rafmacalaba/datause-encoder-data huggingface dataset:
Bruno (2005), which is appropriate for unbalanced data such as ours with a small number of countries and time series observations at higher than annual frequency.<sup>13</sup> Splitting the sample between the pre- and post-crisis periods is likely to reveal whether relative ratings gained increased salience for capital allocation in the post-crisis period. Therefore, the benchmark capital flows specification is estimated separately for the pre- and post-crisis periods. Overall, three specifications are estimated: for all years from 1998 to 2017, for the pre-financial crisis period (first quarter of 1998 until the second quarter of 2008), and the postcrisis period (from the second quarter of 2009 until the fourth quarter of 2017). In order to exclude the effect of the immediate aftermath of the global financial crisis, we omit two quarters following the onset of the global financial crisis in the third quarter of 2008 from the post-crisis sample.

During the post-crisis period, advanced economy central banks were broadly in a monetary easing mode, which included quantitative easing (QE) by the US Federal Reserve and other central banks. This resulted in “spillovers” to developing countries in the form of increased capital flows (IMF 2013, Lim and Mohapatra 2016). In the analysis, we control for this liquidity effect using an indicator of QE based on Lim and Mohapatra (2016). In an alternative specification, we also control for the increase in the US Federal Reserve’s asset holdings as a continuous measure of QE. This can also capture changes in capital flows that are driven not by fundamental or institutional factors but possibly by investor sentiment during the QE implementation.

# **5. Results**

This section discusses our main results on the impact of absolute sovereign ratings and relative risk ratings on net capital flows to emerging and developing countries. The regression specifications used are similar to Ahmed and Zlate (2014) with growth differentials, interest rate differentials, and a global risk (VIX) index as explanatory variables. Our variables of interest, the S&P sovereign rating and relative risk rating, were included as additional explanatory variables in separate specifications.

> 13 Bootstrapped standard errors for the dynamic panel data model were calculated with 100 replications.

15
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Policy Research Working Paper 8182

# **Abstract**

Could a partial subsidy for child education increase children’s participation in paid work? In contrast to much of the theoretical and empirical child labor literature, this paper shows that child work and school participation can be complements under certain conditions. Using data from the randomized evaluation of a conditional cash transfer program in the Philippines, the analysis finds that some children, who were in neither school nor work before the program, increased participation in school and workfor-pay after the program. Earlier cash transfer programs, notably those in Mexico, Brazil, and Ecuador, increased school attendance while reducing child labor. Those programs fully offset schooling costs, while the transfers under the Philippine transfers fall short of the full costs of schooling for a typical child. As a result, some beneficiary children from poor Philippine households increased work to support their schooling. The additional earnings from this work represent a substantive share of the shortfall in the schooling costs net of transfer. The paper rules out several potential alternative explanations for the increase in child labor, including changes in household productive activities, adult labor supply, and household expenditure patterns that, in principle, can arise after a cash transfer and may also affect the supply of or demand for child labor.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at ekandpal@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
# **1. Introduction**

Moldova’s economy grew strongly during the 2001-2008 period, and in subsequent years periods of fast growth were punctuated by episodes of negative growth in 2009, 2012 and 2015. Over the period 2000-2017 GDP per capita in 2011 US$ PPP terms more than doubled, the country registered an impressive reduction in the poverty rate – taking the Lower Middle Income country poverty threshold of US$ 3.2/day in 2011 PPP terms, the share of population below this threshold fell from almost 70 percent in 2000 to just 1 percent of the population in 2017 (Figure 1). According to the national poverty methodology, the poverty headcount fell from 54 percent in 2001 to just below 10 percent in 2015.

<!-- Start of picture text --> Figure 1. Evolution of poverty and GDP growth in Moldova (2002-2017)<br>45 10<br>40 8<br>35 6<br>30 4<br>25 2<br>20 0<br>15 -2<br>10 -4<br>5 -6<br>0 -8<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>national international (3.2) international (5.5) GDP growth (right axis)<br><!-- End of picture text -->

**Figure 1. Evolution of poverty and GDP growth in Moldova (2002-2017)**

Notes: National - below the national poverty line (1562.8 MDL per month per adult equivalent); International - below the international line (3.2 USD/day in 2011 PPP or 759.4 MDL per month per capita); International - below the international line (5.5 USD/day in 2011 PPP or 1305.2 MDL per month per capita). Source: IMF, World Bank, National Bureau of Statistics of the Republic of Moldova.

