rafmacalaba/datause-encoder-data huggingface dataset:
Appendix Table A5 we verify that the results in Table 6 are robust to controlling for our predetermined measure of employee pollution awareness (even columns), as well as to controlling for our standardized index of employee awareness that combines all the outcome variables in Table 7 (odd columns). This further reassures us that the results on manager’s adaptation are not primarily driven by sorting.

# **7 Conclusion**

In developing nations, pollution tends to rise with economic development due to the lack of environmental regulations to reduce emissions (Grossman and Krueger 1995, Selden and Daqing 1995). During a time when pollution is rising in many growing developing country cities, it is important to explore who bears the economic incidence of these costs and who can affect it. Our contribution is to collect new micro data combining high-resolution pollution measurements with a rich firm-level survey and show that managers can play an important role in mediating the severity of the health cost of pollution on worker productivity.

Studying the location choice of managers within polluted cities, we show that they face a trade-off in whether to avoid pollution or maximize profits. Since road traffic bundles product demand and pollution exposure, we show that managers choose to locate on high-traffic and polluted roads to maximize access to customers. At the same time, firms are not passive victims in the face of the threat of pollution exposure, and the extent to which they are able to adapt depends on the ability of the manager: high ability managers better protect their workers from pollution, through both equipment and organizational strategies to minimize their exposure. This result highlights an important, yet overlooked, dimension of welfare inequality across workers in low income countries: employees who work at the best firms earn higher wages but also benefit from lower pollution exposure. In addition, this finding has implications for our understanding of the effects of pollution on aggregate productivity: as those managers who are better able to adapt are also the most productive ones, this may limit the negative effects of pollution on industry productivity (Graff Zivin and Kahn 2016).

Our results have several implications for city leaders. First, they highlight one additional potential benefit of policy interventions aimed at fostering firm growth: as firms grow larger and become able to separate production and retail activities and to invest in marketing, this might allow them to break the bundling problem and locate production in less congested and less polluted areas of the city. Second, industrial parks might help even small firms in breaking the bundling problem, by allowing a critical mass of small firms to relocate away from congested areas while still retaining visibility to customers through the scale of the park. Our data shows that such firm relocation could lead to significant reduction in pollution exposure as there is substantial variation in pollution within developing country cities. For instance, the average 30
> Gurr, Ted Robert, 1970. _Why Men Rebel_ . Princeton, NJ: Princeton University Press.

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Raleigh, Clionadh & Håvard Hegre, 2005. ‘Introducing ACLED: An Armed Conflict Location and Event Dataset’. Paper presented to the Conference on “Disaggregating the Study of Civil War and Transnational Violence”, University of California Institute of Global Conflict and Cooperation, San Diego, CA, 7–8 March 2005. URL: http://www.prio.no/page/Publication_details//9429/46564.html.

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Sambanis, Nicholas, 2003. `Using Case Studies to Expand the Theory of Civil War'. _Conflict Prevention and Reconstruction Working Paper_ No. 5 . May 2003. URL: http://lnweb18.worldbank.org/ESSD/essd.nsf/CPR/WP5 Scarritt, J. and S. McMillan, 1995. `Protest and Rebellion in Africa- Explaining Conflicts between Minorities and the State in the 1980s.', _Comparative Political Studies_ 28(3): 323–349.

