rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

Alan Heston, Robert Summers and Bettina Aten, Penn World Table Version 6.1, Center for International Comparisons at the University of Pennsylvania (CICUP), October 2002

- Ashagrie, Kebebew. 1997. Statistics on working children and hazardous child labour in brief. ILO Bureau of Statistics Manuscript. <http://www.itcilo.it/english/actrav/telearn/osh/wc/child.htm>

- Bhalotra, Sonia, and Zafiris Tzannatos. 2003. Child labor: What have we learnt?  World Bank Social Protection Discussion Paper No 317. Washington, D.C.: World Bank.

- Basu, Kaushik, and Zafiris Tzannatos. 2003. The global child labor problem: What do we know and what can we do? _World Bank Economic Review_ 17:147-173.

- Betcherman Gordon, Jean Fares, Amy Luinstra, and Robert Prouty. 2005. Child labor, education and children’s rights. In _Human Rights and Development: Towards Mutual Reinforcement,_ eds. Philip Alston and Mary Robinson. Oxford University Press.

- Chen, Shaohua, and Martin Ravallion. 2004. How have the world’s poorest fared since the early 1980s?” _World Bank Research Observer_ 19:141-169.

- Dehejia, Rajeev H., and Roberta Gatti. 2002. Child labor: The role of income variability and access to credit across countries.  NBER Working Paper No. 9018. Cambridge: NBER.

Duryea, Susan, Jasper Hoek, David Lam, and Deborah Levison, Forthcoming. “Dynamics of child labor: Labor force entry and exit in urban Brazil.” In Peter F. Orazem, Guilherme Sedlacek, and Zafiris Tzannatos (eds.) _Child Labor and Education in Latin America_ . Washington DC. Inter American Development Bank.

- Edmonds, Eric, and Nina Pavcnik. 2005. Child labor in the global economy. _Journal of Economic Perspectives_ 18:199-220.

- Guarcello, Lorenzo, Fabrizia Mealli and Furio Camillo Rosati (2003) “Household Vulnerability and child labor:  The effects of shocks, credit rationing and insurance.”  Washington DC. The World Bank, Social Protection Unit, Human Development Network.

- Gunnarsson, Victoria, Peter F. Orazem, and Guilherme Sedlacek. 2005. Changing patterns of child labor around the world since 1950: The roles of income growth, parental literacy and agriculture. World Bank Social Protection Discussion Paper No. 0510. Washington, D.C.: World Bank.

- ILO. 2002. _Every child counts: New global estimates on child labour_ . Geneva: International Labour Office.

ILO (2006) _The End of Child Labour: Within reach._ Geneva, International labour Office.

15
**Table 3: Manufacturing share of total employment regressions**

|Coefcients|Marginal Efects|
|---|---|
|Population squared (logarithm)<br>2.390<br>(2.855)|0.130***<br>(0.040)|
|Population (logarithm)<br>-0.011<br>(0.096)||
|GDP per capita (logarithm)<br>-2.177<br>(1.553)|0.041***<br>(0.013)|
|GDP per capita squared (logarithm)<br>0.170*<br>(0.095)||
|Intercept<br>-27.114<br>(22.918)||
|Observations<br>523|523|
|Number of countries<br>11|11|
|Country Fixed Efects<br>Yes|Yes|
|Time Fixed Efects<br>Yes|Yes|

Standard errors in parentheses. Results based on fixed effects fractional logistic regressions in equation (2). _∗p <_ 0 _._ 10 _, ∗∗ p <_ 0 _._ 05 _, ∗∗∗p <_ 0 _._ 010

# **5 Deindustrialization at the SSA subregional levels**

This section explores deindustrialization by subregional groups. This is important for two reasons. First, there is no reason to expect that countries in different regions share the same deindustrialization experience and geographic differences across SSA may mask any evidence of (premature) deindustrialization. Second, examining the evolution of manufacturing for different regions may shed light on why SSA countries may have undergone deindustrialization. We perform subregional analysis for only the manufacturing output shares since we only have a small number of countries with data on manufacturing employment shares. Nonetheless, this is fitting for our purposes as it is also for manufacturing output shares that we find some evidence of deindustrialization in the previous section.

