rafmacalaba/data-use-mentions-extended huggingface dataset:
potential for narrow productivity gains, e.g. by promoting foreign direct investment in retail. But, as Rodrik (2018: 16) put it, “achieving productivity gains along the entire retail sector is extremely difficult in view of the heterogeneity of organizational forms and the range of prerequisites across different segments.” Looking into the drivers of overall growth, we especially found financial development and infrastructure to be key drivers of Ghana’s growth performance since 2000. This is consistent with the picture that extended periods of growth above the 2 percent historical average call for improvements in structural fundamentals and with the two best-performing economies in our comparative analysis, Korea and Malaysia, that also both saw considerable improvements in those variables.

Going forward, our analysis underscores the relation between short-run and long-run policies. In the short run, fixing the macroeconomic stabilization framework should be a priority given the recent boom-bust episode, our result that the most recent episode of growth (since 2010) was not justified by economic fundamentals, and large policy gaps compared to peer countries in this dimension. The need for further improvements of structural fundamentals to enjoy continued substantial growth rates in the longer run is supported by our benchmark exercise (and consistent with firm-level surveys) showing substantial gaps in some variables capturing structural factors, foremost financial development and aspects of infrastructure. The latter will also be important to foster regional and global trade integration. Improving access to finance, together with other reforms such as removing firms’ business constraints and broadening skill levels, may support the development of new firms in less isolated and more productive areas than the traditional wholesale and retail trade sector, boosting growth-enhancing structural change.

Without prudent stabilization policies, necessary investments in many of those growth fundamentals are hard to finance. Additionally, there could be an important macro relation between the exchange rate regime and structural transformation, as moving away from the current overvaluation would also make the tradeable sector internationally more competitive and thus potentially offer job prospects in above-average productivity sectors where the marginal product of labor is not
conditions. Moreover, King (2011) shows that marital status is an important factor determining gender differences in internal migration in Mexico (contrary to the trend of labor mobility reason among men).

**Last, this study also relates to the literature on returns to migration** . Most of the literature on the consequences of migration focuses on the effects of migration from developing countries on wages in developed countries (for instance, Card, 1990, 2001; Card and DiNardo, 2000; Kritz and Gurak, 2001) and the impact of immigration flows on local labor markets (Borjas, 2006).<sup>6</sup> Little is known about the impact on migrants' labor income. Some studies provide evidence of migrants’ income gains from international migration.

**Estimating returns to migration is complex since the estimates may be affected by selection bias, with differences in earnings for migrants and non-migrants reflecting unobserved differences in ability, skills, and motivation rather than the act of moving itself.** Using experimental impact evaluation techniques to isolate the causal effect of migration on salaries, McKenzie et al. (2010) compare the earnings of Togans' winners and losers from a visa lottery to migrate to New Zealand, finding a high average income gain. Clemens et al. (2008) combine Mexico-US household data to control for self-selection, obtaining evidence of wage gains for urban Mexican males in the US.

**The evidence of the effects of internal migration on income is even scarcer.** In developed countries, Korpi and Clark (2017) show the variability in income gains from internal migration in Sweden through a multinomial logit that accounts for different income growth categories. The authors find that the higher educated and those moving to metropolitan areas have the highest probability of obtaining positive income returns. For Mexico, Cazzuffi (2016) evaluates the relationship between adult earnings of internal

> 6 See Dustmann, Schonberg, and Stunhñer (2016) for a literature review on the immigration impacts on local economies
The study was conducted between the July and December 2009 in a rural area in central Malawi. The sample site includes a cluster of five villages located 17 kilometers from a market town which offers a large weekly market, a health clinic, and access to further means of transport. In total, 542 households were listed in the five villages (in a complete census). A circular circuit track connects three of the five villages, with the other two villages within 10 minutes walking distance. This track was identified to serve as the bus route to use in picking up passengers. While there is some evidence of irregular transport on the road between the village cluster and the market town, there was no evidence of a bus or any other mode of transport intended for passengers that operated along the road connecting the village cluster and the market town.<sup>8</sup> We report on the second phase of a two part design.<sup>9</sup> Of the originally listed 542 households, 514 households were successfully interviewed and were eligible to participate; 406 of these households are included in the second phase analysis. Participation in phase one and phase two was by random assignment. After selection, a baseline survey was conducted interviewing both men and women. After the completion of the baseline survey, a meeting was called at each of the five villages. At the meeting, a household-level lottery was explained. One member of each household was asked to select a ticket from a bucket. Each ticket contained a number to signify the price of one round trip on the project bus. Prices of a round trip included the following categories: MK 0, MK 10, MK 20, MK 50, MK 100, MK 300, MK 500.<sup>10</sup> After the household member was assigned the lottery ticket, the number was recorded and the price

> 8 This road was identified as in a good condition for buses to run (during the dry season).
_Figure 4: Drought effect along the consumption expenditure distribution, rural areas._ <!-- Start of picture text --> 10%<br>0%<br>‐10%<br>‐20%<br>‐30%<br>‐40%<br>‐50%<br>‐60%<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Income percentile<br>Drought effect Smoothed Drought Effect 95% Confidence Interval<br>Drought impact on consumption<br><!-- End of picture text --> Source: Authors’ calculations.

