rafmacalaba/datause-encoder-data huggingface dataset:
performance levels among various countries decline. These patterns are robust to alternative definitions of occupational placement, as illustrated by Figures A1 (a and b), and A2 (a and b).

The next issue is whether the strength of the negative correlation survives over time. Figure 3 (Panels a and b) plot the performance of the 1975 arrival migrant over 20 years since migration. The first panel shows the progress between 1980 and 1990 versus initial occupational placement in 1980, while the second plots the placement change between 1990 and 2000 versus the level in 1990. The convergence continues in the second decade since arrival, but at a reduced rate as the slope is smaller. Results are again robust to using the alternative indices of occupational placement (Figures A1 b and c and A2 b and c).

The natural question is whether the convergence suggested by the data leads to full catchup<sup>21</sup> by migrants from countries experiencing low initial performances to the level of migrants from countries that started at a higher level. Figure 4 reveals, the line is flatter than the 45-degree line indicating catch-up but it is not completely flat, indicating that there is no complete catchingup. Figure A3 shows the same patterns for the 1985 arrival using alternative measures of occupational placement.

A common question in the literature focuses on the convergence between the migrants and the natives rather than migrants from different countries. As the data reveal so far, there is significant variation among migrants even if they come from similar neighboring countries. As an exercise, we performed the identical exercises with the inclusion of the American born workers in the sample with a native fixed effect. Even though they overwhelm the sample in terms of size, the inclusion of the native workers do not change the at all. A hypothetical native worker also improves over time and is generally places around 15-18<sup>th</sup> in terms of ranking. The countries that surpass the native worker in terms of performance tend to be smaller or English speaking OECD countries that account for around 12-15% of the migrant population depending on the census.<sup>22</sup> Another potential issue is the performance and assimilation of migrants without tertiary education. We replicated the same exercise for migrant with only high school education where the diploma was again obtained in the home country. We also observe significant variation in terms of initial performance but we fail to find much improvement over time or catching-up by migrants who are placed at the lower end in terms of occupational performance. These results

> 21 We define full catch-up as migrants from countries with low-performance reaching to identical levels of countries that were initially at the high end.

> 22 Detailed results are available upon request.

13
<!-- Start of picture text --> b. Start primary school on time<br>60.0<br>48.8 48.8 50.3 50.3 51.2<br>50.0<br>40.0 35.8 36.6<br>31.0 32.1<br>30.0<br>19.8 20.4<br>HOI<br>20.0<br>11.0 13.5 15.5<br>Coverage<br>10.0 3.3 4.6 5.4<br>0.0<br><!-- End of picture text --> _Source_ : Demographic Health Survey used for African countries, except Côte d’Ivoire, which uses household survey data. All country surveys are from 1998.

# **5. Benefit Incidence Analysis in Côte d’Ivoire**

This section **relates the fiscal policy angle to the new measurement developments of equality of opportunities.** The authors conducted an exercise, expanding one of the most traditional and commonly used distributive analysis methods for fiscal policies, benefit incidence analysis (BIA), to relate it to the concept of equality of opportunities. The authors derived the **Opportunity Benefit Impact Analysis, or Op-BIA** , to present an incidence analysis of public educational spending along the distribution of opportunities and compare it with the traditional BIA based on income distribution. The Op-BIA has two main advantages over the traditional BIA. First, it allows one to analyze directly the allocation of public resources to education against a concept of vulnerability _directly related_ to education, not an indirect concept of per capita household income or consumption as performed by BIA. In other words, it allows a sharper picture of distribution of resources and opportunities directly associated to such resources (in this case, education resources). Second, it provides insights on how multiple factors (all those considered 26
Election Violence, only 98 of the 1,133 conflict deaths occurred in Western Province (Waki 2008). The majority of conflict deaths in Western Province occurred in districts that bordered Rift Valley Province; only 9 deaths were documented in Busia District (Waki 2008). In addition, most KLPS2 respondents (90.0 percent) are members of the Luhya ethnic group, the majority ethnic group in Western Province; as such, they were somewhat less likely to be singled out as clearly aligned with either the incumbent Mwai Kibaki (a member of a different ethnic group) or the opposition candidate Raila Odinga (a member of a third group).<sup>45</sup> Thus, though KLPS2 survey respondents lived through the crisis, both their physical locations and their ethnic identities helped to shield most of them from the worst of the violence.