The subregional group results are presented in Table 4, with the predicted manufacturing output shares plotted in Figure 8. These results highlight significant regional differences. From Table 4, the coefficient estimate on the quadratic term in GDP per capita is only statistically significant for Southern SSA. Figure 8 graphically sums up this finding: only Southern SSA appears to have deindus17
# **Table 1: Sovereign ratings between Jan 2009–Dec 2018**

We group the countries studied in this paper by their credit quality rating, based on Fitch Ratings’ long-term credit ratings. The right-most columns indicate the highest, lowest and average alphabetic ratings observed in the sample. The rating groups used throughout this paper categorize AAA to A- ratings as “A-rated” and BBB+ to BB- as “B-rated”.

|||||Cr|edit quality rating|in sample|
|---|---|---|---|---|---|---|
|Credit quality step|Country|Code|Eurozone|Highest|Lowest|Average|
||Australia<br>|AUS<br>|-|AAA<br>|AAA<br>|AAA<br>|
||Canada<br>|CAN<br>|-|AAA<br>|AAA<br>|AAA<br>|
||Denmark|DNK|-|AAA|AAA|AAA|
||Germany|DEU|Yes|AAA|AAA|AAA|
||Netherlands<br>|NLD<br>|Yes|AAA<br>|AAA<br>|AAA<br>|
||Norway|NOR|-|AAA|AAA|AAA|
||Sweden|SWE|-|AAA|AAA|AAA|
|1|Switzerland|CHE|-|AAA|AAA|AAA|
||United States<br>|USA<br>|-<br>|AAA<br>|AAA<br>|AAA<br>|
||Austria|AUT|Yes|AAA|AA+|AA+|
||Finland|FIN|Yes|AAA|AA+|AA+|
||France|FRA|Yes|AAA|AA|AA+|
||United Kingdom <br>|GBR<br>|-|AAA<br>|AA<br>|AA+<br>|
||China|CHN|-|AA-|A+|AA-|
||Korea, Rep.|KOR|-|AA|AA-|AA-|
||Slovak Repub-<br>|SVK|Yes|A+|A+|A+|
||lic||||||
|2|Ireland|IRL|Yes|A+|BBB+|A|
||Japan<br>|JPN<br>|-|A+<br>|A<br>|A<br>|
||Malaysia|MYS|-|A|A-|A|
||Poland|POL|-|A|A-|A|
||Italy|ITA|Yes|A-|BBB|BBB+|
||Mexico|MEX|-|A-|BBB|BBB+|
||Peru|PER|-|A-|BBB+|BBB+|
||Spain<br>|ESP|Yes|A-|BBB|BBB+|
||Thailand<br>|THA<br>|-|A-|BBB+|BBB+|
||Bulgaria|BGR|-|BBB|BBB-|BBB|
||Colombia|COL|-|BBB+|BBB|BBB|
|3|Russian Feder-|RUS|-|BBB|BBB-|BBB|
||ation||||||
||South Africa|ZAF|-|A|BB+|BBB|
||Hungary|HUN|-|BBB|BBB-|BBB-|
||India|IND|-|BBB-|BBB-|BBB-|
||Indonesia|IDN|-|BBB|BBB-|BBB-|
||Philippines|PHL|-|BBB|BBB-|BBB-|
||Portugal|PRT|Yes|BBB|BB+|BBB-|
||Romania|ROU|-|BBB|BBB-|BBB-|
|4|Brazil|BRA|-|BBB|BB-|BB+|
||Turkey|TUR|-|BBB|BB-|BB+|

Figure 2 shows the wealth composition of the countries studied. Natural capital, or natural wealth, is further divided into renewable and non-renewable resources. The term “renewables” refers to natural capital that can produce benefits in perpetuity under sustainable management. It stands in contrast to “non-renewables”, which refer to extractable, subsoil assets. We do not use the term renewables in the sense of renewable energy sources.

