rafmacalaba/datause-encoder-data huggingface dataset:
changes in patterns of remittances which may be occurring as migration becomes more permanent. Table 3 summarizes key features of per capita _pre-transfer income_ , or household income before receiving transfers, and net transfers, where _net transfers_ are calculated as transfers into the household less transfers out. Considering income and transfers relative to the nutrition-based poverty line for rural China, 875 RMB Yuan per capita (measured in constant Year 2000 RMB Yuan), puts the economic environment of these households into perspective.<sup>13</sup> First, incomes are not high. Average pre-transfer income, both for households with and without migrant children, is less than twice the nutrition-based poverty line in both periods. Second, the share of elderly living in households with pre-transfer per capita income below the poverty line has increased both for households with and without migrant children. In the early period (1995 to 1998), 29.3 percent of elderly households without migrants had pre-transfer incomes at or below the poverty line, and this rose to 38.7 percent by the later period (2000 to 2003). Among the elderly households who have migrant adult children, the share who would be in poverty in the absence of transfers was 37.2 and 44.9 percent in the early and late periods, respectively.

While elderly with adult children who are migrants would appear more likely to be below the poverty line in the absence of transfers, the share of elderly who would be very poor without transfers (pre-transfer income per capita less than one fourth of the poverty line) is somewhat higher for those households without migrant adult children. The higher _very poor_ share likely reflects those households in which elderly family members are too infirm to work and for whom

> 13Of course, poverty lines may be somewhat arbitrary, and thus poverty researchers go to considerable lengths to use objective basis on which to develop a poverty line. Summarizing the general approach to estimating  a nutritionbased poverty lines, the analyst must first estimate the cost of the underlying food requirements for the basket of foods that might be available for someone near poverty are first estimated. To this cost, costs of other durables and housing for someone near poverty are then added to the nutrition cost, and this value is used to determine the poverty line. Researchers working in the poverty field have thought very carefully about the objective calculation of poverty lines and a useful introduction may be found in Ravallion (1996). We use the nutrition-based poverty line calculated for rural China over the period of the study by Ravallion and Chen (2007), who have long experience working with China’s National Bureau of Statistics (NBS) to develop objective, nutrition-based poverty lines for rural and urban China.

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stage regressions. When we only control for province dummies, the first-stage F- statistics is 18.48 (column 1). When we put more controls and include all the 363 oversight government dummies, which likely takes away a significant portion of the variations in _Distance_ , the F-statistics drops to 4.85 (columns 4 and 6). However, even in this case the IV regressions do pass the Anderson-Rubin test, suggesting that the variable Distance is significant even in the event of weak IV (see column 6). Nevertheless, we note that the qualitative results of a significant positive effect of _Distance_ on decentralization remain intact. Since the results from the IV probit and the two-stage least square (2SLS) models are similar, we shall focus on the 2SLS results. As expected, being a Third-Front-Construction SOE would increase log distance by 0.19, and it is statistically significant at the 5% level. Once corrected for endogeneity, the distance remains exerting a positive influence on decentralization, and the coefficient increases to 0.03 (from 0.006). The results thus confirm the key importance of the distance between enterprises and their oversight government.

# **3.5 The Mechanisms of Distance on Decentralization**

In the conceptual framework, we predict that the decentralization-distance link would be stronger for firms with higher communication costs or with higher performance uncertainty. To test this, we need measurements.

We proxy communication costs by three measures. The first is provincial road mileage (road mileage per capita): more convenient transportation significantly reduces the difficulty of on-site inspection, and thus makes information more accurate. The second is provincial telecom density, i.e., the share of people having either mobile or fixed phones. The third is the share of people using internet.

We proxy high uncertainty over a firm by several variables: the average share of intangible assets in total assets within the firm’s industry, and several 22
predominantly accessed via desktop computers during weekdays.

