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

The consequences of climate change for agriculture and food security are of serious concern, not least because food supplies are already inadequate and poverty is severe in many low-income countries, particularly in Africa. Moreover, developing countries are generally considered to be most vulnerable to climate change, mainly due to their reliance on rain-fed agriculture. Previous studies linking climate change to food security have typically used agricultural crop models (see, for example, Parry et al., 2004). Their predictions range from precipitous declines in yields for major African food crops (Schlenker and Lobell, 2010) to more modest reductions (Lobell et al., 2008; Nelson et al., 2010), and even to improvements (Butt et al., 2005).

Previous studies have, however, suffered from at least one of four limitations. First, they often provide global or regional assessments. Yet climate change is expected to vary widely within continents and even countries, and so adaptation policies require higher-resolution information, possibly even at sub-national levels (Lobell et al., 2008). Secondly, despite considerable uncertainty surrounding future climate change, some studies rely on only a few climate projections (see, for example, Butt et al., 2005). Thirdly, many calibrated agronomic crop models exclude “autonomous adaptation” that may offset at least some climate change damages. Finally, previous studies typically measure direct or partial equilibrium production changes, but may exclude indirect and general equilibrium effects, including price and household income changes and inter-sectoral linkages. Since food security depends on both food availability and accessibility, it is inadequate to measure production changes without considering, for example, the impacts of climate change on households’ incomes (Parry et al., 2004; Ahmed et al., 2009).

In this paper we estimate the impact of climate change on agricultural production in Tanzania using detailed sub-national crop models. Four projections are drawn from available general circulation models (GCM) to reflect a range of possible temperature and precipitation changes by mid-century. These climate projections are then used in calibrated crop models to predict crop yield changes, which are in turn imposed on a highly-disaggregated, recursive dynamic economy-wide model of Tanzania. This model captures indirect effects and permits (some) autonomous adaptation. The economic model allows us to evaluate the availability (production) and accessibility (income) dimensions of food security. In the next section, we describe the selected climate scenarios and the crop modeling framework used to translate climate conditions into crop yields. We then describe the economy-wide model and present the results from our simulated baseline and climate change scenarios. We conclude by summarizing our results and identifying areas for further research.

2
<!-- Start of picture text --> <<br>——®— Total inc. Farm inc. ——4&— Non-farm inc.<br>Wage inc. " Currently working?<br>yn<br><b) x<br>><br>D .<br>£—x<br>®<br>co —*<br>o.<br>cx<br>2<br>:<br>3° ;<br>Qa r <<br>fe)<br>=<br>Ox<br>N<br>Apr May Jun Jul Aug Sep Oct Nov Dec<br>Month<br>The income variables are based on whether the household reported an income loss.<br><!-- End of picture text -->
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_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_

**Table 14: Monthly wages by country and occupation**

|||Media|n wage in USD-|PPP<br>Skilld|Ukilld|N|
|---|---|---|---|---|---|---|
|||Managers|Professionals|e<br>production|nse<br>production|on-<br>production|
||Botswana (2006)|486|2,149|496|331|453|
||Gambia (2006)|761|198|331|228|397|
||Ghana (2007)|492|507|213|160|213|
|**e**|Kenya (2003)|1,119|832|256|166|236|
|**hon**|Namibia (2006)|2,552|939|470|282|494|
|**lop**|Swaziland (2006)|237|395|593|289|441|
|**Ang**|Tanzania (2003)|549|442|177|221|148|
||Tanzania (2006)|530|606|253|189|202|
||Uganda (2003)|288|320|128|112|80|
||Uganda(2006)|726|492|258|194|194|
||Total|531|485|242|197|167|
||Benin (2004)|289|491|186|124|144|
||Burkina Faso (2006)|900|750|490|175|325|
||Burundi (2006)|564|146|175|117|146|
||Cameroon (2006)|1,215|996|438|319|319|
|**ne**|Cape Verde (2006)|721|1,189|324|231|216|
|**ho**|DR Congo (2006)|317|251|152|164|175|
|**cop**|Guinea (2006)|205|410|164|148|246|
|**ran**|Madagascar (2005)|1,649|1,539|742|536|660|
|**F**|Mali (2003)|394|315|189|110|132|
||Mauritania (2006)|455|909|505|404|313|
||Rwanda (2006)|2,151|1,340|645|229|376|
||Senegal(2003)|820|601|330|207|270|
||Total|505|681|300|239|258|
|Sour|ce: Authors’ calculations|from Enterp|rise Surveys(for|calculation d|etails, see Tabl|e 19)|

