rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

_“One by one, the factories shuttered and left our shores, with not even a thought about the millions upon millions of American workers left behind”_

- President Trump, Inaugural Address, 2016

In 2018, after many decades of focus on lowering barriers to international trade, the US embarked on a wave of protectionism, unilaterally raising tariffs on imports from many of its major trade partners. Tariffs were levied in several rounds, covering approximately 12 percent of total US imports by the end of 2018. One of the central justifications offered for this unprecedented shift in trade policy was boosting the competitiveness of the US manufacturing sector and protecting US manufacturing jobs. This was a common theme during the Trump Presidency, as is reflected in statements such as that listed above and the promise that ‘we will bring back our jobs.’<sup>1</sup> In response to the US tariffs, from April 2018 onward, the US’s main import partners, starting with China, then followed by Mexico, Turkey, the European Union, Canada and Russia, all levied retaliatory tariffs on US exports. By the end of 2018, these had covered approximately 8 percent of the US’s total exports.<sup>2</sup> While many of the impacts of this trade war, such as on trade flows, prices and consumption, have now been well documented, research on the labor market consequences of the trade war remains more scarce. Did these tariffs really achieve the goal of improving job opportunities for US workers? In this paper, we aim to shed light on this question by studying how the trade war affected the posting of online job adverts during the immediate aftermath of the tariff hikes in 2018. In contrast to official employment statistics, which capture both labor demand and supply-side factors and provide only total employment counts, online job adverts provide an almost real-time proxy for firms’ intentions to hire and include comprehensive and rich information on the characteristics of jobs on offer. This allows us to cleanly pin down the link between trade policy and labor markets.

We investigate three key ways in which the trade war could have affected online job postings in the US: (i) the impact of protection for US firms from import competition (‘output tariff exposure’), (ii) the impact of the higher cost of imported inputs for US producers (‘in-

> 1https://www.whitehouse.gov/briefings-statements/the-inaugural-address

> 2A detailed overview of the US Trade War timeline is provided by Bown and Kolb (2019), and Crowley, ed (2019) presents a discussion of key elements of the conflict, including its origins, costs, and the challenges it poses for the future.

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to alleviating food insecurity (Smith et al., 2017a; 2017b) and the role of local decentralized governance structures.

# **4. Conclusion**

Trust in institutions is crucial during crises like the COVID-19 pandemic since a lack of trust can impede governments’ responses and slow emergency and recovery efforts (OECD, 2013; Bavel et al., 2020; Siegrist and Bearth, 2021). Interpersonal trust is a public good (Ostrom, 1997) based on people’s willingness to follow norms and follow institutional health guidelines to prevent pandemics (Johnson et al., 2020). Trust in friends and family is essential for risk-sharing and social networks that protect against shocks, especially in developing countries (Townsend, 1994; Ambrus et al., 2014; Barrett et al., 2022).

Crime, corruption (Clausen et al., 2011), and inequality are well-studied trust factors, but food insecurity is not. We fill this gap in the literature using data from the 2014–2017 Gallup World Poll Survey. In a global sample of 387,385 adults from 134 countries, food insecurity is negatively associated with both vertical (trust in national institutions) and horizontal (trust in friends and family) measures of trust. The probability of trusting in national institutions is 2.7 percentagepoints lower among the food insecure. The probability of trusting in friends and family is 11.7 percentage-points lower among the food insecure. The magnitude of the association of food insecurity with trust in friends and family is larger than any other explanatory variable. These relationships also vary significantly by GNI per capita country rankings. As a robustness check, we employ an entropy matching strategy to control for the possible endogeneity of food insecurity. Results remain robust and sensitivity analyses using other measures of trust support our results.

Several limitations are worth mentioning. Since the data are cross-sectional, we are unable to control for unobserved individual heterogeneity and thus some hidden bias may remain. While a robustness check confirms that food insecurity is not mediated by poor health or subjective wellbeing, other unobservable factors may bias the results. Thus, the estimates are correlational and only reservedly causal under strong assumptions. Additionally, the survey questions on trust could be defined more precisely. GWP respondents are asked about the confidence they have in national government, without first defining “national government.” Some respondents might equate 18
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

# **Estimating the Capital Stock: Sensitivity to Initial Capital and Depreciation**

We followed a conventional approach in the literature in using the “perpetual inventory” estimates of capital stock based on accumulated gross capital formation (investment) adjusted for depreciation. Such estimates are sensitive to the assumptions employed.

We assumed that gross capital formation was available to facilitate production within the recorded year. Investments in upgrading agricultural land are typically made just prior to planting, so the benefit of the capital stock is enjoyed in the same year. Vehicles and machinery are similarly employed right away.

