rafmacalaba/datause-encoder-data huggingface dataset:
earning rents. These efforts are not necessarily illegal, or even inmmoral. They include much lobbying and some forms of advertising. Some can be efficient, such as an auction of scare and valuable assets. However, economists and public sector management specialists are concerned with what Jagdish Bhagwati termed "directly unproductive" rent seeking activities, because they waste resources and can contribute to economic inefficiency (Bhagwati 1974, see also Krueger 1974).

Corruption and other forms of rent seeking have been well-documented in every society on earth, from the banks of the Congo River to the palace of the Dutch royal family, from Japanese politicians to Brazilian bankers to the New York City police department. Since there is always a positive cost to any effort to prevent corruption, the "optimal" or efficient level of corruption is greater than zero (Becker and Stigler 1974). There are, however, significant differences in the form and extent of rent seeking behavior. Recent economic research on rent seeking has begun to point toward policies and institutional structures that can mitigate the most destructive forms (Klitgaard 1988, Pope 1996, Rose-Ackerman 1978, 1997).

Much rent seeking is part of the decentralized search for private gain by a multitude of private individuals and low level officials. Such activity is the result of vague and restrictive laws, high tax and customs rates, and controls on domestic prices and foreign exchange that produce opportunities for private gain. It is especially likely when citizens do not view the state as legitimate or deserving of respect (Dia 1996, pp. 28-35). Civil service systems that do not reward officials for honesty and competence fuel rent-seeking networks. Without denying the importance of such decentralized activity, we concentrate, instead, on the top of the hierarchy to emphasize cases where the search for personal gain can itself importantly influence the level and 3
industry to export, these results would be particularly relevant. Better education and better institutions are the most important factors. On the other hand, if one were hoping to give certain kinds of firms within particular industries a boost into export markets, then the within-firm results would be important. In other words, if all that mattered was that the best, or the most productive, firms within given industries and locations accessed foreign markets, then more general agglomeration within a location would be an important factor.

In summary, these findings suggest that if, in fact, there are positive externalities from clustering of export-oriented activity, then governments could provide incentives to encourage such co-location. However, the existence of spillovers from more general economic clustering might suggest that governments might be limited in their ability since their effect on generating agglomeration economies is unclear. And lastly, investment in more general education infrastructure and in improving institutional characteristics of regions might also help to reduce the sunk costs of export entry.

# **References:**

- Aitken, B, Hanson, G.H. and Harrison, A.E. (1997) ‘Spillovers, foreign investment, and export behaviour’, Journal of International Economics 43: 103-132.

- Arkolakis, K. (2009) ‘Market penetration costs and the new consumers margin in international trade’, NBER Working Paper No. 14214, Cambridge.

- Baldwin, R.E. and Krugman, P.R. (1989) ‘Persistent Trade Effects of Large Exchange Rate Shocks’, Quarterly Journal of Economics 104(4): 635- 54.

- Becchetti, L. and Rossi, S.P.S. (2000) ‘The positive effect of industrial district on the export performance of Italian firms’, Review of Industrial Organisation 16: 53-68

- Bernard, A. and Jensen, J.B. (2004) ‘Why do some firms export’, Review of Economics and Statistics 86: 561–569.

- Choi, A.G. (2007) ‘Do sunk costs of exporting matter for net export dynamics?’ Quarterly Journal of Economics 122: 289-336.

- Das, S., Roberts, M.J. and Tybout, J.R. (2007) ‘Market entry costs, producer heterogeneity, and export dynamics’, Econometrica 75: 837-873.

- Dixit, A. (1989) ‘Entry and exit decisions under uncertainty’, Journal of Political Economy 97: 620-638.

- Duranton, G. and Puga, D. (2004) ‘Micro-foundations of urban agglomeration economies’, in ‘Handbook of Regional and Urban Economics Volume 4’, Henderson, J.V., Thisse J.F. (Eds).

- Feser, E.J. and Bergman, E.M. (2000) ‘National industry cluster templates: a framework for applied regional cluster analysis’, Regional Studies 34: 1-20.

- Greenaway, D. and Sousa, W.K. (2004) ‘Do domestic firms learn to export from multinationals?’, European Journal of Political Economy 20: 1027-1043.