This pattern is borne out in the data. The second wave of the survey included questions on exposure to violence during the post-election crisis; it documents the fact that very few respondents were, themselves, the victims of physical attacks.<sup>6</sup> 76.3 percent of KLPS2 Wave 2 respondents reported that the crisis prevented them from going to local markets to obtain basic necessities, but only 3.7 percent indicated that someone in their household was physically assaulted during the crisis. Similarly low numbers of respondents had property stolen (5.8 percent) or burned (2.1 percent) during the crisis. Thus, though many of our subjects were directly affected by the crisis because businesses and markets were closed and people were confined to their homes during periods of rioting and insecurity, relatively few were, themselves, victims of violence. Our estimates should therefore be seen as both a lower bound on the impact of civil conflict on risk preferences, and a natural complement to studies which estimate the impact of violence on those individuals and households who were most victimized.

# **3 Measuring Risk Preferences**

We elicit risk preferences by confronting survey respondents with a series of hypothetical decision problems; each decision was a choice between two or three lotteries involving two equally likely

> 4Western Province was an opposition stronghold, and Raila Odinga received 64.6 percent of the votes cast there in the presidential election; thus, individuals perceived as likely supporters of the incumbent were the most at risk.

> 5We thank the World Bank’s Kenya Country Office for guidance on wording.

> 6As discussed above, KLPS2 respondents were randomly assigned to one of two waves of surveying. The postelection violence interrupted the first wave of surveying. The additional survey were included only in Wave 2, so responses can be seen as representative of the entire respondent population.

8
Figure 2: Firm‐level TFP and services regulations in the EU (2013) <!-- Start of picture text --> NL NL<br>BE BE<br>GB DE ATFR GB AT DE FR<br>IE IE<br>SE DK IT DK SEIT<br>FI ES ES FI<br>SI PT PT SI<br>HR HR<br>CZ SK SKCZ<br>EE LV HU PL HU EE PL LV<br>BG BG<br>RO RO<br>.1 .2 .3 .4 .5 .1 .2 .3 .4 .5<br>Services regulation (Entry) Services regulation (Conduct)<br>14 14<br>12 12<br>TFP 10 TFP 10<br>8 8<br>6 6<br><!-- End of picture text --> Source: OECD, and author’s own calculations. TFP figures are based on Ackerberg _et al_ . (2006). The TFP estimates are a weighted average taking the size of the firm into account within its own sector to which it belongs for both goods and services. In our case we use the number of employees of a firm relative to the total amount of employees in the 2‐digit NACE sector. Luxembourg is excluded because of being an outlier.

Figure 3: Regulatory reform in France over time in Transportation services <!-- Start of picture text --> Entry Conduct<br>1970 1980 1990 2000 2010<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br><!-- End of picture text --> Source: OECD 29
**Table 7. Proportion of undergraduate and masters level courses in developing countries pursuing the different learning goals**

|Learning objective|Undergraduatelevel|Masterslevel|
|---|---|---|
|Access existing economics knowledge|1.000|0.952|
|Display understanding of economic knowledge|0.952|0.810|
|Interpret and evaluate economic knowledge|0.857|0.857|
|Apply economic knowledge to an analysis of an economic problem|0.619|0.667|
|Interpret and manipulate economic data|0.286|0.238|
|#countries|17|12|
|#courses|21|21|

33
where  _i_ is a country-specific effect,<sup></sup> _i_<sup>is a country-specific convergence parameter and</sup>  _it_ is a zero-mean error term. (To simplify notation I assume evenly spaced data for now.) Next let the headcount index of poverty be a log-linear function of the mean: ln _H it_   _i_  _i_ ln  _it_  _it_ (7) Where<sup></sup> _i_<sup>is a country-specific effect,</sup> _i_<sup>is interpretable as the (country-specific) elasticity of</sup> poverty to the mean (with the expectation that  _i_  0 ) and  _it_ is a zero-mean error term. The implied model of the growth rate in poverty is then:

* * * for which it is readily verified that  _i_   _i_  _i_  _i_  _i_ ,  _i_  _i_ and  _it_   _it_  _i_  _it_  1(  _i_ )  _it_  1 . The parameters of (6) and (7) (  _i_ ,  _i_ ,  _i_ ,  _i_ ) can vary across counties but (for the sake of this argument) suppose they do so independently of _H it_  . Comparing (6) and (8) it can be seen that the ―speed of convergence‖ for the poverty measures,  ln _Hit_ /  ln _Hit_  1   _i_ , is the same as that for the mean,  ln  _it_ /  ln  _it_  1   _i_ . Thus we have:

**_Proposition 3_** _: In standard log-linear models for growth and poverty reduction, with parameters independent of the initial level of poverty, the speed of convergence will be the same for the mean as the poverty measure._

Turning to the data, we do see signs of mean convergence. Table 1 gives standard convergence tests for mean consumption based on the regression coefficient of<sup>_g_</sup> _i_ (<sup></sup> _it_ ) on ln  _it_  , with and without controls.<sup>25</sup> The controls included initial consumption per capita from the national accounts, primary school enrollment rate, life expectancy at birth, and the price index of investment goods from Penn World Tables (6.2), which is a widely-used measure of market distortions; all three variables are matched as closely as possible to the date of the earliest survey. It can be seen from Table 1 that the survey means exhibit convergence; the  coefficient is -0.013 (t=-3.412) without the controls and -0.042 (t=-7.435) with them. Unconditional

> 25 Alternatively one can estimate the convergence parameter using a nonlinear regression _g_ (  )    [(1  _e_  /)  ]ln    (as in Barro and Sala-i-Martin, 1992). This gave a very similar result to (1) in

> Table 1, namely  ˆ   .0012. (t=-2.865). Clearly, the approximation that _e_   1   (linearizing the nonlinear regression specification) works well.

13
I am hesitant to call Thomas Piketty’s new book _Capital in the 21_<sup>_st_</sup> _Century_ ( _Le capital au XXI_<sup>_e_</sup> _siècle_ in the French original) one of the best books in economics written in the past several decades. Not that I do not believe it is, but I am careful because of the inflation of positive book reviews and because contemporaries are often poor judges of what may ultimately prove to be influential. With these two caveats, let me state that we are in the presence of one of the watershed books in economic thinking.

Thomas Piketty is mostly known as an indefatigable researcher of income concentration. His path-breaking book _Les hauts revenus en France au XX_<sup>_e_</sup> _siecle: Inegalités et redistribution_ (2001) was the basis for several influential papers published in the leading American economic journals. In the book, Piketty documented, using fiscal sources, the rise (until World War I), the fall (between 1918 and the late 1970s) and then again the rise in the share of the top income groups in France. Piketty revived the methodology originally used by the pioneers of income distribution studies Vilfredo Pareto and Simon Kuznets. It consists of the use of fiscal data rather than household surveys, and focuses by its very nature on top incomes. This focus on the top makes both economists and the general public more aware of the rich, their income levels and share in total income than do broader distributional studies using household surveys. The French study was soon followed by a similar long-term study of top incomes in the UK (Atkinson, 2002), in the United States (Piketty and Saez, 2003), the rest of Europe and the developed countries (Atkinson and Piketty, 2007) and most recently in a number of emerging market economies (Atkinson and Piketty, 2010; Alvaredo et al. 2013). The work which is associated mostly with the names of Piketty, Tony Atkinson, Emmanuel Saez, and Facundo Alvaredo includes now many longrun studies, covering in some cases a century or even two, from more than 20 countries. An impressive - interactive database “World Top Incomes Database” (http://topincomes.g <u>mond.parisschoolofeconomics.eu/) has been created. Currently (October 2013), it contains data from</u> 27 countries.