Different from other data sets on natural resources, which account for stock or flows of a particular resource, the wealth data employs a forward-looking approach. Human capital, for instance, is calculated as the discounted expected lifetime earnings of an individual, which is either working age or still in school. A similar rationale applies to the valuation of natural resources. A country’s fossil fuel wealth is calculated as the discounted value of future resource rents, until this non-renewable resource is depleted. Renewable resources distinguish themselves in that their discounting horizon depends on the rate of extraction versus replacement. For instance, forest 6
Table 4: **Heterogeneity: Migrants’ country of origin and FMP location**

||Share of mig<br>who are l|rants in FMP<br>ong-stayers|
|---|---|---|
||(1)|(2)|
|Tone of the news at destination in|||
|_FMPs with a major group of migrants from_|||
|West Africa|-0.0710***<br>(0.0273)||
|East Africa|0.0664<br>(0.0455)||
|_FMPs located in_|||
|West Libya||-0.1570**<br>(0.0698)|
|East Libya||-0.0348<br>(0.0486)|
|Other controls|Yes|Yes|
|FMP FEs|Yes|Yes|
|Year-month FEs|Yes|Yes|
|Country of destination FEs|Yes|Yes|
|Country of origin FEs|Yes|Yes|
|Number of Observations|1,568|1,568|

_Notes:_ OLS regression results. Standardized OLS estimated coefficients are reported. Standardization of coefficients is obtained using the formula<sup>_sd_</sup> _sd_<sup><u>(</u></sup> (<sup>_x_</sup> _y_ )<sup><u>)</u></sup><sup>_βx_,where</sup><sup>_βx_isthepointestimateassociatedtocontrolvariable</sup><sup>_x_,while</sup><sup>_sd_(</sup><sup>_x_)and</sup><sup>_sd_(</sup><sup>_y_)</sup> indicate the standard deviation or respectively control variable _x_ and dependent variable _y_ . Standard errors clustered at the FMP level in brackets. “Other controls” include: _Intensity of night lights within 5 km_ ; _Number of conflicts within 5 km_ ; _FMP with migrants planning to remain in Libya (1 = Yes)_ ; _FMP with migrants planning to return home (1 = Yes)_ . All variables are defined in Tables A1 and A2 and additional details are provided in Section 4. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

27
25 pro-poor than the distribution of income. Unfortunately, the lowest income group in the Foxley et al. study has 30 percent of the households in Chile. To some extent the most interesting part of benefit incidence is what happens within the lowest 30 percent of the income distribution, especially to the bottom 10 or 20 percent of the households in the income distribution. Thus, their results shed little light on benefit incidence in the poorest households.

Baiil et al. examine the distribution of benefits in South Korea, in conjunction with taxes, for 1965, 1968, 1971, and 1974. They analyze benefits for urban and rural residents using household data. The data are separated into current and capital components, and 5 percent of the annual capital expenditure is assumed to accrue to households as capital services in the current year. Debt repayment is used to approximate the current services that accrue to households from past capital expenditures. Because debt repayment is apparently not available by .expenditure category, Bahl et A. do not present benefits for individual functions. However, they do allocate the general components of expenditures to households based on both a household income or on a per household basis. For example, one-half of health benefits is allocated to households based on household income and one-half of health benefits is allocated on a per household basis. Onehalf of education expenditures is allocated to households based on the number of students in the household and the balance is allocated on the basis of household income. They use income, consumption or per household bases to allocate expenditures on other functions to households.

The results suggest that benefits are distributad more equitably amnong family income deciles than income. Moreover, in urban areas, the modernization of South Korea lead to a more equitable distribution of benefits in 1974 than in 1965. But while the distribution of benefits in rural areas is pro-poor in 1974, it is not as pro-poor in 1974 as in 1965.

The results of this study are interesting, but not very rich in comparison to the more elaborate analysis of the health and education sectors that Meerman and others offer. Nonetheless, the Bahl et al. study represents what is feasible when all functions are included in the analysis, several different years are analyzed and the time frame for completing the project is limited.
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to education. As the economy opened to international market, demand for skilled workers increased which provided incentives for getting education. As a result, a virtuous circle was created where education and growth reinforced each other and both contributed to welfare.

