rafmacalaba/datause-encoder-data huggingface dataset:
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39
# **Figure 11.  Correlation Between the Saving and Real Exchange Rate Gaps for LAC1 Countries, 1981-2015**

<!-- Start of picture text --> 1<br>The<br>The Descent into IR The Road to ER Return<br>0.8 of IR<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text --> Sources: Authors' calculations based on UN, WDI and Institutional Investor data. Notes: Sample includes Brazil, Chile, Colombia, Costa Rica, Ecuador, Mexico, Panama, Peru, and Uruguay.

39
*

# 39

and the effect of environmental contamination on child health suggests a conditional analysis based on intervals shorter than the first-year range. Neonatal health is more responsive to inherited biological characteristics and the outcome of prenatal and perinatal care. But postneonatal ages are typically transitional: children are still more or less protected by maternal antibodies, and they start building up natural or artificial resistance to diseases prevalent in their environment. Their exposure to these diseases is intensified by the introduction of foods other than breastmilk and of other child-rearing practices. Thiese differences between neonatal and postneonatal infants means that the critical amount of family resources needed to sustain a child's health typically depends on the child's age group. Consequently, our analysis presents information separately for neonatal and postneonatal ages.

Descriptive and regression analyses are used to assess the changes in child health between 1970 and 1986. The analyses focus on how these changes vary among socioeconomic groups. For the purposes of this paper, we focus on a few family characteristics.

We use family expenditure per capita as an indicator of family permanent income and to classify families by income group. Family expenditure per capita is also used to define the rural poor and the urban poor. The rural poor is defined as faLiilies who fall within the bottom 60 percent of the distribution of family income within the rural areas. The urban poor is similarly defined using the distribution of family income in the urban areas.

Family savings have been recorded in the LSMS. The ratio of the total amount of family savings over total family expenditure is used as an indicator of the
**Table** 7 **Government and Well-Being Regressions: OLS and Instrumental Variable"** (Dependent Variables: Infant Mortality Rate and Life Expectancy)

||Infant M|ortality|Life Ex|pectancy|
|---|---|---|---|---|
|Independent Variables|OLS|IVb|OLS|IV|
|Constant|7.851***|11.872***|0.161***|-6.223***|
||(0.024)|(0.383)|(0.024)|(0.383)|
|Dummy for 1974-83|-0.118|-0.794***|-0.015***|0.336**|
||(0.147)|(0.199)|(0.003)|(0.130)|
|Dummy for 1984-93|-0.428**|-1.119***|-0.016***|0.538***|
||(0.166)|(0.230)|(0.004)|(0.151)|
|Initial Per Capita GDP|-0.780***|-1.128***|0.450***|0.542***|
||(0.097)|(0.137)|(0.060)|(0.089)|
|Per Capita GDP Growth|-5.684*|-31.446***|2.792|8.844*|
||(3.061)|(7.367)|(1.940)|(5.183)|
|Social Spending/GDP|-0.341***|-0.289***|0.155***|0.136***|
||(0.074)|(0.074)|(0.048)|(0.052)|
|Quality of Bureaucracy|-0.865**|0.142|0.076|-0.237|
|(O worst, I best)|(0.392)|(0.446)|(0.236)|(0.282)|
|Political Freedom|0.023|0.034|-0.043*|_-0.045*_|
|(I high, 7 low)|(0.036)|(0.038)|(0.022)|(0.025)|
|Income Inequality|'O.036***|0.003***|-0.012**|-0.012**|
||(0.009)|(0.009)|_(0.005)_|(0.006)|
|Latin America Dummy|-0.051|-0.475*|0.002|0.164|
||(0.192)|(0.210)|(0.111)|(0.122)|
|Sub-Saharan Africa Dummy|-0.405*|-1.341***|-0.195|0.057|
||(0.243)|(0.370)|(0.142)|(0.229)|
|East Asia Dummy|-0.434**|-0.239|0.007|0.005|
||(0.188)|(0.184)|(0.094)|(0.097)|
|Socialist Dummy|-0.106|-0.317*|-0.033|-0.027|
|___|___<br>_(0.223)|(0.184)|(0.141)|_(0.150)_|
|Number of Observations|105|91|105|91|
|R-squared'|0.8723|0.8811|0.8494|0.8404|

*** significant at 1% level significant at 5% level * significant at 1% level

'Standard errors, corrected for heteroscedasticity, are in parentheses.

b For all IV regressions in Table 7, the instruments for the following variables are the previous five year lag of itself and regional decade dummy variables: social spending/GDP; political freedom; and income inequality. The instrument for GDP growth is the prediction from the growth instrumental variable regression reported in Table 6. All other variables in the regression were treated as exogenous.

The R-squared is not an appropriate measure of goodness of fit with instrumental variable regressions.