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- 80 -

in the morning peak or changed modes (Watson and Holland, 1978, p. 7)). In due course, the detours resulted in heavy traffic along the nag road around the Restricted Zone, tating traffic management measures such as the re-timing of traffic lights. On the other hand, on the return trips, traffic traverses directly through the relatively uncongesd downtown area in the afternoon peak. Further, only about one tenth of the carpool were esdmated to make their return trips in the same fashion (others used public transport, taxis, or were collected in private vehicles). As a result, "bumper to bumper congesdon' was observed during the aftnoon peak in 1983, with average traffic speed being 18 kph, in contrast to 30 kph during the morning peak (Behbehani, Pendakur, Armstrong-Wright, 1984, p. 36). Thus the moming ALS scheme was able to solve only part of the congestion problem.

# C. Analvsis of Results (1989-present)

84. The 'mirror image' phenomenon therefore contrbuted dirctdy to the extension of the reincted hours of the ALS on June 1, 1989 (from the moming peak into the aftenoon peak period, initially beginning from 4:30 - 7 p.m. but later curilled to 6:30 p.m. to accommodate business interests and activities).6 ' _85._ The most comprehensive survey to date regarding the impact of the AIS was carried out in Febuary, 1990 by the Public Works Department and the Nanyang Technological Institute (Menon and Seddon, 1991) and the results are briefly analyzed here. One, the traffic survey indicates that there was a marked increase in total in-bound taffic immediately before and after the afternoon peak period came into operation, similar to the historical pattern during the morning peal. Further, compansons of traffic levels traveiog in and out of the Restricted Zone were made between May 1989 (which represents the pro-June 1989 scheme) and May 1990 (which represents the post-June 1989 scheme). Two, as expected, the comparisons indicate that " m Sinsae ALS did not try out 't-id flow' erpimun chur, in which urs- impoed an homard bound trips out of the ceul bins distric **t** o tho sobulbs in n with t min uffic flow, a opposed to the cuirant pvacvic of chargmg for entry _perse_ both ino nmog and the afdl_emn. Thb 'tidal flow' idea is he basi of Long Kong's Bleoctmic Road Picing Systm Scheme B.
Policy Research Working Paper 10466

# **Abstract**

Despite a vast literature documenting the harmful effects of climate change on various socio-economic outcomes, little evidence exists on the global impacts of hotter temperature on poverty and inequality. Analysis of a new global panel dataset of subnational poverty in 134 countries finds that a one-degree Celsius increase in temperature leads to a 9.1 percent increase in poverty, using the US$1.90 daily poverty threshold. A similar increase in temperature causes a 0.8 percent increase in the Gini inequality index. The paper also finds negative effects of colder temperature on poverty and inequality. Yet, while poorer countries—particularly those in South Asia and Sub-Saharan Africa—are more affected by climate change, household adaptation could have mitigated some adverse effects in the long run. The findings provide relevant and timely inputs for the global fight against climate change as well as the current policy debate on the responsibilities of richer countries versus poorer countries.

This paper is a product of the Development Data Group, Development Economics and the Poverty and Equity Global Practice. . It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at hdang@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions 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._ Produced by the Research Support Team
Decomposition of overall environmental performance into Brown and Green sectors yields some additional insight into the impact of demographics and economic structure on regulation. Controlling for income, comparative analysis of the Brown sector indices suggests a very significant country response to environmental pressures from industrialization and urbanization. However, our results do not reveal an equivalent response on the Green side beyond the effect of variations in income per capita.

In summary, our findings suggest that a detailed, quantified analysis of the UNCED reports can yield comparable and plausible indices of environmental policy performance across countries. Cross-country variations in our environmental index are wellexplained by variations in income per capita, de:-ree of urbanization and industrialization, security of property rights, and general administrative efficiency.

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# **2.2 Approaches to and Evidence from NREGS Evaluations**

Not surprising, in light of the program’s size and importance, a large literature aims to assess the impacts of NREGS. Descriptive evidence suggests that the quality of implementation varied across states (Liu and Barrett 2013) but that the program seems to have allowed households to mitigate the impacts of consumption shocks arising, for example, from rainfall variations (Coffey, Papp, and Spears 2011), and to deal with large and covariant swings in asset prices (Johnson 2009). As program wages, relative to what could be obtained in the market, are more attractive for females than for males, it is not too surprising to see positive program impacts on females at the descriptive level, with knock-on effects on their offspring (Dev 2011).

