$1.2 billion in government-guaranteed debt in 2013-2014—roughly 8 percent of the country’s GDP—without the parliament and the public’s knowledge. This lack of public debt transparency led to Mozambique facing severe restrictions in the international credit market when these loans were revealed in 2016 (Economist, 2019; IMF, 2019). More strikingly, a recent study estimates that, due to the incentive to overstate economic growth and a lack of checks and balances, autocratic regimes could overstate yearly GDP growth by 35 percent (Martinez, 2022).

2
# **1. Introduction**

In policy and political discourse, “equality of opportunity” is the new motherhood and apple pie. It is often contrasted with equality of outcomes, with the latter coming off worse. Equality of outcomes is seen variously as Utopian, as infeasible, as detrimental to incentives, and even as inequitable if outcomes are the result of differing efforts. Equality of opportunity, on the other hand, is interchangeable with phrases such as ‘leveling the playing field’, ‘giving everybody an equal start’ and ‘making the most of inherent talents.’ In its strongest form, the position is that equality of outcomes should be irrelevant to policy; what matters is equality of opportunity.

But the application of the broad concept of equality of opportunity requires that it be specified sufficiently precisely to be measurable and implementable. The last two decades have seen a number of attempts to formulate the concept of equality of opportunity with a view to policy application, not least in the World Bank through its World Development Report 2006 (World Bank, 2005) and the subsequent “mini-industry” which has developed in estimating measures of inequality of opportunity using household survey data (for example, de Barros et. al. 2009). The empirical work appears to have given confidence to those who would use the concept of equality of opportunity in the policy context, that it can be measured and quantified, so that policies can be designed and judged according to it.

However, attempts to quantify and apply the concept of equality of opportunity in a policy context have also revealed a host of problems of a conceptual and empirical nature, problems which may in the end even question the practical usefulness of the concept. The object of this paper is to review and critique recent attempts at specifying and quantifying equality of opportunity, and to assess the extent to which they do in fact allows us to discard equality of outcomes as a key part of policy evaluation.

The plan of the paper is as follows. Section 2 sets out the philosophical basics of equality of opportunity, focusing on key writings in the last three decades. Section 3 then introduces the attempts at application and quantification at a general level, particularly in the policy context, and presents a critique. Section 4 carries the argument forward to the specifics of the education and health domains, to see how the general arguments play out in these settings. Section 5 concludes.

# **2. Equality of Opportunity: The Basics**

The core premise of the literature on equality of opportunity is the idea that inequalities in outcomes can be partitioned into justifiable (or legitimate) sources of inequality (let us call them the J’s) and unjustifiable (or illegitimate) sources (let us call them the U’s). The idea is that inequalities in outcomes are to be seen as just (or fair) insofar as they are driven by inequalities in the J’s, and unjust (or unfair) insofar as they are driven by inequalities in the U’s. The practical usefulness of the idea of inequality of opportunity boils 2
|Log of refugees (100km) predicted by Log of<br>Distance IV (square of mean border distance)|||||-0.00552<br>(0.21306)|0.00183<br>(0.21176)|-0.02647<br>(0.24606)|0.07118<br>(0.22566)|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Log of refugees (100km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.11163<br>(0.19594)|0.10884<br>(0.19714)|-0.05718<br>(0.18766)|0.01251<br>(0.17177)|
|Observations|843|842|533|533|843|842|533|533|843|842|533|533|
|Kleibergen-Paap rk Wald F|3.643|3.644|2.950|3.236|4.429|4.435|3.351|3.665|2.879|2.877|2.542|2.778|
|Root MSE|0.518|0.515|0.436|0.369|0.517|0.514|0.436|0.370|0.520|0.516|0.436|0.368|
|Log of refugees (100km) predicted by Log of<br>Distance IV (mean border distance)|0.07325<br>(0.17144)|0.06729<br>(0.15792)|0.06509<br>(0.19841)|0.05537<br>(0.20269)|Panel|D (Non-agricu|lture self-emplo|yment)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square of mean border distance)|||||0.03162<br>(0.15149)|0.03276<br>(0.14153)|0.01116<br>(0.19754)|-0.00291<br>(0.19972)|||||
|Log of refugees (100km) predicted by Log of<br>Distance IV (square-root of mean border<br>distance)|||||||||0.17851<br>(0.24417)|0.16383<br>(0.22284)|0.21811<br>(0.25041)|0.21722<br>(0.25831)|
|Observations|1,068|1,067|669|667|1,068|1,067|669|667|1,068|1,067|669|667|
|Kleibergen-Paap rk Wald F|8.734|9.144|5.631|5.810|10.47|10.90|6.610|6.685|6.193|6.563|4.166|4.406|
|Root MSE|0.517|0.513|0.414|0.325|0.516|0.512|0.413|0.325|0.523|0.517|0.421|0.333|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|
|<br>Other controls (HH size, marital status,|||||||||||||
|<br>education, occupation, other income sources,<br>land ownership)|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|

40