37 I
By adding service activities that are produced _within_ manufacturing firms, the share of indirect services value‐added in manufacturing exports increases to 53 percent. When combined with stand‐alone direct service exports by services exporters, this brings the total contribution of services to exports to two‐thirds.

**Services are an increasingly important driver of global value chains (GVCs)** . Without many input services, such as transport and distribution, value chain trade would not exist. Many service inputs are also re‐ exported by third countries as part of GVCs. Some measures of services can therefore serve as indicators of GVC trade, including both domestic services inputs (indirect domestic services value‐added in exports) and foreign services inputs (foreign services value‐added in exports). While direct exported services increased substantially between 1995–2011, more than 65 percent of the growth of services value added in exports was due to an increase in services embodied in other exports (Figure 1.3). Both domestic and foreign embodied services grew, but the foreign services value‐added component grew the most over the period (Heuser and Mattoo, 2017). The growth of services inputs and foreign services value‐added is an indication of global value chains at work.<sup>3</sup> Finally, services have been shown to be a driver of economic diversification (Cattaneo et al, 2017).

**Figure 1.3: Share of direct, indirect, and foreign services value added in world gross exports, mln USD**

<!-- Start of picture text --> 10,000<br>2155<br>8,000<br>3946<br>6,000<br>4,000<br>3292<br>496<br>2,000 1337<br>1072<br>0<br>1995 2011<br>Domestic direct Domestic indirect Foreign<br>Million USD<br><!-- End of picture text --> Source: Miroudot (2016) and Heuser and Mattoo (2017), based on OECD‐WTO TiVA 2015.

**Service sector reform leads to productivity growth.** Firm‐level studies such as Arnold et al. (2015; 2011), Van der Marel et al. (2016) and Fernandes and Paunov (2012) show that regulatory reform in services has a positive impact on downstream manufacturing and services firms’ productivity. The reason for this productivity effect is that when services as inputs are effectively supplied, this has a knock‐on effect on industries that use services. In other words, services sector deregulation can lead to a more productive allocation of resources, thereby generating economy‐wide productivity effects. Yet, to optimize benefits

3 According to Heuser and Mattoo (2017), the reasons for these developments are variants of the older arguments for why the share of services in GDP tends to grow: the splintering or outsourcing of services activities from manufacturing firms; the growing importance in a GVC world of connecting services like telecommunications and transport; the growing services component in sophisticated manufacturing goods, such as software in cars; and the increase in the prices of services tasks relative to manufacturing tasks because manufacturing tasks are easier to offshore to lower cost locations. However, there is little empirical evidence for these arguments.

5
18 Slovenia, Bulgaria and Russia.*? The second "contributor" to increasing inequality is a rising share of private sector income which is, of all broad income categories like wages, social transfers etc., the most unequally distributed. A very vivid, almost textbook, illustration of the trend is provided by the Bulgarian data. Inequality of private sector income, already higher than that of other sources (see Figure 2) shoots up at the same time as its share in total income rises (Figure 3). The importance of wages (most of them still derived from the state and semi-state sectors) declines, and targeting of social transfers does not change.

Social transfers have failed to mitigate the increase in inequality. As in the past, the concentration coefficient of cash social transfers has remained close to zero.*° This means that the correlation between the level of income and the level of social transfers received, which is expected to be negative, is not statistically significantly different from zero. This is in contrast, to use again the UK as an example, to the situation during the Thatcher era when declining cash social transfers were accompanied by better targeting. Between 1979 and 1989, the share of cash transfers in disposable income of the British population declined from 17 to 13 percent; meanwhile, the concentration coefficient of transfers moved from -18 to -32 (the larger absolute value indicates a sharper focus on the poor).*!

> “For Poland, see World Bank (1994); for Slovenia, Vodopivec and Orazem (1994); for the Czech and Slovak republics, Vecernik (1994) and Chase (1995); for eastern Germany, Bird, Schwarze and Wagner (1992). by poorer“The concentrationhouseholds compared coefficientto providesthe better-off. an index ofIf all transfer progressivity.benefits are receivedIt indicatesby the poorest how muchindividual, of the transferthe concentration is captured coefficient would be -100; if everybody receives the same amount regardless of income, the coefficient is 0; if all benefits accrue to the richest person, the coefficient is +100.

> “See Milanovic (1994, p. 190).
within which the school operates. Household expectations are proxied in an identical manner as in the case of health, in model (1) with a range of household characteristics and in model (2) with a single index constructed using Principal Components from the household characteristics. The sample is restricted to 2,557 households with at least one child of school age who attends a public school for which facility level data are available. Like in the case of health, the models are also estimated separately for households from poor and rich districts, with sample sizes of 1,094 and 1,463 respectively.

The first key difference between the education and health cases is that unlike for health, the list of independent variables to estimate satisfaction with schools does _not_ include indicators of service quality reported by households, such as level of discrimination, speed of service and waiting time.