The reduction in poverty has also been associated with a steady reduction in the degree of income inequality in Moldova. The Gini index of inequality fell from 0.35 in 2007 to 0.26 in 2017, and is at the lower end of the inequality indices among countries in the ECA region (Figure 2, Panel A). The fall in the level of inequality registered in Moldova is quite large, by international standards. Panel B of Figure 2 plots the reduction in the Gini index during the 2006-2016 period against the initial level of inequality in 2006. As one would expect, higher initial levels of inequality are associated, on average, with greater absolute declines in the Gini index. However, countries in the ECA region (orange dots) tend to cluster around the regression line, whereas Moldova (and also Kyrgyzstan) register a much higher reduction in inequality, relative to the starting point.<sup>1</sup>

> 1 This could, however, be a sign of deteriorating data quality. It would be important to compare the inequality statistics with those coming out of 2019 HBS data, as they rely on a new sample drawn from the 2014 Population Census sampling frame.

3
education and the consumption change or the proportional change. Households with a head with secondary education did better than household with a head with primary education. But household with a head with higher education had higher drops than household with a head with secondary education.

Table 4.3. Per-capita expenditure by country regions

|||Pre-cnsis|Post-crisis|Change|Prop. change|
|---|---|---|---|---|---|
|_Largecities._|Mean|165.468|136.693|-28.775|-0.046|
||Std.err.|7.683|6.215|6.840|0.029|
||Median|122.634|107.806|-17.205|-0.141|
||Obs.|368||||
|_Othertowns:_|Mean|106.966|83.532|-23.434|-0.053|
||Std.err.|4.026|3.373|4.100|0.038|
||Median|89.277|67.654|-18.521|-0.214|
||Obs.|317||||
|_Rural:_|Mean|105.165|89.500|-15.665|0.016|
||Std.err.|2.073|1.764|1.762|0.020|
||Median|89.531|77.240|-11.153|-0.145|
||Obs.|1081||||

Table 4.4. Per-capita expenditure by household size

|||Pre-crisis|Post-crisis|Change|Prop.change|
|---|---|---|---|---|---|
|_Hhsize1:_|Mean|150.156|118.562|-31.594|-0.072|
||Std.err.|6.441|4.518|4.894|0.030|
||Median|120.757|96.684|-21.747|-0.196|
||Obs.|325||||
|_Hh size 2:_|Mean|131.010|107.562|-23.448|-0.029|
||Std.err.|5.325|3.801|4.468|0.029|
||Median|107.878|90.557|-17.773|-0.170|
||Obs.|446||||
|_Hhsize3 _|Mean|118.985|95.613|-23.371|-0.030|
||Std.err.|4.268|3.388|3.927|0.035|
||Median|99.739|77.530|-12.474|-0.152|
||Obs.|328||||
|_Hh size 4:_|Mean|100.109|91.324|-8.785|0.041|
||Std.err.|3.560|4.693|4.113|0.034|
||Median|83.692|73.340|-8.839|-0.125|
||Obs.|390||||
|_Hhsize5+:_|Mean|79.701|69.931|-9.769|0.061|
||Std.err.|2.780|2.668|2.900|0.043|
||Median<br>Obs.|69.778<br>277|63.657|-7.962|-0.109|
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 20. Senegal’s infrastructure spending needs are substantial relative to its GDP**

As percentage of GDP <!-- Start of picture text --> SSA<br>LIC-Fragile<br>LIC- Nonfragile<br>ECOWAS<br>Resource-Rich<br>MIC<br>Senegal<br>Niger<br>Mali<br>Ghana<br>Benin<br>Cote d'Ivoire<br>Capex O&M<br>Nigeria<br>Burkina Faso<br>Cape Verde<br>% of GDP 0 5 10 15 20 25 30 35 40<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009. _Note:_ LIC = low-income countries; MIC = middle-income countries; ECOWAS = Economic Community of West African States; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M = operations and maintenance; CAPEX = capital expenditure.

