rafmacalaba/datause-encoder-data huggingface dataset:
the latter, we also use our framework to examine the response of trade flows to trade costs, both in gross flows and value added content, and the welfare gains from trade. We compare our results to a single-stage Eaton and Kortum (2002) model: the gains from trade are relatively larger in countries that tend to specialize downstream and smaller in countries that tend to specialize upstream.

In order to explore the quantitative implications of our framework, we calibrate a numerical version of our model to match key features of input-output relationships in East Asia. This exercise relies on international input-output tables produced by IDE-JETRO. The data cover the US and nine East Asian countries. This region is interesting because production fragmentation there has grown quickly and is highly prevalent. The IDE-JETRO data are unique in that they track flows in four dimensions: from the making industry in the origin country to the using industry in the destination country.<sup>7</sup> To illustrate our findings, we adapt recently developed quantitative measures of firm position (i.e. upstreamness) so that they track the average number of international borders crossed, rather than plant boundaries as in the original. Our calculations indicate increasing international fragmentation over time, especially in key industries like electronics.

While the model has rich implications for trade and the fragmentation of production, its relative parsimony is useful for the purpose of calibration. We calibrate our model by targeting key moments such as GDP per capita, value added, countries’ average position in international supply chains and gross-output-to-value-added ratios. All these moments imply large crosscountry differences in productivity, transaction costs and coordination costs.

We use the calibrated model to conduct counterfactual exercises regarding changes in key structural parameters. We first examine what happens when cross-border trade costs decrease by 10%. In this counter-factual simulation, we find larger gains from trade than predicted by Arkolakis, Costinot and Rodrigues-Clare (2012)’s formula (based on imported final goods), especially in downstream countries. We also examine the response in terms of the VAX ratio (the value-added content of exports) as defined by Johnson and Noguera (2012). We find that a decrease in trade costs leads to a decrease in the VAX ratio for most countries, which can be interpreted as an increase in cross-border fragmentation. In subsequent counterfactual exercises, we simulate a 10% increase in productivity in China and a 10% decrease in Chinese transaction costs. Both the productivity and transaction cost shocks produce similar changes in Chinese and other countries’ welfare, but the shocks have different implications for the organization of international production. The productivity shock causes China to move downstream while the rest of the world moves upstream. The reduction in Chinese transaction costs causes a relative

> 7Other data that report such figures, like the World Input-Output Database (WIOD), impute these values assuming proportional treatments.

4
Vietnam’s output, exports and pollution intensity as measured by the pollution content of its manufacturing, between 1997-2002. We find that output took off after the gradual liberalization under AFTA and USBTA, increasing by about 12 percent between 1997 and 2002. On the other hand, average predicted pollution was roughly constant over this time period suggesting that the there was no dramatic shift in the composition of manufacturing towards cleaner or dirty sectors. However, if we break down the pollution intensity by media, we find that predicted air pollution grew by 8 percent, water pollution by 18 percent and toxic pollution by 13 percent. This suggests that production became more water pollution intensive and less air pollution intensive during this time indicating a slight shift in the composition of industries away from those that are responsible for air pollution and towards those which pollute the water more.

**Figure 3: Overall Manufacturing Output and Pollution Intensity, 1997-2002 (in BVD, in 1994 constant dollars)**

<!-- Start of picture text --> 300000<br>250000<br>200000<br>150000<br>100000<br>50000<br>0<br>1997 1998 1999 2000 2001 2002<br>output airp waterp toxp<br><!-- End of picture text --> Source: GSO, Vietnam and World Bank IPPS On the other hand, manufacturing exports increased by 100 percent during the same period (Figure 4). Disaggregated pollution intensity of exports by source shows that Projection System (IPPS).” The predicted pollution levels are calculated by multiplying each 3-digit manufacturing industries output by the industry’s IPPS coefficient, and then summing across industries to get total predicted pollution (in emissions per $ output produced) for each year for each media. In the case of export, we multiply 3-digit manufacturing exports by the industry’s IPPS coefficient to obtain the predicted exports from dirty and clean industries for each year.