The prominence of the work of Piketty and his associates has also been helped by the revived interest in inequality which coincided with the onset of the Great Recession and the realization that in the United States incomes around the median have been stagnant in real terms for almost 40 years while the top 1%, or even more narrowly top 0.1%, have dramatically increased their share of total income. The confluence of the rise in the political importance of inequality, best exemplified in the _Occupy_ movement and the _99% vs. 1%_ slogan, had its empirical basis in the work done principally by Thomas Piketty and Emmanuel Saez (2003). Their famous graphs of the income shares of US top decile, top 1% and top 0.1%, showing that at the turn of the 21<sup>st</sup> century the rich’s income shares approached 2
# **5.4 Forecasting**

In addition to revealing marginal impacts of various variables on wages the forecasting performance of the models presented in Tables 3, 4 and 6 may also be of interest. Specifically, we are interested in whether our models are valid also after the implementation of ESAP in 1991.

Figure 13 shows forecasts and actual wages for the three models examined previously: (1) total wages with unemployment, CPI, productivity and policy variables; (2) total wages with GDP, CPI productivity and policy variables; and, (3) manufacturing wages. Additionally, 95 percent confidence bands are shown. From these plots, the level of wage growth seems to be captured well by our economic models, i.e., predicted wages fall within the confidence bands in 1992, 1993, and 1994.

**Figure 13 Forecasting and actual values for the three wage models**

<!-- Start of picture text --> DLT.ot_. ror_ _st<br>.48r<br>.44<br>.4<br>.36 -<br>.32<br>.28-<br>.24<br>.2 _<br>A.,, - ,,.-------<br>1991 1992 1993 1994 1995<br><!-- End of picture text --> 31
the demand for inputs; by raising the demand for trade related services (e.g., transport); and through a multiplier effect if workers employed by exporting firms increase consumption in the area.

The data on the number of workers engaged in exports are not available directly at each province level. I construct a province-specific export index ( _Exportpt_ ), using the exports data at the national level as well as the employment and output data at the firm level. The Export Index is the sum of the workers over each industry _j_ aggregated at the province level _p_ , multiplied by the export intensity of each industry, relative to the total economically active population in each province _p_ at time _t_ . Specifically, where _XSjt_ is the export intensity computed as the proportion of exports in gross output for industry _j_ at time _t_ at the national level;<sup>31</sup> _Employmentjpt_ is the number of workers employed by industry _j_ in province _p_ at time _t_ ; and _Employmentpt_ is the size of economically active population in province _p_ at time _t_ ; _j_ represents Vietnam’s industries at the two-digit VSIC level (VSIC 1041), _p_ represents Vietnam’s 61 provinces,<sup>32</sup> and _t_ = 2002, 2004 in my regressions. Thus, the impact of exports on labor demand is likely to be larger, the higher is the export-intensity in the composition of industries, and the larger is the share of workers employed by industries relative to total employment in province _p_ .

> 31 Since the export intensity of each industry is not available at the province level, that at the national level is used. For some industries for some years, the export values from the Comtrade data exceed the production values from the _Enterprise Survey_ data. As a potential source of this incompatibility, Jenkins (2004) points out that the gross output reported in the _Enterprise Survey_ data may refer not to the value of the products produced, but to the processing cost. This is particularly the case of the garment and footwear industries, where exports are commonly contracted on a cut-make-trim (CMT) basis. Following Jenkins (2004), the reported figures for output in the garment and footwear industries are multiplied by 2.5 and 2.0 respectively.

> 32 Between 2002 and 2004, some provinces were subdivided creating three new provinces, namely Dien Bien, Dac Nong and Han Giang. For the purpose of consistency, the definition of provinces is based on the 2002 configurations.

