rafmacalaba/datause-encoder-data huggingface dataset:
the Environmental Performance Index 2020 (Yale Center for Environment Law and Policy, 2020) finds that good policy results are associated with wealth (GDP per capita). Poor environmental regulations combined with large informal sectors might further exacerbate the challenges related to emissions. At the same time, informal firms are excluded from public incentives to reduce emissions such as subsidies to adopt energy efficiency and their workers lack opportunities to access training and skills development programs (International Labor Organization, 2018a). Therefore, understanding the relationship between informality and emissions across countries is critical for informing environmental policies in various countries that aim at reducing emissions both locally and globally.

The paper studies the relationship between the informal economy and CO2 and non-CO2 emissions in developing countries using informal employment as a proxy for the informal economy and emissions per value added. This measure of the informal economy is constructed using data collected through labor force surveys and is available for a majority of developing countries. However, it deviates from the literature, which predominantly uses the share of GDP contributed by the informal sector when studying the relationship between informality and emissions. The share of informal workers in total is data-driven as opposed to measurement of the informal sector using GDP, which is based on various estimates offered in the literature and is thus greatly dependent on the chosen estimation approach and assumptions (e.g. see Schneider (2005), Elgin and Oztunali (2014), or Orsi et al. (2012)). In nearly all developing countries, micro surveys – household and labor force surveys – include questions related to informality such as whether a worker has a contract or employment benefits, belongs to a union, contributes social security payments, or receives renumeration. However, these informality-related questions vary considerably across developing countries. To ensure consistency in the measurement of informality across countries, this paper follows Bürgi et al. (2020) and Loayza et al. (2011) and considers as “informal” only those who are self-employed and non-paid workers. While some surveys include additional information that could be used to determine formality, this would reduce the comparability and consistency of how informality is measured across countries. Finally, it chooses to avoid a comparison over time since, according to the data, the informal sector’s share of the economy remains remarkably stable over time.

Informality in developing countries can impact emissions in two main ways. On one hand, informal firms in developing countries include mostly self-employed workers or small firms with very low capital, hence their contribution to countries’ emissions could be very low. In contrast, formal firms - which include large firms - are to a great extent responsible for emissions as they rely on capital-intensive technologies that burn fossil fuel. On the other hand, informal firms do not necessarily comply with government regulations 3
issue even further, many countries, such as Chile, Peru and Turkey, are individually identified in some of the survey years but aggregated into the regional categories in others, making the analysis close to impossible. To address these complications, we construct the smallest set of countries and regional aggregates that are consistent over time, ending up with 40 geographic units, including the United States. Among these, 25 are separate countries and 15 are regional aggregates of the 150 other countries (or regions) that are listed in birthplace category. The list of our 40 groups and the countries they include are presented in Table 1. This type of aggregation of the world into 40 countries or groups potentially lowers our estimates and the extent of transit migration in the data. For example, if a migrant who was born in Peru goes to Argentina before moving to the United States, we will not be able to identify him as a transit migrant since Peru and Argentina are part of the same category (Other South America) in our data and estimation. For compatibility, we group the countries in the global bilateral migration database and DIOC/DIOC-E along the same lines. Finally, in addition to their country of birth and last residence, we split all migrants as tertiary or non-tertiary educated.

Figures 1 and 2 as well as Tables 2 and 3 provide information on the extent of transit migration to the United States. Table 2 shows what percentage of migrants born in a given country was residing in a different country before migrating to the US. For example, 7% of Canadian born migrants came to the US from a different country. The same ratio is only 1% for Mexican migrants, but over 20% for migrants born in many European countries like the United Kingdom, Italy and the Russian Federation. The same information is presented visually in Figure 1 to highlight regional differences and similarities. We see that transit migration is higher among migrants born in Africa and the Middle East and lower in Latin America, as migrants from the latter region have more direct access to the United States due to geographic proximity and diaspora linkages.

Table 3 presents the percentage of migrants coming from a given location who were born somewhere else and Figure 2 displays the same information visually. The data indicate that transit migration is quite high among the migrants who were living in higher income OECD countries.

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J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 33 Productivity We now turn to consider the estimates of the impact urban form on productivity. For the case of _exchange_ index_ , we note that both the OLS and 2SLS estimates reveal that a higher level of urban compactness is associated with higher levels of productivity. The coefficient on _exchange_index_ is positive and highly significant at the 5% level and the 2SLS coefficient is higher than what was found with the OLS estimate. The 2SLS coefficient on _exchange_index_ suggests that a one standard deviation increase in the level of urban compact shape is associated with an 8-percentage point increase in productivity. Regarding _perimeter_index_ estimates, the results show that while the OLS estimate is positive and significant in statistical terms, the 2SLS coefficient is not significant.

