rafmacalaba/datause-encoder-data huggingface dataset:
growth in total municipal revenues and an increase in the proportion of municipal expenditure spent on capital projects.

The revenue implications of land leasing are summarized in Table 3 for the two largest cities in Amhara and Tigray Regions. The cities have populations ranging from 75,000 to 325,000. Land leasing has become the single largest source of municipal revenue, overtaking the traditionally largest source of revenue, the local fee and tax items covered in Regional Tariff Proclamations. By international standards, of course, the revenue generated from land leasing in Ethiopia is meager. The highest-valued land lease in Mekele, capital of Tigray Region, was sold at auction for approximately US$1.60 per square meter. The infrastructure investments being financed are equally basic; they consist of modest upgrades to water distribution, road surfacing, and the like

# **Table 3 Revenue from Land Leasing: Ethiopia Municipalities in Amhara and Tigray 2003-2004**

|**Municipality**|**Land Leasing**<br>**As % of Total**<br>**Revenue**|**Land Leasing**<br>**As % of**<br>**Capital Spending**|**Growth in**<br>**Land-Leasing**<br>**Revenue**<br>**From 2002-2003**|
|---|---|---|---|
|Bahir Dar(Amhara)|45.3%|140%|NA|
|Gondar(Amhara)|42.3%|105%|35.8%|
|Adigrat (Tigray)|21.5%|77%|144.7%|
|Mekele (Tigray)|24.2%|81%|8.1%|

- a. Municipalities do not have separate capital budgets; amounts of capital spending are estimated from line item expenditures

- b. The US$/Ethiopian Birr exchange rate was unchanged over 2002/03—2003/04

- Source:  Peterson (2005)

The potential exists for further revenue mobilization through land leasing in cities where population is growing and municipal management has an entrepreneurial mindset. A new municipal administration in Mekele, for example, was able to more than double land-leasing revenue in 2004-2005 from the levels shown in Table 3, by implementing a policy that sold a larger share of land leases at public auction and more aggressively identified undeveloped land parcels for leasing. If Ethiopia should succeed in implementing its planned conversion of the annual land rent system to up-front leasing, it would further accelerate revenue mobilization, for a transitional period.

13
# **Education Inequality**

The improvement in education attainment has led to a reduction in education inequality, though different measures of inequality present somewhat different perspectives. During the last fifty years, the region has been able to systematically reduce education inequality, as the Gini coefficient of years of education decreased from approximately 0.70 to 0.43. By construction, the Gini coefficient falls when the mean years of education rises. It is striking, however, that the region’s Gini coefficient in 2010 was higher than that of the advanced economies in 1960. In 2010, the educational Gini coefficient in advanced economies was 0.18 (Figure 3)<sup>7</sup> .

**Figure 3: Evolution of Gini Coefficient in Education 1960 – 2010 Central America and Advanced Economies**

<!-- Start of picture text --> 0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Advanced Economies Central America<br>Data Source: Barro-Lee 2010<br><!-- End of picture text --> Average figures for the region mask important disparities between countries and different indicators also highlight the varying degrees to which disparities have been reduced. As shown by the education Lorenz curve (Figure 4), Costa Rica and Panama are more equal than El Salvador, Honduras and Nicaragua, which share a similar distribution. Guatemala stands out with its markedly higher degree of inequality. Table 1 shows the evolution of inequality between 2001 and 2009 using different indicators. Using the difference in education attainment between the highest and lowest groups (the 75th and 25th percentiles in terms of years of education), none of the countries in the region registered a decline in this measure of inequality. The standard deviation in years of education, another measure of inequality, declined only in one country, Honduras. Based on these two indicators, therefore, the range in education attainment within

> 7 Gini coefficients were approximated using detailed information on levels (years) of education achieved using the Barro-Lee (2010) dataset. “Advanced economies” represents countries classified as such in the dataset.

7
However, the war was short and the country was taken over by a relatively well-organized regime after the end of the hostilities. Armed conflicts often have immediate negative economic impacts and Rwanda is no exception. Rwandan per capita GDP plummeted almost 50 percent and consumer prices increased 64 percent during the genocide but by 1996 had returned to near pre-war levels (all figures from IMF, 1998). Coffee exports, the predominant export crop in Rwanda, declined 54 percent in 1994 but returned to pre-war levels in 1995. Given the rapid return to pre-war economic levels, the long-run impacts might not be severe.

