rafmacalaba/datause-encoder-data huggingface dataset:
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## BelsEium

While the United Nations assumes a small net inflow of immigrants through the year 2000 (25,000 for each five-year period), the World Bank has recently changed its assumption from a declining inflow over time to a small net outflow from 1985 to 2000. I would recommend a constant net inflow of 15,000 per five-year period after 1985 on the basis of information about recent migrant inflows. The net inflow of the foreign population amounted to 4,000 persons in 1985 and 6,600 persons in 1986 (SOPEMI, 1988). In each of those years there were more immigrants and fewer emigrants than the previous year. In 1986, there was a substantial increase in immigrants from Spain, Portugal, Asia, Africa, and the United States, which was partially offset by a lower level of immigration from Morocco. A very significant factor in immigration to Belgium has been an increase in asylum seekers (of whom there were 6,117 persons in 1986). Given the recent rise in net immigrants and the prospect of the conditions that gave rise to increasing immigration continuing, a small net inflow of immigrants seems appropriate.

# Federal ReDublic of Germany

Net international migration to Germany has fluctuated substantially in the 1980s. The decade started off with a very high inflow of foreigners (631,400 in 1980 and 501,100 in 1981) accompanied by an outflow of foreigners of 400,000 per year in 1980 and 1981 (SOPEMI, 1988). However, the inflow declined rapidly to only 273,200 in 1983 at the same time that the outflow was increasing (tc a peak of 545,100 in 1984). Since that time, the inflow has been rapidly increasing and the outflow rapidly decreasing. The outflow was particularly heavy during the period that the Repatriation Assistance Act was in effect, from late 1983 to September, 1984. Although 306,000 aliens left Germany during that period, repatriation did not have much of a lasting impact on the total resident foreign population. In fact, the total stock of foreigners living in Germany increased slightly from 4,453,300 in 1980 to 4,482,500 in 1986.

Most of the international movement of population into Germany consisted of guestworkers who do not have permission to settle in Germany permanently, but Papademetriou (1988) says that there is evidence that the majority of labor migrants to Germany might stay on permanently. Although on a much smaller scale, the inflow of asylum seekers has become a major factor in migration flows to Germany. The number of asylum seekers increased from 19,757 in 1983 to 99,650 in 1986 (SOPEMI, 1988). New controls on asylum seekers were introduced in 1987 and requests for asylum dropped by 45 percent in the first five months of 1987 (compared with the same period a year earlier).

It is interesting to note that the United Nations assumes that there is no net international migration in the case of Germany for the entire period 1980-2000, whereas the World Bank has been assuming net immigration of 200,000 in 1980-1985, 100,000 for 1985-1990, 60,000 for 1990-1995, and 40,000 for 1995-2000. In light of the fact that the foreign population recorded in the municipal registers actually decreased from 4,453,300 in September 1980 to
empirical literature that has investigated the relationships between structural and regulatory factors and performance, access to financing and growth, all as they relate to the competitive structure of the banking systems. Since these papers mostly not attempt to test a specific structural model, we review briefly the general theory on measuring competition and<sup>thenreview</sup> some of the empirical papers that have applied structural competition tests to the financial<sup>sector.</sup>

# A. General Effects of Competition in Banking

As a first-order effect, one would expect increased competition in the financial sector to lead to lower costs and enhanced efficiency, even allowing for the fact that financial products are heterogeneous. Recent research has highlighted, however, that the relationships between competition and banking system performance, access to financing, stability and growth are<sup>more</sup> complex (for a recent review of the theoretical literature on competition and banking, see Vives 2001). Market power in banking, for example, may up to a degree be beneficial for access to financing (Petersen and Rajan 1995). The view that competition is unambiguously good in banking is more naive than in other industries and vigorous rivalry may not be the first best<sup>for</sup> financial sector performance. This literature has also shown that technological progress lowering production or distribution costs for financial services providers necessarily neither leads to more or better access to finance.