**Table 8.** Estimates of the relationship between urban form and ldr by OLS and

2SLS

||(Dependent|variable:ldr)<br>2SLS||2SLS|
|---|---|---|---|---|
||OLS<br>(1)|(IV: Norm.<br>potential<br>exchange)<br>(2)|OLS<br>(3)|(IV: Norm.<br>potential<br>perimeter)<br>(4)|
|Norm. exchange|0.672***<br>(0.1305)|0.958**<br>(0.4298)|||
|Norm. perimeter|||0.366***<br>(0.1198)|-0.370<br>(0.3930)|
|Country dummies|Y|Y|Y|Y|
|Year dummies|Y|Y|Y|Y|
|N|2,757|2,757|2,757|2,757|
|R-squared|0.274|0.142|0.262|0.090|
|**Instrument relevance**|||||
|**1. First-stage statistics**|||||
|F-stat<sup>a</sup>||62.37||29.22|
|F-stat P-val||0.000||0.000|
|**2. Under-identification test**|||||
|Kleibergen-Paap rk LM stat<sup>b</sup>||4.481||5.601|
|Chi-sq P-val||0.0343||0.0179|
|**3. Weak identification test**|||||
|Kleibergen-Paap rk Wald F stat<sup>c</sup>||62.37||29.22|

Note: Robust standard errors clustered at the country level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 All models include geographical characteristics as measured of natural amenities, namely, temperature and coast indicator. a F-test of excluded instruments ( _nexchangep_ or _nperimeterp_ ) in the first stage model. F-stat above 10 indicates that the instrument has considerable explanatory power. b The Kleibergen-Paap rank LM test of under-identification tests whether the excluded instruments are correlated with the endogenous regressor. c The Kleibergen-Paap rank Wald test of weak identification tests the significance of the excluded instruments in the structural equation. The critical values for this test are from Stock and Yogo (2005). The values of this test are higher than the Stock and Yogo (2005) critical values, suggesting that the instruments are not weak.
On balance, however, Botswana has run a fairly prudent fiscal policy, avoiding many pitfalls experienced by other natural resourcerich countries. It has avoided an excessive accumulation of external debt. Rent-seeking behavior has been kept to a minimum, as reflected in Botswana’s excellent governance indicators. Apart from the authorities’ cautious approach, the relative stability of diamond prices has played a role in the maintenance of a stable fiscal policy. In addition, the government’s capacity constraints affecting full implementation of the development budget may have worked to prevent rapid increases in expenditure. In Botswana, actual development spending has been consistently below the budgeted development budget due to capacity constraints to implement projects, despite its preference for ambitious developmental programs. Botswana maintained a large fiscal surplus most of the time in the 1980s and 1990s (Figure 5). These surpluses have been saved in the Government Investment Account (GIA) with the BoB as the government’s portion of the Pula Fund (Box 1), and invested in long-term assets mostly abroad.

Nonetheless, with the declining mineral revenue, the occurrence of fiscal deficits has become more frequent in the last decade. The government has financed the deficits primarily by drawing down on the GIA. Foreign and domestic borrowing has played a very limited role to close the fiscal gap. Issuance of government securities that began in 2002/03 was only intended for the development of local capital markets. At end2007/08, external debt amounted to a mere 2.9 percent of GDP, whereas domestic debt

**Figure 5.  Botswana: Fiscal Operation (in percent of GDP)** /1

<!-- Start of picture text --> 70<br>Fiscal balance<br>Revenue and Grants<br>60<br>Expenditures and Net Lending<br>50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br><!-- End of picture text --> Source: Ministry of Finance and Development Planning 1/ 2009/10 figures are estimates.

**Figure 6.  Botswana: Non-mining Fiscal Operation** (in percent of non-mining GDP) /1

<!-- Start of picture text --> 160<br>140 Non-mineral fiscal balance<br>120 Non-mineral revenue<br>100 Expenditure and net lending<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>-80<br>-100<br><!-- End of picture text --> Source: Botswana authorities. 1/ 2009/10 figures are estimates.

was 2.8 percent. Both of them were well below the statutory limits. Figure 6 shows Botswana’s fiscal operation measured by non-mining GDP, a conventional measure for resource dependent countries. A combination of overall fiscal surpluses, non-resource deficits and a large government characterizes the budgets of major resource-dependent countries.<sup>11</sup>

> 11 Also, resource abundance is often associated with poor development of human capital, as a low degree of economic diversification provides few opportunities for learning and doing (Söderling, 2005).

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**18**

beyond the averages, the exact magnitude of the sectoral multipliers is likely to differ across countries depending on their institutions and structure.