Secondly, quality of public schools is proxied using the following information from the facility survey: (a) the extent of _participatory decision-making_ in school; (b) quality of _infrastructure_ in school; (c) the quality of _teaching staff_ ; (d) _student_ performance (in terms of dropouts and repeats); and (e) _coverage_ of students by the school (the size of enrollment and rate of attendance). For each of these categories, Principal Component analysis is used to collapse the multiple indicators provided by the facility surveys into a single index (see Appendix for a more detailed description).

Thirdly, the institutional and governance environment in which public schools function is captured by the following indicators reported by households: (i) _level of accountability_ , proxied by the responsiveness of service provider to complaints; (ii) an index of _participatory management_ 21
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21
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19
firm competitiveness). In spite of this, if current plans are put into practice, the growth of private sector minimum wages will continue to accelerate at least until 2018. At the same time, productivity growth has lagged behind. Unless productivity growth picks up markedly over the coming years, it will further decouple from the pace of growth in statutory minimum wages.<sup>3</sup>

**Figure 4 – Index for strictness of EPL legislation**

<!-- Start of picture text --> Turkey<br>Mexico<br>Spain<br>Indonesia<br>Greece<br>Vietnam (2014)<br>Portugal<br>France<br>China<br>Norway<br>Luxembourg Protection of<br>Belgium permanent workers<br>Italy<br>Iceland against (individual)<br>dismissal<br>Germany<br>Specific requirements<br>Philippines<br>for collective dismissal<br>Austria<br>Poland<br>Vietnam (2010)<br>Cambodia Regulation on<br>Lao PDR temporary forms of<br>Thailand employment<br>Finland<br>Netherlands<br>Sweden<br>Hungary<br>Czech Republic<br>Slovak Republic<br>Denmark<br>Korea<br>Mongolia<br>Switzerland<br>Japan<br>Brunei Darussalam<br>Australia<br>Ireland<br>New Zealand<br>Malaysia<br>United Kingdom<br>Canada<br>Singapore<br>United States<br>0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00<br><!-- End of picture text -->

**Sources: World Bank staff calculations partly based on World Bank (2014a) and OECD data.**

**Notes: Data are for 2010; higher scores represent stricter employment protection legislation.**

As mentioned above, employment protection legislation is another category of labor market regulations. It consists mainly of mandated risk-management regulations that lower the probability of

> 3 Hansen, Rand and Torm (2015) analyze the effects of the rapid alignment of minimum wages in the domestic and the FDI sector in 2012. They find that the alignment led to significantly higher average wages and a decrease in wage inequality. They do not investigate whether it had effects on employment.

- 10 -
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15
The real effective exchange rate is more competitive during growth accelerations and highly appreciated during decelerations. Correlations suggest that exchange rate appreciation is associated with growth deceleration, whereas depreciation is associated with acceleration. There is no evidence that current accounts change during growth acceleration and deceleration.

**_Trade_** is substantially lower during decelerations. Exports and especially imports drop sharply. Correlations indicate that countries that trade less are more exposed to growth decelerations. Somewhat surprisingly, the terms of trade are lower during growth accelerations. This result may indicate that although high commodity prices trigger growth, they may not be the main factor behind medium-term growth spells in Africa.

**_Aid._** Official development assistance (ODA) as a percentage of GDP is similar in both good and normal times but falls during growth decelerations. Per capita ODA, however, is higher during growth accelerations and lower during decelerations. The correlation analysis suggests that a higher share of ODA in GDP is associated with fewer growth collapses and that countries with higher ODA per capita experience more growth accelerations and have fewer collapses. These results indicate that ODA has been procyclical, reinforcing arguments for greater predictability of ODA to underpin sustained growth.

**_Policies, institutions and governance._** Policies and institutions are also closely associated with both good and—especially—bad times. The World Bank’s Country Performance and Institutional Assessment (CPIA) score, a broad measure of policy and institutional performance, is lower during decelerations, but not significantly different between accelerations and normal times. The correlation coefficients suggest that countries with lower CPIA scores tend to experience more economic collapses.

All the governance indicators—political stability, government effectiveness, rule of law, and control of corruption—are lower for growth decelerations than for the region as a whole.<sup>27</sup> PT Correlation coefficients are negative, suggesting that a deterioration of governance is accompanied by more frequent growth decelerations. Voice and accountability scores are higher during growth accelerations. The correlations also suggest that countries that experience more growth accelerations have more voice and accountability and better regulatory quality.

Minor conflicts are more frequent during growth deceleration episodes than during normal times. Major conflicts are less frequent during acceleration and deceleration episodes than for the region as a whole. The correlation coefficients suggest that minor conflicts are associated with collapses and that major conflicts hamper the chances of a growth acceleration.

These results reinforce the findings of other empirical studies (Dufrénot, Sanon, and Diop 2006; Ndulu et al. 2007) of the close relationship between institutions and

> 27 Governance indicators are available for the following years: 1996, 1998, 2000, 2003, 2004, and 2005.

18
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