B. General Empirical Studies on Banking System Performance and Structure A number of papers have investigated the competitive condition in banking systems. In one of the first papers, Berger and Hannan (1989) investigate the commonly observed relationship between market concentration and profitability using data for US banks during the period 198385. They try to separate the effects of non-competitive price behavior from those of greater 4
# **Figure 9.  Average Saving and Real Exchange Rate Gaps in LAC1 Countries 1990–2012**

<!-- Start of picture text --> 0.5<br>ER high savers IR high savers<br>CHL VEN<br>PAN<br>ARG<br>PER MEX<br>0<br>ECU<br>COL<br>URY<br>BRA<br>IR low savers<br>-0.5<br>CRI ER low save rs<br>-1<br>-0.4 -0.2 0 0 .2<br>Real Exchange Rate<br>National Saving<br><!-- End of picture text --> Sources: Authors' calculations based on UN, WDI and Institutional Investor data. Notes: This figure shows the average national saving and real exchange gaps for LAC1 countries for the 19902012 period. See Section 3 for the details of how the benchmarks are calculated. The dashed line is the linear fit for the per-period version of the whole sample for 1990-2012.

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level utilization for country-product pairs with AGOA utilization rate above 30 percent in pre-suspension year; (ii) medium-level utilization for country-product pairs with utilization rate between 0 and 30 percent; and (iii) no utilization for country-product pairs with zero utilization rate. We expect the AGOA suspension effect to be increasing in the pre-suspension utilization rate.

The results reported in table 5 confirm that the AGOA suspension effect is largest for exports with high pre-suspension utilization rate. We find that the withdrawal of AGOA benefits induces a 65 percent drop in exports with pre-suspension utilization rate above 30 percent (statistically significant at the 5 percent level). The results for the probability of exporting in column (3) of table 5 follow the same pattern. The withdrawal of AGOA benefits from exports with high level of utilization is associated with the largest (4 percentage points) decline in the probability of exporting.

**Table 5: AGOA suspension effect by** **<u>pre-suspension utilization rate</u>**

||ln(Exp<br>|orts)<br>|Export Dummy|
|---|---|---|---|
||Coefficients|Marginal effect||
||(1)|(2)|(3)|
|Suspended×Country×Product×||||
|No utilization|-0.037|-3%|-0.001|
||(0.227)||(0.013)|
|Medium utilization|-0.171|-16%|-0.008|
||(0.277)||(0.015)|
|High utilization|-1.036**|-65%|-0.041**|
||(0.475)||(0.018)|
|Product-year Fes|Yes|Yes||
|Country-year Fes|Yes|Yes||
|Country-product Fes|Yes|Yes||
|Number of observations|584,434|584,434||

The marginal effects throughout this paper are calculated as exp(β)−1 if the dependent variable is lnExports. Robust standard errors in parentheses are clustered by country and product. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

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rates, e.g., when the dynamics in a labor market favor certain kinds of workers. Thus, a series of personal characteristics, namely, gender, ethnicity,<sup>37</sup> educational attainment (measured by total education years) and potential experience,<sup>38</sup> are included.

Changes in Export Index may be systematically correlated with province specific characteristics that affect wage growth (Topalova, 2010). For instance, if provinces which had more initial industrial base were enjoying higher wage growth even in the absence of exports and also experienced larger industrial exports expansion, the coefficient of the change in Export Index might capture a spurious relationship. In order to overcome this concern, I included the initial provincial employment composition at a more aggregate level, namely the proportion of workers in agriculture, construction, industry, government, and service sector in total workers constructed from the VHLSS 2002 (McCaig, 2011; Topalova, 2010). The growth path also may differ between rural and urban areas. Thus, Model B also includes a dummy variable for whether or not the individual lived in an urban area.

Columns 4 through 6 in Table 1 report the results of Model B. Relative to the results obtained by Model A, the inclusion of the additional control variables led to an increase in R<sup>2</sup> . However, the coefficients for the changes in Export Index in Model B turn out to be similar to that obtained in Model A and the results appear to be robust to the inclusion of control variables. Some personal characteristics are found to have affected wage growth. For instance, the coefficients for education are found to be highly positively significant, highlighting the importance of educational attainment in wage growth. The positive impact of education on wage growth is also consistent with the rise in skill premium in Vietnam’s economy as a whole.