Varying levels of drought exposure along the consumption distribution do not explain these differences, as the median drought intensity among the poorest 10 percent of households is similar to the overall average drought exposure. With an average poverty gap of 72 percent, this group is very poor. These households may have relied on humanitarian assistance already before the onset of the drought, thus being isolated from the impact of the drought on consumption.

More drought exposed households were also more likely to experience hunger. As levels of hunger rose across all Somali regions, rural households in highly drought‐exposed areas were most severely affected. Higher drought exposure led to an 11 percent decrease in food consumption, accompanied by a 19 percent increase in the probability of experiencing hunger in December 2017. The effect is much less pronounced among urban households (Table A.3; Table A.4).

_Figure 5: Drought effect on hunger and food consumption._ <!-- Start of picture text --> 40%<br>30%<br>20%<br>10%<br>0%<br>‐10%<br>‐20%<br>‐30%<br>Urban Rural<br>Hunger Food consumption<
17 (2.6%) at the top decile of the distribution in large urban areas (Brazil as a whole). The slope of the expenditure effect PIC is lower in rural areas, where a higher fraction of the population is poor.

The implications of these expenditure effects for summary measures of poverty and inequality are summarized in the third columns of Tables 4, 6 and 8, for large urban areas, rural areas and the whole country, respectively.<sup>11</sup> These tables report poverty incidence (headcount), depth (poverty gap) and severity (squared poverty gap), defined with respect to both an extreme and a moderate poverty line, for the original pre-crisis income distribution (denoted “baseline”, and based on the PNAD 2006), and for each of the impact scenarios. The extreme and moderate poverty lines are drawn from IBGE. They allow for differences in spatial cost of living, and are reported in Table 3.<sup>12</sup> Tables 4, 6, and 8 also report the Gini coefficient of income inequality, both at baseline and for each impact scenario. As noted, column 3 reports poverty and inequality in the income distribution obtained from the expenditure effect PIC only: .

The effects of those relatively large compensating variations on poverty are substantial, particularly in rural areas, where extreme poverty incidence rises by four percentage points, or nearly a fourth. Moderate poverty rises by about a sixth, or six percentage points, and the effect on higherorder FGT indices is proportionately (somewhat) larger. In large urban areas, where people are on average better-off, the effects are smaller: the extreme poverty headcount rises by just over one percentage point, or ten percent. Moderate poverty incidence rises by two and a half points, about 7.5 percent. In the country as a whole, poverty effects fall somewhere between those two extremes, and are certainly not negligible: on their own, the first-order expenditure effects contributed
.57|3,222.11|
|**Log consumption per capita**|8.26|8.27|8.27|8.23***|8.34***|8.29|8.27***|8.37***|8.32|
|**Household wealth per capita**|12,888.57*|11,784.68*|12,327.05|21,110.58***|27,028.18***|24,187.05|23,445.85***|28,067.55***|25,856.56|
|**Log household wealth per capita**|8.42**|8.47**|8.45|9.22***|9.32***|9.27|9.60***|9.71***|9.66|
|**N (observations)**|4,415|4,571|8,986|4,322|4,680|9,002|4,118|4,490|8,608|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ Tests for equality of means across treatment and control estimated in each round with *p < 0.1; **p < 0.05; ***p < 0.01.

12
poverty levels in each of the three survey years can be read from the distribution functions at the point where the function crosses the vertical line that indicates 100 percent of the poverty line (DENEAP, 2010). The distribution can also be compared in welfare terms: any distribution appearing to the right of another is statistically dominant and can be considered a welfare improvement.

**Figure 6: Distribution Function for Revised Real Consumption**

<!-- Start of picture text --> National North<br>20 40 60 80 100 120 140 160 180 200 20 40 60 80 100 120 140 160 180 200<br>% of Poverty Line % of Poverty Line<br>1997 2003 2009 1997 2003 2009<br>Center South<br>20 40 60 80 100 120 140 160 180 200 20 40 60 80 100 120 140 160 180 200<br>% of Poverty Line % of Poverty Line<br>1997 2003 2009 1997 2003 2009<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br><!-- End of picture text -->

**Revised:** national 1996/97 based basket PL with 13 location price deflators and 2002/03 and 2008/09 data partially re-estimated (Sets A and B, Table1)

These conclusions regarding poverty trends hold true at both the national and consolidated macroregion levels. The 2002/03 survey recorded a dramatic reduction in poverty and a general improvement in overall welfare. However, income growth was fastest at higher income levels, and inequality consequently increased. It is worth noting that the unequal growth of consumption has a clear spatial correlation: welfare improvements in the
compared with actual data for all city‐pairs because such data are not available, it is still possible to compare our figures for specific city‐pairs using various sources.

