rafmacalaba/data-use-mentions-extended huggingface dataset:
to less energy-intensive, more labor-intensive sectors in the economy (Solaymani et al., 2015). A recent IMF simulation for manufacturing in Asia and the Pacific utilizes national I/O tables to estimate the potential short-term impact of a $25/ton carbon tax (Dabla-Norris et al., 2021). Sectors that rely on carbon-intensive inputs, typically the downstream, extractive and energy-producing industries, are most affected in terms of output and employment. The amount of projected job loss is primarily driven by the industry’s energy dependence and share of employment. This varies substantially across countries. In Malaysia the largest expected job losses are concentrated in electrical and electronics manufacturing while in Mongolia the vast majority projected job losses are in wood and paper manufacturing.

# **3.3.2. Labor targeting climate change policies**

Our review of the literature points to six potential policies to target the impact of climate change on labor. We refer to these as: green jobs, green skills, labor-oriented adaptation, flexible labor regulation, labor market integration, and social protection. As shown in Table 1, each of these relate to one or more of the above discussed potential labor impacts of climate change. In depth or formal evaluation of these policies has so far remained limited. We discuss each in turn.

**Table 1. Labor targeting climate change policies**

|Impact of climate change on labor|Labor oriented policy response<br>|
|---|---|
||H|
|Demand for labor|<br>Green jobs|
|Supply of labor, time use|Labor-oriented adaptation, regulation for flexible work|
|Productivity|Labor-oriented adaptation, regulation for flexible work|
|Self-employed income, vulnerability, poverty|Green jobs, social protection|
|Reallocation of labor|Labor market integration, social protection|
|Skills and human capital|Green skills, social protection, education and health policies|

Policies that promote “green jobs” aim to increase net labor
# D. Capacity of SOEs to adapt to structural changes

**Can Belarus’ SOEs adapt to the challenges of facing more competition in export markets as well as in the domestic economy?** Two facts shed doubts about SOEs capacity to adapt: (i) the response of production to fall in demand during 2008-2009, and (ii) slow technological innovation. Neither of these facts is proof of the impossibility of SOEs to adapt, but each illustrates the difficulties to do so in the face of critical shifts in market conditions.

**The response of private firm production to the fall in demand in 2008-2009 was markedly different than that of SOEs** . Figure 1.10 documents this fact.  For private firms there is a negative correlation between profits and productivity, for SOEs this correlation is positive.  While a fall in demand results in lower sales and a fall in profits for either of the two groups, private firms shed labor and capital resources and cut production therefore rising productivity (hence the positive correlation);<sup>19</sup> at the same time, SOEs do not shed labor and hence productivity falls at the same time that profits fall (hence the negative correlation).

Figure 1.10 Correlation between changes in productivity and profitability <!-- Start of picture text --> 0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>-<br>(0.10) 2006 2007 2008 2009 2010<br>(0.20)<br>(0.30)<br>State enterprises Private entrprises Mixed with state control<br><!-- End of picture text --> Source: Authors estimates based on the NBRB dataset.

**Why did SOEs respond so slowly to the fall in demand of 2008?  The short answer is that management of
significant effect on co-dependence. Trade over GDP has been previously used in the literature to measure economic integration (Bekaert and Wang 2009). We do not find it to be significant after controls. The Chin-Ito measure quantifies capital control policies and other regulations and restrictions on capital flows (Chin and Ito 2008). It shows up positive and significant. We also consider the impact of social and political integration. We use the KOF political and social 13
parameterized on the basis of a Gini coefficient and an initial headcount poverty rate.<sup>9</sup> The prevalence of undernourishment (the share of the population that does not satisfy its dietary energy requirements, i.e. suffer from calorie poverty) is computed on the basis of similar assumptions and data (per-capita calorie consumption, a Gini coefficient for calorie consumption, and an initial rate of undernourishment). However, in the absence of more