We assumed a depreciation rate of 10%. Others sometimes assume 6% or 8%. We argue for the 10% rate in this case on the grounds that capital investments made in a developing tropical agricultural country in general, and Cote d’Ivoire specifically, depreciate quickly. Tropical arable land will return to forest quickly if not rigorously maintained. Rural building of local construction similarly depreciates quickly, unless kept up. Machinery and vehicles are worked very hard and so also wear out quickly.

The basic algorithm used here was that current physical capital stock equals last year’s capital stock less depreciation of 10%, plus this year’s gross capital formation. Some analysts use the assumption that investment takes time to mobilize in production, and update last period’s depreciated capital with last periods gross capital formation, when computing current period capital stock.

For presenting descriptive statistics we used data from SIMA/DDP database and worked in constant US dollars per capita, to facilitate reader’s comparisons with the statistics of other countries outside the CFA zone. For Figure 10 and for Figure 11 in the main text above, we assumed an initial per worker capital stock of US$ 2,200 in 1995 dollars. For constant CFA, this amount was translated at 240 CFA per dollar. For the productivity regressions, as is common in the literature, we used constant local currency units under the argument that producers make their production choices based on local not world prices. (For CFA Franc countries the differences between dollar prices and local currency unit prices are not very large) Next, we test the initial capital and depreciation rate assumptions in constant 1995 CFA Francs. Figure A-2 shows the profile of the capital stock assumption over time for the baseline, and then with an initial capital stock reduced by the CFA equivalent of $1,000, and then increased by $1,000. Varying initial capital stock by almost 50% has amazingly little impact on the time profile of capital stock over time, and no discernable impact after ten or twelve years. Part of this may be sue to the fact that the baseline assumes a 10% depreciation rate.

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Regarding the impacts of transport accessibility on agricultural production, the main results from the SPTobit models are shown in Table 2. Despite the autocorrelation discussed above, these are considered unbiased and consistent. The estimated elasticities vary across areas and crops but are broadly consistent with our prior expectation: Both rural accessibility and market access are important factors in stimulating agriculture production. Figures 11 and 12 depict the predicted changes in each crop production under the assumption that each element of transport accessibility is improved by 10 percent.<sup>11</sup> Recall that while our rural accessibility is measured by the distance to the nearest road (km), market accessibility is measured by the economic transport cost ($ per ton) to the nearest city or port, depending on the type of crop.

Our estimation results suggest that market accessibility is generally more important than rural accessibility. Particularly, for export crops, access to maritime port is found to be crucial: In some cases (e.g., coffee in the landlocked countries, tea in Kenya and tobacco in Tanzania), a 10-percent reduction in transport costs to the maritime port could boost export crop production by more than 10 percent. These are considered as high-yield crops from the infrastructure investment point of view.

Notably, however, this does not underplay the importance of rural accessibility. For instance, there is no evidence showing that banana production is constrained by market access in Burundi, Kenya, Rwanda and Uganda. But improving rural accessibility would likely result in some increases in banana production. While a 10-percent rural accessibility improvement could increase coffee production by 0.5 percent in Tanzania, the same amount of improvement in port access could increase the production by 4 percent. Thus, both are important to improve, and the relative importance varies across countries.

> 11 The predicted changes are shown only in case the estimated elasticity is statistically significant. In several cases, the predicted impacts are counter-intuitive. For instance, the evidence implies that an improvement in rural accessibility would result in less production of tobacco. This may be interpreted to mean that rural roads are well developed in the tobacco producing area of Kenya. At the same time, there may be a statistical issue that over- or under-estimate the impact, particularly when a large number of observations are censored, as in the case of tobacco in Kenya.
Figure 6: Patterns of economic mobility in Armenia (upper national poverty line) Panel A: 2004 to 2008 Panel B: 2010 to 2015 <!-- Start of picture text --> 45% 60%<br>39.6% 52.2%<br>40%<br>34.2% 50%<br>35%<br>30% 40%<br>25%<br>30% 24.4%<br>20%<br>14.0%<br>15% 12.2% 20%<br>12.0% 11.4%<br>10%<br>10%<br>5%<br>0% 0%<br><!-- End of picture text --> _Source: Authors’ calculations based on ILCS 2004, 2008, 2010 and 2015._ In contrast, during the period between 2010 and 2015, economic growth suffered from the negative impact of the global economic crisis and stayed below potential due to difficult external conditions in the Russian Federation. Still, even in the period of overall low dynamism and characterized by low net poverty reduction, there was significant churning around the poverty line with 24.4 percent of the population escaping poverty and about 11.4 percent falling below the poverty line. Like in the earlier period, a relatively small fraction of the population was monetary poor in both periods (12.0 percent). At the same time, slightly more than half of the sample (52.2 percent) managed to remain above the poverty line in both periods.

