rafmacalaba/datause-encoder-data huggingface dataset:
Increased employment of teachers, health personnel, and other skilled workers may drive up wages and reduce the number of skilled workers available for private sector employment, at least in the short and medium run.

Various approaches have been used both to plan or monitor progress toward achieving the MDGs, and to evaluate the additional (or total) public resources, including foreign aid, needed to meet them. Clemens et al. (2004) and Reddy and Heuty (2004) survey a large number of studies that forecast and cost MDGs. As emphasized by Vandermoortele and Roy (2004), however, data availability and simplifying analytical assumptions severely affect the quality of the quantitative estimates in all these studies.

Four major sets of limitations affect studies on MDG achievement. First, many sector studies have focused on individual MDGs, but even some that consider multiple goals often fail to properly account for the interdependencies that exist among different MDGs and among policies designed to reach them. Second, MDG-related policies interact with the rest of the economy (namely the private sector) by altering prices of specific factors (such as skilled labor) and/or their overall supply. Third, inter-temporal equilibrium consistency is seldom checked. Financing needs, debt accumulation, and the inter-temporal sustainability of fiscal policies need to be integrated in a complete study on strategies to achieve the MDGs. Finally, as stressed by Devarajan et al. (2002), the policy and institutional environment is as important a component of success in achieving the MDGs as the availability of public resources or financial assistance. Keeping these potential limitations in mind, we will briefly report on some recent studies and approaches in use at the UN and the World Bank.

The UNDP Human Development Report (2005, pp. 39-48) covers most MDGs, projecting trends for individual countries, aggregated to regions and globally. Policies and linkages between MDGs are not considered. The authors point out that it is problematic to make projections on a goal-by-goal basis given strong links between different MDGs. Nevertheless, their approach may be adequate for their purpose – to highlight the fact that, if current trends continue, most countries will fail to achieve most MDGs. However, it is clearly not designed for analysis of MDG strategies. With the more ambitious objective of helping countries design PRSPs, Wodon and coauthors have developed SimSIP (Simulations for Social Indicators and Poverty), a set of tools that address different aspects of strategy analysis (also applicable to MDGs), including target setting, and assessments of costs and fiscal sustainability (Christiaensen et al. 2002).<sup>1</sup> The tools, which are Excel-based, have the advantage of being user-friendly and analytically simple. The objective of the target-setting module is to assess the realism of targets related to poverty, health, education, and basic water-sanitation access. It provides alternative specifications for forecasting

> 1 The tools and related documentation can be downloaded from www.worldbank.org.

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advanced technology as it reduces the information on available technological solutions for its needs and could increase its adjustment costs (Cohen and Levinthal, 1989).

Second, even though capital goods are considered good collateral in developed countries, failure to regulate the movable collateral regime in developing countries may constrain smaller firms’ _access to credit_ , restricting their adoption of new technologies. Depending on the technological gap between existing and new technologies and on the associated adjustment costs, complementary physical investments may be needed, in which case access to credit becomes an even more important input for technological update. Based on country, industry, and firm level studies, Levine (2004) finds that there is causal evidence of a role of financial development for growth.<sup>2</sup> Third, based on the potential impact of transport infrastructure on outsourcing and computer use in the production process, infrastructure availability and quality can also play a decisive role in a firm’s decision to acquire and use new vintages of capital. The importance of _physical infrastructure_ for firm productivity and economic growth finds strong support from empirical work for OECD countries as well as developing countries (Roller and Waverman, 2001; Esfahani and Ramirez, 2003; World Bank, 2005a).

# **_II.2 Market Incentives: Private Ownership and Control, Competition, and Contract Enforcement_**

The firm’s technology adoption decision is also likely to be determined by market incentives which refer to four core aspects of a competitive market economy. First, _private ownership and control_ establish profit-maximization as the ‘modus operandi’ of the firm and should drive investment in the most productive technologies with ensuing productivity gains (Brown et al., 2007).