In this paper, we examine the impact of the Rwandan genocide on children’s human capital investment, focusing on primary level schooling as 93 percent of the population under age 35 has not completed primary school. We examine if and how shocks, such as the genocide, affect children’s enrollment and the probability of completing a particular grade for those exposed children. We combine two nationally representative cross-sectional household surveys, one collected in 2000 (6 years after the genocide ended) and one collected in 1992 (2 years prior to the genocide). This is unique as few studies of conflicts have data bracketing a conflict event. The empirical identification strategy uses the pre-war data to control for baseline schooling levels for a given age group and exploits variation in which birth cohorts of children were still in school when they were exposed to the war. We find that school-age children exposed to the genocide experience a drop in educational achievement of almost one-half year of completed schooling and are 15 percentage points less likely to complete third or fourth grade, and the effect is robust to including a variety of control variables, alternative sources for measuring conflict intensity, and an instrumental variables strategy. Based on the existing literature, it is likely that the negative schooling impact of the genocide will have a long-run welfare impact through an adverse effect on future adult wages and productivity.

3
**Figure 3: Outstanding guarantees**

_(as a percent of GDP, 2009)_ <!-- Start of picture text --> 6<br>5<br>5<br>4<br>3.5<br>3<br>1.9<br>2<br>1<br>0.9<br>1<br>0.6 0.6<br>0.5<br>0.4 0.4 0.4<br>0.3<br>0.2 0.2<br>0.03 0.07 0.07 0.1 0.1<br>0<br>Source: Respective PCG schemes<br>Figure 4: Outstanding guarantees<br>(US$ Million, 2009)<br>400 374<br>350<br>292<br>300<br>251<br>250<br>213<br>200<br>200<br>162<br>150 121<br>104<br>100<br>69<br>50 25<br>0.4 8 12 16 4 18<br>0<br>Iraq Jordan Palestine Saudi Arabia Egypt Tunisia Lebanon Morocco<br>2007 2009<br><!-- End of picture text --> _Source: Respective PCG schemes_ 27
# The Many Faces of Deprivation: A multidimensional approach to poverty in Armenia<sup>1</sup>

Diana Martirosova National Statistical Service of the Republic of Armenia Osman Kaan Inan, Moritz Meyer and Nistha Sinha World Bank, Poverty and Equity Global Practice

**Keywords:** measurement and analysis of poverty, multidimensional poverty, development economics, Armenia

**JEL classification: I3, O1, R2**

> 1 This paper was prepared by Osman Kaan Inan, analyst, Poverty and Equity Global Practice, Europe and Central Asia (ECA); Diana Martirosova, of the National Statistical Service of the Republic of Armenia; Moritz Meyer, economist, Poverty and Equity GP, ECA; and Nistha Sinha, senior economist, Poverty and Equity GP, ECA. Corresponding author: Moritz Meyer (mmeyer3 [at] worldbank.org). This draft builds gratefully on work done by the South Caucasus Poverty team in FY14, FY 15 and FY 16 and on information provided by Josefina Posadas. We are also thankful for comments received during a Quality Enhancement Review in which Maria Davalos, Maria Ana Lugo, Luis Felipe Lopez Calva, and Nobuo Yoshida participated. Any errors are ours.
<!-- Start of picture text --> YRD<br>FA3<br>0 .5 1 1.5 2<br>Expenditure-Revenue Ratio<br>1<br>.8<br>.6<br>Density<br>.4<br>.2<br>0<br><!-- End of picture text --> Figure 4: Kernel Densit <mark>y</mark> Estimation of Avera <mark>g</mark> e Ex <mark>p</mark> enditure-Revenue Ratio <mark>,</mark> 1950 and 1957.

a better development performance for their counties. This suggests that the mechanisms must be more efficient resource allocations in the YRD-affliated counties.