41
# Table I.10. Robustness Analysis for the Labor Outcomes Index

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|0.808|0.644|0.782|0.915|1.096*|0.944|0.808|0.644|0.782|0.915|1.096*|0.944|
||(0.740)|(0.724)|(0.717)|(0.673)|(0.578)|(0.652)|(0.740)|(0.724)|(0.717)|(0.673)|(0.578)|(0.652)|
|Obs. left|377|341|368|357|342|352|377|341|368|357|342|352|
|Obs. right|810|631|774|1,125|1,084|1,078|810|631|774|1,125|1,084|1,078|
|Mean values (Non-RAMV)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|1.327|1.177|1.361|1.012|1.276|1.090|1.468|0.961|1.300|0.901|0.992|0.844|
||(1.209)|(1.093)|(1.230)|(0.836)|(0.821)|(0.812)|(1.571)|(1.434)|(1.718)|(0.985)|(0.902)|(0.966)|
|Obs. left|407|362|357|377|362|357|407|362|357|377|362|357|
|Obs. right|842|735|717|1,438|1,390|1,488|842|735|717|1,438|1,390|1,488|
|Mean values (Non-RAMV)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|_Panel C: Polynomial of order tw_|_o_||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|2.400|2.465*|2.218|1.280|0.434|1.309|2.919|2.468|1.300|1.524|1.553|1.257|
||(2.143)|(1.473)|(2.143)|(1.380)|(1.130)|(1.393)|(1.786)|(2.277)|(1.718)|(1.564)|(1.526)|(1.692)|
|Obs. left|348|371|348|348|377|348|348|371|357|348|377|348|
|Obs. right|652|785|670|1,207|1,330|1,189|652|785|717|1,207|1,330|1,189|
|Mean values (Non-RAMV)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
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# The Impact of NAFTA and Options for Tax Reform in Mexico

Jorge Martinez-Vazquez and Duanjie Chen* Professor of Economics, Georgia State University, and Senior Economist, Macrosys respectively. We are grateful to Omar Lopez-Escarpuli for many of the tables and charts in the paper and to Stephen Everhart and Steven B. Webb for helpful comments. This research was conducted as background for the World Bank's Country Economic Memorandum for Mexico, 2000.
To make matters worse, more than a dozen countries suffered a greater than 0.10 decline in relative GDP per capita over the same period. Table 3 displays the list. Many of these countries are middle-income countries that failed to keep pace with the 2 percent GDP per capita growth of the United States over this period. In addition, several oil producing countries failed to diversify their economic base and, as a result, have experienced large declines in their relative income per capita.

**Table 3:  Divergence ―leaders‖**

**(Countries suffering a .10 or greater decrease in relative GDP per capita with respect to the United States)**

||1950<br>|1980<br>|2008<br>|Change<br>1950-<br>2008<br>|
|---|---|---|---|---|
|Bolivia|0.20|0.14|0.09|-0.11|
|Iraq|0.14|0.34|0.03|-0.11|
|Lebanon|0.25|0.19|0.14|-0.11|
|South Africa|0.27|0.24|0.15|-0.11|
|Nicaragua|0.17|0.12|0.05|-0.12|
|Djibouti|0.16|0.09|0.04|-0.12|
|Switzerland|0.95|1.01|0.81|-0.14|
|Argentina|0.52|0.44|0.35|-0.17|
|Uruguay|0.49|0.35|0.32|-0.17|
|Gabon|0.33|0.36|0.12|-0.20|
|N. Zealand|0.88|0.66|0.60|-0.29|
|Venezuela|0.78|0.55|0.34|-0.44|
|UAE|1.65|1.49|0.50|-1.15|
|Kuwait|3.02|0.71|0.41|-2.61|
|Qatar|3.18|1.55|0.56|-2.62|
|_Memo/_|||||
|_Saudi Arabia_<br>_Memo/_|_0.23_|**_0.71_**|_0.27_|_0.04_|

_Former Soviet Union/E. European countries with data only since 1990_

||_1990_|_2008_|_Change_|
|---|---|---|---|
|_Moldova_|_0.27_|_0.11_|_-0.15_|
|_Georgia_|_0.33_|_0.19_|_-0.14_|
|_Serbia/Montenegro/Kosovo_|_0.22_|_0.12_|_-0.10_|
|_Ukraine_|_0.26_|_0.16_|_-0.10_|

Source: Authors‘ calculations based on Maddison data.