Second, an important additional condition is strong _product market competition_ from domestic and foreign rivals and/or demand from consumers which encourage incumbents to direct their investments towards new and more productive technologies - product upgrades and/or costs reductions - rather than towards rent-seeking activities (Baumol 1990; Aghion and Schankerman 2004).<sup>3</sup> Third, sound legal institutions that guarantee adequate _protection of property rights and contract enforcement_ are critical for growth and for the development of market economies _(_ North, 1990; Acemoglu et al., 2004; Djankov et al., 2006). Acemoglu et al. (2007) show how greater contractual

> 2 See also Gatti and Love (2006) on the links between firm performance and access to finance.

> 3 Consistent with that argument, Comin and Hobijn (2004) find that the importance of openness to trade in determining the speed at which a country adopts advanced technologies is due to the fact that openness introduces pressures from foreign competition on incumbents, thereby reducing their payoff from lobbying the government to deter the adoption of new technologies.

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temperature and precipitation within each day could include important information to identify extreme or unusual weather conditions (Deschênes and Greenstone, 2007; Fisher et al., 2012; Schlenker et al., 2006; Schlenker and Lobell, 2010; Schlenker and Roberts, 2009).

# **2.3 Regression analyses**

In the literature there are two econometric approaches used to estimate the income effects of weather variation (Dell et al., 2012; Deschênes and Greenstone, 2012, 2007; Fisher et al., 2012; Mendelsohn et al., 1994): Differences between households and locations are estimated from a cross‐section of data observed at one point in time following a Ricardian approach. Alternatively, changes over time are estimated from panel data observing the same households or locations at various points in time.

The data set used for this study allows estimating the following regression model: where Y denotes per‐capita income observed for individual _i_ in commune _j_ in year _t (i.e._ 2010, 2012, 2014 _)_ . W measures weather conditions n commune _j_ in year _t_ using five sets of weather variables as described in section 4. _β_ is the parameter of interest that indicates the income effects of weather variation. _X_ is a set of household‐specific controls that vary over time, such as education, labor and land endowments.<sup>5</sup> _T_ measures time‐fixed effects to neutralize common trends over time. _Z_ includes commune‐specific effects that do not change over time. _U_ is a random, idiosyncratic error term.

A particularity of this data set is that for some households information is available for two or three survey years. These households form a _Panel_ data set. Other households were only observed in one of the three years. Using this cross‐section data as well and treating all observation as independent observations provides a _Pooled_ data set.

A main concern when fitting models to estimate weather impacts on economic outcomes is endogeneity bias (dell et al., 2014). Reverse causation is unlikely to be a problem as weather conditions are exogenously determined. Yet the model is likely to suffer from omitted variable bias caused by the potential correlation of weather variables with other commune characteristics (e.g. long‐term climate, geo‐graphical, agro‐ecological conditions) that determine living standards. To the extent that such variables cannot be measured, the estimates of β will be biased.

To minimize this omitted variable bias, a fixed‐effects ( _FE_ ) linear model is fitted by using a within‐ regression estimator based on the _Panel_ data set ( _Panel FE_ ).<sup>6</sup> These analyses allow investigating the determinants of income changes between 2010 and 2012 and 2014 due to differences in weather

> 5 The data sets allow to control for land and labor inputs of some income activities. These are, however, not included in the regression analyses due to potential endogeneity biases.

> 6 A within‐regression estimator is always consistent, but a General Least Squares estimator can be more efficient. The GLS –estimator based on a random‐effects model was also tested, but a Hausman test rejects that the GLS estimator is efficient.

6
Figure 13: Initial impact and recovery of exporters by region <!-- Start of picture text --> (a) Change in sales (b) Probability of falling into arrears<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>68 61 62<br>62<br>60 50<br>48<br>-20 - 16<br>-23 40 30 30<br>- 28<br>- 31<br>-40 -39<br>20<br>-46 - 47<br>-50<br>-60 0<br>EAP ECA SSA SAR EAP ECA SSA SAR<br>First round Latest round First round Latest round<br>P-value of test that exporters in EAP are different than exporters in other regions: P-value of test that exporters in EAP are different than exporters in other regions:<br>ECA: First round: 0.000; Latest round: 0.016 ECA: First round: 0.000; Latest round: 0.000<br>SSA: First round: 0.249; Latest round: 0.000 SSA: First round: 0.975; Latest round: 0.068<br>SAR: First round: 0.305; Latest round: 0.000 SAR: First round: 0.572; Latest round: 0.000<br>Change in sales (%)<br>Probability of falling into arrears<br><!-- End of picture text --> _Notes:_ The sample includes 43,361 firms in 45 countries in panel a and 37,875 firms in 41 countries in panel b.

