rafmacalaba/datause-encoder-data huggingface dataset:
# Table 3: General Economic Effects

||||||**Simulation Modeling**||
|---|---|---|---|---|---|---|
|**GENERAL ECONOMIC IMPACTS**|**Case**<br>**studies**|**micro‐econ.**<br>**Studies**|**macro‐econ.**<br>**Studies**|**fixed**<br>**employment**|**full employment**|**non‐full**<br>**employment**|
|**Direct impacts on industry**|1|1|1|1|1|1,2|
|How is the coal industry affected by<br>climate policy?|||||||
|**_Indirect impacts on industry_**|1|1,2|1|1,2|1,2|1,2|
|_How is manufacturing affected by_<br>_increased energy prices?_|||||||
|**_Non‐carbon‐intensive non‐energy‐_**<br>**_intensive industry impacts_**|||1|1,2|1,2|1,2|
|_How is the service sector affected?_|||||||
|**_Aggregate effects_**|||1|||1,2|
|_How would a carbon tax affect the_<br>_unemployment rate?_|||||||
|**Short‐run vs. Long‐run impacts**|||1|||2,3|
|How quickly does the economy adjust<br>to climate policy?|||||||
|**Effects on household welfare**||||1,2|1,2|1,2|
|Which households are made worse off<br>by climate policy? How does this vary<br>by income/skill/location?|||||||
|**Unemployment and household welfare**||1,2||||2,3|
|How much are households harmed by<br>unemployment? How does this vary by<br>income/skill level?|||||||
|**_Wage impacts_**<br>_How are wages affected by climate_<br>_policy? Does the impact vary by_<br>_industry? Skill level?_||1,2||1|1|1,2|

43
(which is based on equations 12-17), the CGE model is typical of models of small open economies with optimizing behavior for households and producers, domestic markets for commodities and factors cleared by flexible prices and wages, respectively, and a government that consumes, saves, taxes, and both receives and pays transfers. With regard to its treatment of factors, in distinction from the producer maximization problem, the CGE model also requires equilibrium conditions for factor markets, which are defined over _f_ and _a’_ (one condition for each factor stock and activity of origin). These conditions may be stated as follows: �������� �,��,� = �����,�� ����� �������� � where �����,�� is the available physical quantity of factor _f_ that has the capabilities required in source activity _a_ ’. The condition imposes equality between this quantity and the sum of the physical quantities of labor _f_ from _a_ ’ that are allocated to any activity _a_ . It is cleared by the wage variable, �����,��, which is exogenous in the producer model but endogenous (and in upper case) in the CGE model.

# _2.3 Data_

The bulk of the dataset for our model consists of a social accounting matrix (SAM) and sectoral employment quantities (both for the simulation base-year), complemented by a set of elasticities (for production, consumption, and trade). Due to our proximity-based treatment, additional data is needed for the proximity parameter. We will here present the model database with special emphasis on the construction of the data for the proximity parameter.

In this application, the SAM is designed to capture the sectoral structure of an SSA country that is not strongly reliant in natural resource exports.<sup>14</sup> The SAM is used to define base-year values for the bulk of the model parameters, including production technologies, sources of commodity supplies (domestic output or imports), demand patterns (for household and government consumption, investment and exports), transfers between different institutions, and tax rates. The disaggregation of our country SAM coincides with that of the rest of the model database. As shown in Table 1, it is disaggregated into 23 sectors (activities and commodities) – 3 in agriculture, 2 in mining, 13 in manufacturing, and 7 in services – with each activity producing a single commodity for which it is the only domestic producer. The factors are split into labor, private capital, and natural resources (5 types: agricultural land, forestry land, fishing resources, and two natural resources used in extractive industries). The institutions are split into households, government, and the rest of world. A set of auxiliary accounts cover 14 Specifically, our SSA SAM is based on the 2010 supply and use tables for Uganda.

11
drop in exports during the global recession in 2009 (Biesebroek and Volpe Martincus, 2016). However the COVID pandemic is different as it hit the export sector directly by disrupting global supply chains. How has the pandemic affected the functioning of TPOs? Were they able to adapt? These are the questions addressed in the World Bank survey of TPOs.