While the program effectively targets the poor, significant rationing remains (Dutta et al. 2012a), so that some benefits may be captured by elites (Niehaus and Sukhtankar 2012), thereby reducing the program’s effectiveness in transferring resources to the poor (Shankar, Gaiha, and Jha 2011). This is consistent with the finding that access to information significantly affected poor people’s ability to benefit from the program (Jha, Bhattacharyya, and Gaiha 2011b), and the presence of a positive association between landholding and NREGS that results in less poverty targeting (Jha et al. 2009).

Establishing a control group to more rigorously assess impacts is made difficult by the fact that the program now operates nationally and that phasing in was not random but instead gave preference to poorer districts. A number of studies use the phasing in of the program to assess district-level impacts of the program, often relying on repeated cross-sections of National Sample Surveys (NSSs). To the extent that the underlying assumptions are justified, this would provide an estimate of the _intention to treat_ effect of the program on wages or employment at the district level.

One contribution using this approach (Imbert and Papp 2011) finds that the program provided direct and indirect benefits and that direct gains and indirect gains (via general equilibrium effects) are of similar magnitude. The quality of implementation varies significantly across states, as indicated by the fact that estimated program effects almost double (to 9 percent) in the states with the best implementation performance. Increases were focused on low-wage, low-skilled public employment; in fact, wages for better-paying jobs decreased. Seasonality in wage labor demand was also important: the average daily earnings of casual laborers increased by 4.5 percent during the dry season but were unaffected in the rainy season. This result implies that program-induced wage increases redistribute income from net buyers to net suppliers of labor but that the impact on labor force participation remains limited. Using the same framework, Azam (2012) finds that increases in female wages are larger than increases in male wages. Wages for female casual workers were estimated to have increased 8 percent more in NREGS districts as compared to non-NREGS districts.

6
Kellenberg, D. K. (2008), “A Reexamination of the Role of Income for the Trade and Environment Debate,” _Ecological Economics_ , 68(1-2): 106-115.

- Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004a), “Estimating the Ad Valorem Equivalent of Barriers to Foreign Direct Investment in the Telecommunications Services Sectors in Russia.” Available at http://www.worldbank.org/trade/russia-wto.

- Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004b), “Estimating the Ad Valorem Equivalent of Barriers to Foreign Direct Investment in the Maritime and Air Transportation Service Sectors in Russia.” Available at http://www.worldbank.org/trade/russia-wto.

- Kuik, O. J. and R. Gerlagh (2003), “Trade Liberalization and Carbon Leakage,” _The Energy Journal_ , 24: 97- 120.

- Krugman, P. (1979), “Increasing Returns, Monopolistic Competition, and International Trade,” _Journal of International Economics_ 9:469-479.

- Krugman, P. (1980), “Scale Economies, Product Differentiation, and the Pattern of Trade,” _American Economic Review_ 70: 950-959.

- Markusen, James R, Thomas Rutherford and David Tarr (2005), “Trade and Direct Investment in Producer Services and the Domestic Market for Expertise,” _Canadian Journal of Economics_ 38: 758-777.

- Markusen, James R. (1989), "Trade in Producer Services and in Other Specialized Intermediate Inputs," _American Economic Review_ , 79:85-95.

- McGuire, Greg and Michael Schuele (2000), “Restrictiveness of International Trade in Banking Services,” in Findlay, Christopher and Tony Warren (eds.), _Impediments to Trade in Services: Measurement and Policy Implications_ , London: Routledge.

- Melitz, M. J. (2003), “The Impact of Trade on Intra-industry Reallocations and Aggregate Industry Productivity,” _Econometrica_ 71: 1695–1725.

- Mielnik, O. and J. Goldemberg (2002), “Foreign Direct Investment and Decoupling between Energy and Gross Domestic Product in Developing Countries,” _Energy Policy_ , 30: 87-89.