_Third_ , at the onset of the pandemic, some economies imposed much more stringent restrictions to stop the spread of the virus, which may have had differential impacts on globally engaged firms across countries. Counter-intuitively, exporters in countries with above median initial stringency of lockdown conditions exhibit a smaller decline in total sales and a lower probability of falling in arrears, but their recovery has been slower (Figure 14). These results are in sharp contrast to Cirera et al. (2021a) who find that the average firm in countries with higher severity of initial lockdown suffered more, although their recovery was comparable. It is possible that exporters are highly agile and react promptly by adjusting their operations, products and markets in response to domestic conditions, as we will next explore in greater detail.

Figure 14: Initial impact and recovery of exporters by severity of country initial lockdown <!-- Start of picture text --> (a) Change in sales (b) Probability of falling into arrears<br>0 60<br>42 44<br>40<br>-20 -18 40 38<br>- 24<br>- 29<br>-40 20<br>- 40<br>-60 0<br>Below median Above median Below median Above median<br>First round Latest round First round Latest round<br>P-value of test that exporters in above the median countries are different than exporters in below median: P-value of test that exporters in above median countries are different than exporters in below median:<br>First round: 0.000; Latest round: 0.000 First round: 0.873; Latest round: 0.000<br>Change in sales (%)<br>Probability of falling into arrears<br><!-- End of picture text --> _Notes:_ The sample includes 43,361 firms in 45 countries in panel a and 37,875 firms in 41 countries in panel b.

26
The fact that the units of the assessment of learning matter is not in and of itself a problem for the _LAYS_ approach. It does mean, however, that _LAYS_ should be thought of not just “average years of schooling measured in terms of the learning productivity of the top performer” but as “average years of schooling measured in terms of the productivity of the top performer _according to the metric used to determine that productivity_ .” But given that the alternative unitfree measure yields results in line with the basic _LAYS_ results, in practical terms that distinction may not be as significant as it first seems.

# **_4.2 The assessment used: Using PISA instead of TIMSS_**

TIMSS is not the only assessment that could be used to calculate _LAYS_ . PISA is another international assessment with wide coverage: in 2015, the PISA assessment covered 72 countries and economies (35 of which are in the OECD). PISA is an assessment of 15-year-olds in secondary school. Since these students are not necessarily all in the same grade, here we take a slightly different approach to calculating _LAYS_ , using age rather than grade when we define the average learning profile. So, for example, we now divide the score on the assessment by 9 (in the equivalent of Equation 3), since this is the number of years of learning that a student who started learning at age 6 would have acquired by age 15. For alternative calculations that allow for learning to start before age 6, we could then proceed as before (that is, as per Equation 4) and add in additional years of learning. Implementing _LAYS_ in this way using PISA 2015 mathematics scores yields the _LAYS_ estimates shown in Figure 4.2, which are generally speaking in line with the results that use only TIMSS.

was already large using either the average or the “low” benchmark for the adjustment. See Annex 1 Figure 1.