# **5.6 Faction, Local Accountability or Something Else?**

Another concern is that the main results may be driven by the fact that cadres affiliated with FA3 and YRD factions might be different in terms of their skills in managing economic affairs. After all, FA3 cadres specialized in fighting wars because they spent most of their careers in the formal troop led by the Chinese Communist Party. In contrast, YRD cadres may be more experienced in working with locals because they originated from the revolutionary bases in Hebei and Shanxi province. If this was the case, the difference in the growth rate for two groups of counties can presumably be driven by skill difference of the local leaders, not by political survival incentives due to different levels of connections with provincial leaders or by local accountability.

To address this concern, we argue that, if either skills and/or knowledge in economic development are important driving forces behind the differences in the development performances of FA3- and YRD-affiliated counties, then we would expect the growth gaps between these two groups of counties to decrease (and eventually disappear) over time, as FA3-affiliated cadres obtained more experience on their positions.

In Table 16, we present the regression results, where we replicate the regressions in Tables 6-7, but replace the dependent variable with the annual real growth rate between 1984 and 1998. Comparing the results in Table 15 with those in Tables 6-7, we find that for every specification, the coefficient estimate for YRD is larger in the 1984-1998 period than that in the overall 1978-1998 period. Thus, skills and/or knowledge on the job does not seem to be the main factor in explaining the growth gaps between FA3 and YRD counties; if anything, the gap increases rather than decreases over time.17

> 17If FA3 cadres, however, prefer to promoting their former colleagues, this kind of official selection practice will prolong their disadvantage in managing economic affairs and might cause persistent gaps in growth rate between these two kinds of counties. As we do not observe cadres’ skills or knowledge directly, it is impossible to definitively reject the mechanism of social heritage.

41
The data from the mostly small and medium-sized enterprises included in the Investment Climate Surveys—the surveys used in the analysis in this paper—are broadly consistent with the macroeconomic data.<sup>5</sup> Manufacturing enterprises included in the surveys were less likely to export in most of the countries in Sub-Saharan Africa than similar enterprises in Asia. Furthermore, enterprises in Sub-Saharan Africa also exported less as a share of total output— only about 12 percent on average—compared to about 22 percent of output in Asia (see Table 2).

Although the average is relatively low for the eight countries in Sub-Saharan Africa, it is important to note that there are large differences between countries within Africa. For example, manufacturing enterprises export more in Senegal, Kenya and Zambia than in the other countries for which data were available. Whereas 58 percent of Kenya enterprises and 43 percent of Senegalese enterprises included in the samples were involved in exporting, only 7 percent of firms in Ethiopia and 12 percent of firms in Mozambique did the same.

In principle, the large pools of workers with relatively modest levels of education in most countries in Sub-Saharan Africa would appear to make these countries attractive platforms for exporting labor intensive goods to capital-rich industrialized economies. In practice, however, the small and medium-sized enterprises included in the Investment Climate Surveys mostly export to neighboring countries within Sub-Saharan Africa rather than to Western Europe or other industrialized economies. In all countries other than Ethiopia, enterprises were more likely to export to neighboring countries than they were to export to more distant European markets (see Table 3). This pattern appears to be true for both landlocked countries (e.g., Uganda and Zambia) and countries with access to the sea (e.g., Tanzania and Kenya).

Although Ethiopia appears to be an exception to this general rule—Italy, the United Kingdom and Germany are the three most important export destinations for Ethiopian enterprises—it is important to note that very few Ethiopian enterprises export at all (see Table 2). Although over 50 percent of exporters in Ethiopia export goods to their main industrial market (Italy), this represents less than 4 percent of Ethiopian enterprises. In contrast, although only

> 5 Given that a small number of large enterprises are responsible a large share of exports in most countries, it would be possible that the unweighted average for the small and medium enterprises included in the Investment Climate Assessments could be quite different from the macroeconomic data.

5
Productivity growth is the sine qua non for economic growth and consequently poverty alleviation. While factor accumulation is important, increases in capital and labor inputs alone do not explain cross-country differences in growth. Economic research has shown that improvements in the efficiency with which countries use their inputs—their total factor productivity (TFP), —explains most of the increase in output for fast-growing economies. No country has experienced sustained economic growth without also achieving rapid gains in productivity. The questions then for policymakers are these: What determinants of productivity cause producers in one country to be more efficient than those in competitor countries? And where should reform efforts be targeted to have the greatest impact on productivity and thus poverty reduction?