3. Unprecedented global diffusion and widespread usage in middle-income economies. Just 16 months since ChatGPT’s release, it has reached 209 of 218 economies worldwide. As of March 2024, the top five economies for ChatGPT traffic are the US, India, Brazil, the Philippines, and Indonesia. The US share of ChatGPT traffic dropped from 70% to 25% within one month of ChatGPT’s debut. Middle-income economies now contribute over 50% of traffic, showing disproportionately high adoption of generative AI relative to their GDP, electricity consumption, and search engine traffic. Low-income economies, however, represent only less than 1% of global ChatGPT traffic.

4. Higher income levels, a higher share of youth population, better digital infrastructure, and stronger human capital are key predictors of higher generative AI uptake. Specialization in digitally tradable services and English fluency are strongly associated with higher chatbot usage.<sup>1</sup> 5. Generative AI tools are already disrupting online traffic patterns and altering user habits. Information, language processing, and professional question and answer websites like Wikipedia, Grammarly, Google Translate, and Stack Overflow experienced significant traffic drops immediately after GPT-4 launch, though some recovery has been observed as sites integrate generative AI capabilities. People are increasingly using ChatGPT for information acquisition and aggregation, skill learning, and language processing, while more complex cognitive and analytical tasks have been augmented by ChatGPT.

Existing evidence in advanced countries shows that AI adoption by firms remains very low and is dominated by large firms and young firms. Acemoglu (2024), Babina, Fedyk, He, and Hodson (2024), and Goldfarb, Taska, and Teodoridis (2023) used online job posting data to measure firms’ AI adoption and subsequent effects. Miric, Jia, and Huang (2023) and Webb (2019) used patent data as a proxy for AI adoption. Zolas, Kroff, Brynjolfsson, et al. (2021) and McElheran, Li, Brynjolfsson, et al. (2024) used the US Annual Business Survey data to track AI use among businesses. Even though AI development and use has been ongoing for decades, AI usage among US businesses remains very low and highly uneven. Fewer than 6% of firms used any of the AIrelated technologies, and adoption is heavily tilted towards very large businesses, businesses owned by more educated and younger owners, and in superstar cities. Bonney, Breaux, Buffington, et

> 1Digitally tradable services include, for example, information and communication technology and business process outsourcing, finance and insurance, professional services, and research activities.

5
Using all firms, we see that for each percentage point increase in net employment that would result from the labor regulations being more flexible, dismissals as a percentage of employment would increase by 0.45 percentage points and that this result is significant at the 0.05 level. This result implies that for each percentage point increase in net employment, additional hires as a percentage of employment would increase by 1.45 percentage points. The raw data underlying this estimation are plotted in figure 1.

There are two additional noteworthy observations on the results from table 3. The first observation is to note that the analogous estimations done separately for manufacturing or non-manufacturing firms and the estimations done separately by size categories all yield positive coefficients, although not all of them are significant. It is worth mentioning, however, that these disaggregated regressions do not pick up reallocations across broad sectors or across size categories that would arise in response to labor reform. It is for this reason that the coefficient of 0.45 from the aggregate regression is larger than any of the coefficients from the disaggregated models.

The second observation is to note that there are insignificant results using job destruction as the dependent variable. That is, the relationship between net growth and worker flows is stronger than the relationship between net growth and job flows. This result demonstrates that labor reform has important effects on worker turnover that are not being captured in papers that only study job flows such as Caballero, et. al. (2006), Haltiwanger, et. al. (2006), and Micco and Pagés (2007).

In light of the previous literature on the effects of labor reforms, one might conjecture that the countries that could gain the most by reforming their markets in terms of net employment growth would be those with heavily regulated labor markets. The results from table 3 would therefore suggest that the particularly high gains in terms of net employment that could be obtained by labor reforms in highly regulated countries would be accompanied by the displacement of particularly large fractions of their workforces. I address this possibility in table 4.