Senegal’s infrastructure spending needs look particularly high relative to the country’s GDP, since they would absorb 20 percent of its GDP. Investment alone would absorb around 12 percent of GDP, not far from the 15 percent China invested in infrastructure during the mid-2000s. These high numbers are on par with the average GDP share that other low-income, nonfragile African countries would need to spend, which amount to 22 percent of GDP (figure 20).

Senegal already spends a sizable amount ($0.91 billion per year) to meet its infrastructure needs (table 21). About 70 percent of the total is allocated to capital expenditure and 30 percent to operating expenditures. Operating expenditure is entirely covered from budgetary resources and payments by infrastructure users. Public and private investment account for around 33 and 35 percent of capital funding respectively, followed by official development assistance (ODA) (16 percent). Non-OECD finance and household self-financing of the sanitation sector also play a smaller but nonetheless significant role, amounting to 8 percent of capital funding each.

**Table 21. Financial flows to Senegal’s infrastructure, annual averages over the period 2001–06**

$ millions per year

||O&M||||Capital e|xpenditure|||
|---|---|---|---|---|---|---|---|---|
||Public sector<br>and users’<br>tariffs|Public<br>sector|ODA|Non-OECD<br>financiers|PPI|HH self-<br>financed|Total<br>CAPEX|Total<br>spending|
|ICT|1|1|2|3|205|0|211|212|
|Power|257|93|18|12|16|0|138|396|
|Transport|9|51|47|23|5|0|125|133|
|WSS|4|65|37|14|0|49|117|121|
|Total|270|210|103|53|225|49|640|911|

36
<u>Table 2: SAM for Guinea-Bissau 2001</u>

|Co|nsumer|Untaxed|Domestic|Exports|Imports|Foreign|Govt|
|---|---|---|---|---|---|---|---|
|Untaxed|-48.20|48.20||||||
|Domestic|-5.67||4.93||||**0.74**|
|Exports||||24.60||**-27.21 **|**2.61**|
|Imports|**-57.71**||||53.41||**4.30**|
|Investment|**-18.92**|11.73|1.20|5.99||||
|Foreign Exchange|26.20||||-53.40|27.20||
|Formal Capital|2.36||-0.39|-1.97||||
|Informal Capital|48.36|-28.85|-2.93|-16.58||||
|Formal Labor|9.45||-1.58|-7.87||||
|Informal Labor|34.61|-31.08|-0.92|-2.61||||
|Capital Taxes|||-0.15|-0.77|||**0.92**|
|Labor Taxes|||-0.16|-0.79|||**0.95**|
|Transfers|9.51||||||-9.51|

Figures represent a percentage of GDP at market value. Positive figures are revenues received by the column from the row. Negative figures are payments made by the column to the row. Figures in bold are taken directly from national accounts, all others are calculated using the calibration process.

tive entries are payments to factors or factor taxes. In the government’s column, positive figures are tax revenues, the negative figure is the transfer to consumers. The ‘Foreign’ column represents the purchase of exports and the sale of imports by the rest of the world, using foreign exchange.

# **3.1 Data**

The data are given in Appendix B. Country-specific data comprise values of exports, imports and investment as a percentage of GDP, tax revenues for each of the five taxes, and legal tax rates for domestic goods, capital and labor. These data were obtained from IMF country report statistical annexes, for the most recent years available. The data also include labor-output ratios for _untaxed_ , _domestic_ and _exports_ that are, by assumption, common for all countries. The labor-output ratios are the averages of ratios derived from SAMs prepared by IFPRI for Malawi 1998, South Africa 1999, Tanzania 2001, Zambia 1995, and Zimbabwe 1991.

Data for tax revenue from sales of _domestic_ are derived from tax revenues from domestic VATs. In the absence of a VAT, general sales tax revenues are used. Corporate income tax revenues are interpreted as tax revenues from _formal capital_ . Personal income tax revenues are equated with revenues from _formal labor_ . Data for tax revenues from _exports_ and _imports_ are taken directly from the national 9
high in SSA and in Latin America and the Caribbean (LAC). Trade costs declined less in agriculture than in manufacturing over 1995-2019, falling from 194 percent to 170 percent, in part because of slower progress in reducing tariffs and the narrower coverage of trade agreements.

