rafmacalaba/datause-encoder-data huggingface dataset:
35 except Poland (13%): Czech Republic - 13 percent; Hungary - 15 percent; Slovakia - 23 percent; and Slovenia - 20 percent (Table 14).

The growth of trade in motor vehicles and parts including such components as piston engines, electrical equipment, etc. has been spectacular.16 The total value of CEC-10 exports from automotive network increased from US $1.4 billion in 1993 to 10.1 billion in 1998 (Table 14).

Table 14: Motor Vehicle EU-based networks, 1993 and 1997-98 (in million of US dollars)

|||E|xports||I|mports||Netwo|rk's|Memo: Share|of|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||Final<br> <br>Products|Compo P<br>-nents|arts|Final<br> <br>Products|Compo-<br>nents|Parts|Exports I|mports|Exports in<br>manufactures<br>exports to EU|Imports in<br>manufactures<br>imports from<br>EU|
|Bulgaria|1993|<br>9|1|3|131|<br>2|24|12|157|<br>1.6|<br>12.0|
||1997|<br>9|1|7|114|3|26|17|143|<br>1.0|<br>8.3|
||1998|<br>7|1|8|179|3|29|16|211|<br>0.9|9.6|
|Czech Rep.|1993|<br>382|7|87|487|13|162|476|663|<br>9.1|<br>9.2|
||1997|<br>1,088|37|569|1,102|<br>187|738|1,693|2,028|14.7|<br>12.9|
||1998|<br>1,856|106|869|916|279|883|2,831|2,078|19.3|<br>12.5|
|Estonia|1993|<br>1|0|0|46|1|4|1|51|<br>0.7|<br>24.7|
||1997|<br>1|0|5|<br>227|<br>4|48|7|279|<br>0.7|<br>13.2|
||1998|<br>3|0|7|202|<br>5|41|10|248|<br>0.8|<br>10.1|
|Hungary|1993|<br>20|107|60|638|<br>28|178|188|843|<br>4.6|<br>12.6|
||1997|<br>300|1,632|417|734|<br>110|1,563|2,350|2,407|<br>20.9|<br>17.9|
||1998|<br>525|2,456|556|1,037|<br>93|1,218|3,537|2,347|<br>24.1|<br>15.2|
|Latvia|1993|<br>2|0|0|83|1|9|3|93|<br>1.6|<br>27.4|
||1997|<br>1|0|1|<br>143|<br>3|44|2|189|<br>0.4|<br>15.0|
||1998|<br>3|0|1|<br>177|<br>4|25|4|206|<br>0.6|<br>13.6|
|Lithuania|1993|<br>2|0|0|80|1|15|3|95|<br>1.2|<br>23.5|
||1997|<br>1|0|1|<br>228|<br>3|86|2|317|<br>0.2|<br>16.0|
||1998|<br>3|0|1|<br>227|<br>7|39|4|272|<br>0.3|<br>13.0|
|Poland|1993|<br>536|4|71|950|<br>98|320|610|1,368|<br>9.2|<br>13.2|
||1997|<br>1,073|28|354|<br>1,719|<br>246|1,728|1,454|3,693|<br>11.7|<br>15.1|
||1998|<br>1,217|29|461|1,622|<br>290|1,430|1,706|3,342|<br>11.8|<br>12.5|
|Romania|1993|<br>10|0|4|<br>143|<br>5|21|14|169|<br>0.8|<br>7.5|
||1997|<br>12|1|43|<br>176|<br>16|41|57|233|<br>1.2|<br>4.7|
||1998|<br>10|1|51|<br>368|<br>17|44|63|429|<br>1.2|<br>6.9|
|Slovakia|1993|<br>43|1|8|<br>73|<br>2|34|52|109|<br>3.8|<br>6.8|
||1997|<br>515|1|221|<br>316|<br>85|410|<br>737|811|<br>18.7|<br>16.9|
||1998|<br>1,654|1|265|<br>294|<br>226|828|<br>1,919|1,348|<br>34.4|<br>23.4|
|Slovenia|1993|<br>303|33|95|<br>427|<br>65|265|<br>430|757|<br>12.7|<br>21.4|
||1997|<br>655|60|135|<br>676|<br>174|309|<br>849|1,159|<br>17.4|<br>19.3|
||1998|<br>904|66|190|<br>652|<br>240|390|<br>1,160|1,282|<br>21.6|<br>20.2|
|Ceec-lO|1993|<br>1,004|121|234|<br>2,631|<br>150|767|<br>1,359|3,548|<br>6.6|<br>11.7|
||1997|<br>3,001|1,700|1,617|<br>4,759|<br>656|4,685|6,318|10,100|<br>13.2|<br>14.3|
||1998|<br>5,278|2,594|2,218|<br>5,022|<br>924|4,537|10,090|10,482|<br>17.0|<br>13.3|