**Table 2. GINI Index of Education**

|_Countiy_|_1970_|_1975_|_1980_|_1985_|_1990_|_1995*_|
|---|---|---|---|---|---|---|
|Brazil|0.413|0.367|0.373|0.369|0.314|0.260|
|Chile|0.279|0.275|0.267|0.263|0.266|0.270|
|China|0.450|0.452|0.447|0.442|0.411|0.379|
|Colombia|0.415|0.386|0.397|0.399|0.409|0.420|
|India|0.370|0.394|0.363|0.393|0.422|0.452|
|Korea|0.439|0.382|0.351|0.296|0.242|0.189|
|Mexico|0.420|0.415|0.428|0.410|0.365|0.320|
|Malaysia|0.445|0.439|0.426|0.413|0.398|0.383|
|Peru|0.428|0.424|0.392|0.391|0.379|0.367|
|Philippines|0.368|0.320|0.314|0.313|0.309|0.305|
|Thailand|0.378|0.369|0.268|0.327|0.348|0.370|
|Venezuela|0.425|0.426|0.351|0.350|0.347|0.345|

* 1995 data is based on projections. See Appendix I for methodology.

# _Policy_ **_Reform and Education:_** _Thte Case of_ **_Chiina_**

The Chinese government started to invest heavily in education and public health since the 1950s. Its social indicators outstripped those of other low-income countries. Primary enrollment in 1980 reached 103 for girls and 121 for boys, and secondary enrollment was 37 and 54. But factors such as a lack of openness, restricted labor mobility, distorted wages and prices, prevented educated workers from finding the most productive use of their talents.

Economic growth did not take off until the policy reforms started in 1978. China started with easy reforms (such as rural and trade reforms) and only later moved on to more complex and politically more difficult reforms (such as state enterprise and banking reforms). Learning has been incorporated in the process of reforms, as reflected by Mr. Deng Xiaoping's famous saying, "crossing the river by feeling the stones at the bottom." China opened its doors early on to foreign trade and investment by experimentation. It established four special economic zones in 1980; by 1993 it had more than 9,000 economic zones, which played an important role in attracting foreign direct investment, stimulating trade, bringing in advanced technology, and upgrading industries. Similarly China's price reform was gradual. The scope of planning has been systematically reduced. A dual price approach was adopted in 1984 that combined some price setting by the government for products and materials allocated by the plans, and price setting by markets otherwise. This approach allowed decisionmaking to be determined at the margin by market prices. By 1994, 90 percent of price had been liberalized.

Reform of state-owned enterprises has been a process of learning by doing as well. The first phase (1979-86) focused on giving a certain amount of autonomy to enterprises in exchange for greater efficiency, and the second phase (1987-91) focused on improving managerial mechanism. Since 1992 attention has shifted to clarifying property rights. By early 1998, 800 companies were listed on China's two stock exchanges and thousands of firms are going through ownership 7
below (above) **the** full tariff-inclusive (subsidy-inclusive) price. **These relatlv,** price effects **are** ln fact **the** key channal through which customs fraud affects **the** real equilibrium.

We formlize **_these_** points below ln a veraion of. the static modl of **the** previous section. To keep things syLple, **we** abstract from smuggling and focus only on fraudulent involcing. Since the analysis for overinvoLcing of exports la syuwetric to that for undarinvoicing of imports, **we** do the full analysis only for **the** latter case.

For Liporter J, let us denote the amount of imports by Hj and tho degre of underinvoLiing by Aj, where (1 - Aj) is **the** ratio of the reported prlce to **the** true world prieo. By definition of Aj, **the** importer choosing N and Xi deprives the government of tariff revenue of e - SAJEP*Mj. In general, the (expected) cost of underinvoicLng should be **some** functlon of **the** three variables A, V and M. **Ue** choose the followLng simple form: C(A,WN) - p(A)(bW + *W), b,a k 0, p' > 0, p" 2 0. One possible literpretatLon, consLitent wLth liporters being rLsk-neutral, is that p(.) is the probabliLty of being "caught" (we require p(.) **6** [O,1 j and bM **+ aW is the** penalty condltional on being caught.<sup>3 5</sup> An iLporter who is caught therefore loses the illegally approprlated tarlff revenues W plus an addltlonal amount 35 In a smuggllng context, (1-Aj)/Aj can be lnterpreted **as** the ratio of illegal to legal iLports brought ln bsy Lmporter J. **The** smuggler in this **case** ts using legal trade **as** a 'cloakw tc avoid detectLon of smuggling. The analy ss here is therefore closely rAlated to that of Pitt (1981) and Martln and PanagarLya (1984) and Macedo (1987), who speclfy the probabilLty of detectlon of sauggling **as** a functlon of thLs ratio.

