rafmacalaba/datause-encoder-data huggingface dataset:
|_____________<br>Country<br>_______________|____________<br>Mode<br>_____________|__________<br>l<br>___________|________________<br>All<br>firms<br>_________________|________________<br>10 - 19<br>employees<br>__________________|______________<br>20 – 49<br>employees<br>________________|_______________<br>50 – 249<br>employees<br>_________________|______________<br>250 – 499<br>employees<br>______________|____________________<br>500 and more<br>employees|
|---|---|---|---|---|---|---|---|---|
|Sweden|Pooled|ß|25.734|25.609|26.617|26.871|30.604|-2.955|
|||p|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.517]|
||Fixed|ß|3.458|2.327|2.327|5.760|11.628|5.866|
||effects|p|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.214]|
||N<br>NxT||11,446<br>55,015|6,726<br>23,548|4,514<br>17,171|2,451<br>11,385|403<br>1,526|312<br>1,385|
|UK|Pooled|ß|11.271|27.950|9.173|11.056|8.724|10.317|
|1995 - 2004||p|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
||Fixed|ß|4.873|14.527|8.557|4.338|7.957|-3.511|
||effects|p|[0.000]|[0.009]|[0.000]|[0.000]|[0.000]|[0.072]|
||N||9,903|600|1,950|5,180|1,070|1,103|
||NxT||55,286|2,228|9,594|29,421|7,122|6,921|

__________________________________________________________________________________________________________________________________ Note: Results are for firms from ISIC industries 15 – 36 with at least 10 employees at the median over the years covered in the panel. The firms with the bottom / top one percent of labor productivity (defined as total sales per employee) in a year are excluded from all computations. Firms are classified into size classes according to the median of the number of employees over the years covered. ß is the estimated regression coefficient from an OLSregression of log (labor productivity) on a dummy variable for exporting firms, controlling for the log of the number of employees and its squared value, the log of wages and salaries per employee, and a full set of interaction terms of 4digit industry-dummies and year dummies; the fixed effects model adds firm fixed effects. To facilitate interpretation the estimated coefficients for the exporter dummy variable have been transformed by 100(exp(ß)1). p is the prob-value. N ist the number of firms, NxT is the number of observations.

73
# _I.INTRODUCTION_

Trade liberalization has produced in the past two decades steady growth in imports of intermediate and capital goods across countries. The endogenous-growth literature has provided theoretical arguments for the role of foreign intermediate inputs in enhancing economic growth and productivity gains (Ethier, 1979, 1982; Grossman and Helpman, 1991; Rivera-Batiz and Romer, 1991).<sup>2</sup> The specific influence of trade in capital goods on economic growth has also been emphasized in a number of theoretical and empirical works (Lee, 1995; Eaton and Kortum, 2001; Goh and Olivier, 2002). Importing capital goods is found to be a relevant channel of foreign technology transfers and R&D spillovers across countries (Xu and Wang, 1999). Trade liberalization is therefore expected to improve economic growth, by decreasing the cost of both foreign intermediate goods and capital equipments (Amiti and Konings, 2007; Topalova and Khandelwal, 2011; Goldberg et al., 2010; Eaton and Kortum, 2001).

This paper investigates the link between input-trade liberalization and foreign technology adoption embodied in imports of capital goods. Input-trade liberalization may affect technology adoption through a _direct_ channel: the reduction of tariffs on capital goods decreases their price and allows firms to import a larger volume of these goods. In this work, we take a different perspective and focus on an _indirect_ channel. We look at the effect of tariff cuts affecting variable inputs on firms’ decision to upgrade foreign technology in imported capital goods. We emphasize unexplored mechanisms through which trade liberalization affects firms’ technology choice: a supply shock of input tariff reductions and a complementarity channel between imported variable intermediate goods and capital equipment. Such complementarity is observed in our micro-data of Indian firms used in the empirical analysis. We first show that only a subset of firms in our sample import capital goods and almost all of them also import intermediate inputs. This feature of the data suggests that importing capital goods is associated

> 2Recent firm-level studies have confirmed that input-trade liberalization played a key role on firm productivity growth (Schor, 2004; Amiti and Konings, 2007; Topalova and Khandelwal, 2011), the ability to introduce new products in the domestic market (Goldberg et al., 2010), export performance (Bas, 2012; Bas and Strauss-Kahn, 2015) and mark-ups changes (DeLoecker et al., 2016). Other works highlight a positive link between imports of intermediate goods and firm productivity (Kasahara and Rodrigue, 2008; Halpern et al., 2015).