Strand, Håvard; Lars Wilhelmsen & Nils Petter Gleditsch, 2004. 'Armed Conflict Dataset Codebook Version 3.0', PRIO, 12 October. URL: 35
|**Table 2: Interactio**<br>Dependent variable: Labor Productivity|**n term results fo**<br>**(1)**|**r level of lab**<br>**(2)**|**or producti**<br>**(3)**|**vity**<br>**(4)**|**(5)**|
|---|---|---|---|---|---|
|(logs)||||||
||**Panel A: Regressi**|**on results**||||
|Ethnic Fractionalization*SME Y:1 N:0|-1.654***|-1.386***|-1.339***|-1.394***|-1.247***|
||(0.438)|(0.454)|(0.440)|(0.414)|(0.413)|
|Ethnic Fractionalization|-0.637|0.028|0.077|0.207|0.121|
||(0.487)|(0.442)|(0.440)|(0.421)|(0.402)|
|SME Y:1 N:0|0.193|0.056|0.288|0.405**|0.350**|
||(0.180)|(0.171)|(0.174)|(0.166)|(0.166)|
|GDP per capita (logs)||0.590***|0.562***|0.559***|0.506***|
|||(0.090)|(0.087)|(0.086)|(0.113)|
|Age of firm (logs)|||0.182***|0.195***|0.195***|
||||(0.058)|(0.059)|(0.058)|
|Manager experience (years, logs)|||0.035|0.011|0.002|
||||(0.059)|(0.059)|(0.055)|
|Exports (proportion of sales)|||0.420***|0.376***|0.403***|
||||(0.112)|(0.106)|(0.104)|
|Foreign Ownership Y:1 N:0|||0.272**|0.256**|0.252**|
||||(0.105)|(0.104)|(0.104)|
|Firm is Financially Constrained Y:1 N:0|||-0.319***|-0.277***|-0.275***|
||||(0.079)|(0.079)|(0.073)|
|Female Top Manager Y:1 N:0||||-0.262***<br>(0.071)|-0.239***<br>(0.069)|
|Firm Provides Training Y:1 N:0||||0.002***|0.002***|
|||||(0.001)|(0.001)|
|Courts Obstacle Y:1 N:0||||-0.010|0.000|
|||||(0.064)|(0.059)|
|Total hours of power outages||||-0.001**|-0.001**|
|||||(0.000)|(0.000)|
|Firm Purchased Fixed Assets Y:1 N:0||||0.171***|0.187***|
|||||(0.062)|(0.064)|
|Time Tax||||-0.001|-0.000|
|||||(0.002)|(0.002)|
|Population (logs)|||||0.033|
||||||(0.055)|
|Primary Education|||||<br>-0.009*|
||||||<br>(0.005)|
|Freedom from Business Regulations|||||<br>0.033|
||||||(0.098)|
|Merchandize Trade to GDP ratio|||||<br>-0.002|
||||||(0.003)|
|Growth rate of GDP per capita (%,|||||0.022|

46
indices on “Public Accountability” and “Administrative Quality” by aggregating the existing relevant data sets for these features of governance.

Our choice of indicators has been limited by the lack of annual data available for a large sample of countries over long periods of time. Considering the existing classifications of governance data, this paper categorizes the governance variables which are likely to affect individual investors’ decision into three broad clusters: “Administrative Quality” ( _QA_ ), “Public Accountability” ( _PA_ ), and “Political Stability” ( _PS_ ).

# **2.1. Quality of Administration (** **_QA_ )**

The first set of candidates is intended to provide information on the ability of government to deal with investors and to provide them with an investment-friendly and reliable context in which to conduct their investment projects. Following the World Bank (2004), we have defined the first governance variable as the “Quality of Administration” **.** This variable incorporates four indicators from the International Country Risk Guide ( _ICRG, 1999_ ), namely: (a) “Control over Corruption”, (b) “Quality of Bureaucracy”, (c) “Investment Profile”, and (d) “Law and Order” (see definitions of variables in _Annex 3_ ). These institutions are part of the investment climate of a country. They promote investments by reducing the costs and risks of doing business.

