rafmacalaba/datause-encoder-data huggingface dataset:
sociological perspective (see, for instance, Ndiaye and Tidjani, 1995). The available economic studies, in turn, are based on quite small samples. The interesting thing, however, is they all find that union members earn less than similar, nonunionized wage earners.

The available estimates of the union wage premium in CFA countries are reported in Table 8. The figures in this table indicate the percent change in earnings an individual with "average" characteristics would experience by shifting from a non-unionized to a unionized activity. These figures are based on Mincerian equations which include a dummy variable for unionization among their explanatory variables. The fact that union wage premia are negative suggests that union members may get other, non-wage benefits that compensate for the lower earnings. But this is also the case in other countries, while the negative premium is highly atypical from an international perspective. In developing countries, union members usually make 5 to 30 percent more than their non-unionized counterparts.

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Therefore, it is unlikely that changes in foreign demand are correlated with the _distribution of unobserved factors_ that _differentially_ drive changes in local labor markets.

As shown in the bin-scatter below, a visual representation of the first-stage regression, changes in exposure to exports growth strongly correlate with changes in exposure to foreign demand shocks. The F-statistic is 1,545, so the instrument is extremely relevant. If the instrumentation strategy is valid, then the instrument satisfies the exclusion restriction. In that case, we can interpret the results in this section as the causal dynamic effect of exports on local labor markets in Indonesia.

**Figure 4.1: Indonesian Districts’ Labor Market Exposure to Merchandise Exports** (binscatter that partitions the support of the x-axis into 100 bins; x-axis: regional exposure to foreign demand growth, in pct change; y-axis: regional exposure to export growth, in pct change)

<!-- Start of picture text --> .6<br>.4<br>.2<br>0<br>-.2<br>-.4 -.2 0 .2 .4<br>Regional Exposure to Foreign Demand Growth, pct change<br>The underlying regression has n = 8,558, with unconditional ß = 0.697 and t-stat = 39.31<br>Regional Exposure to Export Growth, pct change<br><!-- End of picture text --> Source: Authors’ calculations with data from Sakernas and UNCOMTRADE.

# **5. Main results**

The results in this section should be interpreted in **relative terms,** as in a difference-in-differences estimator **.** Below we plot the results of a given region being relatively more exposed to exports growth (as instrumented by foreign demand shocks) over time, in other words, the causal dynamic treatment effect of exports on those outcome variables.

In each Figure we present here, the red line shows the coefficients βh, estimated using two-stage least squares, as explained in the methodology section. The confidence interval shows 95 percent confidence bands with standard errors clustered at the district-level.

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business environment. Year-fixed effects will capture global shocks affecting finance and growth, such as the effect of the recent financial crisis that is common for all countries in the same quarter. Thus, PVAR is a methodology that is well-suited to the questions this study aims to address.

Our main findings can be summarized as follows. Applying PVARs to a global panel that consists of quarterly data for 41 countries in ECA, Asia, Latin America, and the Middle East and Africa for the period 2000-2011, we document that private credit growth is highly sensitive to cross-border funding shocks around the world, and particularly sensitive in the average ECA country. Moreover, we show that countries with high loan-to-deposit ratios or high reliance on foreign funding exhibit a stronger response of private credit to foreign funding shocks. Higher loan-to-deposit ratios make banks more vulnerable to general wholesale funding shocks while high reliance on foreign funding specifically signifies sensitivity to foreign shocks. At the same time, we also show that foreign ownership _per se_ does not explain the different credit responses during the crisis in our sample of countries. These findings are robust to additional sample splits designed to disentangle the differential impact of ownership and funding structures on credit growth in ECA and non-ECA countries. Taken together, our findings suggest that funding model differences with the rest of the world were at the heart of ECA’s protracted post-crisis credit growth contraction, not its high prevalence of foreign bank ownership.

These findings have important policy implications. Rather than trying to curtail and scale back foreign bank presence, this paper suggests regulators ought to focus on business models of local affiliates of international banks and the regulation of cross-border funding. Indeed, Western European banks are already adjusting their business models by deleveraging, derisking, shifting to local, more stable sources of funding, and reducing loan-to-deposit ratios (e.g. Impavido, Rudolph and Ruggerone (2013), Raiffeisen (2013), and Unicredit (2013)). However, eradicating cross-border funding altogether and promoting a fully domestically funded subsidiary model also appears suboptimal as it may lead to costly pockets of inert liquidity and capital within the banking group. This raises various regulatory issues which we will discuss in our concluding 5
Fankhauser, S, Sehlleier, F, and N Stern (2008): _Climate change, innovation and jobs_ , Climate Policy, Vol. 8, pages 421-429.

