rafmacalaba/datause-encoder-data huggingface dataset:
Figure 3: Variation of city premiums after controlling for sorting<sup>a</sup> .

<!-- Start of picture text -->
1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>ECU CHL ARG PER BOL COL BRA URY CRI SLV NIC PAN DOM MEX GTM HND<br>South America Central America and the Caribbean<br><!-- End of picture text -->

> a This chart is organized in descending order of the median level of estimated city premium within each subregion. Estimated city premium measures sub-national variations in underlying productivity after controlling for observable worker characteristics within the broad sample (all wage/salary employees aged from 14 to 65 years old). The upper and lower caps respectively indicate the maximum and the minimum estimated city premiums for each country. The bottom of the blue box, the border of two colors, and the top of the red box respectively depict the first quartile, the median, and the third quartile of the estimated city premiums in each country. ARG = Argentina, BOL = Bolivia, BRA = Brazil, CHL = Chile, COL = Colombia, CRI = Costa Rica, DOM = Dominican Republic, ECU = Ecuador, GTM = Guatemala, HND = Honduras, MEX = Mexico, NIC = Nicaragua, PAN = Panama, PER = Peru, SLV = El Salvador, URY = Uruguay.

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# <u>2. The changing composition of global inequality, 1820-2002</u>

While the social tables and (obviously) the survey data for the early 19<sup>th</sup> century are not fully available so that global inequality must always be estimated quite approximately, Maddison’s GDP data allow us to compute with much more precision Concept 2 inequality. Concept 2 or international population-weighted inequality is an inequality index computed across GDPs per capita of countries at a given point in time, where each country's GDP per capita is weighted by country’s population (see Milanovic, 2005).

Concept 2 and Concept 3 (or global) inequality are, in the case of Gini, linked as in relation (1). Concept 2 inequality also represents the between component of global inequality. This is inequality that would exist if each individual had mean income of his/her country, i.e., all individual country distributions were egalitarian. The within component (the part of global inequality due to income inequalities within each country) is the rest of expression (1).

Concept 2 inequality where yj = mean income or GDP per capita of country _j_ , pj = population share of country _j_ , πj = income share of country _j_ (country _j_ -th share in global income), μ = global average per capita income, and L = overlap term (which can be treated as part of the within component; it is greater than zero any time there are people from a poorer country that are better off than some people from a mean-richer country).

The difference between the Concepts 2 and 3 consists of _intra_ -national inequality or, in other words, of the within component. Today, the between component—on account of huge differences in mean incomes between the countries—represents at least 85 percent of global inequality.<sup>9</sup> But was this the case in the past?

> 9 See Milanovic (2005).

10
<!-- Start of picture text -->
[|<br>[a AlGrades ||Religious ann<br>PT<br>[b. _-KthroughéTMgrade] TT<br>CO<br>|e. 7*through12"grade] | TT<br><!-- End of picture text -->

Note: Each row indicates the fraction of students of the corresponding race enrolled in each type of school. For each row, sum across columns equals 100. Data from all years has been pooled for the table.
a persistent gender wage gap. In addition there is also a low participation rate for men, and a high rate of unemployment for the youth. More than half of the working age population (53.3 percent) is out of the labor force, the vast majority being women. In 2016 the labor force participation rate was around 26.3 percent for women, compared to 71.6 percent for men. Among the young (15-29 years of age) these rates are 34.9 percent and 53.4 percent, respectively. As a result Morocco ranks in the bottom 20 percent of countries in the world with the lowest female participation in the labor force. In terms of levels of education, 3 out of 5 (63 percent) inactive women do not have any degree whereas a small share (8 percent) has a tertiary education. Moreover, the participation rate among women aged 15 and over has declined steadily since the late 1990s, by almost 3.5 percentage points between 1999 and 2016 (see Ragbi et al. (2017)). Once employed, women are also the most exposed on average to job insecurity, informal work and lower pay. Moreover, they often leave their jobs when they marry, partly as a result of social norms and partly because available jobs do not offer flexible schedules.

Women are also disproportionately affected by unemployment. While the overall unemployment rate, at 9.4 percent in 2016, is high, it particularly affects women, whose unemployment rate is 14.7 percent compared to 8.8 percent for men. This gap is especially large for skilled workers: 24.9 percent for women and 14.6 percent for men, compared with 2.9 percent and 4.1 percent, respectively, among unskilled workers.<sup>3</sup> Similar patterns are observed in other countries in the region, including Tunisia (Mouelhi and Goaied (2017)).