In Table 4, we corroborate our results using data from Tan and Reja (2018). In this paper, the authors conducted interviews with China based companies and collected information regarding the time it takes to ship products by train for certain destinations. While still featuring some differences, our pre‐BRI figures are broadly consistent with the quotes from private companies.

_Table 4: Comparison between shipment time declared by survey participants (Tan and Reja (2018)) and our “pre‐BRI’ results._

||**Survey**|**Our “Pre‐BRI” scenario**|
|---|---|---|
|**Lanzhou to Almaty**|10 days|8.95 days|
|**Lanzhou to Astana**|10 days|8.40 days|
|**Manzhouli to Yekaterinburg**|9 days|‐|
|**Qiqihar to Yekaterinburg**<br>(Qiqihar is 650km east of Manzhouli)|‐|12.2 days|

_Note: This table focuses on rail links_ Along the same lines, Table 5 presents a comparison between our own estimates and quotes that can be found online from a company specialized in maritime transportation.<sup>11</sup> Again, we find that our estimates are not far from the information we can gather from private companies, which is another validation of the method and the assumptions used.

_Table 5: Comparison between commercial information and our “pre‐BRI’ results._

||**CFC**|**Our “Pre‐BRI”**<br>**scenario**|
|---|---|---|
|**Dalian to Hamburg**|34 days|
# **5. Results**

In this section we present our main results. Many outcome variables are measured at the household level. However, several of these outcomes are also reported separately by different individuals within households which allows us to explore gender-differentiated impacts. In Tables 4 to 13 we present the main impact estimates for the Land Titling Program. All tables follow a similar format. Column 1 provides the sample size for the pooled regression and Column 2 the mean of the dependent or outcome variable of interest in the control group for the third survey round. Columns 3 to 5 report the estimates for each survey round separately which correspond to the coefficients on the interaction terms in Equation 1. Column 6 reports the estimates from the pooled regression and Columns 9 to 11 show the results from estimating the SAR regression model on those respondents for whom household-level GPS data were available.<sup>14</sup> We start our examination of impacts with the most proximate indicator that the program might be working: individuals reporting that the land was registered and their perceptions of security. Table 4 shows these variables. Land in the treatment area is significantly more likely to be reported as registered by the respondents, and this coefficient increases across our three rounds for women. Although it is hard to capture in quantitative data, there is some suggestion that perceptions of security have also improved. A significant fraction of both men and women are worried that they will lose their plot if they leave it fallow (about a third in the control group in round 3). For men we see significant reductions of this for the pooled estimate, although this effect may have tapered off by the

> 13 The distance decay can also be formulated as a power function and the elements of W matrix can be inverse 1 distances with a distance decay factor, , ∅= , where i denotes the distance between units and i 14 The GPS measurements of plots in our data are incomplete and, as mentioned above, we rely on a repeated γγ
Policy Research Working Paper 5463

# **Abstract**

Energy poverty is a frequently used term among energy specialists, but unfortunately the concept is rather loosely defined. Several existing approaches measure energy poverty by defining an energy poverty line as the minimum quantity of physical energy needed to perform such basic tasks as cooking and lighting. This paper proposes an alternative measure that is based on energy demand. The energy poverty line is defined as the threshold point at which energy consumption begins to rise with increases in household income. This approach was applied to cross-sectional data from a comprehensive 2005 household survey representative of both urban and rural India. The findings suggest that in rural areas some 57 percent of households are energy poor, versus 22 percent that are income poor. For urban areas the energy poverty rate is 28 percent compared with 20 percent that are income poor. Policies to reduce energy poverty would include support for rural electrification, the promotion of more modern cooking fuels, and encouraging greater adoption of improved biomass stoves. A combination of these programs would play a significant role in reducing energy poverty in rural India.