Fields, GS (1975): _Rural-urban migration, urban unemployment and underemployment, and job search activity in LACS_ , Journal of Development Economics, Vol. 2, pages 165-187 Fullerton, D, and G Heutel (2007): _Who bears the burden of a tax on carbon emissions in_ _Japan?_ Environmental Economics and Policy Studies, Vol. 8, pages 225-270.

Fullerton, D, and G Heutel (2010): _Analytical General Equilibrium Effects of Energy Policy on Output and Factor Prices_ , NBER Working Paper No. 15788, February.

Fullerton, D, and GE Metcalf (1997): _Environmental taxes and the double-dividend_ _hypothesis: did you really expect something for nothing?_ NBER Working Paper No. 6199, September.

GCN (2010): _Low-carbon Jobs in an Interconnected World_ , Global Carbon Network discussion paper No. 3 and literature review, March.

GHK (2009): _The Impacts of Climate Change on European Employment and Skills in the Short to Medium-Term: A Review of the Literature_ , Final Report to the European Commission Directorate for Employment, Social Affairs and Inclusion Restructuring Forum, Vol. 2, May.

Goettle, RJ, and AA Fawcett (2009): _The structural effects of cap and trade climate policy_ , Energy Economics, Vol. 31, Special Supplement 2, pages 244-253.

Greenpeace/EREC (2011): _The Advanced energy [R]evolution: a Sustainable Energy Outlook_ _for South Africa_ , Greenpeace and the European Renewable Energy Council, May. Harris, JR, and MP Todaro (1970): _Migration, unemployment, and development_ , American Economic Review, Vol. 60, pages 126-142.

Hatfield-Dodds, S, Turner, G, Schandl H, and T Doss (2008): _Growing the green collar_ _economy: Skills and labour challenges in reducing our greenhouse emissions and national environmental footprint._ Report to the Dusseldorp Skills Forum, CSIRO Sustainable Ecosystems, Canberra, June.

Hughes, G. (2011): _The myth of green jobs_ , The Global Warming Foundation Report No.3, London.

IEA (2008): _World Energy Outlook 2008_ , International Energy Agency, Paris. ILO and CEDEFOP (2011): _Skills for green jobs: a global view_ , ILO, Geneva.

Kammen, DM, Kapadia, K, and M Fripp (2004): _Putting renewables to work: how many jobs_ _can the clean energy industry generate?_ Report of the Renewable and Appropriate Energy Laboratory, University of California, Berkeley, April (corrected January 2006).

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industry demand curve will be downward sloping, making the domestic<sup>price</sup> endogenous from the viewpoint of the industLy. Allowing for the endogeneity of<sup>the</sup> domestic price requires a model of the domestic demand for the output of<sup>the</sup> manufacturing sectors. Thus, we model jointly firm output supply and input demand functions and domestic demand for domestically produced goods.

The short-run focus of this study is motivated by methodological<sup>as well as</sup> policy considerations. First, analyzing the long run response implies treating the'<sup>capital</sup> stock as endogenous and modeling investment behavior and credit markets. This<sup>task is</sup> further complicated by difficulties in measuring the price of capital services. Second, from a policy point of view, the uncertainty associated with this particular policy<sup>reform</sup> in C6te d'Ivoire suggests that a sensible approach is first to evaluate the immediate short-run effects.

On the supply side, our empirical model follows closely the production theory framework of Diewert and Morrison (1988) (see also Kohli, 1978). The major<sup>new</sup> element that we introduce to their framework is the modeling of the demand side. Recent studies such as Zilberfab (1980), Aspe and Giavazzi (1982), and Faini (1988) have discussed the role of domestic market conditions and relative prices in modeling export supply, but have not treated domestic prices as endogenous and identified empirically the influence of exogenous export and import prices on domestic<sup>prices.</sup> Our work also differs in that we use sector-level data aggregated from individual<sup>firm</sup> records, whereas Diewert and Morrison use aggregate data. We also allow<sup>for non-</sup> constant returns to scale.

We compare results from a model that estimates only the supply side with<sup>one</sup> that estimates jointly supply and demand. We simulate the model to yield<sup>domestic</sup> supply and export supply elasticities, as well as measures of the sensitivity<sup>of the</sup> domestic price and domestic demand to exogenous variation in export and<sup>import</sup> prices. The empirical results are very sensitive to the assumption regarding the endogeneity of the domestic price and to the inclusion (or exclusion) of the demand function in the model. Not allowing for domestic prices to change and affect<sup>the</sup> relative profitability of exporting versus selling domestically leads to a large bias<sup>in the</sup> estimate of the export elasticities.