> 37 The minority is defined to represent all ethnic groups other than the Kinh majority and Hoa (the Chinese). 38 Potential experience is calculated as age minus six minus education years.

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a control function approach for a dynamic model. To deal with the dynamic nature of the model, we consider two possibilities. We ...rst use a “pure”random e¤ects approach which assumes that unobserved heterogeneity is independent of the observed exogenous covariates and initial conditions. Next, we relax this strong assumption by employing the dynamic correlated random e¤ects model introduced by Wooldridge (2005). This approach is not only more relevant for analyses of poverty persistence, but also more ‡exible and computationally straightforward than alternative approaches currently in use.

We then implement our empirical approach using panel household and village data from rural China. Following the market-oriented reforms introduced in the early 1980s, there was a pronounced decline in the proportion of China’s population living below the poverty line (Ravallion and Chen, 2007). While much of the literature examining growth in China’s rural areas has focused on incentive e¤ects related to reform and on the role of local non-farm employment, there has been relatively little research demonstrating the relationship between increasing migration and the probability that households within villages have consumption levels below the poverty line. Our empirical analysis demonstrates an economically signi...cant causal relationship between migration and poverty reduction in rural China. In performing this exercise, we highlight the usefulness of our econometric approach to settings in which the researcher must work from binary indicators of poverty status, which is often the only information available from administrative data sources.

The paper proceeds as follows. In the Section 2 below, we ...rst review approaches to estimation of dynamic binary response panel data models, and then propose a general approach to estimating these models when there is an endogenous regressor. In Section 3, we introduce the rural China setting, and motivate a speci...c implementation of the model developed in Section 2, and ...nally describe a strategy for identifying the e¤ect of migration on poverty within China’s villages. In Section 4, we discuss our estimation results and the performance of the model, and then in Section 5 we summarize our results and discuss the potential value of the estimator introduced in the paper.

3
three times the standard deviation from the upper (lower) quartile in the corresponding two-digit NACE industry, country, and year are considered outliers and dropped from the sample.

Given that respondents to the BEEPS were asked to answer questions with respect to business operations occurring in the previous year, BEEPS 2002 and 2005 data are assumed to capture the characteristics of the business environment in 2001 and 2003, respectively, in order to fit the first-difference model with two-year changes in firm productivity regressed on lagged two-year changes in the business environment. BEEPS 2002 and 2005, therefore, is match merged with Amadeus 2002 and 2004 observations, respectively, on country, sector, subnational location, and firm size. Specifically, averages of variables from the BEEPS manufacturing dataset are first calculated for groups defined by country, sub-national location, and firm size in each respective year using only the responses of manufacturing firms (NACE codes 15-36). There are three sub-national location categories: capital city, large city (defined as a non-capital city having a population of 250,000 or greater), and small city (defined as a noncapital city having a population less than 250,000); and two firm size categories: small (defined as employing 2 to 49 full-time workers) and large (defined as employing 50 or more full-time workers). These country-location-size-year averages of BEEPS variables for the manufacturing sector are then match merged to each AMADEUS observation on this identical set of variables. To illustrate, the average number of days in 2001 that large-sized manufacturing firms located in small cities in Bulgaria experienced power outages or surges from the public grid is first calculated from the BEEPS 2002 database, and then this value is assigned to all observations in the 2002 AMADEUS sample that operate in the manufacturing sector, employ 50 or more fulltime workers, and are located in cities with populations less than 250,000 in Bulgaria.

The final sample that will be used for the econometric analysis of the effect of changes in the business environment on firm-level TFP growth over the period 2001 to 2004 consists of 22,004 manufacturing firms in 8 countries: Bulgaria, Croatia, the Czech Republic, Estonia, Poland, Romania, Serbia, and Ukraine. The distribution of the merged AMADEUS-BEEPS balanced panel dataset by countries is as follows: Bulgaria 221; Croatia 1,780; Czech Republic 964; Estonia 1,253; Poland 1,133; Romania 12,576; Serbia 2,237; and Ukraine 1,840. The above inclusion criteria create the most comparable sample of firms across countries. Note, however, that strong conclusions at the international level cannot be derived from direct cross-country 8
methodology explicitly takes into account the underreporting of household surveys at the top of the income distribution, and only relies on household survey data to compute the Gini coefficient for the segment of the income distribution that lies below the threshold _b_ .