Several key findings emerge from the survey. First, there is significant heterogeneity in TPOs’ size in terms of budget: the budget of the median TPO in high-income countries is 20 times larger than that of the median TPO in low-income countries. TPOs’ budget responses during the pandemic also varied across income levels: TPOs in high-income countries increased their budget whereas TPOs in low-income countries saw a decline in their budget even after lockdown measures eased. Second, TPOs in high-income countries have a stronger hierarchical structure with the number of board members per employee being eight times smaller than in TPOs in low-income countries. This could partly reflect the finding that TPOs in high-income countries tend to have broader mandates whereas TPOs in low-income countries mainly focus on export promotion. These two observations put together may be explained by the fact that a broader mandate may require a more hierarchical structure. Third, TPOs target generally medium-size established exporters, rather than large or small non-exporters or occasional exporters. The focus on established exporters strengthened in high-income countries during the pandemic, whereas non-exporters became relatively more important in low-income countries. Marketing activities remained the main expenditure item of TPOs during the pandemic. However, its relative importance declined in low-income countries as the pandemic hit. This is partly explained by the fact that TPOs in low-income countries were not as quick to adapt to virtual tools such as business-to-business (B2B) matching events. Indeed, at the time of the survey, among low-income countries there was no TPO that had put a COVID recovery plan in place. Among high-income countries 59 percent had a recovery plan in place. Interestingly, one of the most popular virtual tools used during the pandemic in both high- and low-income countries was “Training on e-commerce” (as popular as “B2B matching events”). This was also the virtual program with one of the lowest expenditure shares, suggesting such programs are not only popular, but also relatively 2
22 16. We believe that a zero entry for housing expenditure is more properly interpreted as missing data rather than a corner solution of the consumer choice problem; it is now rare for a household to be homeless in Java, and even more unlikely that such a household would get interviewed for the SUSENAS. There are very few zero entries for rice consumption in the data, as it is the main food-staple, and most of those that do occur are also more plausibly missing data than corner solutions (van de Walle, 1988a).

17. The use of unit values in demand modelling can result in a aqualitybias* (Deaton, 1987), though this is unlikely to be a serious problem in this application; see van de Walle (1988a) for further discussion.

18. Our estimated income elasticities for housing are higher than some past estimates for developing countries (see the survey by Malpezzi and Mayo, 1987). However, past estimates for Indonesia have indicated an income elasticity of unity or higher, both from grouped data (Chatterjee, 1979) and on applying the same methods here to a different data set (Ravallion, 1989). Note also that past micro-level housing demand studies have generally been confined to urban samples, where average income elasticities are likely to be lower than in rural areas. Our data set spans both. Our rice demand elasticities accord reasonably well with past estimates; for further discussion see van de Walle (1988b).

19. Noting that, although average urban rice prices are higher than in rural areas, rice is a necessity (P2<0)- If housing prices were no higher in urban areas, then the urban cost-of-living index would decrease as reference income increases.
# **Notes**

1 Mogridge 1997.

2 This financing technique is known as project finance. See Yescombe (2002, 2007).

3 See Wilson (1987, ch. 7).

4 For example, by an amount equal to a third of the upfront investment in the case considered in Engel, Fischer, and Galetovic (2003) and by an even larger amount in the case considered in Albalate and Bel (2009). 5 Tirole 1997.

6 Based on Cheng (2010).

7 Cheng 2010.

8 For the Indiana Toll Road, depreciation expenses for 2009–10 totaled $73.6 million.

9 Cheng 2010.

10 See Engel, Fischer, and Galetovic (forthcoming [b]).

11 Engel and others 2009.

12 Yescombe 2007.

> 13 According to the 2011 census, 6.2 million people live in the Santiago metropolitan area ( _Gran Santiago_ ), and the city covers 711.2 square kilometers (71.1 hectares or 274.6 square miles). Densities are 8,700 inhabitants per square kilometer, 87 per hectare, or 22,578 per square mile. _Gran Santiago_ is divided into 37 municipalities.