Table 4 studies the relationship between an indicator of labor-market flexibility and the job- and worker-flows statistics used in the paper. The top panel of table 4 presents the results from OLS models. The independent variable is always the standardized value (multiplied by minus one) of the labor flexibility indicator from the EFW report as described in the data section. Larger values for the indicator imply that the labor market is more rigid. When the net percent change in employment from making labor 11
Table D.3. Heterogeneous Impacts on Mental Health

|Variables are indicators|Mental Health<br>Issues<br>Index<br>(1)|Felt Little<br>Interest<br>in Doing<br>Things<br>(2)|Felt<br>Depressed<br>or Hopeless<br>(3)|Had<br>Eating<br>Problems<br>(4)|Suicidal<br>Thoughts<br>(5)|Ask<br>Mental<br>Health<br>Assistance<br>(6)|
|---|---|---|---|---|---|---|
|_Panel A. Taliban Exposure_<br>|||||||
|_β_1=_ETi ×_I[Female]|0.037|0.092|0.069|0.008|-0.018|-0.452|
||(0.063)|(0.155)|(0.134)|(0.103)|(0.035)|(0.612)|
|_β_2=_ETi_|0.055|-0.009|0.063|0.125*|0.018|2.036***|
||(0.046)|(0.113)|(0.098)|(0.075)|(0.026)|(0.590)|
|_β_3=I[Female]|0.005|-0.009|0.009|0.005|0.010|0.179|
||(0.016)|(0.040)|(0.035)|(0.027)|(0.009)|(0.155)|
|Diff. Effect=_β_1+_β_3|0.042|0.083|0.079|0.013|-0.008|-0.273|
||(0.048)|(0.118)|(0.102)|(0.078)|(0.027)|(0.466)|
|R-squared|0.031|0.025|0.033|0.039|0.031|0.407|
|_Panel B. Contested Territories Exposure_|||||||
|_β_1=_ECi ×_I[Female]|0.008|0.084|-0.031|0.002|-0.017|-0.136|
||(0.024)|(0.059)|(0.051)|(0.039)|(0.013)|(0.233)|
|_β_2=_ECi_|0.015|-0.043|0.010|0.081***|0.011|0.549***|
||(0.017)|(0.043)|(0.037)|(0.029)|(0.010)|(0.186)|
|_β_3=I[Female]|0.013*|0.001|0.031**|0.007|0.008**|0.085|
||(0.007)|(0.016)|(0.014)|(0.011)|(0.004)|(0.056)|
|Diff. Effect=_β_1+_β_3|0.021|0.085|0.001|0.009|-0.009|-0.050|
||(0.021)|(0.052)|(0.045)|(0.035)|(0.012)|(0.209)|
|R-squared|0.030|0.025|0.032|0.040|0.031|0.398|
|_Panel C. U.S.- Allies Exposure_|||||||
|_β_1=_EAi ×_I[Female]|-0.009|-0.057|0.011|-0.003|0.012|0.108|
||(0.019)|(0.046)|(0.040)|(0.030)|(0.010)|(0.177)|
|_β_2=_EAi_|-0.013|0.027|-0.011|-0.057**|-0.008|-0.459***|
||(0.014)|(0.033)|(0.029)|(0.022)|(0.008)|(0.146)|
|_β_3=I[Female]|0.019|0.048|0.020|0.009|-0.001|0.000|
||(0.012)|(0.031)|(0.026)|(0.020)|(0.007)|(0.121)|
|Diff. Effect=_β_1+_β_3|0.011|-0.009|0.031|0.006|0.010**|0.109|
||(0.009)|(0.023)|(0.020)|(0.015)|(0.005)|(0.081)|
|R-squared|0.031|0.025|0.032|0.040|0.031|0.403|
|Mean Dep. Variable|0.165|0.294|0.215|0.112|0.011|0.781|
|<br>Observations|5,590|5,554|5,578|5,573|5,568|259|
|_Controls in All Panels_|||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables: (i) Mental Issues Index is constructed using the average of the variables in columns (ii) to (vi). The Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

68
decline than the baseline model does. The impact on net exports ranges from 0.9 percent for the transport equipment sector (with the lowest aggregate energy intensity) to 4.4 percent for the basic metals sector (with the highest). For the European Union as a whole, the inputoutput model predicts an overall impact of a 1.9 percent reduction in net manufacturing exports annually. This is almost three times the size of the reduction predicted by the baseline model. The difference reflects multiplier effects within the European Union where there is intensive trade in intermediate goods between EU firms.