Source: Derived from EU trade data as reported to the UN COMTRADE database.

16 See Table 1 for the list of parts, components and final products included in the automotive network.
# _IV.4 Inequality and poverty_

We also examine the spatial pattern of expenditure inequality (the Gini coefficient) in Vietnam. The provincial estimates of the Gini coefficients can be found in Table A.10 in the Appendix. Inequality varies across provinces and districts albeit with small differences. Average inequality (based on expenditure) is rather low at 0.27 for provinces and 0.25 for districts. The province with the lowest Gini (0.23) is Thai Binh, while the province with the highest Gini (0.35) is Lam Dong. At the district level, the Gini coefficient varies from 0.17 to 0.47. Meo Vac disitrict of Ha Giang province has the lowest Gini (0.17), while Da Lat city of Lam Dong province has the highest Gini (0.47).

Interestingly, low levels of inequality are found in both the poorest provinces and the richest provinces. Figure 3 plots the relationship between poverty and inequality. The quadratic relationship is highly significant both at the province and district level. Inequality tends to be lower in areas with relative low poverty and areas with relatively high poverty rates, although the differences are not enormous. This finding is consistent with the Kuznets hypothesis that inequality first increases as the economy develops, and then decreases once a high level of economic development is reached.

**Figure 3 Inequality (Gini index) and poverty (P0)**

<!-- Start of picture text --> 0 20 40 60 80 0 20 40 60 80 100<br>The poverty incidence in 2006 The poverty incidence in 2006<br>Source: Authors’ estimation from VHLSS 2006 and ARFC 2006<br>.35 .5<br>.4<br>.3<br>.3<br>Gini coefficient Gini coefficient<br>.25<br>.2<br>.2 .1<br><!-- End of picture text -->

# _IV.5 Poverty change during the period 1999-2006_

Figure 4 compares the poverty maps for the years 1999 and 2006. This shows how poverty has been reduced across the country during 1999-2006. Virtually all provinces experienced a reduction in the poverty rate. The areas where progress has been slow are the poorest areas of the country (the North West of Northern and the North West of Southern Vietnam).

13
benchmark.<sup>10</sup> Moreover, if we compare our precision-weighted estimates of governance with an alternative set of aggregate indicators based on simple averages of the underlying indicators, we find that the two estimates of governance are very similar, with correlations of 0.99 on average across all our indicators and periods. This reflects the fact that all of our underlying data sources do, in most cases, provide fairly similar crosscountry ratings of governance. We have also experimented with alternative weighting schemes that equally weight each type of governance indicator (of the four types identified in Table 1). Again we find that the correlations are very high with our benchmark indicators (see Kaufmann, Kraay, and Mastruzzi (2007a), Critique 8, for details).

# **3.  Estimates of Governance 1996-2007**

In Appendix C we report the aggregate governance indicators, for all countries, for each of the six indicators, for all periods. The aggregate indicators, as well as almost all of the underlying indicators, are available at <u>www.govindicators.org. The units in</u> which governance is measured follow a normal distribution with a mean of zero and a standard deviation of one in each period. This implies that virtually all scores lie between -2.5 and 2.5, with higher scores corresponding to better outcomes.<sup>11</sup> This also implies that our aggregate estimates convey no information about trends in global averages of governance, but they are of course informative about changes in individual countries’ relative positions over time. Moreover, as we discuss below, we find little evidence from our individual data sources of any systematic trends in world averages of governance. As a result, relative and absolute changes in countries' governance scores are likely to coincide quite closely.