**Table 3: Dynamic Linkages of the Non-agricultural Sector**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
|**Non Agricultural GDP**||||||
|**per capita**|**Full sample**|**Middle inc**|**Middle inc**|**Low inc**|**Low inc**|
|**Non-agr. GDP t-1**|1.49|1.28|1.27|0.928|0.803|
||_0.000_|_0.000_|_0.000_|0|_0.000_|
|**Non-agr. GDPt-1 * DSSA **|||-0.18||0.227|
||||_0.111_||_0.047_|
|**Non-agr. GDP t-2**|-0.499|-0.261|-0.232|||
||_0.000_|_0.283_|_0.3629_|||
|**Non-agr. GDP t-3**||-0.121|-0.117|||
|||_0.425_|_0.4803_|||
|**Agr. GDP t-1**|0.0407|-0.0818|-0.117|0.138|0.259|
||_0.209_|_0.172_|_0.0643_|0.0639|_0.0459_|
|**Agr. GDP t-1 * DSSA**|||0.256||-0.313|
||||_0.1119_||_0.0267_|
|**Time dummies**|_Jointly sign._|_Jointly sign._|_Jointly sign._|_Jointly sign._|_Jointly sign._|
|**Constant**|-0.0959|1.24|1.27|-0.188|-0.0163|
||_0.298_|_0.064_|_0.010_|0.4204|_0.9567_|
|**Observations**|730|300|300|526|526|
|**# of countries**|104|42|42|74|74|
|**# of instruments**|81|60|60|72|72|
|**P-value f/ no AR(1)**|0.0000191|0.0487|0.0553|0.00142|0.00261|
|**P-value f/ no AR(2)**|0.695|0.0785|0.0889|0.864|0.94|
|**Sargan test**|0.185|0.992|0.989|0.281|0.588|

Source: Authors’ calculations
Table 11: MNCs and Wage Bill in Manufacturing, 2013~2019

|Panel A. Depen|dent Variab<br>|le: Logof A<br>|verage Wa<br>|ge<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|MNC_i ×_Post_t_|0.0661***|-0.0093|-0.0094|-0.0082|
||(0.0070)|(0.0075)|(0.0074)|(0.0068)|
|Observations|41,036|41,036|41,035|41,030|
|R-squared|0.7227|0.7310|0.7323|0.7431|
|Panel B. Depen|dent Variab<br>|le: Logof R<br>|eal Wage B<br>|ill<br>|
||(5)|(6)|(7)|(8)|
|MNC_i ×_Post_t_|0.0336*|-0.0405**|-0.0423**|-0.0377***|
||(0.0193)|(0.0163)|(0.0165)|(0.0130)|
|Observations|41,036|41,036|41,035|41,030|
|R-squared|0.9535|0.9546|0.9551|0.9583|
|Fixed Effects:|||||
|Firm|Yes|Yes|Yes|Yes|
|Year|No|Yes|No|No|
|Region-Year|No|No|Yes|No|
|Industry-Region-Year|No|No|No|Yes|

_Notes:_ MNC _i_ is an indicator variable that equals 1 if a firm _i_ has foreign affiliates in the years 2013~2016 and 0 otherwise. Post _t_ is an indicator variable that equals 1 if the time _t_ is after 2016 (i.e., 2017, 2018, and 2019) and 0 otherwise. Standard errors are clustered at the industry level. *** p<0.01, ** p<0.05, * p<0.1.

(1) through (4) in Panel A of Table 11. In column (4), the preferred specification, the coefficient estimate is -0.0082 and statistically insignificant, invalidating the idea that there was labor-labor substitution within Korean MNCs. Replacing the dependent variable in equation (2) with real wage bills in log, Panel B of Table 11 summarizes estimation results, whereby column (8), the preferred specification, shows that the coefficient estimate is -0.0377 and statistically significant at the 1% level. Quantitatively, the real wage bill of the Korean firms with foreign affiliates declined by 3.8% more than that of the Korean firms with no foreign affiliates, which is contrary to what the labor-labor substitution hypothesis would predict for Korean MNCs.<sup>37</sup>

# **6.3 Foreign Demand Shocks**

Global trade has been sluggish since the recovery from the global financial crisis (Bems et al., 2013; Boz et al., 2015; Aslam et al., 2018; Shin, 2019; Constantinescu et al., 2020). If global trade slowdown reduced overall global demand, then Korean MNCs may be affected by external demand shocks and thus reduced employment. Therefore it is plausible to hypothesize that our baseline findings simply reflect the fact that weak global

> 37In Appendix Figure B.8, the event study analyses and the dynamic treatment effects are presented, which corresponds to Table 11.

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7-11 years old due to almost full enrollment in urban areas (Acción Social, 2010a). Like the _Oportunidades_ program, the transfers are specifically given to mothers, a mechanism designed to ensure that the money is invested in children and as an incentive for empowering women within their communities.