Patterns of economic mobility during the pre-crisis and the post-crisis period highlight the opportunities but also the vulnerabilities of households to poverty. For both time intervals the share of households left behind has been relatively small, and a significant share of the sample was able to escape poverty over a four (five) year time window.<sup>14</sup> However, poverty reduction does not seem to be a one-way street and over the same time period the share of the population falling below the poverty line is high. In particular, one concrete implication of the results in Figure 6 is that about half of those who were poor in 2015, were actually non-poor in 2010.

> 14 The literature uses the term chronic poor in different contexts. Here, the term “chronic poor households” refers to the group of the population which lived in poverty in the initial and the final period under consideration (see Vakis et al. (2016)).

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exogenous for the sample of poorer countries whose changes in IPR regime are more exogenously induced. We rerun the exercise using just countries whose distance from the frontier are below those of Mexico in 1996-2000 and the results doing this yield patterns very similar the left side of the graphs above with declining returns with distance from the frontier.

# **4.4 Controlling for labor quality**

One explanation for the upward trend in the coefficient on labor, particularly as we near the frontier, is that the quality of labor is increasing across level of development. This suggests the potential importance of incorporating education into the analysis. We create an augmented labor variable by controlling for years of education, drawn from Barro and Lee (2001). We then follow Caselli (2005), who in the tradition of Hall and Jones (1999), augment the labor factor according to its quality (understood as the average human capital) so that labor now enters as _hL_ where _h_ = _exp_ ( _φ_ ( _s_ )), _s_ is the average years of schooling, and _φ_ ( _s_ ) a piecewise linear function with varying slopes.<sup>13</sup> We re-estimate equation 6 with the augmented labor variable. In the parametric results (not shown), for both gross and net R&D, labor growth is now significant in all specifications. Returns to physical investment remain broadly unchanged. R&D loses significance in several specifications, however, where significant, the magnitudes are very similar to the previous case. As documented earlier, however, parametric specifications hide important variance across level of development. Figures 8a and 8b are the corresponding analogues to Figures 6a and 6b respectively. We immediately note that the marked upward trend in the coefficient on labor growth among advanced countries has now been eliminated, and now there is no obvious trend across distrance from the frontier. The upward trend in the coefficient is due to the higher quality of labor. The pattern of physical investment is similar to before. The coefficient on initial output changes little with, again, a discrete fall around -1.5.

> 13 _φ_ ( _s_ ) = 0 _._ 134 _s_ if _s ≤_ 4, _φ_ ( _s_ ) = 0 _._ 134 _×_ 4+0 _._ 101( _s−_ 4) if 4 _< s <_ 8, _φ_ ( _s_ ) = 0 _._ 134 _×_ 4+0 _._ 101 _×_ +0 _._ 068( _s−_ 8) if 8 _< s_ .

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households on the basis of census data without any community involvement, was more effective at targeting poor communities than at targeting poor households within them.

A series of studies on social funds, which have become a popular mechanism for public service delivery, also provide evidence on the targeting performance of antipoverty programs. In a typical social fund, community infrastructure is built with local participation in the selection and management of facilities. In most cases, community representatives send project proposals to a central public agency, which allocates funds on the basis of such criteria as extent of community involvement, community capacity for collective action, and other factors affecting the feasibility of the proposed project. Social fund rules almost always require that funds be targeted to poor communities, in accordance with specific targeting criteria, and often require some cofinancing from fund recipients. Most social funds restrict the menu of feasible projects to a limited number of public goods, usually schools, clinics, roads, and water and sanitation facilities.

Paxson and Schady (2002) assess poverty targeting in the Peruvian social fund Foncodes using district-level data on expenditures and poverty. They find that the fund, which emphasized geographic targeting, successfully reached the poorest districts but not the poorest households in those districts—better-off households were slightly more likely than poor households to benefit. Using propensity score matching, Chase (2002) compares targeted communities in the Armenia Social Fund with communities that had not received projects but were in the pipeline for them. He finds that while the social fund was targeted to areas with the poorest infrastructure, these were not always the poorest 21
between bank-based and market-based systems. Finally, regarding firms' characteristics, the results suggest that tradable producers bias their maturity structure to the short-term. There are no differences between bank-based and market-based economies, except that tradable producers have a higher level of internal financing in market-based systems.

Access to bond markets increases long-term debt and extends the maturity structure of debt, both in market-based and bank-based financial systems. Results do not only capture differences between firms, but also within a given firm. Companies with access to bond markets seem to react in the same way in bank-based and market-based financial systems. In bank-based systems, the **OLS** and IV estimates show that firms that access international bond markets also increase their debt-equity ratios, suggesting that they are not just replacing bank debt with bonds. The results suggest that bank-based systems seem to be liquidity constrained, given that firms increase their leverage as they access to international bond markets. Also, _within_ regressions show that, in market economies, firms with access reduce internal financing.