18
9 data sets include time series information on changes in government, coup attempts (successful or unsuccessful) and political motivated riots, among others, and canv, thus, be used to both classify countries into different political categories and to construct Indexes of political instability. In Appendix II of this paper we include a detailed discussion on possible alternative ways of actually measuring political variables and we explain, at some length, the procedures that we have actually used in this paper. In Table 2 we report some preliminary evidence consistent with the two main implications of the Persson-Tabellini model on credibility and reputation. This table reports the results of estimeting a simple OLS regression of avera3e inflation against various measures of political instability and polarization, on cross-country data (see Appendix II for greater details on measurement issues,. In the first spi.Jification of Table 2, political instability is measured as the frequency of (regular and irregular) govern-aent changes during the relevant time interval. We interpret this variable as beirxg a proxy for the probability -f the governmenc being replaced. In the second specification we distinguish between the frequency of regular government changes and the frequency of coups. Since the latter form of government transfer is likely to Involve a more radical change in the ideology of the government, the frequency of coups is a measure of both instability snd polarization of the political system. The results are quite striking: the estimated coefficients are always positive and generally highly significant for most time periods.10 10The same results are obtained if we replace the actual frequency of government change with the expected probability of a government change, estimated from a probit model. ThiF alternative measure of political instability, used for the first time in Cukierman, Edwards and Tabellini (1989), i. d'4scussed later in the text and described in greater detail Appendix.
|**Appendix Table 9.4: Impacts on Steps**|**Towards Investmen**<br>Contacted|**t**|Has mentor|||
|---|---|---|---|---|---|
||outside<br>investor|Made<br>a pitch|helping raise<br>finance|Entered into<br>negotiations|Aggregate<br>Index|
|**Panel A: Impact at Six Months**||||||
|Assigned to Treatment|‐0.082|0.016|0.078|‐0.008|‐0.017|
||(0.074)|(0.068)|(0.063)|(0.057)|(0.098)|
|Sample Size|239|240|232|279|240|
|Control Mean|0.509|0.549|0.236|0.323|0.008|
|Control S.D.|0.502|0.500|0.427|0.470|0.720|
|**Panel B: Impact at Two Years**||||||
|Assigned to Treatment|‐0.019|0.006|0.050|0.068|0.044|
||(0.057)|(0.047)|(0.040)|(0.059)|(0.092)|
|Sample Size|282|282|279|279|282|
|Control Mean|0.324|0.184|0.097|0.328|‐0.032|
|Control S.D.|0.470|0.389|0.297|0.471|0.760|

70
from commodities. Income commodity dependence varies widely across countries. This comparison, however, is unlikely to be very useful because of differences in coverage. An example of this is Argentina, which in spite of being a country with a relatively important agricultural sector, the household survey suggests that only 3.2 percent of household income is, on average, derived from commodities. Clearly, this is more a manifestation of the urban focus of the survey than of the economy. In the discussion that follows, it is thus convenient to focus more on within country comparisons across quintiles. A regularity in most of our Latin American data is that lower quintiles exhibit higher shares of income derived from “commodities.” The differences are sometimes important. In Paraguay, for instance, while the bottom quintile earns 44.4 percent of income from commodities, the top quintile earns only 14.7 percent.

Table 4 uncovers similar patterns in the African data. The income commodity dependence in Africa is very high (higher than in Latin America) and varies across countries. The lower quintiles also tend to show higher commodity dependence. In Ghana, for instance, the share of income derived from commodities is 71.4 in the first quintile, and 27.1 in the top quintile. Table 4 shows that, in the data, most of the dependence from commodities in Africa is related to dependence on agriculture. Since the data from both continents indicates that poor households are on average highly dependent on commodities on both the consumption and income sides, the distinction between net consumers and net producers will become particularly relevant for the literature that attempts to assess the distributional and poverty impacts of commodity-price fluctuations.

As a preliminary step in this direction, it is worth analyzing the evolution of consumption and income dependence on commodities during a period of time when global commodity prices rose. The purpose of the empirical exercise is to describe the changes in income shares observed in the data from 2004 to 2008. Transitions in and out of dependency can be studied with panel data, which is unavailable in our sample countries. We can, however, build pseudo-panels of households using repeated cross-section with the method proposed by Dang, Lanjouw, Luoto and McKenzie (2011). Since only the income surveys in Latin America are repeated cross-sections, we focus here solely on _income_ commodity dependence 5