Corruption often has adverse effects on economic activities. This fact is well documented and is often described as one of the major constraints facing enterprises in the developing world (see the World Bank 2005). In his cross-country analysis, Mauro (1995) shows that corruption reduces growth. Gupta, Davooli and Alonso-Terme (2002) stress that corruption exacerbates income inequality and poverty. Mo (2001) documents a 9
# **Mind the Gap?**

# **A Rural-Urban Comparison of Manufacturing Firms**

Bob Rijkers<sup>1</sup> , Måns Söderbom<sup>2</sup> and Josef Loening<sup>1</sup>

> **1** World Bank, Washington DC, USA, **2** Department of Economics, University of Gothenburg, Sweden Corresponding author: Bob Rijkers, PREMPR, the World Bank, e-mail: <u>brijkers@worldbank.org</u>

_We would like to thank the Ethiopian Central Statistical Agency for being able to make extensive use of the survey data, seminar participants at the 2008 Oriel College Oxford Joint Academic Forum on Economics and Finance, the Ethiopian Ministry of Finance and Economic Development, the Ethiopian Ministry of Agriculture and Rural Development, as well as Magdi Amin, Kathleen Beegle, Paul Collier, Mulat Demeke, Louise Fox, Cornelius van der Meer, Gbemisola Oseni, Pierella Paci, Peter Lanjouw and especially Francis Teal for useful discussions and comments. Financial support from the Research Committee of the World Bank is gratefully acknowledged. The views expressed in this paper are entirely those of the authors; they do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent_
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14 production and investment decisions based on the expected economic rewards to their family members, provincial and local political interventions limit the role of economic incentives for many firms. Indeed, these interventions by government officials and politicians in individual SOEs – in particular in the case of investment, export and employment – is the background for Gregory Chow’s characterization of China as “a bureaucratic market economy” (1997, 2002, Chapt. 19). As a result, there are tensions between market signals and political signals for the management of SOEs, with unclear responsibilities and principal agent problems as a consequence.

The important role of economic incentives for agriculture farms directly translates into strong economic incentives (economic rewards and punishment) for individuals working on the farms. By contrast, the role of economic incentives for individual employees in industry and services depends on the system of wage formation. Indeed, while state firms today are quite free to set wages for their employees, the latter still often enjoy higher wages than employees in other firms. This is the case, in particular, when state firms have monopoly positions in product markets (such as in the case of public utilities).

It should be noted, however, that low marginal tax rates on work and saving contribute to maintaining economic incentives in all sectors, as compared to the situation in a number of high-tax developed countries today. Very few individuals are subject to marginal income tax rates above 20 percent, although a small group of high-income earners pay 45 percent. An important explanation is, of course, that total government spending is less than 20 percent of GDP (18 percent in 2004 according to the 2005 revision of the national accounts). Another explanation might be that the authorities have been careful not to diminish economic incentives for entrepreneurship and investment in human and physical assets.

Competition has certainly also increased dramatically during the reform period – for firms as well as for individuals. But it is difficult to determine whether the degree of competition among _firms_ (dimension 7 in Figure 1) is stronger in agriculture than in industry and services. The character of this competition obviously differs: atomistic in agriculture but strategic – reflecting “rivalry” – in most other sectors. It seems, however, reasonable to argue that _individuals_ are less protected against competitive forces in agriculture than in industry. While market forces automatically expose individual
16

**Table 4 (continued)**

|Years:<br>||||19|98|||
|---|---|---|---|---|---|---|---|
|Education|Experience|_OLS_|Q10|Q25|Q50|Q75|Q90|
|2|10|_0.040_|0.030|0.033|0.038|0.046|0.052|
|2|20|_0.026_|0.017|0.019|0.024|0.031|0.037|
|2|30|_0.011_|0.003|0.005|0.010|0.015|0.021|
|2|40|_-0.003_|-0.010|-0.008|-0.005|0.000|0.006|
|6|10|_0.041_|0.031|0.035|0.041|0.047|0.052|
|6|20|_0.027_|0.018|0.021|0.026|0.032|0.037|
|6|30|_0.012_|0.004|0.008|0.012|0.016|0.022|
|6|40|_-0.002_|-0.009|-0.006|-0.002|0.001|0.006|
|11|10|_0.043_|0.032|0.037|0.044|0.049|0.053|
|11|20|_0.028_|0.019|0.024|0.030|0.033|0.037|
|11|30|_0.014_|0.006|0.010|0.015|0.018|0.022|
|11|40|_-0.001_|-0.008|-0.003|0.001|0.002|0.007|
|16|10|_0.044_|0.034|0.040|0.047|0.050|0.053|
|16|20|_0.029_|0.020|0.027|0.033|0.035|0.038|
|16|30|_0.015_|0.007|0.013|0.018|0.019|0.022|
|16|40|_0.000_|-0.006|-0.001|0.004|0.003|0.007|