2
Policy ReseaRch WoRking PaPeR 4900

# **Abstract**

This paper examines the role of male-dominated international migration in shaping labor market outcomes by gender in migrant-sending households in Albania. Using detailed information on family migration experience from the latest Living Standards Measurement Study survey, the authors find that male and female labor supplies respond differently to the current and past migration episodes of household members. Controlling for the potential endogeneity of migration and for the income (remittances) effect, the estimates show that having a migrant abroad decreases female paid labor supply and increases unpaid work. However, women with past family migration experience are significantly more likely to engage in self-employment and less likely to supply unpaid work. The same relationships do not hold for men. These findings suggest that over time maledominated Albanian migration may lead to women’s empowerment in access to income-earning opportunities at the origin.

This paper—a product of the Poverty Team, Development Research Group—is part of a larger effort in the department to study the impact of migration on poverty and other outcomes in source countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at gcarletto@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Child Labor in C6te d'Ivoire: Incidence and Determinants

# TABLE OF CONTENTS

||Pae|
|---|---|
|1.|Introduction ..........................................<br>.<br>1|
|2.|Trends in Child labor in C6te d'Ivoire in the 1980s ............................. 6|
|3.|Child Labor and Schooling in 1988<br>...................................<br>15|
|4.|Multivariate Analysis<br>..................<br>........................<br>27|
||A Model for the Determinants of Child Labor ................................... 31|
||Results for Urban Areas ...........................................<br>40|
||Results for Rural Areas<br>..........................................<br>52|
|5.|Conclusions ......................................<br>....<br>63|
|Appe|ndix:<br>Multinomial<br>Logit Results..<br>.<br>.<br>...<br>68|
|Refe|rencesf....<br>73|

1
fundamentally different from those to be encountered in the developed world, but they are much more difficult to address, notably because of the absence of an efficient judiciary system.

There are several possible ways which can be considered to improve this situation:

- Firstly, the strengthening of relevant established institutions to give them the necessary independence, legal and financial resources to accomplish correctly the functions they are supposed to fulfill. For instance, a number of countries have competition laws, but there are no mechanisms and appropriate organizations to ensure their enforcement.

- Secondly, the establishment of efficient counter powers, made notably of users of the public services, including entrepreneurs, to get those services better functioning, less sensitive to corruption, etc.  Such initiatives have notably been implemented in the education sector to ensure an appropriate use of public funding<sup>20</sup> and could usefully be extended to other sectors, such as public procurement to ensure more transparency in funding allocations and selections of providers.

- Thirdly, the implementation of institutional audits, focusing on innovationrelated obstacles.  Such audits could be built, or grafted, on those broader surveys implemented by the World Bank to evaluate the business environment<sup>21</sup> or governance conditions<sup>22</sup> .   Such audits – to be implemented by high level, independent bodies - should not be conceived as one shot events, but should be designed as processes, through which recommendations made by the relevant bodies would be followed-up and monitored in their applications and effects.  Note that these audits could also be implemented by international instances, modeled on the peer review mechanisms put in place in Africa within the NEPAD for governance and democracy issues<sup>23</sup> .

# **6. Challenging global trends and policy responses**

Globalization and technological change represent both opportunity and challenge for developing countries, and so the question of how developing countries can harness the globalization process to turn potentially adverse spillovers into windows of opportunity, how they can tap into what is an increasingly rich pool of global knowledge. This section looks at how to turn FDI volatility, the north’s research concentration, patent asymmetry and brain drain into potential sources of new knowledge for the developing world.

# **_FDI volatility_**

Foreign direct investment can be crucial in stimulating change and innovation, and in bringing new technology and knowledge to a country, as illustrated by the examples of China and Malaysia. Equally, it is an important driving force behind improvement of a country’s business climate and governance conditions.

> 20 E.g. in Uganda, increasing by more than 50 per cent the use of funds received from the donors – which used to be lost because of corruption practices.

> 21 See Doing Business, World Bank, 2003.

> 22 As designed and implemented by the WBI Governance program.

> 23 Which begin to work, a number of countries having offered to be reviewed.