- Ministry of Energy of the Russian Federation (2010), _Energy Saving and Energy Efficiency Improvement until 2020_ . Available at: http://minenergo.gov.ru/upload/iblock/b61/b612746a17d6adae790262ad58b4c9d9.do c

- Ministry of Natural Resources and Environment of the Russian Federation (2014), _State Program of the Russian Federation for Protection of the Environment, 2012-2020_ . Available at: http://www.mnr.gov.ru/upload/iblock/34e/Gosprogramma_OOS.pdf

- Rauscher, M. (1997), _International Trade, Factor Movements and the Environment_ , Oxford: Oxford University Press.

- Romer, P. (1994), “New Goods, Old Theory, and the Welfare Costs of Trade Restrictions,” _Journal of Development Economics_ , 43: 5–38.

35
We obtain data on the regions of Russia from the publication by Rosstat (2002, 2003) entitled _Regions of Russia_ .<sup>8</sup> The regions have several names in Russian; the most common legal jurisdiction is referred to in Russian as an “oblast.” Oblasts are analogous to states in the United States or provinces in Canada. But there are also jurisdictions known as territories, federal cities, autonomous districts and an autonomous region.

We group several contiguous regions into what we call “Regional Markets” (RMs). The mapping of regions into regional markets is shown at the top of table 2. In this paper, we shall analyze effects at the level of the Regional Market. Value-added, exports and imports by sector for our ten regional markets of Russia are presented in tables 3-11.

We assume that firms and sectors operate at the Regional Market level, primary factors of production are not able to move between Regional Markets (unless otherwise noted). We assume a nested CES structure of demand (see figures 1, 2 and 3). Since this implies that the structure of demand is both homothetic and weakly separable; consumers and firms in a representative Regional Market r employ multiple stage budgeting for all goods.

# **_4.1.2 Price Determination for Competitive Goods and Services Sectors_**

Firms in each Regional Market have three choices for sales: sell in their own Regional Market; sell to other parts of Russia; or export to the rest of the world. This is depicted in figure 4. Firms maximize revenue for any given output level based on their transformation possibilities between the three types of goods which is defined by a constant elasticity of transformation production function. For all firms within the same Regional Market, the product they export to other parts of Russia (including other regions within their own regional market) is homogeneous. It follows from our assumptions of homogeneous demand and production outside of the own Regional Market, that for each competitive good, say good g, there will be only three prices for good g of Regional Market r: the price of good g in Regional Market r; the price of good g from Regional Market r in other parts of Russia; and the price of good g from Regional Market r in the rest of the world.

The structure of demand for goods or services from competitive sectors is shown in figure 2. Consumers and firms in a representative Regional Market r first optimize their choice of expenditures on foreign goods versus goods from Russia. Subsequently they optimally allocate their expenditures between goods from other Russian Regional Markets and their own Regional Market. Finally, they optimally allocate their expenditures between goods from the other Russian Regional Markets. This structure assumes that consumers differentiate the products of producers from different regional markets; but, they regard as

> 8 The number of regions of Russia has varied over time. This study groups regions of Russia based on data from Rosstat (2002; 2003), which listed 89 regions, and had data on 88.