30
# **Table 11: Deep Integration in the Tripartite Agreement (Results are percentage change from initial equilibrium)**

|<br>|**Tripartite**<br>**regional**<br>**integration,**<br>**central:**<br>**(Trade**<br>**Facilitation**<br>**plus services**<br>**and NTB**<br>**liberalization**<br>**and tariff**<br>|**Tripartite**<br>**regional**<br>**integration**<br>**(only Trade**<br>|**Tripartite**<br>**regional**<br>**integration (onl**<br>**services**<br>|**y**<br>**Tripartite**<br>**regional**<br>**integration (only**<br>**NTB**<br>|
|---|---|---|---|---|
|**Scenario definition**<br>**Benchmark**|**reform)**|**Facilitation)**|**liberalization)**|**liberalization)**|
|**Trade faclitation: 20% reduction in trade costs with Tripartite countries***<br>No<br>**Trade faclitation: 5% reduction in trade costs with non-Tripartite countries**<br>No|Yes<br>Yes|Yes<br>Yes|No<br>No|No<br>No|
|**Services Liberalization: 50% reduction of discriminatory FDI barriers for Tripartite countries***<br>No|Yes|No|Yes|No|
|**Non-Tariff Barriers: 20% reduction of NTB costs with Tripartite countries***<br>No|Yes|No|No|Yes|
|**Aggregate welfare**|||||
|Welfare (EV as % of consumption)|||||
|<br>Kenya|3.1|1.3|1.4|0.2|
|Tanzania|2.2|1.1|0.1|0.6|
|Uganda|2.4|1.9|0.2|0.1|
|Rwanda|2.3|1.7|0.2|0.1|
|COMESA|1.5|0.3|1.0|0.0|
|SADC|0.8|0.6|0.2|0.0|
|**Aggregate trade**|||||
|<br>Aggregate exports|||||
|<br>Kenya|5.9|6.3|-2.3|1.5|
|Tanzania|10.1|4.9|0.2|3.4|
|Uganda<br>Rwanda|7.7<br>12.9|6.9<br>10.9|0.1<br>0.4|0.1<br>0.4|
|COMESA|3.4|1.8|0.8|0.3|
|SADC|2.7|2.3|0.1|0.1|
|**Factor earnings**|||||
|Kenya|||||
|Capital|4.5|1.4|2.2|0.7|
|Unskilled labor|4.1<br>|2.6<br>|0.3<br>|1.3<br>|
|Skilled labor|0.9|0.4|0.2|-0.1|
|Resource|7.3|5.0|0.2|2.7|
|Tanzania|||||
|Capital|0.6|0.9|0.1|-0.3|
|Unskilled labor|1.4|1.1|0.0|0.2|
|Skilled labor|0.8|0.4|0.0|0.2|
|Resource|0.1|0.9|0.0|-0.6|
|Uganda|||||
|Capital|3.5|2.4|0.1|0.3|
|Unskilled labor|10.7|7.8|0.0|1.2|
|Skilled labor|3.1|2.1|0.1|0.1|
|Resource<br>Rwanda|0.7|1.3|0.0|-0.2|
|Capital<br>|1.8<br>|0.8<br>|0.2<br>|0.0<br>|
|Unskilled labor|11.1|7.8|0.1|0.2|
|Skilled labor|0.0|-0.3|0.1|0.0|
|Resource|-0.3|0.7|0.2|0.0|
|COMESA|||||
|Capital|0.3|0.1|0.2|0.0|
|Unskilled labor<br>|0.6<br>07|0.4<br>02|0.2<br>04|0.0<br>00|
|Skilled labor<br>Resource|.<br>0.4|.<br>0.4|.<br>0.0|.<br>0.0|
|SADC|||||
|Capital|0.9|0.6|0.2|0.1|
|Unskilled labor|1.1|0.7|0.1|0.1|
|Skilled labor|0.5|0.4|0.1|0.0|
|Resource<br>*Tripartite countries are EAC, COMESA and SADC<br>Source: Authors'estimates|0.6|0.6|0.0|0.0|

72
skewed.<sup>10</sup> As the scatter plot in Figure 5.1 suggests, the association between overall environmental problems and lending is very strong for those countries that have received environmental loans.