In a global economy where technology diffuses rapidly and capital is mobile, the persistence of productivity differences among countries can be largely explained by differences in the investment climate—the policy, institutional, and regulatory environment in which businesses must operate. Countries where property rights are secure, the government provides efficient services, and infrastructure is well developed are considered to have a good investment climate. And a good investment climate reduces the cost of doing business and leads to higher and more certain returns on investment.

But merely knowing that the investment climate matters is not enough. Policymakers are constrained by limited resources, and the path of reform is difficult and often slow. While some policy reforms can be instituted relatively quickly, many changes—such as building 2
Table 2: Descriptive Statistics: Services sector

||Mean|sd|p10|p25|p50|p75|p90|N|
|---|---|---|---|---|---|---|---|---|
|2008|||||||||
|**ICT variables**|||||||||
|Share of labor that uses computers|27.47|34.74|0.00|0.00|10.00|50.00|90.00|6,241|
|Share of labor that uses Internet|22.56|31.15|0.00|0.00|9.00|33.00|80.00|6,241|
|Stock of computer equipment/worker|7.35|53.48|0.00|0.24|1.25|4.37|13.08|6,241|
|**Employment and wages**|||||||||
|Number of white-collar workers|23.08|100.58|2.00|3.00|6.00|14.00|39.00|6,241|
|Number of blue-collar workers|119.77|1853.09|10.00|15.00|29.00|71.00|190.00|6,241|
|Annual real wage white-collar workers|119.11|159.62|27.00|48.00|80.25|140.00|236.17|6,241|
|Annual real wage blue-collar workers|67.22|74.59|19.20|29.78|48.00|78.47|127.40|6,241|
|White/Blue-collar workers|0.25|0.23|0.05|0.09|0.18|0.33|0.57|6,241|
|White/Blue-collar wages|2.01|1.48|0.63|1.00|1.56|2.58|4.10|6,241|
|**Firm characteristics**|||||||||
|Capital/worker|214.10|507.08|0.56|10.06|66.72|243.09|545.45|6,241|
|Total employment|149.03|1883.58|14.00|21.00|39.00|95.00|248.00|6,241|
|Firm age|23.78|18.13|9.00|12.00|19.00|28.00|43.00|6,241|
|Share of FDI|3.75|17.66|0.00|0.00|0.00|0.00|0.00|6,241|
|2013|||||||||
|**ICT variables**|||||||||
|Share of labor that uses computer|34.06|35.97|0.00|1.00|20.00|60.00|100.00|6,241|
|Share of labor that uses Internet|32.68|35.48|0.00|1.00|20.00|58.00|100.00|6,241|
|Stock of computer equipment/worker|11.02|75.50|0.02|0.38|1.67|6.06|17.47|6,241|
|**Employment and wages**|||||||||
|Number of white-collar workers|32.96|418.77|1.00|3.00|6.00|15.00|40.00|6,241|
|Number of blue-collar workers|132.75|1974.33|9.00|14.00|29.00|76.00|210.00|6,241|
|Annual real wage white-collar workers|91.23|90.41|25.64|39.98|67.38|109.61|177.44|6,241|
|Annual real wage blue-collar workers|60.05|58.50|20.11|28.42|42.95|69.49|116.28|6,241|
|White/Blue-collar workers|0.25|0.23|0.05|0.09|0.17|0.32|0.56|6,241|
|White/Blue-collar wages|1.83|1.35|0.63|0.99|1.41|2.31|3.69|6,241|
|**Firm characteristics**|||||||||
|Capital/worker|247.03|787.23|0.64|13.03|71.93|257.57|609.07|6,241|
|Total employment|174.09|2175.95|14.00|21.00|40.00|105.00|283.00|6,241|
|Firm age|21.46|15.27|7.00|11.00|18.00|26.00|42.00|6,241|
|Share of FDI|4.53|20.09|0.00|0.00|0.00|0.00|0.00|6,241|

_Source:_ Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI 16
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recovered until the turn of the century, when another economic slowdown becomes apparent.