> 8 In Appendix 1 (on the MAMS database), we discuss our treatment of the links between productivity and government capital stocks. 9 It is widely accepted that the lognormal provides a good approximation for within-country income and consumption distributions even though it may fail to account for phenomena such as consumption smoothing (Easterly 2007, pp. 5-6; Lopez and Servén 2006, p. 2). However, in this application, some of the simulations (especially the targeted transfer schemes) invalidate the assumption of an unchanged distribution. To account for this, the following procedure was applied in the computation of poverty and inequality results: (1) a synthetic distribution was generated on the basis of initial rural and urban Gini coefficients; and (b) for each simulation, household consumption observations were scaled on the basis of the change in household consumption per capita for the model household to which the observation was mapped. Poverty and inequality indicators were computed from the resulting distribution. The mapping between model households and observations was possible given that the model households initially represent the top and bottom halves of the income distribution in rural and urban areas. Except for the targeted transfer simulations, this procedure had little impact on the results; however, for the targeted transfer simulations, it made a significant difference.

5
adjustment costs, and dynamic adjustment. This paper exploits the exogenous variation in Mexico’s trade with the United States to study the employment and wage effects of trade shocks with unique data on formal labor markets from Northern Mexico. The data from social security records allow tracking individual workers across industries, which is critical for estimating the effects of trade on employment and wages whilst allowing for such effects to operate through labor mobility across industries. In addition, the data allow for a careful matching of the data on labor by industries to bilateral trade data from U.S. customs records. This combination of trade and employment data results in a quarterly dataset of employment and wages that permits the estimation of labor-market models with leads and lags around the time of the Great Trade Collapse.

The econometric results revealed some interesting and novel patterns. First, imports appear to be complements to labor in Northern Mexico, which is consistent with outsourcing patterns whereby Northern Mexico is a processing stage in North American supply chains. We wonder whether the bulk of the empirical literature on trade and labor (and even the literature on trade and productivity) to some extent has confounded the import-competing and importedinputs effects in models that utilize industrial classifications at medium levels of aggregation, which could partially explain the largely small estimated effects of trade that have been reported in the literature.

Second, a significant portion of hiring decisions tends to occur prior to the realization of exports. Hiring and firing decisions seem to be more important than wage setting because most of the adjustment to trade shocks in Northern Mexico, including during the trade collapse at the end of 2008 and early 2009, seems to have taken place through adjustments in employment, 31
Consequently, the welfare results reported in Table IX might overstate the true cost of trade liberalization. We therefore also calculate the welfare effects for an economy in which the average income risk, _σε_<sup>2,andallchangesinincomerisk,∆</sup><sup>_σ_</sup> _ε_<sup>2,arescaleddownbya</sup> factor of 0 _._ 7. The factor 0 _._ 7 is derived from the fact that the estimate of income risk, _σε_<sup>2,</sup> obtained by Carroll and Samwick (1997), Gourinchas and Parker (2002), Hubbard, Skinner and Zeldes (1994), and Storesletten, Telmer and Yaron (2004) using income data with a very long panel dimension is roughly 70 percent of our estimate of income risk using a much shorter panel dimension (see our discussion in Section II.5 for details). Using the scaled down values for income risk and income risk changes, we find the following welfare cost of a five percent tariff reduction for a degree of risk aversion of _γ_ = 1. First, the one-year increase in income risk immediately following the tariff reduction is equivalent to a decrease in lifetime consumption by _._ 68 percent. Second, a five percent decline in GDP growth leads to an increase in income risk that is equivalent to a loss of lifetime consumption of _._ 27 percent before the tariff reduction, and this loss increases to _._ 68 percent after the tariff reduction (that is, the difference is _._ 41 percent). Thus, although the welfare cost of trade liberalization are somewhat smaller than for the baseline case, they are still quite substantial.