# **5 Conclusion**

This paper seeks to contribute to the literature on energy prices and international trade by taking into account the effect of embodied energy costs (costs of direct plus indirect energy consumption) on exports. We start with the premise that energy price shocks affect not only the cost of direct energy consumption (at the final stage of production) but also the cost of indirect energy consumption embodied in intermediate inputs. In addition, with the rapid expansion of global value chains, domestic energy prices may not tell the full story if a firm relies heavily on imported intermediate goods that are energy intensive. We develop a theoretical framework that incorporates tradable intermediate inputs in a Meliz-type trade model and apply the theoretical model to energy, trade, and multi-country input-output data for 10 manufacturing sectors in 43 countries from 1991 to 2012.

We identify a robust effect of energy costs on export performance. We use the estimated trade elasticities to simulate two counterfactuals: the removal of energy-cross subsidies in Bulgaria and India, and a unilateral 10 percent increase in electricity prices in the European Union. We find that removing cross-subsidies which impose a 15 percent implicit electricity tax on industry in India could increase its net manufacturing exports by as much as 9.5 percent, or $6.1 billion a year. A carbon tax that unilaterally increases electricity prices by 10 percent in the European Union could cause an EU-wide reduction in net manufacturing exports of 1.9 percent annually.

The simulation analysis also yields a nuanced understanding of the causal link between energy price shocks and exports. First, even if a sector is not energy intensive on the basis of direct energy consumption, it may still be vulnerable to energy price shocks if 22
equivalent to regressing investment disbursement on lagged PTA variables, which is one of the common techniques used when there is concern about endogeneity. Another advantage of using the fDi Markets database is the availability of company level investment data. This allows us to address endogeneity concerns related to the fact that large investment decisions may determine the adherence to certain disciplines in PTAs by host countries, which would imply that it is large investments that determine PTAs rather than the converse.<sup>13</sup> To tackle that endogeneity concern, in the final part of our analysis we remove the largest investments from the sample, as well as the sectors that spend the most resources on lobbying for specific contents in PTAs, to check the robustness of results to that potential source of endogeneity.

Overall, the data covers more than 60,000 companies from 177 source countries investing in 162 destination countries from 2003 to 2015. Each company’s project is classified according to 39 industries and 18 business activities. Table A2 shows how the different sectors and activities overlap in the data. Business activities are defined as the actual function of a project, whereas sectors are based on the FDI recipient company’s core business area. Examples of project announcements are “Wiseway Group (Australia – a transportation services provider) is investing in China in the Transportation sector in a project related to Sales, Marketing and Support activity”; “ProLogis (United States – a leasing and property management company) is investing in Sweden in the Real Estate sector in a construction project”. This means that an industry traditionally classified as manufacturing may receive investments to develop a service-related activity and vice versa. In the data for example, 65 percent of the investment received by the “consumer electronics” sector is dedicated to manufacturing activities but 16 percent falls in retail, 5 percent in research and development, 4 percent in marketing, 2 percent in logistics, distribution and transportation, and so on (all service-related activities). We think that these activities

> 13 For example, the bilateral investment treaty signed between Finland and Uruguay in 2002, which entered into force in 2004, was largely driven by the undergoing negotiations for a large Finnish investment in the cellulose paste production sector in Uruguay.

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# 5. Robustness

The robustness of this analysis’ main findings on the drought impact on rural poverty is verified in several ways. First, the results’ sensitivities to the inclusion and exclusion of various groups of control variables are tested. The results are robust across all tested specifications, and do not depend on the inclusion of certain groups of control variables. With the inclusion of subsequent groups of control variables, the drought impact point estimates vary between 19 and 30 percent increase in the probability of being poor.