14 Between 1940 and 1960, Santiago’s population roughly doubled, from about 1 million to 2 million. Santiago’s surface area doubled too, from about 100 square kilometers to 200 (10,000 hectares to 20,000, or 38 square miles to 77). By 1970, the population had increased by another million and surface area by another 100 square kilometers.
could nonetheless be significant in an economic sense. This is particularly true for the sectors under consideration here, which are of considerable importance to developing countries in the early stages of industrialization. These results highlight, however, that in terms of assessing the impact on exporting countries, harmonization needs to be considered within the context of the overall level of compliance costs involved.

4.1 Instrumental Variables Results It is important to be sure that these results are not biased due to the possible endogeneity of product standards (and tariffs) with respect to export variety.”° It is commonplace to view endogenous tariffs as the outcome of a lobbying process in which a government balances support from lobbies seeking protection against the national welfare costs of imposing that protection (e.g., Grossman and Helpman, 1994). Since harmonization can, like tariff liberalization, lower the cost barriers facing foreign firms, local incumbents have an interest in lobbying against it. For the moment, the political economy of this process remains to be examined in detail in the literature.” I draw on the general framework that has emerged from the endogenous tariffs literature to identify some likely determinants of endogenous standards, which I then use as instruments. In the EU context, the type of firm-to-government lobbying envisaged by Grossman and Helpman (1994) is accompanied by an additional layer of government-to-government lobbying within centralized EU bodies.” This is due to the fact that both trade policy and the EU standards under consideration here are not decided unilaterally by national governments, but by European bodies (the Commission and CEN). To capture the lobbying potential of industries on an EU-wide basis, I use total sector value added and employment.”* The first of these measures proxies a sector’s °Moenius (2004) addresses endogeneity by using five year lags of his standards variables as instruments for their current levels. Chen and Mattoo (2004) use harmonization of closely related sectors as an instrument for harmonization of a given sector. In both cases, instrumental variables estimation does not substantially change the results. *1Essaji (2005) is the only empirical study dealing with the political economy of standards in a trade setting. 2Balaoing and Francois (2006) report that after controlling for the size of an industry, its "nationality" remains an important determinant of the level of protection it receives through the EU common external tariff. °3 An alternative approach could be to instrument using data on antidumping actions taken by each industry, using the dataset compiled by Bown (2007). This is a promising avenue for future research, but it is not pursued here for two reasons. First, the instruments used in this paper are arguably more likely to be exogenous to partner country 19
Second, we have developed a control group for the trainees, using panel data on a random sample of individuals drawn from the 1990-91 quarterly urban labor force surveys (ENEU).

We begin the report by providing a broad overview of the characteristics of unemployment in Mexico And of the PROBECAT program. We also describe several surveys that have Deen fielded to monitor the progress of trainees. We use these surveys, and data from the Urban Labor Force Survey (ENEU) to compare trainees and unemployed individuals that did not participate in training. This is followed by discussion of the methodological issues that arise in manpower training evaluations, and our approach to resolving them in this study. The next two sections report our estimates of the effects of training on probabilities of employment, time-to-first job, monthly earnings, hours worked per week, and hourly wage. In the concluding section, we summarize the most important findings, present estimates of the cost-benefit ratios of training for various groups of participants, and discuss the implications of these findings for the PROBECAT program and, more broadly, for other manpower training evaluation studies.

# II. UNEMPLOYMENT AND THE PROBECAT PRGRM: BASIC FEATURES

We begin by summarizing the main characteristics of unemployment in Mexico. Next, we describe the salient features of the PROBECAT program: its organization, the kinds of vocational training provided, arid the criteria used to select individuals into the training program. Finally, we describe the data on PROBECAT trainees and a matched sample of unemployed individuals which we use for the evaluation.