Source: computed from results reported in Table 2.
Table 6 below summarizes the results of SEI calculations according to the various weighting scenarios. Scenario 1 shows the results for the 1<sup>st</sup> principal component when no restrictions are imposed. This linear combination can explain up to 29% of the variation in the data. The second column shows the results when PCA‐determined weights are constrained to be all non‐negative. As one can see in Scenario 2, some of the resulting weights are zero.

Scenario 3 introduces an additional constraint to PCA so that no variable contributes less than 5% to the composite SEI score. This ensures that all criteria –carefully selected by consensus between experts and stakeholders– contribute, albeit minimally, to the scores given to the projects through the SEI. While each subsequent constraint reduces the power to explain the variation in the data, the weights derived in Scenario 3 are considered to be the most objective given the constraints and can still explain 21% of data variance. Therefore, these are used as the weights applied in the final IPF results.

NPD suggested that existing safe water coverage should receive the highest priority/weight, followed by prevalence of water borne diseases, poverty level, beneficiaries per million dollars invested. The lowest priority was recommended to be accorded to jobs created per million dollars invested. The jobs created criterion was deemed of lowest priority by the NPD since the direct jobs primarily reflect temporary jobs created during construction. In contrast, long‐term direct jobs associated with operation and maintenance of the project comprise only a small percentage (on average, 0.03%) of the total number of direct jobs created.

Scenario 4 below shows the results that emerge when the analysis is based on the subjective PCA weighting recommendation of the NPD.<sup>17</sup> Following this rule, there is a significant loss in the efficiency of the resulting SEI in terms of the data variation that can be explained compared to previous estimations (see Scenario 4, Table 6).

Finally, a simple average weighting method (Scenario 5 in Table 6) is also estimated. Overall, while there are differences in some individual rankings (as can be expected) due to the different weights, the general trend of the projects that are the highest and lowest on the spectrum does not vary significantly (more details in Section 4.1.5).

> 17 The NPD references were used as additional constraints in the generation of weights through PCA.

21
is true of restrictive labor market regulation—see e.g., Besley and Burgess (2004) and Bolaky and Freund (2004).

Some commentators—e.g., Ocampo (1994)—worry that liberalization induces an increase in the marginal propensity to import, which in turn causes tightening foreign exchange constraints to curtail growth at an earlier phase in the business cycle than in less open economies. This, they argue, reduces long-run growth prospects. This view is essentially a Keynesian one whereby demand, in this case domestic demand, is the driver of growth. It ignores the potential supply-side benefits of a liberal trade regime and also the fact that the more rapid emergence of current account constraints may lead governments to rely less on domestic demand stimuli to induce growth and rather pursue more stable macroeconomic regimes, which experience has long suggested lie behind sustained expansions. It is also worth noting that even in Keynesian terms it is not inevitable that raising the average propensity to import (i.e., increasing openness) inevitably raises the marginal propensity, and that if it does exchange rate depreciation offers an antidote. It has long been understood that successful trade liberalizations typically require real depreciations—e.g., Thomas and Nash (1991)—which also have political economy benefits in terms of sustaining support for reforms as they reduce the pressure of imports on domestic competing sectors.