Within each municipality FA targets the poorest households based on a proxy-means test system constructed with information from a census of poor people (known as _Sisben_ ). In Colombia, all households surveyed by _Sisben_ are assigned to one of six brackets of a poverty index score called _Sisben_ that is used to identify the most vulnerable population. The index runs from 0 to 100, and it is a function of a set of household characteristics and variables related to the consumption of durable goods, human capital endowments, and current income to calculate a score that indicates the household economic well-being. The first level includes households that are extremely poor. While many social programs target the population with scores falling in the first two brackets, FA is only offered to households in the first bracket. Municipal governments are responsible for ensuring adequate coordination with schools and health centers working with the FA program to ensure its successful local implementation.<sup>9</sup> Local banks deliver the cash transfers to beneficiaries every two months. It was estimated that the annual costs of the program in 2009 were going to be equivalent to 0.27 percent of GDP in that year (World Bank, 2008).

The program FA expanded over the years to reach national coverage in 2010. Initially, the program was targeted geographically. Only municipalities that were not departmental capitals, with fewer than 100,000 inhabitants, with at least one bank branch working in the municipality, and had access to facilities that allowed for the implementation of the program were eligible (691 out of the 1,024 municipalities). Within these communities, a total of 340,000 households in 622 municipalities were registered to participate (Attanasio et al., 2006). In 2005, the program was extended to

> 9 Municipalities prepare the list of families to receive the subsidies ( _Sisben_ level 1 families with children 18 years old or younger). The list is consolidated from municipalities every three years. The subsidies are also contingent on verification of compliance with the conditions that involves beneficiary mothers obtaining attendance certificates from schools every two months and delivering them to the municipal coordination office, who then sends that information to the regional and finally the national coordination unit. In each community, a committee of beneficiary mothers is elected to monitor program implementation.

10
the length of a period corresponds to a year. To facilitate the matching of equations and parameters between this section and Section II, we follow the presentation of the model in that section.

# **1.  Calibration**

The model is calibrated for 2005, the most recent year for which we were able to construct a complete set of macro accounts. Data on national accounts and fiscal accounts were used to produce estimates.

Consider first the production of health services. The share parameter βHC, which determines the roles of public capital in infrastructure and public capital in health in determining the “effective” capital stock in the production of health services (see equation (1)) is set at 0.3. The share βH of medical personnel in the public production of health services (see equation (2)) is set at 0.6. In the same equation, the fraction χGH of the total public labor force that is employed as medical workers is set at 0.015, which corresponds to the value for Haiti in 2005. In equation (3), the share χPH of total private spending allocated by households to expenditure on health services is calculated as 0.032 for Haiti as well.

The share parameter βT in equation (4), which determines how the prevailing stock of educated labor in the private sector and health services are combined to create effective (educated) labor, is set at 0.7.

In the production of commodities, the share parameter βJ, which determines how effective labor and the private capital stock are combined to produce the intermediate input J (see equation (5)), is set at 0.7. Similarly, the share parameter βV, which determines how the composite input J and raw labor are combined to produce the intermediate input V (see equation (6)), is set at 0.8.

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supervisor assessment of Cr-FAs, who improved their performance on microcredit but worsened their social outcomes, are mixed, and not very different in magnitude nor statistical power from that of FAs in control FUs.

# 7 Conclusion

In this paper, we provide evidence of the role of cost and production complementarities in an organization with multiple goals.

From a methodological perspective, an assessment of whether cost and production complementarities exist requires researchers to isolate the impact of rewarding one task on the other outcome, <u>i</u> that is, ∂b<sup>∂y</sup> j<sup>, i=jandthisisonlyfeasiblewithanexperimentaldesignthatincentivizesonetask</sup> at a time. Comparing the optimal contract that rewards both tasks simultaneously to a fixed wage contract would perhaps yield improvements in both outcomes but would tell us little about existing complementarities.

Although cost complementarities may exist, e.g. effort on a particular task may increase the cost of exerting effort on another task, we uncover important production complementarities as well. The credit bonus unsurprisingly improves the performance of the microcredit program but worsens CO quality and thus social empowerment. In contrast, at least among staff working alone, the social bonus improves social outcomes and is as effective as the credit bonus at improving credit outcomes. This result cannot obtain with disutility cost complementarities alone.

Given that the social bonus undermined the intrinsic motivation of all employees regardless of whether they worked alone or in teams, and it increased the propensity to free-ride among those working in teams, the results suggest that rather than incentivizing both tasks simultaneously, a fixed wage may be optimal. In fact, shortly after the study concluded NRSP stopped collecting the data needed to pay the social bonuses.

Our results highlight the challenge that organizations with multiple goals face. We take as an example a development organization where an emphasis on sustainability may undermine its overarching development mission. As anti-poverty programs become increasingly multi-faceted by including microcredit, asset transfers, skill training and other interventions and are delivered through a community participation driven approach, the results suggest that the effectiveness of such programs rely not only on how these interventions are designed but also on how the delivery of different services is incentivized.

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