Table 4 summarizes some of the key features of our governance indicators. In the top panel we show the number of countries included in each of the six indicators and

> 10 Related to this, the main reason why standard errors vary across countries is because some countries appear in more data sources than others, and not because countries vary in the average precision of the data sources in which they appear.

> 11 These boundaries correspond to the 0.005 and 0.995 percentiles of the standard normal distribution.  For a handful of cases, individual country ratings can exceed these boundaries when scores from individual data sources are particularly high or low.  Note also that small adjustments to this distribution of scores are made for earlier years to correct for compositional effects driven by expansion of the sample of countries covered.  See Appendix D for details.

16
|Quality of workfare implementation|Share of those who agree<br>or strongly agree (%)|Share of those who<br>disagree or strongly<br>disagree (%)|
|---|---|---|
|1. Selected projects benefit only a<br>narrow group of people in village<br>2. Workers were provided with<br>adequate tools for completing the<br>projects|19.6%<br>8.7%|78.10%<br>90.70%|
|<br>3. Projects had adequate supply of<br>materials|25.7%|73.20%|
|4. The project will provide benefits for<br>one yearor longeronce completed<br>Note: The questions were asked to EGPP pa|47.7%<br>rticipants.|47.80%|

# **7.  Conclusions**

Workfare is an important policy instrument for social protection for the poor in developing countries. Many of these programs are in part financed by donors, and thus evaluating these programs is critical to assess the development effectiveness of aid. Despite its popularity and importance, there is surprisingly limited evidence regarding how well and efficiently workfare programs work. This paper contributes to building a robust evidence base for social policies by examining the first-order impacts of a large-scale, modern workfare program, EGPP, on poor households receiving traditional social assistance benefits.

Utilizing the fact that EGPP requires a bank account and direct transfer of cash benefits to beneficiaries, we used access to banks as an instrument variable for the evaluation. Our findings show that EGPP participation has both qualitative and quantitative impacts on households’ welfare. Program participation not only increases a household’s per capita consumption, it also improves the quality of food consumption by increasing the expenditures on better quality protein (meat and dairy products as well as fish) and increases investments in health for both genders. In addition, the program benefits include substantially reducing households’ outstanding loans, and reducing the reliance on loans in response to shocks, which in turn alleviates the burden of indebtedness.

Based on these findings, we also discuss the effectiveness of workfare compared to its operationally simpler alternative, unconditional cash transfers.<sup>29</sup> The poverty-reducing impacts of the two, in

> 29 Unconditional cash transfers are considered to be operationally simpler compared to public works which tend to involve construction and compliance verifications in addition to wage transfers. However, this does

32
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- Dicker, D., G. Nguyen, D. Abate, K. H. Abate, S. M. Abay, C. Abbafati, N. Abbasi, H. Abbastabar, F. Abd-Allah, J. Abdela, et al. (2018). Global, regional, and national age-sex-specic mortality and life expectancy, 19502017: a systematic analysis for the global burden of disease study 2017. The lancet 392 (10159), 16841735.

- Eichenbaum, M. S., A. Atkeson, G. Barlevy, F. Ciocchini, and W. Cornwall (2020). The Macroeconomics of Epidemics. NBER Working Paper .

- Ferreira, F. H., S. Chen, A. Dabalen, Y. Dikhanov, N. Hamadeh, D. Jollie, A. Narayan, E. B. Prydz, A. Revenga, P. Sangraula, U. Serajuddin, and N. Yoshida (2016). A global count of the extreme poor in 2012: data issues, methodology and initial results. Journal of Economic Inequality 14 (2), 141172.

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25
Figure 1. The evolution of the bottom, middle and top decile’s share of total income (in percent of total income) <!-- Start of picture text --> top decile<br>fifth decile<br>bottom decile<br>1990 1995 2000 2005<br>year<br>30<br>20<br>10<br>percentage share of total income<br>0<br><!-- End of picture text --> Note: The unweighted (unbalanced panel) average for 26 economies.