8
now dropped to 700 gCO2/kWh. However, the heat rate of JLEC, outperforms ONEE’s older coal plants, such as Jerada, which requires almost twice the quantity of coal to produce the same amount of power. While JLEC has a heat rate of 2,195 kilocalories per kilowatt-hour, the values for ONEE’s Jerada and Mohammedia coal plants, commissioned over three decades ago, are 3,850 and 2,534, respectively.<sup>32</sup> These stable system-wide indicators are underpinned by a growth and diversification of Morocco’s electricity generation mix in at least three ways. First, there are more energy sources today than there were in 1990. The fuel-type concentration index has declined steadily over the years (Figure 4) from 0.48 in 1990 to 0.36 in 2017. In the 1990s, there were three main fuel sources, oil, hydro and coal. Therefore, the fuel-type concentration index reached a peak of 0.57 in 1992 when oil constituted 73 percent of generation (7,087 GWh) and coal contributed about 17 percent (1,668 GWh). The concentration index dipped to 0.39 from 1997 when hydro-electric generation more than tripled to an all-time high of 2,083 GWh. There was another shift in the 2000s, when the generation mix became more concentrated, at 0.63 in 2000. At this time, hydro-electric output collapsed to 718 GWh and coal-fired generation began an upward trend to 8,783 GWh that continued for the next decade. This was in part, due to the drought between 1997 and the year 2000. Over the next decade, other energy resources were added to the mix, including gas, wind, and solar. Gas for instance, contributed 126 GWh in 2004 and 2,003 GWh in 2005 due to imports through regional interconnections with Algeria.<sup>33</sup> In 2014, for the first time, windgenerated electricity surpassed hydroelectric power as the biggest contributor to renewable energy generation (Figure 5).

_Figure 4: Electricity Generation in GWh (1990-2017)_ <!-- Start of picture text --> 35,000 0.70<br>30,000 0.60<br>25,000 0.50<br>20,000 0.40<br>15,000 0.30<br>10,000 0.20<br>5,000 0.10<br>0 0.00<br>Hydro Geothermal Other renewables<br>Coal Oil Gas<br>Biofuels and waste Nuclear Total final consumption<br>Fuel type concentration index<br>GWh<br>Fuel Type Concentration Index<br><!-- End of picture text --> _Source: Authors based on data from ONEE_

> 32 (Camos, Bacon, Estache, & Hamid, 2018)

33 Morocco plans to expand the share of natural gas in the longer term. Several new gas supply routes are being discussed, including imports of natural gas from Nigeria, and the construction of an LNG terminal, which can provide access to contract and source flexibility (IEA, 2019, p. 12).

15
are insured up to 400,000 UDIs (currency units), but the training increased this number to 20 percent in the treatment group. Similarly, 24 percent of the control group knows what CAT (total annual cost of credit) is, and this number increased to 28 percent in the treatment group due to the training. The training also increased the self-assessed level of financial literacy, with 63 percent of the treatment group saying their knowledge is satisfactory or higher, compared to only 58 percent in the control group.

When interpreting the magnitude of the effects, it is important to keep in mind that only about 30 percent of the treatment group attended the financial literacy course. The LATE estimates in the last column of table 4 take into account this relatively low take-up rate. They show much larger impacts on individuals who actually went to the training as a result of being invited through the intervention. The knowledge index increased by 8.7 percentage points due to the course for these individuals and the probability of answering some of the specific knowledge questions correctly increased by up to 20 percentage points.

# **6.5 Impact on Savings Behavior and Outcomes**

The financial literacy course emphasized specific behaviors that may help individual save more money, including: checking financial institution transactions regularly and keeping track of expenses, making a budget and setting a savings goal, identifying necessary and unnecessary expenses to reduce overall expenditures. The follow-up survey asked whether individuals engage in these behaviors and we code five dummy variables indicating that they do, as listed in panel A of table 5. We aggregate these behaviors into a savings behavior index by taking an average of the five dummies, giving the fraction of five behaviors that individuals engage in.