22
# **Figure 21: Education Indicators**

## A. School Attendance by Age, 1986 to 2002 (%)

<!-- Start of picture text --> 6 10 15 20 25<br>Years of Age<br>86 88 90 92 94<br>96 98 00 02<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> C. School Attendance by Level among the 15 to 21 Year-Old

## B. School Attendance by Level among the 6 to 25 Year-Old

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>1988 1990 1992 1994 1996 1998 2000 2002<br>Primary Lower Secondary Upper Secondary Vocational University Outside School<br><!-- End of picture text --> D. Primary Gross Enrolment Rates (%), 1985 to 2000 <!-- Start of picture text --> 100%<br>90% 115<br>80% 105<br>95<br>70%<br>85<br>60%<br>75<br>50% 65<br>40% 55<br>30% 45<br>20% 35<br>25<br>10%<br>15<br>0% 5<br>1988 1990 1992 1994 1996 1998 2000 2002 1985 1990 1995 2001<br>Lower Secondary Upper Secondary Vocational University Outside School<br>Thailand Philippines Malaysia Korea Indonesia<br>E. Secondary Gross Enrolment Rates (%),1985 to F. Tertiary Gross Enrolment Rates (%),1985 to 2000<br>2000<br>115<br>105 115<br>95 105<br>85 95<br>75 85<br>65 75<br>65<br>55<br>55<br>45<br>45<br>35 35<br>25 25<br>15 15<br>5 5<br>1985 1990 1995 2001 1985 1990 1995 2001<br>Thailand Philippines Malaysia Korea Indonesia Thailand Philippines Malaysia Korea Indonesia<br><!-- End of picture text --> 52
# **Table 3. Impact Evaluations of Business Literacy Training Programs for Entrepreneurs**

|**Study**|**Method**|**Sample**|**Intervention(s)**|**Impact**|
|---|---|---|---|---|
|Karlan and<br>Valdivia<br>(2011)|RCT|~3000 female<br>microfinance<br>clients in Peru|Weekly or bi-weekly 30- to 60-<br>minute training sessions designed<br>to teach basic business practices|+ Business knowledge, record keeping, and client<br>retention<br><> No change in business profits, revenues, or<br>employment|
|Bruhn and<br>Zia (2011)|RCT|445 young male<br>and female<br>microfinance<br>clients (18-35) in<br>Bosnia-<br>Herzegovina|Six comprehensive modules on<br>basic business concepts, accounting<br>skills, and business investment and<br>growth strategies|+ Business practices, investments, and loan terms for<br>surviving businesses<br>+Business performance and sales, for those with higher ex-<br>ante financial literacy<br><> No effect on business survival, or business entry by<br>clients with exploratoryloans|
|Berge et al.<br>(2011)|RCT|644 male and<br>female<br>microfinance<br>clients in<br>Tanzania|Comparison between:<br>1) 21 weekly 45-minute sessions on<br>entrepreneurship, management,<br>and marketing<br>2) Cash business grant equivalent<br>to 50% of average annual<br>investment|+ Business training increased profits (via increased sales,<br>not profit margins), for male entrepreneurs only<br><> No impact of business grant for either males or females<br><> No impact for female entrepreneurs for either<br>treatment. Accompanying lab experiment suggests<br>behavioral and social constraints|
|Drexler et al.<br>(2010)|RCT|1, 200 micro-<br>finance clients in<br>the Dominican<br>Republic|Comparison between:<br>1) Training with traditional<br>accounting module<br>2) Training with simplified rule-of-<br>thumb accountingmodule|+ Rule-of-thumb intervention significantly increased<br>practice of separating accounts, keeping records, and<br>calculating monthly revenue, relative to traditional<br>intervention<br>+ Increase in sales in bad weeks|
|Field et al.<br>(2010)|RCT|597 Hindu and<br>Muslim female<br>microfinance<br>clients in India|Two-day training module on basic<br>financial literacy and business skills<br>training, encouragement to identify<br>concrete financial goals and<br>business aspirations|+ Increase in borrowing and business income for upper-<br>caste Hindu women, who face more social restrictions<br>than lower-caste women<br><> No impact for Muslim women, who face the most<br>restrictions. Thus, findings may indicate non-monotonicity<br>in impact of social restrictions|
|Mano et al.<br>(2010)|RCT|167<br>entrepreneurs in<br>metalwork<br>cluster in Ghana|Three weeks of classroom training<br>on entrepreneurship, production<br>and quality management, and<br>record-keepingand costing|+Positive impact on business practices and performance,<br>but impact is highly heterogeneous|

33
collection of customs duties whether the coffee enters as one or the other tariff line. Thus, the tariff line “other” encompasses both arabica and robusta coffee, and the import unit values for the arabica and robusta coffee cannot be calculated separately. Therefore in Figures 2-5 all imports of green coffee are pooled together and a single import price is calculated. These prices can only be used for comparison with producer prices in countries which predominantly produce and export either arabica or robusta coffee. For countries that export both (Brazil, India, Tanzania) the US import unit values are not good indicators of world prices, as they represent a composite price for both arabica and robusta coffee. Lastly, US import prices are not available for all countries for every month in the considered period. Green coffee from a number of countries, namely Madagascar, Togo, Angola, Cameroon, Central African Republic and Ghana was not imported every month in the period under investigation, and the monthly price data would therefore have a lot of missing observations.