Goods and services trade are complementary. Tradable services are key links between stages of value chains and “enablers” of trade in goods, particularly communications, finance, business and logistics services. As a result, services account for almost one-third of the value added of manufacturing exports (Ariu et al. 2019; OECD 2022). Comparable cross-country data on services trade costs and on policies affecting trade in services are scant. The few attempts in the literature to quantify trade costs in services either rely on observed trade and value-added flows, akin to the methodology embedded in the UNESCAP/World Bank database for goods trade costs (Miroudot, Sauvage, and Shepherd 2010), or rely on an inventory of services trade restrictions (Benz 2017). Both types of studies suggest that trade costs for services are considerably higher than trade costs for goods, and that, unlike trade costs for goods, they have not fallen since the 1990s.

# V. Correlates of trade costs

Trade costs include the full range of costs associated with trading across borders. These include transportation and distribution costs (Marti and Puertas 2019; Staboulis et al. 2020), trade policy barriers (Bergstrand, Larch, and Yotov 2015), the costs of information and contract enforcement (Hou, Wang, and Xue 2021), legal and regulatory costs, as well as the cost of doing business across cultures, languages, and economic systems (Anderson and van Wincoop 2003).

## V.1 Candidate correlates

A number of correlates of trade costs have been identified. They include trade policies, shipping and logistics, regulations, uncertainty, and other factors.

## Trade policies: tariffs and trade agreements

Import tariffs raise trade costs. The contribution of tariffs to total trade costs has decreased in the post-war period, including through steep reductions since 1990 in tariffs imposed by EMDEs. Thus tariffs in EMDEs averaged 7.7 percent of the value of imports in 2020, down from 16.0 percent in 1995, although this is still much higher than the average tariff of around 1.9 percent in advanced economies (figure 6). As a result of tariff reductions, tariffs now amount to a small portion of trade costs—about one-twentieth. Agricultural tariffs remain higher than manufacturing tariffs, by one-fifth in EMDEs and two-fifths in advanced economies.

The decline in tariffs in recent decades has been accompanied by the establishment and expansion of regional trade agreements (RTAs). The number of RTAs more than quintupled between the early 1990s and the mid-2010s and their focus has shifted from
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Table 2.1 Potand: SQctoral date, January-Deceirrer 1990 1/ Jan-Dec. 1989u100

|.................................................................................................................................|
|---|

|<br>|Eaoltoy2nt<br>P<br>|roductivity|Productfon|Produceor<br>Pricoo|Real<br>Profits|Product<br>"|Consuinr<br>wage|Nominal<br>tdages|
|---|---|---|---|---|---|---|---|---|
|................<br>.......................................................................<br>COAL|.................................................<br>91.80|78.00|71.60|1,078.3|42.1|46.8|63.4|441.3|
|FUEL|99.70|78.74|78.50|1,190.8|168.1|52.0|75.8|527.9|
|P09ER|101.80|88.41|90.00|1,272.1|313.8|44.9|68.7|478.1|
|FERROUS KETALLURGY|94.40|88.03|<br>83.10|1,098.1|117.9|55.2|74.7|519.6|
|NMO-FERROUS METALLURGY|97.20|78.50|<br>76.30|1,090.6|113.7|51.3|72.9|507.6|
|METAL PRODUCTS|90.60|79.25|71.80|836.5|75.6|64.3|67.5|470.0|
|ENGINEERING|89.20|90.36|80.60|781.8|74.2|69.4|68.5|476.7|
|PRECISION INSTRUMENTS|89.70|92.75|83.20|650.1|60.8|80.9|65.7|457.1|
|TRANSPORT EQUIPMENT|91.50|80.77|73.90|885.0|70.4|63.8|68.7|477.9|
|EL-TECH ENS. & ELECTRONICS|<br>92.00|85.54|78.70|734.6|57.4|70.9|65.5|456.0|
|CHEMICALINDUSTRY|93.90|80.19|75.30|938.9|80.6|59.1|68.2|474.7|
|BUILDINGMATERIALS|93.20|82.40|76.80|892.1|91.9|60.6|67.6|470.8|
|GLASS & GLASS PRODUCTS|94.60|76.53|72.40|873.0|58.3|62.5|67.0|466.2|
|POTTERY&CHINA|97.00|76.39|74.10|734.1|51.7|72.4|66.8|465.0|
|WOWD<br>&WOOMPRODUCTS|89.80|84.19|75.60|730.7|35.4|73.9|66.0|459.6|
|PAPER & PAPER PRCPt(CTS|92.30|83.75|77.30|949.8|70.5|58.8|67.7|471.1|
|TEXTILE PRODUCTS|88.90|66.93|59.50|638.1|21.3|78.0|61.8|429.8|
|WEARING APPAREL|89.20|77.80|69.40|496.8|27.4|97.7|62.1|431.9|
|LEATHER PRODUCTS|90.10|73.58|66.30|565.3|17.6|84.6|60.3|419.8|
|FOOD INDUSTRY|98.20|75.66|74.30|727.6|52.0|82.2|66.0|459.2|
|,,,,,,,,,...............................|...............................|.......................|||<br>|<br>|||
|<br>Umweighted<br>averages|,,<br>93.26|<br> <br>80.89|<br>?S.4|8S8.2|......<br>...<br>80.0|.......<br><br>66.5|............<br>67.2|463.0|
|<br> <br>Std. deviation|3.70|6.03|6.33|204.62|63.98|13.46|3.85|26.82|
|Cooff.<br>of variation,<br>in%|3.96|7.45|8.39|23.84|79.9S|20.21|5.73|5.73|