This paper—a product of the Agriculture and Rural Development Team, Development Research Group—is part of a larger effort in the department to understand the determinants of rural energy poverty and its influence in rural income and poverty. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at skhandker@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
(0.01)|(0.02)|(0.03)||(0.03)|||
|home||||||||
|The job she is in|0.98|0.96|0.98||0.97|-0.01|-0.00|
|pays verywell|(0.01)|(0.01)|(0.01)||(0.01)|||
|She has to|0.67|0.46|0.63||0.38|0.07**|0.08**|
|occasionally travel|(0.02)|(0.02)|(0.03)||(0.03)|||
|for work outside||||||||
|the village/city||||||||
|Her job is|0.70|0.50|0.65||0.43|-0.01|0.07**|
|<br>physically|(0.02)|(0.02)|(0.03)||(0.03)|||
|demanding||||||||
|She works in a|0.51|0.29|0.48||0.24|0.03|0.05|
|hotel/restaurant|(0.02)|(0.02)|(0.03)||(0.03)|||
|Observations|85|0||3|00|1|,150|

55
The construction of an individual measure of relative labor deprivation also allows for the use of this measure in distributional analyses by constructing Pen’s parades, Cumulative Distribution Functions (CDFs) or Lorenz curves. As an example, in Figure 1 (left hand panel) we plotted the Lorenz curves using the two different assumptions of joint and separate reference groups. The results illustrate graphically what we have learned so far. If the reference group is gender specific, as opposed to being relative to everyone in society, the feelings of deprivation would be lower and the societal RLD is less prominent. Indeed, if we estimate RLD within each gender, we find that RLD for men is much more pronounced than RLD for women, as we noted when we looked at the indexes (right hand panel, Figure 1).

# **5.3 How good is the indicator of relative labor deprivation?**

The concept of relative labor deprivation developed in this paper is an artificial construct that claims the ability to capture subjective feelings of deprivation. In this section, we attempt to determine whether the measure of relative deprivation proposed is effectively related to subjective feelings of satisfaction or deprivation as reported by respondents in the MHYS. For a sub-sample of the population (2,128 young individuals aged 15 to 29 who are currently out of school) we can examine the relation between relative labor deprivation and subjective wellbeing. The survey we use includes questions on selfreported satisfaction with different aspects of life. It also includes a question on the intention to migrate, a variable we presume to signal discontent with the current situation, especially with labor market status. We should expect a good measure of deprivation to be inversely related with measures of subjective satisfaction as found for example in D’Ambrosio and Frick (2004) and in Silber and Verme (2010, 2011).

Table 8 shows the results of an ordered probit regression of young people (aged 15-29) where the dependent variable is satisfaction with opportunities to improve social status in the future.
of opportunity. It is one that has gained increased attention in recent years, however, especially after the global financial crisis and recognition of the need for more stringent regulatory frameworks in the labor and financial sectors (Stiglitz 2013; see also Ferreira and Peragine 2015). By developing a methodology for the collection of comparable cross-country global data, the researchers aim to create a framework for the identification of gaps in the legislation of countries around the world that, if addressed, may help tackle the marginalization of minorities and ultimately strengthen the economy of those countries.

The rest of the paper is structured as follows: Section II provides definitional clarifications. Section III discusses the theoretical underpinnings of the research and relevant literature on equality of opportunity and equality legislation. Section IV presents the survey design and data collection methodology. Section V is divided into six subsections and is a summary of some of the major findings in the six pilot countries for the six indicators. Section VI concludes.

# II. Definitions

_Discrimination_ was defined in the 1989 Human Rights Committee’s Comment on Non-Discrimination as follows: any distinction, exclusion, restriction or preference which is based on a number of identified grounds and which has the purpose or effect of nullifying or impairing the recognition, enjoyment or exercise by all persons, on an equal footing, of all rights or freedoms.<sup>2</sup> The definition of _minorities_ used in this paper is the one used in the United Nations human rights system, which was provided in 1977 by Francesco Capotorti, special rapporteur of the United Nations Sub-Commission on Prevention of Discrimination and Protection of Minorities, who stated the following:

[A minority is] a group numerically inferior to the rest of the population of a State, in a nondominant position, whose members—being nationals of the State—possess ethnic, religious or linguistic characteristics differing from those of the rest of the population and show, if only implicitly, a sense of solidarity, directed towards preserving their culture, traditions, religion or language.
in Hawassa.

- Clark, G. (1994). Factory Discipline. _The Journal of Economic History 54_ (1), 128–163.

- Dean, J. (2018). Noise, Cognitive Function, and Worker Productivity. _Working paper_ .

- Deserranno, E. (2014). Financial Incentives as Signals: Experimental Evidence from the Recruitment of Health Workers. _Working Paper_ .

- Elkan, W. (1971). An African Labour Force. In _Developing the Underdeveloped Countries_ , pp. 240–246. Springer.

- Farber, H. S., D. Herbst, I. Kuziemko, and S. Naidu (2018). Unions and Inequality Over .

- the Twentieth Century: New Evidence from Survey Data. _NBER Working Paper_

13