The control group means in panel A of table 5 show that many individuals check their financial institution transactions regularly (69 percent), keep track of expenses (79 percent), and make a budget (77 percent), even without having taken the financial literacy course. We do not detect a significant treatment effect of the training on these behaviors, perhaps because these behaviors were already quite common in the control group. The remaining two savings behaviors we study are not as frequently used in the control group: 57 percent of individuals have a savings goal and 59 percent have cut expenses in the past three months. We find that the course increased the percentage of individuals who cut expenses during the past three months to 63 percent, but this effect is only statistically significant at the ten percent level.

In addition to studying savings behavior, we also examine whether the training had an impact on savings outcomes. Panel B of table 5 lists three measure of personal savings (i) a dummy variable indicating whether the individual has any type of savings/money set aside, (ii) a dummy 22
differently to changes in overseas economic conditions. For “life-cycle” migrants, improved economic conditions in host countries—say, increased wages—can lead to longer overseas stays (as long as substitution effects dominate any income effects). For “target-earners,” on the other hand, improved economic conditions should lead to shorter overseas stays, as migrants reach their earnings goals more quickly.

Empirically, attempts to distinguish between the two alternatives typically examine the correlation between return migration and migrants’ overseas earnings. The evidence has been inconclusive. Borjas (1989) finds among the foreign-born in the US that higher earnings are associated with less return migration. By contrast, Dustmann (2003) documents, among immigrants in Germany, that higher migrant wages (instrumented by parental education) are associated with more return migration (shorter overseas stays). Constant and Massey (2002) find no statistically significant relationship between earnings and migrant returns in the same German dataset, although migrants who are unemployed or marginally employed are more likely to return.

A key methodological concern with existing empirical work on this topic is that the independent variable of interest—foreign earnings—is not randomly assigned across migrants, so any observed relationship between foreign earnings and return migration may simply be caused by unobserved third factors. For example, a finding that migrants with higher earnings have shorter lengths of stay overseas need not imply that higher earnings cause shorter migration durations. Rather, higher-wage migrants could simply have other characteristics that make early return attractive (such as better job prospects at home, or stronger family ties).

Yang (2006) exploits the exchange rate shocks experienced by Filipino overseas migrants, making possible a causal estimate of the effect of migrant economic conditions on return migration. In so doing, it also sheds light on the relative importance of life-cycle versus target-earnings explanations for return migration. Overall, the paper finds that more favorable exchange rate shocks lead to fewer migrant returns, which supports the “life-cycle” explanation for return migration. A positive exchange rate shock raises the marginal benefit of staying overseas (by raising the domestic-currency value of foreign wages), and leads to less return migration on the margin. However, the paper also finds that even though life-cycle 10
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|Perkoa MiningExploration and Technical Assistance Project||
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|Perkoa MiningExploration and Technical Assistance Project||
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|Perkoa MiningExploration and Technical Assistance Project||
|Fertilizer and Food PolicyCredit Project||
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|Education Project(03)|Makha Ndao|
|Education Project(03)|Makha Ndao|
|Education Project(03)|Makha Ndao|
|Education Project(03)|Makha Ndao|
|Education Project(03)|Makha Ndao|
|Education Project(03)|Makha Ndao|
|Education Project(03)|Makha Ndao|
|Education Project(03)|Makha Ndao|
|Education Project(03)|Makha Ndao|
|Education Project(03)|Makha Ndao|
|Health Services Development|Bruna Vitagliano|
|Health Services Development|Bruna Vitagliano|
|Health Services Development|Bruna Vitagliano|
|Health Services Development|Bruna Vitagliano|
|Health Services Development|Bruna Vitagliano|
|Health Services Development|Bruna Vitagliano|
|Health Services Development|Bruna Vitagliano|
values are declining, e.g. because of cheaper lower-quality varieties flooding the market.

Some recent work has pointed out that quality regulation can create trade, but brings only data from individual countries. Macedoni and Weinberger (2022) show theoretically that under perfect information quality regulation can create trade, through reallocation toward high-productivity firms, and find evidence consistent with this model among domestic firms in Chile. Disdier et al. (2023) examine theoretically an imperfect information environment, in which quality regulation can create trade even for low-productivity firms by conveying information about product quality, and confirm the models predictions among exporting firms in France. We incorporate these insights into our model, and provide additional evidence using a panel dataset of importing and exporting firms in many countries.