- 1/ Oue to uwavaflabitity of data On produrer pricoo by actcore for Oecczbar 1990, the product wage referS to JanUary-Noves5ar.
land; and (iv) land rental markets are more active in areas with higher levels of economic activity as proxied by either infrastructure access, light intensity, or urban gravity.

With 54% of those renting in land in Malawi (74% in Nigeria, 35% in Tanzania, and 25 and 24% in Niger and Uganda) being landless, land rental allows households who are either landless or have significantly less land per adult than the rest to gain land access. Households with higher endowments of effective labor (including human capital) are more likely to rent in and make productive use of available resources including available family labor. Beyond improving equity, land rental is thus also likely to allow better overall land utilization.

Relatively younger household heads are more likely to use land renal markets and expand their agriculture production, although with an average age of the head of well beyond 40 - from 43 in Malawi to 51 in Nigeria. While the share of illiteracy is lower among those renting in than the rest everywhere, pointing towards a minimum level of educational attainment, participation in rental markets does not seem to be associated with high levels of formal education except in Malawi where educated households seem to access land through rental markets. Households headed by females are less likely to rent in land in Ethiopia, Malawi and Niger, something that may partly be a result of their lower overall labor endowment. To the extent that information on the value of assets is available, we note that lessees in Niger and Nigeria but not in Uganda hold more agricultural assets. Interestingly, land is transferred to wealthier households ($2,741 vs. $1,604 value of durable goods but not agricultural assets) in Malawi.

Non-farm diversification appears to drive some of the observed land rental activity as illustrated by the fact that in Malawi, Nigeria, and Uganda, land markets are more active in EAs with greater non-agricultural employment. This may suggest that land markets contribute to structural transformation by allowing interested individuals to take up non-agricultural employment without losing the safety net function implied by land ownership and making effective use of their land (Deininger et al. 2011b).

# **3.4 Evidence on land sales market participation**

The fact that data on land sales are available only for two countries -Uganda and Niger- illustrates that, to obtain good information on land sales, issues of questionnaire design and sampling need to be considered. While including questions on land acquisition in samples of agricultural cultivators is important, land may increasingly also be acquired by firms that may not be visible in household surveys (Headey and Jayne 2014; Schoneveld 2014). Analysis of such transactions can be of great policy relevance (Sitko and Jayne 2014) and will be important especially in land-abundant countries where, in the context of the post-2008 food price spike (Deininger and Byerlee 2011), magnitudes involved are likely to be large and thus will affect the agricultural sector more broadly (Jayne _et_ 9