In addition, many low-income countries are still trapped in poverty, and they are failing to achieve the MDGs. While the global headcount poverty MDG (MDG 1) is likely to be achieved, many countries remain off-track—in particular, countries in Sub-Saharan Africa are not likely to meet the MDG for halving headcount poverty (Figure 7(a) and (b)). In addition, 11
more, and is nominal or real rigidity more important? If the source of the market malfunctioning at the macroeconomic level is not understood, it is difficult to work out what effect a second-best employment creation policy such as new environmental investments would have (as opposed to a first-best policy of removing the offending rigidity). All of these questions are relevant to investigating the likely labor market consequences of green policy initiatives ranging from carbon pricing to changes in the pattern of government subsidies to energy to deficit-financed green infrastructure investments.

Understanding the interaction of business cycles and labor markets better would help to establish what the scope is for green fiscal stimuli to counter increases in involuntary unemployment and underemployment in developing countries. Although governments in developing countries may on average have less of a capacity to time and manage fiscal stimuli appropriately (Kraay and Servén, 2008), business cycles may be more of a problem in developing countries, especially those engaged in international commodity trade, than in developed economies (Kraay and Ventura, 1998; Otero, 2000). Arguably, development economics has not taken sufficient account of the importance of fluctuations in aggregate demand, instead paying more attention to capital accumulation and technological innovation (Dutt, 2007).

Taking a much longer-term perspective, further analysis is needed of the scope for inducing higher long-run growth in developing countries by raising the rate of innovation. That could in principle speed up the re-allocation of labor inputs to more productive sectors of their economies, reducing underemployment and increasing real wage growth (if the new skills necessary are developed through appropriate training). Although the literature on green jobs tends to focus on the current characteristics of renewable energy supply and energy efficiency, integrated assessment modeling of climate-change mitigation is paying more attention to induced technological progress in low-carbon technologies. Are developing countries going to depend on spill-overs from R&D and learning in developed countries, as is largely the case in the WITCH model (Bosetti et al., 2006), or can they benefit sooner, for example, from advances in technologies more fitted to developing country circumstances (e.g. biofuels in Brazil, concentrated solar thermal power in North Africa, forest management in Indonesia)? Can developing countries with large manufacturing sectors benefit from a new global Industrial Revolution driven by a Schumpeterian search for competitive advantage (Perez, 2002)? Several of the jobs studies for countries such as South 23
A. Sanghi and D. Johnson <!-- Start of picture text --> 750<br>4000 Automotive OEM<br>Automotive Components Building & Construction Materials<br>Automotive OEM Business Machines & Equipment<br>Communications Business Services<br>500 Consumer Electronics Communications<br>Financial Services Consumer Electronics<br>Food & Tobacco 2000 Consumer Products<br>Industrial Machinery, Equipment & Tools Financial Services<br>250 Metals Food & Tobacco<br>Metals<br>0 0<br>Industry Sector Industry Sector<br>(a) Chinese FDI job creation by sector (b) Total FDI job creation by sector<br>Figure 26: Chinese companies create most jobs in the communications sector (2003-2015)<br>Source: fDi Intelligence from The Financial Times Ltd 2015<br>1500 20000<br>Business Services<br>15000 Construction<br>1000 Business Services Customer Contact Centre<br>Design, Development & Testing Design, Development & Testing<br>Education & TrainingHeadquarters 10000 ExtractionHeadquarters<br>Manufacturing ICT & Internet Infrastructure<br>500 Sales, Marketing & Support Logistics, Distribution & Transportation<br>5000 ManufacturingSales, Marketing & Support<br>0 0<br>Industry Activity Industry Activity<br>(a) Chinese FDI job creation by Industry Activity (b) Total FDI job creation by Industry Activity<br>Figure 27: Chinese companies in manufacturing sector create the most jobs (2003-2015)<br>Source: fDi Intelligence from The Financial Times Ltd 2015<br>1500<br>400<br>Business Services<br>300 Construction<br>1000 Business Services Customer Contact Centre<br>Design, Development & Testing Design, Development & Testing<br>Education & Training Extraction<br>Headquarters 200 Headquarters<br>Manufacturing ICT & Internet Infrastructure<br>500 Sales, Marketing & Support Logistics, Distribution & Transportation<br>Manufacturing<br>100 Sales, Marketing & Support<br>0 0<br>Industry Activity Industry Activity<br>Jobs Created Jobs Created<br>Jobs Created Jobs Created<br>Average Jobs Created Average Jobs Created<br><!-- End of picture text --> <!-- Start of picture text --> (a) Average Chinese FDI job creation per project by (b) Average total FDI job creation per project by Industry<br>Industry Activity Activity<br><!-- End of picture text --> Figure 28: Chinese companies in manufacturing sector create the most jobs per project (2003-2015) Source: fDi Intelligence from The Financial Times Ltd 2015 China is also a major employer for Kenya. Figure 29 shows the number of jobs that each country creates through FDI. China ranks fifth overall with 2,170 jobs, and India is number one with 7,422 jobs. Hence, China and India are not only important sources of trade and investment, 28
Similarly to our paper, recent work by Alfaro and Chor (2023) shows that US sourcing has been reallocated away from China and toward other locations, notably Mexico and Vietnam, and that prices of imports from these countries are on the rise.