19
**Table 5. Poverty and inequality measures by spatial deflation methodologies**

||||Food||F+|NF||F+N|F+H||
|---|---|---|---|---|---|---|---|---|---|---|
||GLSS|price|CPI (item)|CPI(variety)|CPI(item)|CPI(variety)||CPI (variety)+|GLSS housi|ng|
||||||||i|i<br> <br>WCP|i<br> <br>D +|i<br>|
||GEKS-F<br>(1)|WCPD<br>(2)|WCPD<br>(3)|WCPD<br>(4)|WCPD<br>(5)|WCPD<br>(6)|Medan<br>paid rent<br>(7)|Medan<br>imputed rent<br>(8)|Hedonc<br>paid rent<br>(9)|Hedonc<br>imputed rent<br>(10)|
|**Poverty head**|**count ratio (FGT**|**0)**|||||||||
|Ghana|24.4|24.0|24.1|24.5|24.5|24.6|24.1|24.4|24.3|24.3|
|Urban|8.1|8.0|7.8|8.1|8.4|8.3|8.5|8.3|8.4|8.3|
|Rural|41.3|40.5|40.9|41.5|41.1|41.5|40.1|41.0|40.5|40.7|
|**Poverty gap (**|**FGT1)**||||||||||
|Ghana|8.7|8.4|8.5|8.7|8.7|8.9|8.5|8.8|8.6|8.7|
|Urban|1.9|1.9|1.9|1.9|1.9|2.0|2.0|2.0|2.0|2.0|
|Rural|15.8|15.1|15.4|15.7|15.7|16.0|15.1|15.8|15.5|15.6|
|**Poverty squar**|**ed gap (FGT2)**||||||||||
|Ghana|4.5|4.2|4.3|4.4|4.4|4.6|4.3|4.5|4.4|4.4|
|Urban|0.7|0.7|0.7|7.0|0.7|0.7|0.7|0.7|0.7|0.7|
|Rural|8.3|7.8|8.1|8.2|8.3|8.5|8.0|8.4|8.2|8.3|

_Source:_ Authors’ calculations based on the GLSS7 and the CPI price data. _Note:_ F = Food; H = Housing; NF = Non-food; Variable weights are applied to the WCPD regression.

**Table 6. Regional poverty rates by spatial deflation methodologies**

||||Food||<br>F+|<br>NF||F+N|F+H||
|---|---|---|---|---|---|---|---|---|---|---|
||GLSS|price|CPI (item)|CPI(variety)|CPI(item)|CPI(variety)||CPI (variety)+|GLSS hous|ing|
||||||||Mdi|Mdi<br>WC|Hdi<br>PD +|Hdi|
||GEKS-F|WCPD|WCPD|WCPD|WCPD|WCPD|ean<br>paid rent|ean<br>imputed rent|eonc<br>paid rent|eonc<br>imputed rent|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Western|19.1|19.2|19.1|20.8|23.9|21.1|21.3|21.0|21.4|21.3|
|Central|17.8|16.3|14.4|14.2|14.3|14.2|13.5|13.7|13.7|13.7|
|Greater Accra|2.7|3.2|3.2|2.7|3.1|2.7|3.2|3.2|3.2|3.0|
|Volta|37.8|37.2|40.7|41.4|32.5|36.6|34.8|36.4|34.8|35.5|
|Eastern|15.7|16.6|15.2|16.2|17.6|15.5|15.5|14.9|14.9|15.2|
|Ashanti|14.6|14.2|14.0|14.1|12.7|14.1|13.8|13.7|13.9|13.9|
|Brong Ahafo|25.7|27.5|28.6|28.9|31.4|29.6|28.9|28.8|29.0|29.0|
|Northern|55.6|53.0|52.4|53.6|54.6|57.3|55.0|57.7|56.7|56.5|
|Upper East|67.1|60.3|60.6|60.7|62.2|62.1|61.0|61.1|61.3|61.1|
|Upper West|71.6|70.9|75.4|75.6|78.7|78.5|76.1|77.4|76.8|77.2|
|Ghana|24.4|24.0|24.1|24.5|24.5|24.6|24.0|24.4|24.2|24.3|
|Urban|8.1|8.0|7.8|8.1|8.4|8.3|8.5|8.3|8.4|8.3|
|Rural|41.3|40.4|40.8|41.4|41.1|41.5|40.1|40.9|40.5|40.6|

_Source:_ Authors’ calculations based on the GLSS7 and the CPI price data. _Note:_ Variable weights are applied to the WCPD regression.