# **VI. Conclusions**

This paper studies empirically the relationship between trade policy and _individual_ income risk. The analysis proceeds in three steps. First, longitudinal data on Mexican workers are used to estimate individual income risk in various manufacturing sectors. Second, the variation in income risk and
all cohorts and bear a direct influence on wages (equation (8)). Similarly, changes in cc ii(ww)i and i apply only to the young cohort but these ττ tt yy yy changes have general equilibrium effects that have implications for other cohorts. Second, in all the ii ii ττ zz counterfactuals, I keep occupation specific TFPs i fixed to focus on the effect borne by the barriers and preferences. Finally, I consider only the share of output that is produced in the market occupations so that it maps to real GDP following the method in Hsieh AA _et al._ (2019).

**6.1   Aggregate output and labor earnings** Let me begin by investigating the change in market output when I replace i , i ,2015(h) and i ,2015 with their prior values. A decline in market output will indicate that the changes contributed cc ,2015(ww) yy ττ ττ yy to economic growth and vice versa.<sup>18</sup> Thus, this exercise assists in answering two vital questions. First, zz which of the three channels is critical in explaining the aggregate growth over the last two decades? Second, has any channel provided headwinds to economic expansion? If so, how large has been this drag?

> 17 The robustness checks on the baseline results are relegated to the appendix.

> 18 A more intuitive approach will be to plug the 2015 values of barriers and preferences into a model calibrated to earlier periods. However, calibrating the model for prior periods requires information on wages and occupational data from the pre-1985 years. Unfortunately, the surveys do not go as far back for any country.

26
the debt maturity is longer or the debt rollover is lower. However, the difference from the benchmark case is not necessarily large because the long-term interest rates and debt rollover ratios in the benchmark case are already low. These results imply that modestly higher inflation, even if accompanied by some financial repression, could reduce the public debt burden only marginally in many advanced economies.

We focus on advanced economies and assume that a temporary inflation shock, such as a temporary overshooting of the inflation target, does not affect a central bank’s credibility and monetary policy framework.<sup>3</sup> We also assume that the inflation shock is not correlated with other types of shocks, such as growth shocks or fiscal policy shocks. With these in mind, we use a larger sample of 19 advanced economies compared with previous studies on the effects of inflation on public debt. Other features of our analysis include the combination of simulation and estimation approaches and consideration of financial repression cases in both approaches. Moreover, our estimation approach attempts to add more granularity and robustness relative to existing studies by distinguishing long and short- maturities, identifying the inflation shock in multiple ways, and running regressions at different frequencies.

The remainder of the paper proceeds as follows. Section II briefly reviews the literature on the simulation and empirical analysis of the effects of inflation shocks on public debt. Section III presents our simulation approach, and Section IV presents our estimation approach. Section V concludes.

# **II.   LITERATURE REVIEW**

Table 1(a) summarizes recent studies on the effects of inflation shocks on public debt using simulations. The closest approach to ours is Akitoby et al. (2014), where the effects of a persistent inflation shock that raises inflation to 6 percent for the next five years on the public debt-to-GDP ratio is simulated using the IMF’s _World Economic Outlook_ (WEO) data for G7 countries. Compared to them, we use updated WEO data and expand sample countries. We also take account of the
Many surveys ask for a reason for the item non-response. Both NIDS and Statistics South Africa household surveys allow “refusal” and “don’t know” answers to earnings questions. In the Statistics South Africa surveys, “don’t know” responses are overwhelmingly given by proxy respondents, who are answering on behalf of another household member. In some sense then, don’t know item nonresponse is similar to non-contact unit non-response, but it is a specific household member who cannot be contacted to obtain answers to some questions that another household member does not know the answer to.

Lepkowski (2005) and Si et al. (2023) highlight that item non-response for total individual or household income or wealth may be a combination of item non-response or response for several questions on each subject for the same person or household, making measurement of total income, consumption or wealth even harder. In South Africa wave one of the National Income Dynamics Study (NIDS) in 2008, 41% of households had at least one of a detailed set of consumption items missing (Finn et al. 2009). We discuss solutions to item non-response in the sections that follow.