_Table 3: Robustness of results across various specifications._

||(I)|(II)|(III)|(IV)|(V)|(VI)|
|---|---|---|---|---|---|---|
|**_Sample_**|||Full ru|ral sample|||
|**_Outcome variable_**|||Po|orPPP|||
|**Drought Impact**|0.192***|0.256***|0.222***|0.230***|0.301***|0.264***|
|**S.E.**|(0.0629)|(0.0733)|(0.0679)|(0.0739)|(0.0769)|(0.0812)|
|**_Outcome variable_**|||ln(Core C|onsumption)|||
|**Drought Impact**|‐0.107**|‐0.189***|‐0.146***|‐0.152***|‐0.169**|‐0.146**|
|**S.E.**|(0.0428)|(0.0598)|(0.0555)|(0.0551)|(0.0668)|(0.0665)|
|**Controls**|||||||
|Regional|No|Yes|Yes|Yes|Yes|Yes|
|Household|No|No|Yes|Yes|Yes|Yes|
|Dwelling|No|No|No|Yes|Yes|Yes|
|Conflict|No|No|No|No|Yes|Yes|
|Assistance|No|No|No|No|No|Yes|
|**Observations**|1,591|1,591|1,563|1,536|1,536|1,536|
|**R‐squared**|0.032|0.226|0.359|0.487|0.501|0.522|

Source: Authors’ calculations.

Note: ***p<0.01, **p<0.05, *p<0.1. Poverty status results estimated using Probit, Consumption results estimated using OLS. Drought effect expressed in standard deviations of NDVI loss.

Second, the analysis is replicated on households only in regions overlapping between wave 1 and wave 2 of the SHFS. This reduces the geographical scope of the analysis to rural areas in the north‐west of Somalia and to urban areas in the north‐west and Mogadishu. The results from this subsample are in line with the main findings from the full sample of households (Table A.5). In rural areas, more drought‐exposed households are 36 percent more likely to be poor, experiencing a 15 percent reduction in consumption, slightly higher than the 26 and 15 percent, respectively, in the full sample. No significant effect was found in urban areas.

Third, the sample is restricted by iteratively removing from the analysis households from north‐eastern regions (Table 4, columns I and II), central regions (Table 4, columns III and IV), and south‐western regions (Table 4, columns V and VI). The main results are largely unchanged when removing north‐eastern regions, while a larger drought effect is found when removing south‐western households. In contrast, the results are weaker when excluding central regions, though point estimates are still in a similar range. This indicates that the drought effect was weaker in south‐west and particularly strong in central regions.

_Table 4: Regression results with restricted samples._ Rural, NE excluded Rural, Central excluded Rural, SW excluded

**_Sample_**

8
17

# Output Demand

Operating earnings do appear sensitive to fluctuations in the demand for manufactured goods (Table 3, 8). A 10 percent drop in expenditures on manufactured goods reduces operating earnings by almost 12 percent. Or, using the fact that operating earnings are about .22 of net worth, the effect is to reduce net income by an amount equal to .026 of net worth. One can thus imagine that the sudden reduction in Argentine demand for Uruguayan products which took place in 1981 had a major effect on industtLal sector profitability.

# Borrowing Costs

By equation 1, increases in real borrowing cost reduce net income to the extent that firms have net financial ilabilities in the assoiciated currencies. Conversely, increases in the inflatlon rate reduce net income to the extent that firms have net monetary assets. To give an idea of the magnitudes. involved it is useful to consider some examples based on the consolidated balance sheet for the entire sample. Specifically, in 1980, the ratio of net peso liabilities to net worth was about .15, so a 10 percentage point increase in real peso interest rates (e.g., from 0 to 102) would have cut net income by an amount equivalent to about .015 of net worth. By similar calculations, a IC percentage point increase in the cost of dollar credit would have cut net income by about .035 of net worth, and an increase ln the inflation rate by 10 percentage points would have resulted in capital losses on monetary assets of about .015 of net worth. The average ratio of net income to net worth was about .07 in 1980, so fluctuations on this order of magnitude clearly are signficant. It is noteworthy that by 1980 firm were