The main and somewhat surprising result is that manufacturing producers<sup>in</sup> C6te d'Ivoire are able to expand their exports in the short run in response to an increase in export prices. However, most of this expansion comes at the expense of sales in the - 3 -
We have that the simple or un-weighted average tariff in year t is: The weighted average tariff is:

# **Tariffs for the the Years 2011-2020—Intuition for the Mathematics**

We have to calculate the ad valorem equivalents of the specific tariffs in future time periods and also calculate trade weights of tariff lines for the purpose of the trade weighted average. We need trade data for both of these requirements. Since trade data are unavailable after June 2011, we make two assumptions. First, we assume that the average of the trade weights from 2009, 2010 and the first half of 2011 apply in future time periods. Rather than use the most recent year of available data, we call this 2.5 year period our base period for the calculation of tariffs in later periods. We take the import statistics for 2009, 2010, and the first half of 2011 in order to reduce yearspecific shocks when computing the tariff projections for 2011-2020.

Second, we must also deal with the complication that new tariff lines appear in the years 2011-2020 that did not exist in the base period. For example, comparing the tariff codes of 2010 with those of January 1, 2012 (our classification for 2011) , 8,907 tariff lines were identical, but there were 2218 new tariff lines in the tariff classification of January 1, 2012 that did not exist in 2010. For 2020, 10,351 tariff lines were identical to those in 2010, but 1,206 did not match any tariff line in 2010 due to changes in classification.

For ten-digit tariff lines in the years 2011-2020 that are identical to tariff lines in our base period, calculation of the AVE of the specific tariff is relatively straightforward. We call these ―matched‖ tariff lines. We compute the tariff rate on tariff line h by averaging over all countries from which there are positive imports in all three time periods, 2009, 2010 and 2011*. This is analogous to equations 1 and 2.

For tariff lines h in the years 2011-2020 with no match in the base period, we look for a match at the eight-digit level in the base period. If there is no match at the eight-digit level, we look for a match at the six-digit level; in a few cases, we had to find a match at the four digit level. Our calculations, however, are always based on ten-digit data. So if there is a match at the eight-digit level, we use all ten-digit tariff 9
Another stream of the literature has exploited micro-data to investigate the effect of various shocks, such as conflict and weather conditions, on multiple socio-economic characteristics. Several studies document how adverse weather conditions reduce agricultural incomes and can push households into poverty. Hill and Mejia-Mantilla (2017) describe the negative effects from droughts in Uganda. Porter (2012) finds that weather shocks reduce consumption in the long run among rural households in Ethiopia. Similarly, Hill and Porter (2016) conclude that in Ethiopia consumption declined by 9% in rural areas due to a moderate drought. For the case of Somalia, Pape and Wollburg (2019) estimate an increase in poverty of 9 percentage points among rural households attributed to a drought shock.

In terms of the drivers of conflict, Dube and Vargas (2006) use municipality-level data in a difference-in-difference framework to understand the dynamics of Colombia’s civil war. They find a negative relationship between coffee prices and the incidence and intensity of conflict, while a positive relationship between oil prices and violence. Besides, large evidence supports the adverse impact of conflict-related violence on welfare conditions. Mercier, Ngenzebuke, and Philip (2016) use household-level panel data for Burundi and conclude that exposure to violence condemns vulnerable households into chronic poverty. Hill and Mejia-Mantilla (2017) find a negative effect from conflict and prices on poverty in Uganda. In South Sudan, Müller, Pape, and Ralston (2019a) investigate the effects of conflict-induced cancellation of programs on their designated beneficiaries and describe the welfare status of households displaced by violence. In the same country, Pape and Phipps (2018) analyze the impact of conflict on the socio-economic and psychosocial well-being of teenage girls, including on income opportunities, aspirations and marriage. Other authors provide evidence on how conflict increases the likelihood of chronic poverty due to disruption of income-generating activities and depletion of infrastructure and basic services (Bratti, Mariapia, and Alfonso 2009; Bozzoli and Brück 2009; Bozzoli, Brueck, and Muhumuza 2016).

# 3. Empirical analysis

The World Bank implemented Wave 1 (2016) and Wave 2 (2017-18) of the Somali High Frequency Survey to better understand livelihoods, vulnerabilities and poverty across 6
Figure 8: Coefficient plot of PISA-based people management index x intermediate outcomes, by country <!-- Start of picture text --> Teacher Shortage Index Teacher Motivation Index<br>COL CHL BRA URY PER ARG MEX CRI ARG COL URY PER BRA MEX CHL CRI<br>Teacher Effort Index<br>ARG COL URY MEX CRI CHL BRA PER<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>-.2 -.2<br>-.4 -.4<br>Coefficient on people management Coefficient on people management<br>.6<br>.4<br>.2<br>0<br>-.2<br>-.4<br>Coefficient on people management<br><!-- End of picture text --> Notes: Each marker represents the coefficient (and vertical spike represents the associated 95% confidence intervals) from regressions of the people management index on the intermediate school outcome indices for each country in Latin America. The people management index is built out of the school questionnaire from PISA 2012 using the methodology from Anderson [2008]. All specifications include country fixed effects, school controls and PISA _school final weights_ . School controls include school location, student-teacher ratio, log of the number of students, share of government funding relative to total school funding, ratio of computers connected to the web as a proxy for school resources, and average student socio-economic status. For control variables, missing variables are replaced with a value of -99 and we include an indicator variable with a value of 1 for each imputed value, for each variable with imputed values dummies are added to the specifications.