# Background: Basic Characteristics of Unemployment in Mexico

According to official unemployment statistics, the open unemployment rate in Mexico is quite low. In 1991, it stood at 2.8 percent of the labor force, and even in the worst years of the adjustment crisis, it did not rise beyond 6.1 percent. These unemplo-_..nt figures, however, have a number of shortcomings. First, they refer only to urban unemployment. Second, they are based on a fairly loose definition of employment, which counts an individual as employed if he/she works at least one hour per week. Third, and most important, they include only those individuals who are actively searching for a job. However, research on a number of countries suggests that the distinction between "unemployed" and "not in the labor force" based on intensity of
||||<br>For all of the beneficiaries 12 years are required for the<br>program to have apositive netpresent value.||
|---|---|---|---|---|
|**Argentina (1994)**<br>Programa Joven.|Almeida and<br>Galasso<br>(2007)|Difference-in-difference methodology to<br>compare the outcomes of interest for project<br>participants before and after the intervention<br>with those of non-participants. A baseline<br>household survey was administered to 309<br>participants and 244 non-participants.|<br>Those with entrepreneurial skills, female household<br>heads and more educated individuals are most likely to<br>take up self-employment.<br><br>No evidence of average income gains to participants<br>and their households in the short run.||
|**Mexico (1982)**<br>PROBECAT (Programa de<br>Becas de Capacitación para<br>Trabajadores)<br>.|Wodon and<br>Minowa<br>(2001)|Use the PROBECAT at the state level is used<br>as an instrumental variable to control for<br>endogenity of program placement to compare<br>a sample of PROBECAT participants and a<br>sample of unemployed individuals from<br>Mexico’s urban employment survey.<br>Heckman Sample Selection Model is used to<br>estimate the impact of PROBECAT on<br>monthly earnings corrected for selection into<br>the program. Cox Proportional Hazard Models<br>are estimated to assess the impact of training<br>on the time necessary to find employment.|<br>No impact on employment and wages found.|This result contrasts<br>with earlier<br>evaluations; this study<br>concludes that the<br>positive results in the<br>past evaluations were<br>obtained because<br>limited attention was<br>given to the problem<br>of sample selection.|
|**Mexico(1984)**<br>PROBECAT (Programa de<br>Becas de Capacitación para<br>Trabajadores)|Revenga,<br>Riboud and<br>Tan (1994)|Comparison Group Analysis with quarterly<br>National Urban Employment Surveyy<br>administered to the 1990 cohort of<br>PROBECAT participants with the non-<br>participants comprised of unemployed<br>individuals<br>Heckman Two Stage Selectivity Correction<br>Proceudure is used to correct for selectivity<br>into the program. Cox Proportional Hazards<br>Model of unemployment duration on the<br>pooled trainee and comparison group samples.|<br>PROBECAT fairly effective in shortening the duration<br>of unemployment only for trainees with prior work<br>experience.<br><br>It also improved the likelihood of employment over a<br>longer run<br>(three-six months for male and one year for female)<br><br>It raised post-training earnings of men but not women<br>and the effects were greater for males with 7-9 years of<br>schooling.<br><br>For both men and women, training induced an<br>increase in the number of hours worked per week.<br><br>The study confirms that program evaluation results can<br>be sensitive to the way in which training effects are<br>measured. A key source of bias is that arising from<br>nonrandom selection of participants into the training<br>program and it is important to correct for selectivity<br>bias.||

23
# **I Data and indicators**

## **Trade data sources**

The data used in this study come from Chinese Customs and provide export flows aggregated by province, year, product, and destination country over the 1997-2007 period.<sup>1</sup> We reaggregate the original eight-digit-level data into HS4-level data (more than 1,200 product lines). An interesting feature of this dataset is that it allows us to identify whether the export flows emanate from domestic or foreign firms<sup>2</sup> and whether they correspond to processing trade or to ordinary trade.<sup>3</sup> The processing trade includes all trade flows by firms operating in the assembly sector; that is, firms that import and process inputs in China and then re-export the final products abroad. Firms engaged in this type of activity might be less embedded in their local environment and might consequently generate fewer (and possibly benefit less from) externalities.