# **5. The estimation results**

The results of the stationarity tests conducted for the price variables are reported in Tables 3 and 4. The properties of each price time series are analyzed first. In all cases, except Ghana and to a smaller degree Tanzania, the ADF test does not reject the null hypothesis that the price time series follow a unit root process. However, testing the same hypothesis for first differences allows us to reject the unit root hypothesis at 1% level for all countries. This leads us to conclusion that price differentials can be used in the error-correction model.

Turning to the long-term cointegration between the domestic and the world prices we note that for most countries we cannot reject the hypothesis of no cointegration before the reforms, which is consistent with our expectations. Given the high degree of government intervention in the sector prior to liberalization we can expect the domestic prices to be driven by policy decisions, rather than by the world market prices. After the reforms cointegration is detected in some countries: In almost half of the cases the null of the residuals following a unit root process was rejected at 5% level of significance. In the pooled model with structural breaks the null of no cointgration is rejected at 10% significance in 9 countries out of 14. The exceptions are Colombia, Togo, Angola, Cameroon and Central African Republic. In all these countries, except Angola, a cointegrating relationship was found in the period after the reforms.

The estimates of the pre- and post-liberalization shares of producer prices in world prices are reported in Table 5, together with the results of the error-correction model. For countries where the model with 13
Table 17: Income and Wealth Mobility, Uganda (ISAs 2005-2010)

|||Frac<br>t|tion (<br>hat Le|%) of <br>ft the|House<br> Quint|holds<br>ile|
|---|---|---|---|---|---|---|
|||1st|2nd|3rd|4th|5th|
|Income|||||||
|US||23|37|40|39|23|
|Uganda<br>▷|Rural|71|71|74|72|51|
|▷|Urban|70|69|72|71|46|
|Wealth|||||||
|US||25|39|41|37|20|
|Uganda<br>▷|Rural|58|69|71|66|45|
|▷|Urban|53|66|69|64|39|

_Notes_ : The figures of economic mobility use the 2005-6 — 2009-10 Uganda ISA panel. The definition of household consumption, income, and wealth is discussed in Section 2. The data are divided by quintiles with 1st denoting the poorest quintile and 5th the richest.
Table 1. The Destinations of Mercosur Countries Trade: Selected Periods from 1979-81 to 1994.

|||World<br>($ millions)|OECD||of which:<br>||Non-OECD|Of which:|
|---|---|---|---|---|---|---|---|---|
|Exporter|Years|||NAFTAT|<br>USA|Europe||Mercosur|
||||||Percent o|f total exports_(%)_|||
|Argentina|1994|15,803.3|40.2|13.1|10.9|25.6|59.8|30.4|
||1993|13,114.4|43.6|11.9|9.7|29.3|56.3|28.1|
||1990-92|<br>12,187.1|49.5|14.5|11.7|33.3|50.5|16.8|
||1984-86|<br>7,785.2|44.9|14.4|11.2|28.3|_55.1_|9.5|
||1979-81|8,322.7|45.0|11.4|8.7|32.5|55.0|13.4|
|Brazil|1994|43,355.2|57.4|24.2|20.6|29.0|42.6|13.7|
||<br>1993|<br>38,679.4|56.1|24.5|20.7|27.6|43.9|13.9|
||1990-92|32,987.8|63.6|25.2|21.4|32.8|36.4|7.8|
||1984-86|25,008.6|64.7|29.8|27.2|29.1|35.3|4.7|
||1979-81|<br>19,556.3|58.2|21.6|18.0|32.7|41.8|8.1|
|Paraguay|1994|816.8|36.3|7.6|7.0|29.1|63.7|52.0|
||1993|725.2|45.8|8.2|7.3|38.0|54.2|39.6|
||1990-92|784.1|41.9|_5.0_|4.7|36.8|58.1|37.6|
||1984-86|290.3|48.8|3.7|3.6|43.7|51.2|36.3|
||1979-81|303.6|52.7|6.6|5.7|41.3|47.3|38.4|
|Uruguay|1994|1,918.1|30.4|10.1|6.8|21.7|69.6|46.7|
||1993|1,603.3|32.6|12.3|9.2|21.9|67.3|41.2|
||1990-92|1,629.9|38.8|13.1|10.1|26.4|61.2|34.7|
||1984-86|953.3|41.7|14.9|13.5|25.1|58.3|28.4|
||1979-81|1,021.0|44.0|9.8|8.5|33.6|56.0|30.6|
|MERCOSUR|1994|61,893.3|51.8|20.7|17.5|27.9|48.1|19.5|
||1993|<br>54,122.2|52.2|20.9|17.6|28.0|47.8|18.5|
||1990-92|<br>47,588.9|58.8|21.7|18.3|32.8|41.2|11.5|
||1984-86|<br>34,037.4|59.5|25.6|23.0|28.9|40.6|6.7|
||1979-81|29,203.6|53.9|18.2|14.9|32.7|46.1|10.7|