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**Figure 5: Effect of Tariff on: (a) Consumer/Producer Surplus, and (b) Price**

<!-- Start of picture text --> 1,401.60<br>1,192.50 1,189.50 1,187.90 1,187.90<br>0 1 5 10 15<br>% Tariff<br>CPS US$Mn<br><!-- End of picture text --> <!-- Start of picture text --> 856.69 860.1 861.8 861.8<br>624.11<br>0 1 5 10 15<br>% Tariff<br>US$/ton<br><!-- End of picture text --> Source: Based on model simulation In summary, what we observe from a policy to increase tariff in line with the ECOWAS position is, on the one hand, a reduction in the sum of consumer and producer surplus from the baseline level, but on the other hand, a narrowing of the trade balance and an increase in employment in the rice sector. This outcome assumes that labor is mobile, including between ecologies and that farmers who increase their acreage of rice beyond the level that can be supported by the available family labor will be able to find temporary labor to hire at the current wage.

# _Minimum wage increase simulation_

Since Sierra Leone has a minimum wage, which was established by the Regulation of Wages and Industrial Relations Act No. 18 of 1971, and the said Act provides for a review of the minimum wage every two years, it is useful to understand the impact of an increase in the minimum wage on the rice sector. Since job creation is also an important issue for the government, the model is used to: First, simulate the impact of a 10 percent increase in the minimum wage, keeping other things the same. Second, simulate the impact of a 10 percent increase in the minimum wage, assuming a 1 percent tariff on imported rice and, finally, simulate a 10 percent increase in the wage rate under the assumption of a 5 percent tariff on imported rice. The results of the simulations are shown in Table 13.

Increasing the minimum wage under a zero-tariff regime has very little impact on the employment in the rice sector, in large part because much of the labor are from farm-owning households and there are no temporary workers hired by farms.

Increasing the minimum wage by 10 percent under a 1 percent tariff regime, changes both the labor and rice market dynamics. Household labor falls by 1 percent and the demand for temporary labor drops by nearly 6 percent on account of the higher wages. The reason for the former is less clear. But one plausible explanation, in keeping with labor market theory, is that the 10 percent increase in wage would meet the reservation wage threshold of some household-farmers, who would then be willing to hire themselves out, including as temporary labor in other non-farm sectors. Importantly, the higher cost of labor pushes 23
East Asia did not have high inflation or a high black market premium in the previous period.

On balance, the policy changes in Latin America relative to East Asia correctly predict that Latin America's growth should have risen relative to East Asia's in 1990-93. The strongest effect comes from Latin America's reduction in inflation, with another strong effect from its reduction in the black market premiumm Another important. non-policy determinant of growth is the change in initial level of income, which is strongly positive in East Asia and close to zero in Latin America. According to the standard convergence result, the increase in initial income was a disadvantage for East Asia in 1990-93.

Adding all effects together, our estimated equation explains most or all of the fall in the East Asia - Latin America growth differential. Our resuhs suggest that there is nothing "miraculous" about the relative growth performances of East Asia and Latin America during the period of Latin American reform. Latin America's growth performance is _not_ falling short relative to the degree of policy change, even compared to the "miracle" economies of East Asia. If Latin American reform did not close the gap with East Asia even more strongly, it was because financial depth, openness, and investment were also improving in East Asia.

# IV. Conclusions

The response of economic growth to reforms in Latin America has not been disappointing. Latin American growth has responded to changes in policy variables as would have been predicted by the experience of other times and places, as summarized by a panel regression spanning a large number of countries and (mostly) 5-year periods from 1960 to 1993. Part of the perception of disappointing growth in Latin America in the 1990s mav really be reflecting the disappointing growth in the world as a whole, since the 1990s have registered poor growth rates in all countries on average, 18
**<u>Figures & Tables</u>**

**Figure 1: Diversification & Innovation**

<!-- Start of picture text --> Diversification Discoveries Patents<br>0 5000 10000 15000 20000 25000 30000 35000<br>1995 GDP Per Capita (PPP)<br>Source: Author’s Calculations<br>Predicted Value / Sample Maximum<br><!-- End of picture text -->

**Figure 2 Effects of Returns on Discovery: Non-Linear Effect of Barriers**

<!-- Start of picture text --> Lower Bound<br>Point Estimate<br>Upper Bound<br>1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35<br>Sub-Sample Rank (low to high barriers)<br><!-- End of picture text --> Source: Author’s Calculations. Dashed lines indicate 90% confidence interval 15