## **Explained variable: creation of new export linkages**

The creation of a new export transaction is measured by a dummy that takes the value 1 if the domestic firms in province _i_ begin exporting product _k_ to country _j_ at time _t_ + 1 and 0 otherwise. A specific database is constructed that incorporates the set of alternatives faced by

> 1We do not have firm-level data, but we believe that province/firm-type/trade-type/product/destination country data are suitable for the investigation of micro-phenomena such as export spillovers. The information that we have is very detailed. Feenstra and Hanson (2005) argue, for example, that their city/firm-type/tradetype/product/destination country dataset approaches the precision of a firm-level dataset. Moreover, with firm-level data, we would have information on the overall size or productivity of the firm, but we would lack information on the firm/product-specific ability. Finally, we have over four million observations for our regressions. For the analysis of the determinants of entry on export markets, firm-product-destination country data would hardly be tractable.

> 2The data are separately reported by firm type, including foreign-owned enterprises, Sino-foreign joint ventures, collective enterprises, private enterprises, and state-owned enterprises. The first two categories are considered foreign firms, and the other categories are considered domestic firms. Unreported regressions, available upon request, show that the results hold when restricting domestic firms to state-owned or private firms. In addition, the foreign export spillovers appear to emanate similarly from both fully-foreign and joint-venture

> 3The data also refer to a third category (“Others”) that groups other flows, such as aid, border trade, and consignment, together representing less than 1% of the total trade value per year. When considering the processing/ordinary trade distinction, this category is dropped. 7
- Ravid, S., 1996, Debt maturity: a survey, _Financial Markets, Institutions and Instruments_ 5, 1-69

- Rey, P. and J. Stiglitz, 1993, Short-term contracts as a monitoring device, _NBER Working Paper_ 4514

> Rodrik, D. and A. Velasco, 1999, Short-term capital flows, _NBER Working Paper 7364_

- Smith, C. and R. Watts, 1992, The investment opportunity set and corporate financing, dividends and compensation policies, _Journal of Financial Economics_ 32, 263-292

- Stiglitz, J., 1974, On the irrelevance of corporate financial policy, _American Economic Review_ 64, 851-66

- Stiglitz, J. and A. Weiss, 1981, Credit rationing in markets with imperfect information, _American Economic Review_ 71, 393-410

- Stoh, M. and D. Mauer, 1996, The determinants of corporate debt maturity structure, _Journal of Business_ 69, 279-312

- Von Thadden, E., 1995, Long-term contracts, short-term investment and monitoring, _Review of Economic Studies_ 62, 557-575.

28
the direct domestic value added embodied in gross exports over the years 2009‐2011, from 54.4% to 50.6%.

This is somewhat different compared to Brazil’s peer countries. For instance, Argentina shows higher direct value added in exports in 2011, of around 55.1 percent whereas its indirect value added in exports was 44.9 percent and which has been stable compared to 1995. For Canada this pattern is also reversed, and to a higher extent.

In GVC sectors, however, the domestic direct value‐added share generated through exports is more modest, and has also been decreasing (Figure 4). For instance, in agribusiness the direct share in 2011 reached almost 25%, but had declined from as share in 1995 of 31.1%. For metals, the direct share in 2011 was 39.2% but was much higher in 1995 where it had a direct share of 51.2%. The automobile sector has more or less stabilized its direct share although slightly decreased as well from 27.2% in 1995 to 24.3% in 2011. Finally, the chemical sector has seen a direct share decline from 46.5% in 1995 to 32.4% in 2011. This all means that, conversely, Brazil’s indirect share in all four sectors has increased throughout these years as the re‐imported value‐ added share is minimal.

Figure 4: Decomposition of domestic value added embodied in gross exports, by sector (2011) <!-- Start of picture text --> Agribusiness 2011 BRA Metals 2011 BRA<br>0.1% 0.1%<br>24.9%<br>39.2%<br>60.7%<br>75.0%<br>Direct Indirect Re-imported Direct Indirect Re-imported<br>Automotive 2011 BRA Chemicals 2011 BRA<br>0.2% 0.1%<br>24.3%<br>32.4%<br>67.5%<br>75.5%<br>Direct Indirect Re-imported Direct Indirect Re-imported<br><!-- End of picture text --> Source: Authors’ calculations using OECD/WTO TiVA.

The direct shares in other countries continue to be higher than in Brazil across the GVC sectors. In short, for Brazil the indirect supplying industries play a relative larger role in generating 8