According to survey results by the country’s Haut-Commissariat au Plan, several factors help to explain the low labor force participation rate among women (see World Bank (2018)). Of the 10 million women not currently participating in the labor market, 52.7 percent (more than half) consider household responsibilities as the main reason for staying home–more than 22 points higher than the survey estimate reported by the International Labour Organization (2017, Table 5) for Northern Africa as a whole. Indeed, time use surveys indicate that women continue to perform the vast majority of household tasks (see Attioui et al. (2017)). This factor, however, decreases as the

> 3For workers with tertiary education only, the data are similar: the unemployment rate is 29.7 percent for women and 14.4 percent for men. he unemployment rate for the unskilled (men and women) is lower than for the skilled because they cannot remain openly unemployed for long and often seek employment in the informal economy.

5
Rao, V., and R. Ban (2007). 'The political construction of caste in South India'. _Processed._ Washingon DC: the World Bank.

<mark>Revankar, R. G. (1971).</mark> _<mark>The Indian constitution--: a case study of backward classes</mark>_ <mark>. Fairleigh Dickinson Univ Press.</mark> Rodgers, G. <mark>, and J. R</mark> odgers (2001). ‘A leap across time: When semi-feudalism met the market in rural Purnia’. _Economic and Political Weekly_ <mark>, 36:</mark> 1976–1983.

<mark>Sanyal, P., Rao, V., & Majumdar, S. (2015). Recasting culture to undo gender: A sociological analysis of Jeevika in Rural Bihar, India.</mark> <mark>Seymour, S. C. (1999).</mark> _<mark>Women, family, and child care in India: A world in transition</mark>_ <mark>. Cambridge: Cambridge University Press.</mark> <mark>Sharma, A. N. (2005). ‘Agrarian relations and socio-economic change in Bihar’.</mark> _<mark>Economic and Political Weekly</mark>_ <mark>, 960-972.</mark> <mark>Srinivas, M.N. (1962). ‘Caste in modern India and other essays’.</mark> _<mark>Caste in modern India and other essays.</mark>_ <mark>Srinivas, M. N. (1967). ‘The cohesive role of Sanskritization’.</mark> _<mark>India and Ceylon: unity and diversity</mark>_ <mark>, 67-82.</mark> <mark>Srinivas, M.N. (1976).</mark> _<mark>The remembered village</mark>_ <mark>(No. 26). California: University of California Press.</mark> Srinivas, M. N. (1977). _The changing position of Indian women_ . Man, 221-238.

Srinivas, M. N. (1996). _Caste: Its Twentieth Century Avatar._ New Delhi: Penguin Books.

23
Finally, we use the index to shed light on some of the factors that lie behind the incidence of graft. In particular, we study whether excessive regulation is associated with corruption. For that we use data from the World Bank’s _Doing Business_ project. The latter ranks countries according to the extent and nature of the regulatory and legal obligations that firms have to meet to be able to operate in an economy; 6 more obligations translate into a lower rank. We also consider whether democratic governments, more accountable to their citizenry, do a better job in containing petty bribery. We find that both excessive regulation and weak democracies increase the likelihood that firms will be the target of bribes. Our quantitative results imply that differences in the incidence of graft in Africa and Latin America would disappear if the former had levels of democracy and regulation similar to those that exist in the Western Hemisphere.

The rest of the paper is organized as follows. In Section 2, we address the relationship between experience with corruption and the perception of corruption. We show that perceptions adjust only gradually to changes in the true extent of graft. In Section 3, we explain how we calculate the _Graft Index of Firm Transactions_ , apply the methodology to our sample of countries, and present our estimates _._ In Section 4, we look at some of the correlates of the index at the country level. Although the data do not allow us to identify the causes behind graft, findings here regarding the link between regulation, democracy, and corruption are in line with those suggested in the literature and explored by other authors. Section 5 concludes.

# **2 Do perceptions of corruption match incidences of graft?**

In this section, we discuss the relationship between perceptions and incidences of corruption. We begin by discussing the difficulty with measuring the extent of corruption using perceptions data —which are at present almost exclusively what is used to measure corruption. By comparing firm-level data on the incidences of bribes with firm-level data on perceptions of corruption, we show that perceptions and incidences are imperfectly matched but likely to be updated depending on recent experience. With respect to updating of corruption perceptions, the data show that when firms have a positive experience with honest, uncorrupt officials, these same firms are prone to have more positive perceptions about the extent of corruption than firms that had no record of transactions with officials or those that had transactions and were asked for bribes to complete them.