22
**Figure 3: Export-employment (upper) and export-income elasticities (lower panel), by disaggregated sector**

<!-- Start of picture text --> 0.49 0.47<br>0.42<br>0.35 0.31<br>0.21 0.18 0.15 0.11 0.11<br>-0.11 -0.13 -0.16<br>-0.23<br>-0.30 -0.33<br>1.36<br>0.78 0.75 0.72 0.71 0.68 0.64 0.63 0.63<br>0.56 0.53 0.48<br>0.38 0.36 0.35 0.33 0.32 0.31 0.28<br>0.16<br>-0.25<br><!-- End of picture text --> Source: Own computations. Data: OECD TiE and TiVA data, covering 48 countries (see Appendix 2) and 45 sectors for the period 1995-2018. Note: Trade-employment (trade-income, resp.) elasticities at the country-sector level have been estimated using employment (labor income, resp.) (in natural logs) as dependent variable and the instrumented trade variables (in levels and natural logs) as independent variable. For more information on the instruments, see Appendix 1. The panel data regressions controls for country and sector fixed effects. Only results significant at the 10% level and above the critical value for the Kleibergen-Paap test statistic are reported.

In sharp contrast to manufacturing, aggregate business services exports show no significant association with within-sector employment creation, while their potential to generate labor income is much higher. Across the sample of 45 disaggregated sectors, only wholesale and retail trade, land transport, and professional services exports are positively associated with employment growth in these sectors over the period 1995-2018. But the link with labor incomes is even stronger in these business services sectors, in the case of trade and transport more than six times higher. Exports of health services, on the other hand, can reduce employment within the sector, if medical services providers such as doctors or nurses temporarily move to another country to export services. In addition, health services can also be offered to foreign consumers within a country, known as medical tourism. It is therefore not surprising that health exports create higher labor income, as the relative supply of labor in the sector declines with export expansion, while the relative demand for health providers increases which is exacerbated in the case of medical tourism. Interestingly, accommodation and restaurant exports which are associated with tourism do not increase the number of jobs in the sector but raise labor incomes.

7
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en
results with more than two thirds (i.e., 17 out of 24) and almost two thirds (i.e., 15 out of 24) of the point estimates respectively pass the 50 percent mark and 100 percent mark.

# **VI.5. Consumption Dynamics between Different Groups**

A common way to study consumption dynamics is to divide the consumption distribution into five groups (quintiles), and look at the 5x5 transition matrix for mobility between two periods. We provide such estimates using data from Vietnam in 2006-2008 in Table 7, where the synthetic panel estimates are shown in panel B and the actual panel estimates shown in panel A. While estimates are off with some of the row and column totals, it is perhaps more useful to focus on the inner transitions since the former do not offer as much insight into mobility as the latter.<sup>34</sup> Estimation results are, again, rather encouraging with the majority of the different consumption categories (i.e., four-fifths of the inner transitions) falling within the 95 percent CIs of the true estimates, which are presented in bold. These estimates also pass the 100 percent mark of the coverage test.<sup>35</sup> Furthermore, some of the remaining estimates that fall just outside these 95 percent CIs around the true estimates appear practically close to the latter (e.g., the transition from quintile 3 to quintile 4 or from the richest quintile to quintile 2).

> 34  The row or column totals should sum up to 20 percent by definition and serve mostly as an indicator of prediction accuracy for these totals only. In addition, it may be useful to highlight the fact that our validation is predicated on the assumption that the true panel data for Vietnam have good quality. If the mobility in the true panel data is partly caused by spurious changes due to measurement errors (or attrition bias) in household consumption, our estimates based on the synthetic panel data would be more accurate since cross sections are free of such data issues. See also Dang and Lanjouw (2014) for a related study of mobility between three consumption groups: poor, vulnerable, and middle class.

> 35 Other useful statistics that can be calculated from Table 7, panel B, include the percentages of the population that have seen either an improvement or a decline or remained in the same quintile over time, which are respectively 24.7 percent, 27.3 percent, and 48 percent. These estimates are within the 95 CIs around those based on the actual panels.

35