This paper is organized as follows. After presenting the methodology and its properties in Section 2, the paper demonstrates an application to the case of Colombia (Section 3). Although in recent years an increasing number of governments have made income tax data available to the public, Latin American countries have lagged behind. Colombia is the first country in the region to make personal income tax micro-data available to researchers, which determined the case study selection in this analysis. Section 4 offers conclusions.

# **2 Methodology**

This section describes a simple procedure to estimate the entire income distribution in a population by combining the information in two data sets that are only informative about conditional income distributions. In this case, we focus on the fact that household surveys are representative of the bottom segment of the income distribution but not the top, while tax records capture the top end of the income distribution but not the bottom. The proposed methodology applies a result of Dagum (1997), who showed how to decompose a population’s Gini coefficient into a combination of the Gini coefficients of its subpopulations. A related methodology has been applied by Alvaredo (2011) to measure income inequality in the United States and Argentina. The end of this section summarizes the main differences between the proposed methodology and the previous literature.

## **2.1 Setup and Assumptions**

Suppose that the cumulative distribution function of income _y_ in a population is _F_ ( _y_ ). We would like to obtain an estimate of _F_ ( _y_ ) and some related statistics (like the Gini coefficient, _G_ [ _F_ ]). However, we can only obtain consistent estimates of some _conditional_ distributions of _y_ . In particular, we assume: 4
**Figure 16. Upward economic mobility is associated with initial year household characteristics** Share of households in type of transition ($2.5/day) by characteristic **a. Household characteristics and human capital**

<!-- Start of picture text --> 80<br>69 1999<br>70 64<br>60 59<br>60<br>50 47<br>42<br>40<br>30<br>23<br>20<br>12<br>10 4.3 3.8 3.5 6<br>0<br>Household size Household dependency Rural Tertiary education<br>rate (share of members)<br>stayed poor escaped poverty non-poor<br>80 2006 74<br>68<br>70<br>58<br>60<br>48 49<br>50 45<br>40<br>30<br>22<br>20<br>11<br>10 4.6 4.1 3.6 2<br>0<br>Household size Household dependency Rural Tertiary education<br>rate (share of members)<br>stayed poor escaped poverty non-poor<br><!-- End of picture text --> 22
driven by the quality of the data and the specific measures of outputs and inputs used to estimate the model. In sum, the models are not really that useful in answering the original questions about the scope for improvement in outputs or in costs across sectors.

|**Ta**<br>|**ble 6 Average ef**<br>|**ficiency levels (19**<br>|**90–2002)**<br>||
|---|---|---|---|---|
|**Country income**<br>**categories**|**Transport**|**Energy**|**Health**|**Education**|
|High-income|0.36|0.59|n.a.|0.93|
|Low-income|0.33|0.23|n.a.|0.74|
|Lower-middle-income|0.29|0.30|n.a.|0.92|
|Upper-middle-income|0.37|0.32|n.a.|0.91|

Because we could not really come up with a reasonably good model for health, we do not report the results from the time-series analysis, although we will discuss results from a cross-section of 192 countries at the end of this section.<sup>19</sup> Within each of the other sectors, however, the supporting econometric results are robust enough, and a story can be squeezed out for each subsector. That story differs across the subsectors. A discussion of the evolution of each of these for each country group generates a fuller picture of the global evolution of government expenditure. Figure 4 provides a graphical presentation of this evolution. The main potential use of these results is to get a sense of the relative efficiency levels, even if it is sometimes useful to ignore some of the outlier years.

> 19 The estimated model has the wrong signs on some of the parameters.