6 See <u>www.doingbusiness.org.</u>
Dix-Carneiro and Kovak Trade Reform and Regional Dynamics Figure 13: Individual Average Months Formally Employed - Tradable Sector Worker Sample - 1990-2009 <!-- Start of picture text --> 9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>-1<br><!-- End of picture text --> Each point reflects an individual regression coefficient where the dependent variable is the average number of months formally employed per year from 1990 to the year listed on the x-axis, calculated using RAIS. For all years, the independent variable is the regional tariff change reflecting tariff changes from 1990-1995 (described in the text), with various controls (described in the text). Positive estimates imply fewer average months formally employed for workers initially employed in regions facing larger tariff declines. Vertical bar indicates that liberalization was complete in 1995. Dashed lines show 95 percent confidence intervals. Standard errors adjusted for 112 mesoregion clusters.

52
31 (2) number of years between the devaluation and the next government change. Additionally countries were classified according to their political regime into three groups: presidential democracies, where the date of the election is predetermined; parliamentary democracies and dictatorial rule. Classifying some of these countries into a political regime is not completely straightforward. Two particularly difficult cases refer to Egypt under Nassar and Cyprus under Makarios, where the leaders term was extended beyond what was considered 'normal". Both cases, however, were labelled as democratic presidential regimes.

Table 9 contains summary statistics for the timing of these 39 devaluations episodes. This information is quite impressive, showing that, as was expected from the discussion, in democratic regimes devaluations indeed tend to take place during the early years of each administration; very few devaluation attempts have indeed taken place during the last few years. An additional interesting piece of information in this table is that there is no evidence that dictatorships front-load their devaluations. IV.1.3 Polical Determinants of Successfu Devaluations The most important empirical implication of our previous discussion is that countries with a weaker, more unstable and polarized political environment will generally have greater difficulties in implementing the fiscal adjustment required for a devaluation to be successful. This suggests that it should be possible to "predict" the degree of success of a devaluation through the use of political variables only. This is indeed what we do in this section through the implementation of a series of discriminant analyses on our 39 devaluation episodes. We used the Jodice and Taylor data set to define three groups of political indicators for the different devaluation episodes. The first set
<!-- Start of picture text --> Average Prices, Potato<br>(December 1991 - August 1992)<br>25-<br>0- --- @---------Z------------ ----------<br>,.'<br>5' '4; ...--,,,,,---,-,,,,;;,,-,.-:.---- ,^@"""..................... ..................................<br>5 *eC91 01/28 02/11 02/25 031 0414 0428 05/12 0&02 06 6 030 07/14 07/28 Q8J11 0=i<br>Date<br>Market Price .... State Pfice<br><!-- End of picture text --> 3
limited period of time, and only for broad categories of investments such as ‘trees’ in general. In this paper we can exploit a large plot level four-round panel data set covering 1994 to 1999, with time-varying information on perceptions of transferability and across different areas and can focus on actual allocations to specific tree crops, rather than propensities to invest.

In the next section, we first give an overview of the recent experience with land rights and security in Ethiopia, as well as any evidence on its consequences. In section 3, we present the data available and in section 4 we explain the modelling approach. In section 5 we present the results. A discussion of the policy implications of these results concludes the paper.

# **2. Land rights in Ethiopia**

The land tenure system in Ethiopia has its own peculiarities. After ousting the imperial regime, the military government (the Derg) nationalized land in 1975 and subsequently distributed use rights to cultivators through local peasant associations. This system strictly prohibited private ownership of land, and transfer of land by sale, lease or mortgage. Periodic land redistribution was based primarily on family size. This was to accommodate the needs of new claimants, but as a result widespread land tenure insecurity was instigated in the rural areas. For example, in the data set used in this paper, more than a third of the households reported having lost land at one point or another during this period.

After the fall of the Derg regime in 1991, land redistribution was temporarily suspended without any provision to address the needs of the landless and the land hungry. The practice of repeated land redistribution had been already frozen in 1989, as part of the market-oriented reforms undertaken by the Derg. But the land policy has basically stayed the same and the 1995 constitution has simply reiterated the previous policy with just minor amendments. It has restated that land remains the collective property of the state and the peoples of Ethiopia and a mandate is given to regional governments for its administration. Accordingly, a farmer who wants to make a livelihood from farming is entitled to have a plot of land free of charge (Federal Republic of Ethiopia, 2002). In line with this guiding principle, the policy provides usufruct rights to rural households while 7
incentives to renewable-energy companies. Whereas previous studies have examined the impact of ITBIS incentives in the DR, the effects of CIT expenditures on investment and growth have yet to be fully assessed. The following analysis is designed to address that gap in the literature.