# _Measurement error_

Even if individuals respond to a survey and to the questions on incomes, there is still a concern that the true income of household members is not accurately captured. The obvious example of measurement error when thinking about the top tail of the income distribution is under-reporting by respondents who do not want to reveal their true incomes, perhaps because of concerns that these will be reported to tax collection agencies (such data sharing is illegal in some countries). Other types of measurement error affecting the top tail could occur when survey questions ask about gross income, but respondents give after tax income or even after other deductions such as medical insurance or pension fund contributions (which could be important in the top tail even in low-income countries and more important in middle income countries).

It is hard to find direct evidence of under-reporting because this requires matched
age-friendly jobs (i.e. jobs that are more conducive for older workers) are also more desirable among women, who may prefer flexibility (Acemoglu et al. 2022). That aside, these findings generally suggest that in high-income countries, older and younger workers are not substitutes; instead, they are more likely to be complements (Kalwij, Kapteyn and De Vos 2010). For example, older workers may have job-specific knowledge resulting for their experience, while younger workers are yet to accumulate such experience but may be more technologically adept.

**Similarly, there is virtually no evidence of an adverse relationship between elderly employment and the labor market outcomes of younger persons in developing countries at the macroeconomic level.** In a sample of eleven Latin American countries (eight of which are developing countries),<sup>8</sup> Apella (2024) finds significant positive relationships between the employment of older persons and the labor market outcomes in terms of employment and wages of youth across genders. This analysis was conducted using a panel dataset that was built using household surveys from each of the countries. In their analysis of China, Zhang (2012) finds significant positive relationships between the employment rate of older persons and younger persons across genders. They also find some evidence of significant positive relationships between the employment rate of older persons and the income earned by younger men and women. This is consistent with Munnell and Wu (2013), who do not find any evidence that the employment of older

> 7 The 12 OECD countries are Belgium, Canada, Denmark, France, Germany, Italy, Japan, the Netherlands, Spain, Sweden, the United Kingdom, and the United States.

> 8 The 11 countries are Argentina, Bolivia, Brazil, Chile, Costa Rica, Ecuador, Mexico, Panama, Paraguay, Peru, and Uruguay.  Out of these, Chile, Panama, and Uruguay are categorized as high-income countries by the World Bank in 2023.

8
_Uganda’s roads are on average in better condition compared to several East African countries_ (figure 9) _._ Around 87 percent of Ugandan roads are in good or fair condition compared to only 72 percent of the roads in its low-income peers (see table 4 and figure 9). And when compared to its East African peers, Uganda boasts some of the best quality roads—although Uganda’s share of roads in good condition is relatively low, its share of roads in fair condition is exceptionally high.

**Figure 9. Main road network conditions in East Sub-Saharan Africa, 2005**

<!-- Start of picture text --> Uganda<br>Tanzania<br>Ethiopia<br>Kenya<br>Sudan<br>Eritrea<br>0% 20% 40% 60% 80% 100%<br>Percentage Main Road Network Length<br>Good Fair Poor<br><!-- End of picture text --> _Source:_ AICD Road Sector Database on Southern Sub-Saharan African countries.

_A number of recent efforts have aimed at strengthening road sector institutions_ . The Ministry of Works and Transport, which is responsible for policy formulation, leads and periodically formulates and updates the government’s National Transport Policy and Strategy (NTPS), which includes a Road Sector Development Program (RSDP). Following a long-term transitional arrangement that created the Road Agency in Formation Unit (RAFU), which is mostly externally funded, the government established the Uganda National Roads Authority (UNRA), which become operational in 2008. The UNRA is responsible for executing the road development and maintenance plans of national roads as well as projects financed by donors and development agencies. The Uganda Road Fund (URF) was legally established in 2008 as a Second Generation Road Fund and become operational only in 2010. The URF has private participation in its board and has been given the mandate to provide funding for maintenance on all roads. The legislation makes provision for road user charges
on how much of an exogenous revenue shock they convert into education spending. Appendix Table A.3 presents first and second stage IV results, which use the same excluded instruments. I compare this table with the baseline results. Once again, _θ_ 1 and _δ_ 1 are robustly negative. Comparing the coefficients _θ_ 1 and _δ_ 1 to the mean of education spending, and doing the same for the baseline results, one sees that the magnitude of the effects changes very little.<sup>43</sup> This supports the findings of the constrainedness sub-sample analysis.