Source: All data compiled from United Nations COMTRADE records. Trade statistics for Mexico are included in the OECD totals.
<!-- Start of picture text --> ESS18<br>pe 0 oe<br>a<br>ot LIL LL Ue<br>of AIO UUae<br>eHLILILILILILU Ye<br>ste<br>AHH ae<br>PHO Ue<br>otLL LT TE TT TT TP<br>1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text -->
# Table 7

# Percentas Sha.r of CACM EXPOrte Facina Non-Tariff Barriers In<sup>OECDMrke-te. 1989</sup> (By Co todltY GroU)

||U.S.|EC.|Jaspan|Canada|
|---|---|---|---|---|
|Commodity<br>Groups|||||
|FruitsandNuts|2.8|3.7|n.a.**|n& **|
|Coffee|0.0|0.0|n.a.|na.|
|Other Food Products|89.2|26.1|na.|n.e.|
|Other Primary ncl.<br>MetalsAMinorals|11.3|0.6|n.s.|na.|
|Clothing<br>ATextiles|38.0|21.6|na.|n.a.|
|Other Manufactures|6.0|0.4|n.s.|n.a.|
|All Goods|16.0|2.0|82.3|89.4|
|of which:<br>Hard Core NTBs .|7.1|1.8|0.9|1.8|

_ These involv, including voluntary quotas or other quantitative export rostraints, tariff quotas, restraints of various reference priceskinds, and variable levies.

oc Not available.

Source: UNCTAD Data Base on Trade Control Measures.
**The relationship between average return on assets and ownership** (Figure 1.4a) **is inverse.** This result suggests that private sector ownership is more efficient than government ownership. Even mixedownership, SOEs with state participation have on average higher returns on assets. During the crisis, profitability of enterprises decreased, but the inverse relationship between returns and state ownership remained valid.

Figure 1.4a: A relationship between average returns on assets and ownership: Industry, Trade, Construction and Transport Sectors <!-- Start of picture text --> 8.0%<br>7.0%<br>6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>Private SOE (Mixed) SOE (pure)<br>Before Crisis (2004-2008) After Crisis (2009-2010)<br><!-- End of picture text --> Figure 1.4b: Relative shares of loss-making enterprises (all sectors of economy) <!-- Start of picture text --> 25.0%<br>2006 2007<br>20.0%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br>Private SOE (Mixed) SOE (Pure)<br><!-- End of picture text --> Source: Authors estimates based on Belstat dataset.

**Second, the share of loss-making enterprises in the private sector is significantly lower than that among mixed-ownership or state-owned firms** (Figure 1.4b). This fact suggests that if resources allocated to the private sector do not generate income sufficient to recuperate their cost firms do not survive.  In contrast, the percentage of loss-making firms among mixed-property or state ownership is fairly large; this suggests that the natural cleansing mechanism of a market economy may be working very slowly or not at all in Belarus.  Borrowing from parent companies within a conglomerate, borrowing from state-owned banks or under state guarantees are among the mechanisms that may be making this possible.  Notice also (Figure 1.4b) that if there are firms systematically showing losses, capital and labor may be stuck in sectors where revenue cannot cover opportunity cost of factors of production.

# C. Recent shifts in demand and supply

**Recent shifts in both supply and demand factors imply that the SOE growth model may be exhausted** .

**On the supply side, the post-soviet industrial structure has not been renovated at a sufficiently fast pace (accumulated depreciation is estimated in excess of 60 percent) which hurts competitiveness in the Russia and CIS market** . Excess production capacity in several of the main sectors of the economy was almost exhausted around 2006 (. The absence of new investment and introduction of modern production methods has limited the capacity of Belarus’s firms to respond to the increase in demand in the Russia market (Figure 1.5a, 1.5b and 1.5c).

9