# **IV. The Data Set**

Data from tax returns can be used to analyze whether the DR’s CIT-related tax expenditures correlate with significant difference in firm-level outcome indicators. In 2016, the Ministry of Finance provided an anonymized CIT data set to the World Bank as a part of the authorities’ ongoing fiscal policy dialogue. The panel data include administrative CIT declarations for 2006-2015 from more than 180,000 firms in 31 provinces. Though anonymized, the data set contains important information about firm characteristics, including the economic sectors in which the firms operate, their ownership and capital structures, and their performance as measured by the outcome indicators described below. The data set also records estimated forgone CIT revenue for each firm—i.e., the amount of tax each firm did not pay due to fiscal incentives. Researchers rarely have access to such extensive and detailed tax information, and analyzing this data set may yield important insights into the much debated but still ambiguous relationship between tax incentives and firm performance.

# **V. Outcome Indicators**

To estimate the impact of CIT exemptions, multiple outcome indicators were selected as proxies for firm performance. Profitability and firms’ performance are the major aspects of firm welfare. The profits of firms translate into income for shareholders and generate spillover and multiplier effects at the individual, household, and economy-wide level. Profitable firms attract more investors and raise greater amounts of capital to finance larger and more sophisticated 8
TEEP was implemented not randomly but in the divisions identified as socially most depressed in the presidential Social Reform Agenda. Figure 3.1 shows the distribution of school districts by income category in TEEP and non-TEEP groups. School districts are concentrated in income categories 1, 4, and 5—that is, the highest income and the two lowest income rankings—for both TEEP and non-TEEP. Though we observe that more school districts are in income category 4 (and fewer in 1) in the TEEP group than in the non-TEEP group, the difference does not look significant. Further, Figure 3.2 shows the distribution of schools in the TEEP and non-TEEP groups. Our basic observation remains valid here. Therefore, it is likely that we can find (and compare) school districts that share similar socioeconomic conditions in both TEEP and non-TEEP divisions.<sup>15</sup> For TEEP implementation information, we have the Division Education Development Plan data, which was part of the TEEP completion reports. This dataset has aggregated TEEP inputs during SY 2000/01 to SY 2004/05. However, it does not identify implementation timing and inputs of different components of TEEP. Furthermore, the completeness and quality of the data substantially vary across divisions. To overcome this gap in the data, we visited 23 TEEP division offices to find the raw data on TEEP investments. The raw data we collected reveal details of different TEEP investments: textbooks, training, school-based management, school building, school innovation and improvement fund, equipment/furniture, and supplementary instructional materials. For training, we identified the starting date of teacher training and calculated the total number of man-

> 15 In general, migration occurs from TEEP provinces since poverty rate is higher in those provinces. In the case of Visayas, we observe migrations to Manila and Cebu City (non-TEEP provinces which are not included in our analysis). Though the implementation of TEEP might have created an incentive for students to move from nonTEEP to TEEP schools, this is not realistic since many provinces are divided by mountains or sea (i.e., islands). See Figure 1.

10
Figure 23: Temporary employment by gender. (Early 2000s/Mid-2010s) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s<br>Georgia Kyrgyzstan Turkey Armenia<br>Female Male<br><!-- End of picture text --> Source: Own calculations based on Household surveys In summary, the evidence does not suggest a clear trend across countries regarding the prevalence of non-standard employment but rather identifies a heterogeneous panorama by countries and types of non-standard employment.

# 4.2.2 Profile of Non-Standard Employment

In this section, we investigate possible changes in the profile of non-standard employment, usually associated with lower productivity and greater vulnerability. Like in the case of the LAC countries, the analysis of the employment profile was made based on the education, wages and the content of tasks performed.9 From the educational point of view, a general tendency can be observed in the countries analyzed to improve the profile of workers linked to non-standard work contracts. In fact, in most of the countries in our sample, it is observed that taken together, the prevalence of workers with secondary and tertiary educational levels increases to the detriment of workers with a lower educational level.

In the case of part-time employment (Figure 25), there is a first group of countries which present a decrease in the prevalence of workers at the secondary level, but, this decrease is compensated by the greater incidence of tertiary workers, so that, taken together, workers at the secondary or higher education increased their share within part-time employees (Russia, Kyrgyzstan, Armenia, and Moldova). Therefore, we can conclude from this group that part-time employment presents a better

> 9 For limitations in the data, there is not included an analysis in terms of informality. For limitations in the databases, Albania was not included in the analysis of educational profile. Also, due to limitations in the identification of occupational categories, Turkey was not included in the task content profile.

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