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**<u>Table 2. Descriptive statistics of major variables</u>**

||**Whole**<br>**Sample**|**No Demand**<br>**(mean**|**Extreme**<br>**High**||**Nor**|**mal Deman**|**d**||
|---|---|---|---|---|---|---|---|---|
|**Variable**|**(mean**<br>**value)**|**value)**|**Demand**<br>**(mean**<br>**value)**|**Mean**|**Obs**|**Std.**<br>**Dev.**|**Min**|**Max**|
|**Social and demographical**<br>|||||||||
|**characteristics**<br>|||||||||
|Worker (1=worker;<br>0=otherwise)<br>|0.499*|0.463***|0.607*|0.533|838|0.499|0|1|
|Male (1=male;<br>0=otherwise)|0.706|0.746**|0.7679*|0.699|838|0.459|0|1|
|<br>Age (years)|36.820|38.695***|35.000*|36.323|838|10.746|16|77|
|Education (1=Secondary|||||||||
|school or higher;|0.938|0.912*|0.976*|0.931|838|0.254|0|1|
|0=otherwise)|||||||||
|Married<br>(1=married;0=otherwise)|0.840|0.887***|0.762*|0.827|838|0.379|0|1|
|<br>Religion (1=yes; 0=no)|0.091|0.101*|0.095|0.080|838|0.271|0|1|
|**Economic characteristics**|||||||||
|Income (household<br>income last year, yuan)|13230|12614**|24411**|13397|838|8495|600|70000|
|Yuncertain (1=not sure|||||||||
|<br>about future income;|0.160**|0.195***|0.071*|0.134|838|0.340|0|1|
|0=otherwise)|||||||||
|**Physical and health**|||||||||
|**characteristics**|||||||||
|Rcancer (1=relative<br>diagnosed with cancer;<br>|0.095|0.071|0.202***|0.088|838|0.284|0|1|
|0=otherwise)|||||||||
|<br>Smoking<br>(1=yes;0=otherwise)|0.421|0.465*|0.405|0.421|838|0.494|0|1|
|Healthcheck (1=Regular<br>check-up; 0=otherwise)|0.382|0.359|0.417|0.374|838|0.484|0|1|
|**Perceptions about the**<br>**preventive medicine**|||||||||
|<br>Need (1=do not need the<br>medicine at all,|1.814|1.606***|1.952*|1.856|829|0.766|1|3|
|2=somewhat, 3=need)|||||||||
|Length (1=expected|||||||||
|length of the preventive<br>effect 1=less than 1|1.574|1.594*|1.829***|1.539|831|0.652|1|3|
|year; 2=1year; 3=longer|||||||||
|than 1 year)<br>Trust (1=not at all trust|||||||||
|in the medicine;<br>2=somewhat. 3= totally|2.067*|1.922***|2.250***|2.104|836|0.527|1|3|
|trust)|||||||||
|<br>**Regional dummies**|||||||||
|<br>Guizhou|0.310**|0.290***|0.254***|0.353|838|0.478|0|1|
|Tianjin|0.352**|0.273**|0.327|0.309|838|0.462|0|1|

Note: The stars are to indicate the statistical significance of the differences from respondents in category 7, based on the student T test.. *** represents a significance for 1%; ** for 5%; * for 10%.

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# **II. Latvia’s Industrial Structure**

During the Soviet period, Latvia was one of the USSR’s major high tech production centers – along with Russia, Ukraine, and Kazakhstan. But despite its abundance of human capital and legacy of science-intensive production, Latvia’s principal attraction today for foreign investors and its principal comparative advantage is its supply of low wage labor performing relatively unskilled tasks while working in comparatively low productivity, low technology enterprises.

For example:

# <u>A. Productivity</u>

- As of 2002, labor productivity in Latvia was approximately one-third of the EU average.  Even more worrisome, Latvian labor productivity lags behind all NMS’s except Bulgaria and Romania.

**Figure 1 Labor productivity per employed person in 2002**

<!-- Start of picture text --> Slovakia<br>Romania<br>Turkey<br>Slovenia<br>Poland<br>Latvia<br>Lithuania<br>Hungary<br>Estonia<br>Czech Rep.<br>Bulgaria<br>CCs Mean<br>EU-15<br>Japan<br>US<br>0 20 40 60 80 100 120<br>GDP in purchasing power standards (PPS) per employed person, as % of EU-15 average<br><!-- End of picture text --> _Source_ : Eurostat, 2003

- As of 2001, three industry groups -- wood and wood products, transport equipment, and textiles -- accounted for 70% of Latvia’s manufacturing exports.

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