The employment story is rather different when we turn to the short run or adjustment period following trade liberalization, the period that structuralist models focus on. The churning that reform induces could clearly reduce employment temporarily, as could conceivably a Keynesian shock emanating from increased import competition. In Chile, for instance, Edwards and Edwards (1996) find a positive association between the degree of liberalization a sector experienced and the extent of layoffs; the sectors experiencing the greatest liberalization were also the ones where the duration of unemployment was longest. (We return to sectoral evidence below.) Overall, however, there is surprisingly little evidence on the nature and extent of transitional unemployment in developing countries, at least partly because of the difficulties of measuring or even defining the phenomenon in dualistic economies. A multi-country study of trade liberalization before 1985 (Papageorgiou, Michaely and Choksi 1991) argued that 7
# **6. Summary and Conclusions**

It is important to examine the impact of past decentralization in Turkey as the country is going through significant reforms, including public administration reform, in the EU accession process. More decentralization is expected in Turkey as the Parliament has recently approved a new law (Law No. 5747) that could substantially reduce the number of municipalities in provinces starting in 2009. In the midst of these reform efforts, we find mixed results in our analysis of past decentralization in Turkish provinces. First, creation of new provinces by separation from existing provinces seems to have had no significant impact on development or growth in those existing provinces. On the other hand, decentralization through increase in the number of local governments per capita seems to have had a negative effect on at least the level economic development. The other decentralization measure, number of local government per square kilometer of province land gives us different results in different regressions. The crosssectional regressions that used more recent data show that enhanced competition through more municipalities per square kilometer produced favorable results in terms of economic development levels across provinces.

The evidence that there is a negative effect of decentralization through number of municipalities per capita may be pointing to the importance of economies of scale in public service provision. At the same time, there seems to be weak evidence that Tiebout style local government competition may have enhanced economic efficiency in Turkish provinces, which requires further study.

As a future extension, one can expand on Neyapti (2005) and seek panel data for the period from 1980 to 2000 to examine the impact of extensive “fiscal decentralization” 15
Not only are health professionals not the majority of high-skilled emigrants, but they appear to have lower emigration rates on average than other skilled professionals. We compare the overall brain drain rates for tertiary educated migrants who emigrated at age 22 or higher (Beine et al, 2007) to the medical brain drain rates in Bhargava et al. (2010). Across 161 countries, the median medical brain drain rate is 5.4 percent, compared to a median skilled brain drain rate of 8.4 percent. The skilled brain drain rate exceeds the medical brain drain rate for 69 percent of the countries in this sample.

Mattoo et al. (2010) note that there is often a concern that not all educated migrants end up working in skilled occupations after they have migrated— a phenomenon which they call ―brain waste.‖ However, Table 1 shows that the most common occupations for educated migrants are skilled occupations, particularly those in the so-called STEM fields (science, technology, engineering and mathematics). Moreover, using the same 2008 sample we calculate that 79 percent of working migrants from developing countries with a bachelors‘ degree or more are working in occupations in the United States in which the majority of workers have postsecondary education, as are 90 percent of those with a masters degree or more, and 96 percent of those with a Ph.D. The stereotype of foreign workers with Ph.D.s driving taxis is certainly the exception; only 2 out of 1,936 developing country migrants with Ph.D.s in the American Community Survey sample are taxi drivers. This is in line with Mattoo et al.‘s (2010) work using the 2000 census, where they find it is mainly skilled migrants from non-English speaking countries with poor quality education systems who struggle to find skilled work—a finding which might mean that the actual skill level of these migrants is lower than their education would suggest.

# **Question 2: Why Should Economists Care About Brain Drain?**

Recently we heard from editors of two of the professions‘ top economics journals that work on brain drain is ―of great interest to specialists, but insufficiently broad in scope for our general-interest audience,‖ and that one might equally ―consider brain drain from the state of Florida to the rest of the US and other countries‖. It therefore appears worth summarizing some of the reasons economists should care about brain drain, and some ways in which it differs from internal mobility.

5