# **4.2 Magnitude of Job and Worker Flows in Mexico**

Table 2 presents all of the statistics on worker flows and job flows discussed in the methodology section for each year 1986-2001. Table 2 is the central table of our paper and we will discuss its implications quite thoroughly. Table 2 contains twelve columns that are grouped and numbered. Several of the columns are algebraically related. Column 1, net employment growth, is equal to the sum of columns 2 and 3. Column 1 is the difference between jobs created (column 7) and jobs destroyed (column 10). Furthermore, the sum of accessions and separations (column 4) can be decomposed as the sum of job creation and destruction (column 11) and excess worker flows (column 12). To further illustrate the main trends, figure 2 graphs columns 4, 11, and 12.

(Table 2 here) Job flows in Mexico on average appear to be not very different from job flows in the U.S., although both job creation and job destruction are somewhat higher in Mexico. For example, using data from West Virginia, Spletzer (2000), finds an annual job-creation percentage of 15.8%, which is lower than the 19.0% we observe on average in our data (column 7). Spletzer reports an annual job-destruction percentage of 14.4%, which is slightly lower than our figure of 14.8%.

Davis and Haltiwanger (1992) cover the period 1972-1986. If we compare their results to our Mexican results, we again find that both job creation and job destruction are higher in Mexico. If we restrict the sample to manufacturing establishments, we get an average jobcreation percentage of 16.4%, substantially higher than the figure of 9.2% from Davis and 10
because the intensity target, which is indexed on GDP, might presumably do a better job at controlling that particular variable. Also, total costs expressed as a fraction of GDP represent a better indicator for the effective impact of climate mitigation on a country’s economy than absolute costs. Total costs relative to GDP are as The term _E_<sup>1</sup><sup>_−α_</sup> in Eq.(14) translates the fact that the additional costs of abatement caused by an increase of the effort _R_ by _dR_ is the marginal cost of abatement at effort _R_ , _C_ 0( _R_ ), times the additional amount of carbon that is abated by increasing the effort by _dR_ . Given the definition of _R_ (Eq.5), that additional amount is equal to _E_<sup>1</sup><sup>_−α_</sup> _dR_ .

The variance of _RC_ is given below. The equation is written for a GIT, but the quota case is easily obtained by setting _m_ = 0 and the LIT case by setting _m_ = 1. Detailed calculations can be found in Appendix B.

From Eq.(15), the condition under which an intensity target reduces uncertainty on total relative costs vis-`a-vis a quota follows.

**Proposition 4** _A general intensity target qY_<sup>_m_</sup> _with m >_ 0 _and q >_ 0 _leads to a lower variance of total abatement costs relative to GDP than a cap if and only if_

# **_Proof:_** _See Appendix B._

Condition (18) reveals three major differences between marginal abatement costs and total 17
as large compared to when educational quality is not accounted for.

The second and third columns of Table 5 show the corresponding development accounting results. Human capital per worker gaps between the United States and poor countries are substantially higher compared to Table 1. As a result, the _Success_ measure jumps from 8% to 32%.

In a subsequent study, Hendricks and Schoellman (2018) use data on wage gains at migration to provide yet another perspective on cross-country differences in human capital. This approach treats human capital as a residual: wage gains at migration are informative on combined effect of technology and physical capital, and the cross-country dispersion that remains after acocunting for these factors can be attributed to human capital. Formally, from the production function in (4) , the wage of a migrant from country _c_ (and corresponding human capital _hc_ ) when working in the US can be written as while the pre-migration wage of the same worker is (10) with _kUS, yUS_ and _AUS_ replaced by the corresponding quantities for country _c_ . It follows that the wage gain upon migration is so that the relative human capital per worker can be found as This approach is not subject to concerns on migrants’ selection, as wage gains are constructed at the level of individual migrants (therefore keeping constant both observable and unobservable human capital). Moreover, the authors do not need to impose any structure on the human capital production function. Since _hc_ is measured as a residual, its variation across countries might reflect a combination of the quantity and quality of education, as well as of all other country-specific inputs relevant for human capital formation.

Hendricks and Schoellman (2018), for confidentiality restrictions, display only the average wage gains across five groups of countries, corresponding to the five quintiles of the GDP per worker distribution in 2005. To implement my version of their development accounting exercise, I assign to each country in my sample the average wage gain of the relevant group (implictly assuming no change in this quantity between 2005 and 2010). The results are reported in the last two columns of Table 5.

Since wage gains upon migration are small relative to GDP gaps, human capital per 18