The total number of inequality observations we have is 209. At the beginning of the period (1990 and 1991), we have observations for only 6 and 7 countries respectively, and at the very end of the period (2005) for 9 countries.<sup>4</sup> In between, for all other years, the number of countries included ranges from 15 to 21. This is because annual data for each of the 26 countries are not available whether because the surveys were not conducted, or because (less frequently) we did not have access to them. The list of countries with their number of observations, and the average top and bottom decile shares is given in Annex 1. We therefore have an unbalanced panel where the number of observations ranges from 16 ( _i.e_ ., available for all years) for Poland to only 2 (for Bosnia, Croatia, and Montenegro). The average number of observations per country is about eight (209 divided by 26 countries).

> 4 Azerbaijan and Turkmenistan are not included in the analysis because of unreliability of their household surveys.

7
The primary reason for the government's decision to corporatize MPTC was that it had agreed to as one of the conditions of a sector loan from the African Development Bank.' However, at this time, it would have been very unlikely that the Government would have attempted a more ambitious sector reform. **_First,_** Malawi had relatively low credibility with foreign investors. For example, Malawi was ranked lower on the International Country Risk Guide, a commercial index commonly used by international investors, than any of the early reformers were (e.g., Ghana, C6te d'Ivoire, and Tanzania).<sup>2</sup> The low ranking reflected the fact that Malawi had started the democratization process only recently - after thirty years of singleparty rule and very tight state control over the country, in general, and the communications sector, in particular. Second, at this time, bolder reforms, such as introducing competition or privatizing state-owned operators, were relatively rare in Africa. By 1994, of 42 countries in Sub-Saharan Africa for which data is available, only 4 had privatized state-owned operators and only 2 had introduced competition even in cellular.<sup>3</sup> Malawi's history of tight state control made it very unlikely that Malawi would be a leader in this respect and, therefore, it is not surprising that the Government responded with a relatively modest reform.<sup>4</sup> As well as corporatizing MPTC, the Government also licensed Malawi's first cellular operator in 1995. The new operator, Telekom Networks Malawi (TNM), was 40 percent owned by MPTC and 60 percent owned by Malaysia Telekom. Although this might not seem very ambitious, it is worth noting that many countries in Sub-Saharan Africa did not introduce cellular until even later. By 1995, cellular operators were providing services in 25 Sub-Saharan African Field interview with Post Master General of MPTC (October 1999).

> **2** A higher score on the scale indicates a more favorable environment for investment, while a lower score indicates a less favorable one. Malawi scored 4.90 points on this scale in 1994 compared to 6.53 for Tanzania, 6.33 for Ghana, and 6.27 for C6te d'lvoire.

The 42 Sub-Saharan African countries are the countries covered in International Telecommunication Union (1998ia).

_5_
# **3. Data and Main Variables**

Our main data source is the World Bank’s Women, Business, and the Law database<sup>4</sup> and the World Development Indicators (WDI). This is complemented with other data sources mentioned below. Our main specifications as well as the IV specification take advantage of the panel nature of the data by including both country and year fixed effects. The sample is an unbalanced panel of up to 95 countries for which data are available.<sup>5</sup> The time period covered is 1990 to 2012. The equation we estimate is as follows: Subscripts i,t denote the country and year, respectively. Y is the dependent variable, X is our main explanatory variable for domestic violence legislation. Z is the vector of various controls that vary across countries and years. YFE and vi denote year fixed effects and country fixed effects respectively; uit is the error term.

Ideally, the dependent variable would be adult mortality rate due to domestic violence, however the data for this variable are scarce and unreliable due to gross underreporting. Thus, we use the overall mortality rate, which biases our results downward especially if domestic violence has a low prevalence. However, domestic violence legislation may have far-ranging effects on women’s empowerment and thereby mortality beyond just domestic violence, giving some support

> 4 http://wbl.worldbank.org

> 5 List of countries: Algeria, Angola, Argentina, Armenia, Azerbaijan, Bangladesh, Belarus, Benin, Bolivia, Botswana, Burkina Faso , Burundi, Cambodia, Cameroon, Chad, Chile, Colombia, Congo, Dem. Rep., Congo, Rep., Costa Rica, Côte d'Ivoire, Denmark, Djibouti, Dominican Republic, Ecuador, Egypt, Arab Rep., El Salvador, Equatorial Guinea, Ethiopia, Fiji, Gabon, Georgia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, Indonesia, Iran, Islamic Rep., Ireland, Kazakhstan, Kyrgyz Republic, Lao PDR, Lebanon, Lesotho, Liberia, Madagascar, Malawi, Malaysia, Mali, Mauritania, Mauritius, Mexico, Morocco, Namibia, Nepal, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Rwanda, Senegal, Sierra Leone, Slovak Republic, Slovenia, South Africa, Sri Lanka, Sudan, Suriname, Swaziland, Sweden, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Togo, Tunisia, Uganda, Ukraine, Uruguay, Uzbekistan, Venezuela, RB, Vietnam, Yemen, Rep., Zambia, and Zimbabwe.