**Figure 5.1:  World Bank Environmental Lending vs. Overall Environmental Problems (Log Scale)**

<!-- Start of picture text -->
-4 -2 0 2 4<br>Log Environmental Index<br>8<br>6<br>4<br>Log Environmental Lending 2<br>0<br><!-- End of picture text -->

In a log-log regression of environmental lending on the overall environmental indicator (Table 5.1, column 1), the estimated response elasticity is .70, with an associated t-statistic of 10.3 and regression R<sup>2</sup> (adjusted for degrees of freedom) of .53.

> 10  Conventional regression and correlation analysis assume that variable distributions do not contain extreme “outlier” observations, because such outliers can sharply skew the results.  In this case, both nominal and per-capita distributions are extremely skewed.  Log measures, on the other hand, have regular, approximately-normal distributions with no outliers.

12
# _Remittances_

**Moldova receives large inflows of remittances (Figure 5)** . Prior to the economic crisis, remittances represented over 30 percent of GDP in Moldova. During the crisis period, they collapsed to 22 percent of GDP, with a decline also of absolute remittances inflows. It is estimated that the stock of emigrants represents around 22 percent of the total population.<sup>3</sup> Migrants are mostly well educated and work in the Russian Federation (around 65 percent) and the European Union (around 20 percent).

**Figure 5. Moldova receives large inflows of remittances, which were impacted by the crisis**

Remittances as a share of GDP (right axis) and in millions of US dollars (left axis) <!-- Start of picture text --> 2500 40<br>35<br>2000<br>30<br>25<br>1500<br>20<br>1000<br>15<br>10<br>500<br>5<br>0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Remittances, received (% of GDP) Remittances, received (million current US$)<br><!-- End of picture text --> Source: WDI.

_Poverty and Inequality Trends_

**Analyses of consumption poverty trends over the long term in Moldova need to account for survey methodological changes** . The Household Budget Survey for Moldova, from which poverty numbers are obtained, had a substantial change in methodology in 2006. Changes included improvements to the questionnaire and changes in sampling and survey design. As a result, the poverty series for Moldova cannot be used to assess long-term poverty changes, but the analysis has to be divided into two periods: pre 2006 and post 2006. Therefore, the analysis that follows on poverty, inequality and economic mobility analyzes two separate periods with methodological consistency within each one of them.<sup>4</sup>

**Following sharp increases in poverty during the Russian financial crisis of 1998, Moldova achieved a remarkable reduction in economic poverty, particularly during the first half of the 2000s (Figure**

> 3 Source: IMF International Financial Statistics database, World Bank DEC Prospects group, World Bank Migration and Remittances Factbook 2011; own calculations.

> 4 The consumption aggregate used, based on the harmonized regional ECA poverty dataset, excludes health, durables and housing expenses. 2005 was excluded due to data concerns regarding household and individual characteristics.

6
**Figure 24. Export Revenue and Market Size by District**

# B. **Revenue from all exports**

<!-- Start of picture text --> 20<br>18<br>16 Moscow<br>Republic of Tatarstan<br>Sakhalin Region<br>14 Penza Region<br>Amur Region<br>12<br>11 12 13 14 15 16<br>Population (log)<br>Oper revenue ALL exporters / Number of firms (in logs)<br><!-- End of picture text -->

## **B. Revenue from services exports**

<!-- Start of picture text --> 16<br>Moscow<br>Sakhalin Region<br>14<br>Republic of Tatarstan<br>12<br>10<br>Penza Region<br>8<br>11 12 13 14 15 16<br>Population (log)<br>Oper revenue SERVICES exporters / Number of firms (in logs)<br><!-- End of picture text --> _Source:_ RUSLANA.

48