The financial liberalization variable is negatively associated with both short-term and long-term debt in market-based economies. However, the maturity structure moves to the short term and internal financing increases.

Regarding the crisis variables, the Mexican crisis does not have sizable effects on capital structure, except that the maturity structure increases in market-based systems. During the Asian crisis, market-based economies were affected first, in **1997,** with increases in most leverage ratios. During **1998,** both systems were affected. However, bank-based economies were able to increase the maturity structure of debt. Most probably, the increase in interest rates during the crisis is behind higher debt-equity ratios. Short-

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|**Table 16. Oil Prices and**<br>|**Fiscal Balances of F**<br>**Fixed**<br>|**uel Exporters in First Di**<br>**Effects**<br>|**fferences with Country**<br>|
|---|---|---|---|
|Dependent variable:|Change in fiscal<br>balance/GDP (%)|Change in total<br>revenues/GDP (%)|Change in total<br>expenditures/GDP<br>(%)|
|Change in ln (real $|15.90***|12.17***|-3.36|
|oil price)|(5.18)|(4.72)|(-1.67)|
|Change in ln (real $|-0.407|-0.918|-0.358|
|oil price) if > 0|(-0.24)|(-0.79)|(-0.25)|
|Political stability|0.0212|0.0272|0.0099|
|(0 to 100)|(0.52)|(0.82)|(0.22)|
|Time trend|-0.0.0673|-0.0180|0.0503|
|(=0 in 2000)|(-1.12)|(-0.38)|(0.93)|
|n|328|336|328|
|No. of countries|33|33|33|
|RMSE|5.07|4.50|4.38|

_Note_ : Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries with fuel >25 percent of total exports. Country fixed effects are included in the regression. RMSE = root mean square error, or sample standard deviation.

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# **Firm Dynamics, Job Outcomes, and Productivity: South African Formal Businesses, 2010–14**<sup>1</sup>

Reyes Aterido, World Bank Ayanda Hlatshwayo, National Treasury of South Africa Duncan Pieterse, National Treasury of South Africa Andre Steenkamp, National Treasury of South Africa Keywords: Job creation; productivity; efficiency of allocation; firm dynamics JEL codes: D22, D24, D61, E24

> 1 The authors would like thank the National Treasury of South Africa and the South African Revenue Service for providing access to the data for this study. Marek Hanusch and Sara Nyman provided valuable comments.
# **A. Introduction**

Many economists are beginning to see signs of an end to the global economic crisis. Talk has turned now to how long the recovery will take. But there is another concern about how this crisis may have reshaped the global economy and how it may have changed globalization in ways that will hinder recovery in many countries. Some Finance Ministers are concerned whether changes in globalization will help or hinder the pace of economic recovery.

Past experiences with global recessions show that when global recessions are financial in nature and synchronized, it typically takes much longer to recover (World Economic Outlook, IMF, April 2009). This crisis is the mother of all crises. Its scope and amplitude are unprecedented. Thus it is likely that the recovery process may be long and painful and thus the concern about the timing of recovery.

South Asia has witnessed a growth miracle in the past five years. The growth rate has been exceptional by any standard. Globalization has accelerated growth in the region and contributed to poverty reduction. But the current global crisis may potentially change globalization itself, as developed countries adjust to global imbalances that contributed to the crisis. It appears that the three aspects of globalization - capital flows, trade flows, and economic management - may not be the same in the future. Trade flows will be diminished and exports may not be a powerful engine of growth as developed countries try to correct the imbalance by consuming less and saving more. Also, growth in the developed world is either negative or stagnant. Thus, the chances of external demand for traded goods picking up anytime soon are bleak. Capital flows are not likely to reach its pre-crisis levels in the short-term, and South Asia’s rising public debt means there is less room for economic management, such as fiscal stimulus.

So, how will these changes in globalization impact South Asia’s recovery and prospects of medium term growth? South Asia’s recovery will be determined by a number of factors. Particularly critical are trade, foreign capital flows, and economic management. In the present paper we analyze these three major channels of globalization to study whether the changed globalization is going to hinder or assist the recovery of South Asia. We show that South Asia has followed a very peculiar path of growth and the distinctive features of the region are going to help them in recovering faster than most other regions and also in sustaining high growth in the medium term.

South Asia’s foreign trade has grown considerably over the last decade, which has contributed to rapid growth. Trade is always low during crisis, but rebounds quickly during recovery. Many countries in past crisis have accelerated their recovery with the help of expanding exports. The recovery of East Asian countries following the crisis in the 1990s was achieved by exporting to developed countries. Given that the current crisis is synchronized and global in nature, and world economic growth may be slower, it leaves less room for an export led recovery.

4