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# TABLE III

# First Stage Process - Teacher Level

||(1)|(2)|(3)|(4)<br>Pan|(5)<br>el A: All teac|(6)<br>hers|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|||_Y_0|||_Y_2|||_Y_3||
||Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|
|Started or completed|0.61|0.29|0.33<sup>_∗∗∗_</sup>|0.65|0.48|0.17<sup>_∗∗∗_</sup>|0.71|0.64|0.07<sup>_∗∗∗_</sup>|
|the certifcation process|[0.49]|[0.45]|(0.02)|[0.48]|[0.50]|(0.02)|[0.45]|[0.48]|(0.02)|
|Completed the|0.19|0.18|0.01|0.61|0.38|0.23<sup>_∗∗∗_</sup>|0.65|0.50|0.14<sup>_∗∗∗_</sup>|
|certifcation process|[0.40]|[0.39]|(0.01)|[0.49]|[0.49]|(0.02)|[0.48]|[0.50]|(0.02)|
|Certifed and paid|0.11|0.12|-0.01|0.55|0.27|0.29<sup>_∗∗∗_</sup>|0.62|0.39|0.24<sup>_∗∗∗_</sup>|
|the certifcation allowance|[0.32]|[0.33]|(0.01)|[0.50]|[0.45]|(0.02)|[0.49]|[0.49]|(0.02)|
|||||Panel B:|Target teach|ers only||||
|||_Y_0|||_Y_2|||_Y_3||
||Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|Treatment|Control|Diference<br>(F.E)|
|Started or completed|0.73|0.18|0.54<sup>_∗∗∗_</sup>|0.86|0.55|0.32<sup>_∗∗∗_</sup>|0.90|0.82|0.08<sup>_∗∗∗_</sup>|
|the certifcation process|[0.45]|[0.39]|(0.03)|[0.35]|[0.50]|(0.03)|[0.30]|[0.38]|(0.02)|
|Completed the|0.00|0.00|0.00|0.81|0.39|0.43<sup>_∗∗∗_</sup>|0.86|0.62|0.24<sup>_∗∗∗_</sup>|
|certifcation process|[0.00]|[0.00]|_d.n.a._|[0.39]|[0.49]|(0.03)|[0.35]|[0.48]|(0.03)|
|Certifed and paid|0.00|0.00|0.00|0.72|0.18|0.54<sup>_∗∗∗_</sup>|0.83|0.40|0.45<sup>_∗∗∗_</sup>|
|the certifcation allowance|[0.00]|[0.00]|_d.n.a._|[0.45]|[0.38]|(0.03)|[0.38]|[0.49]|(0.03)|

_∗ ∗∗ ∗∗∗ Notes: p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Table compares average values between treatment and control groups for all teachers (Panel A) and target teachers (Panel B). Estimates are based on a model that includes district-triplet fixed effects, which are the strata used for randomization. Within-group standard deviations are reported in brackets in columns (1), (2), (4), (5), (7) and (8). School-level clustered standard errors of the estimated mean differences between treatment and control are reported in parentheses in columns (3), (6) and (9).
Specifically, we use ENEMDU to estimate regional income using aggregate earnings at the canton level. In addition we also include the employment-to-population ratio (for natives) in the canton and the share of the native population with secondary and tertiary education.

The empirical model we estimate is the following: _V zc_ denotes the number of Venezuelans who settled in canton _c_ (though we will also consider a logarithmic transformation of this variable). The right-hand side contains the (logs) of the distance to the three gateways (denoted by a single regressor ln _Distc_ for simplicity) and the economic indicators of the region ( _Xc_ ) along with a disturbance term: We estimate this model using data on the geographic distribution of Venezuelans in 2019 and ENEMDU data for that same year. Table 2 collects the estimates. In columns 1-3 the dependent variable is the number of Venezuelans settled in the canton in 2019 (estimated using the _Telefonica_ data) in thousands. Column 1 includes only our measure of regional income, based on the wage bill in each canton.<sup>21</sup> The estimates show a positive and significant coefficient, suggesting that Venezuelans were attracted to higher-income cantons. Column 2 adds the logs of the distance from the canton to the three main gateways. We expected negative coefficients, indicating that migrants tend to remain close to the points of entry. However, this is only the case regarding the airports but the coefficients are not statistically significant at conventional levels. In both specifications, the coefficient for the log of the wage bill is around 3, implying that regions with 1% higher income receive about 3,000 more Venezuelan migrants than otherwise similar cantons. In column 3, we decompose the wage bill into two components: employment and average wage per worker. In both cases the coefficients are highly significant and are close to 3. Thus Venezuelan migrants are equally attracted to regions with higher levels of employment and higher wages per worker.

Columns 4-6 estimate the elasticity of migration to regional income, by employing logarithmic transformations of both the dependent and the explanatory variables. In column 4 the transformation is simply the log of the corresponding variables. In columns 5 and 6 we use the inverse hyperbolic sine transformation, which is defined at zero and thus preserves the sample size. These estimates show that the elasticity is around 0.5,

> 21Recall that, in many contexts, overall GDP is close to proportional to the wage bill. From a theoretical viewpoint, this is exactly the case in a competitive economy when the aggregate production function is Cobb-Douglas.

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