9
sales rather than earnings to avoid the problems generated by the volatility and manipulability of earnings. To control for time-varying industry-level effects, we include each firm’s global industry _q_ , which is computed by averaging across all corporations within the firm’s industry.

To control for country factors, we include real GDP growth, which comes form the World Bank World Development Indicators. In robustness tests, we control for additional country traits that might affect not only a firm’s _q_ but also its willingness and ability to access international markets, including a country’s institutional quality, shareholder rights, legal origin, domestic market capitalization, and an index of accounting standards.

After removing financial firms (since highly leveraged and heavily regulated financial institutions could be valued differently from nonfinancial firms), firms with missing data, firms from the United States and the United Kingdom (since these are financial centers where most internationalization is taking place), and firms with less than three observations, we are left with a sample of 9,096 firms from 74 countries covering the period 1989 to 2000, totaling 66,963 firm-year observations. Appendix Table 1 lists the countries, the number of domestic and international firms per country, the coverage period for each country, and summary statistics on _q_ . Some countries do not have any international firms. We keep these in the sample as a control group, but emphasize that this paper’s results hold when we exclude countries with zero or only one international firm. Also, Japanese firms represent about 30 percent of the total firms in our sample. We therefore re-did our analyses excluding Japanese firms and reached the same conclusions reported below.

# **3. Results: Before and After Internationalization**

This section tests whether there is a significant increase in _q_ after firms internationalize. We compare the average valuation of firms in the years before they internationalize to average 10
Table 6 shows the results of the estimation of equation (2), the consumption equation.<sup>17</sup> We first estimate equation (2) by OLS, using the standard specification adopted in these types of poverty analysis. We find that larger households, households with a higher ratio of dependents to working adults, and households headed by a female have lower consumption levels. Households with an older head also have lower consumption levels, although this effect diminishes as the household head ages. The average household head is 44 years old. The education level of the head of household head matters. Literacy in Kiswahili or English is positively correlated with household consumption. Surprisingly, however, the education levels of other household members do not show a significant impact on household consumption.

Households primarily dependent on farming for income generation have significantly lower levels of consumption than households in other sectors. This result is consistent with the finding of the analysis of the national 2000/01 Household Budget Survey (HBS), which shows that poverty is highest among farming households (NBS, 2002). Households primarily dependent on fishing for income generation are neither worse off nor better off, in terms of consumption levels, than households in other occupations. Rural households depend on a variety of income sources. After controlling for household size and number of productive adults relative to the number of dependents, the results on Table 6 show that having such a diversified income base increases household consumption levels. This finding is also consistent with findings of the 2000/01 national HBS. However, nationally 65 percent of households report 3 or more income sources. In our sample of the coastal population, only about half of the households surveyed report having 3 or more income sources. Providing opportunities for income diversification along the coast should therefore increase consumption levels and reduce poverty.

Households with more assets also have higher consumption levels, despite the very low level of ownership of productive assets. Surprisingly, land ownership and the amount of land farmed do not significantly impact household consumption. Despite experimenting with several alternative measures for social capital, in general, social capital does appear to have a

> 17 We do not report results with village distance variables as in nearly all instances these variables were statistically insignificant. We drop the distance variables from the analysis because missing distance data for some villages resulted in a large number of observations being excluded from the analysis.

33
and anticompetitive conduct cases vary according to their complexity and the need to allocate different categories of specific human capital skills and financial resources.

<!-- Start of picture text --> Figure 7<br>Mergers per Lawyers and Economists<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>ECA LAC EAP<br>Mergers per lawyer Mergers per economist<br><!-- End of picture text --> 12