This analysis also has limitations. Simulated revenue may affect public pre-primary investment through channels other than education spending, violating the exclusion restriction.<sup>44</sup> However, it is meaningful that both of these constrainedness analyses confirm the main results.

# **7.3 Correlates of inequality and income**

I treat inequality and median income as broad concepts that summarize important dimensions of society. However, my findings may be less interesting if income and inequality are simply picking up the effect of some variable correlated with them that also conditions how revenue is spent. For example, they may be correlated with higher initial enrollment in public pre-primary (‘saturation’ from the start, and less room for growth), or with urbanization (creating more economies of scale in provision, and thus more room for enrollment growth).

To ensure that this is not the case, I individually enter four new interactions with revenue into the model: the 1997 enrollment rate in public pre-primary education, and the year 2000 fraction of the municipality that was urban, labor force participation rate, and index of racial fractionalization.<sup>45</sup> Due to problems of weak instruments, I cannot enter all of these in the same specification. However, as Appendix Table A.4 shows, their individual inclusion has negligible effects on the main point estimates and their statistical significance. My findings seem to come from what income and inequality
A. Sanghi and D. Johnson <!-- Start of picture text --> 600<br>ACTION RELATING TO DEBT<br>400 ECONOMIC INFRASTRUCTURE AND SERVICES<br>Education<br>Health and Population<br>HUMANITARIAN AID<br>MULTISECTOR<br>Other social infrastructure and services<br>200 PRODUCTION SECTORS<br>PROGRAMME ASSISTANCE<br>UNALLOCATED/UNSPECIFIED<br>0<br>Country<br>ODA (USD Millions)<br><!-- End of picture text --> Figure 31: Kenya receives the most aid in health and populations services (China excluded) (2013) Source: OECD DAC aid database 2015

# **4.2 Most Chinese financing does not qualify as ODA**

The OECD Development Assistance Committee (DAC) calls export credits and loans with a less than 25 percent grant element other official flows (OOF); much of China’s financing overseas would fall under this category. The Chinese development bank, for example, created an China Africa development fund which provides equity investment capital. The ministry of finance also subsidizes the Exim bank’s preferential export credits and foreign aid loans, paying the difference between the interest rate and the actual cost of the loan. 97 percent of Chinese Exim Bank financing is in suppliers’ credits and other official flows, and the remaining three percent is in foreign aid (Br ̈autigam 2011). China gives most of its official financing at competitive commercial rates with a maturity of 12 to 15 years and a grace period of two to five years (Br ̈autigam 2011).

## **4.2.1 China loans the most to ministries of energy and petroleum and transport and infrastructure**

China’s aid to Kenya is almost entirely loans. The government of China gave a total of KSH 20.6 billion (US $200.6 million) in loans for development expenditure for the fiscal year ending in 2015,
# Abstract

This paper uses a large cross country data set to look at the factors explaining the size of government and the consequences of government for income growth and other measures of wellbeing, such as infant mortality and life expectancy. We find no robust link between government size and per capita income. Relative prices, the age dependency ratio, years of independence, political freedom and openness are found to be important explanatory factors behind growth in government. Our results partially support the view that governments use consumption to buffer external risk, particularly in low income countries. With respect to the consequences of government for growth, we find a robust negative association with government consumption and with an index of policy distortions and a positive relationship with quality of bureaucracy. Finally, we find that social sector spending can exert a positive influence on infant mortality and life expectancy.

JEL classification: 047, Hil 1, I31 2