Our results have important policy implications. Standardizing quality regulation across countries has been a significant focus and achievement of the WTO. Under the WTO Agreement on the Application of Sanitary and Phytosanitary Measures and the WTO Agreement on Technical Barriers to Trade, more countries adopted quality regulation similar to that of higher-income countries (Rodrik, 2018). Our results, which show benefits for low-income exporters, can rationalize the poverty-reducing benefits of access to larger markets through international trade agreements including the World Trade Organization (Goldberg and Reed, 2023). Yet, they also suggest that capturing these benefits may require government intervention, especially in lower-income countries.

Consider two recent experiments in emerging markets that provide quality certification to firms. Bai (2025) provides melon vendors in local markets in China with laser cut branding labels, as would be required by a TBT, for premium melons. These labels increase demand and profits. Deutschmann, Bernard and Yameogo (2021) provide groundnut farmers in Senegal with a technology to screen for aflatoxin, which must be below a certain level for exports to conform with an SPS measure in the European Union, and guaranteed price premium for conforming groundnuts. In both experiments, farmers adopt the quality certification technology initially. In the China experiment, farmers continue branding their premium melons when the intervention (subsidy) is withdrawn, consistent with the label conveying valuable information to consumers without much cost, as with a TBT. In contrast, in the SPS experiment, farmers stop screening their groundnuts when the price premium is withdrawn, consistent with the technology increasing costs, as with a SPS.

Like these experiments, our evidence suggests that lower-income country producers will not have trouble conforming with TBT measures. Government support with conformity may be less needed in this area. In contrast, ensuring conformity with SPS measures required for export success is costly, suggesting a potential role for government support. Firm-level subsidies as in the Senegal experiment could be justified for longer periods, allowing firms to 6
higher since future consumption gains are discounted at a lower rate.

Even though the representative agent framework is unable to account for distributional costs, it is informative regarding potential distributional implications. Our analysis suggests that while ICT may have a large effect on the distribution of labor income, it has only a moderate effect on the distribution of income between capital and labor.

Our results further suggest that automation is unlikely to be the sole cause of the declining labor income share. In particular, our measurement suggests that only half of the decline in the labor income share is directly countered by an increase in the ICT capital income share. The remainder is due to an increase in the non-ICT capital income share, particularly in the post-2001 period and primarily driven by housing. This suggests that, in order to fully understand the sources of the declining labor income share, it is necessary to study the mechanisms driving changes in non-ICT capital income, in particular residential capital income. We leave this challenge for future work.

# References

> Acemoglu D. 1999. Changes in unemployment and wage inequality: An alternative theory and some evidence. _American Economic Review_ **89** : 1259–1278.

> URL http://ideas.repec.org/a/aea/aecrev/v89y1999i5p1259-1278.html

- Acemoglu D, Autor D. 2011. _Skills, Tasks and Technologies: Implications for Employment and Earnings_ , volume 4 of _Handbook of Labor Economics_ , chapter 12. Elsevier, 1043–1171.

URL http://ideas.repec.org/h/eee/labchp/5-12.html

- Acemoglu D, Autor D, Dorn D, Hanson GH, Price B. 2014. Return of the Solow Paradox? IT, Productivity, and Employment in US Manufacturing. _American Economic Review_ **104** : 394–99.

URL http://ideas.repec.org/a/aea/aecrev/v104y2014i5p394-99.html

- Akerman A, Gaarder I, Mogstad M. 2013. The Skill Complementarity of Broadband Internet. IZA Discussion Papers 7762, Institute for the Study of Labor (IZA).

URL http://ideas.repec.org/p/iza/izadps/dp7762.html

- Atkinson AB, Piketty T, Saez E. 2011. Top Incomes in the Long Run of History. _Journal of Economic Literature_ **49** : 3–71.

- URL http://ideas.repec.org/a/aea/jeclit/v49y2011i1p3-71.html

- Autor D, Dorn D, Hanson GH. 2013. Untangling Trade and Technology: Evidence from Local Labor Markets. IZA Discussion Papers 7329, Institute for the Study of Labor (IZA).

URL http://ideas.repec.org/p/iza/izadps/dp7329.html 34
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