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 studies the impact of the trade war on the reconfiguration of supply chains. Section 4 takes a closer look at where production moved after the shock. Section 5 concludes.

# **_2. Data_**

We use 10-digit import data at the country level from US Customs for 2017 and 2022. There are more than 17,891 products and 157 countries.<sup>2</sup> Tariff data are from Bown (2023). Strategic industries are defined as the 2-digit categories identified as Advanced Technology Products by the US government (see Appendix Table 1 for a list of the industry codes).

Information on country and country-pair characteristics has been obtained from various sources. The Centre d’Etudes Prospectives et d’Informations Internationales (CEPII) GeoDist database (Mayer and Zignago 2011) provides information on bilateral distance in kilometers and an indicator variable that captures if two countries share a border. Data on population and GDP per capita are from the World Bank’s World Development Indicators (WDI). Data on UN voting alignment with the US are from Voeten (2004). Measures of revealed comparative advantage (RCA), export similarity with China, and intraindustry trade with China have been computed using trade data from UN Comtrade. Data on Free Trade Agreements (FTAs) are from Mario Larch’s Regional Trade greements Database based on Egger and Larch (2008). A summary of the main variables is presented in Appendix Table 2.

Prima facie evidence on the reshuffling of the top trade partners from 2017 to 2022 is presented in Figure 3. China’s share fell 5.3 percentage points (ppts). For strategic products, this decline is even more massive, from 36.8 percent in 2017 to 23.1 percent in 2022. Focusing on the overall shares, the countries with the biggest gains in market share were Vietnam (1.9 ppt), Taiwan, China (1 ppt), Canada (0.75 ppt), Mexico (0.64 ppt), India (0.57 ppt), and the Republic of Korea (0.53 ppt). These six countries more than account for China’s 5.3 percentage point decline. Their combined gain does not, however, mean that these countries are the main or only beneficiaries, as they may be increasing their market share in products that China does not export or for reasons unrelated to the tariffs on imports from China. These

> 2 As is common in the literature, we exclude countries with population less than 1 million.

5
During 1993-94, the rural CPR for the age group of 15 to 24 years was 52.7 percent, a figure which increased to 55.5 percent during 2011-12 (Table 8). Moreover, the FLFP rate for this age group declined by 19.3 percent (from 42 percent to 22.7 percent) by 2011-12, and during this period the decline was more than compensated by a gain of 22.1 percent in educational participation among working age females. Similarly, in urban areas, the CPR increased by 11 percent (from 49.2 to 60.2 percent), despite a decline in the FLFP rate by only 3.8 percent (from 18.3 to 14.5 percent) during 1993-94 to 2011-12. These figures support the argument that the decline in the FLFP rate for females between 15 to 24 years of age was to a large extent (or may be completely) due to an increase in female enrollment in education.

While evidence from the latest NSSO data corroborates this fact for the age group of 15-24 years (Panel I in Table 8), it falls short to explain the overall drop which occurred for the entire working age female population (Panel II). In terms of change, during 1993-94 to 2011-12, the rural CPR declined by 7.9 percentage points and the urban CPR declined by 0.8 percentage points (Table 8). In rural areas, the decline in CPR was caused by a 13.2 percentage point decline in the FLFP rate, which outweighed a 5.2 percentage point rise in educational attendance. This suggests that the decline in the total FLFP rate during 1993-94 to 2011-12 across all age groups was not in fact due to a rise in educational attendance. To be specific, 62 percent of the total drop in the FLFP rate which occurred in rural areas during 1993-94 to 2011-12 was due to reasons other than increased attendance in educational institutions. In urban areas, only 28 percent of the total drop in FLFP was due to reasons other than attending educational institutions.

The above analysis on CPR dismisses the hypothesis that the overall drop in FLFP of working age females is the result of higher school attendance among the younger cohort, especially in rural 14