rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> By going beyond the mean and estimating the distribution of treatment effects we were able<br>fact, the estimated variance implies that the standard deviation is about 70% of the estimat-<br>ed average effect and is also statistically insignificant. This finding of little treatment hetero-<br>geneity is corroborated by both the deconvolution estimate of the distribution and estimates<br>While relevant for policy, such a null result falls short of illustrating what we can learn<br>feature of the particular program being studied rather than a deficiency of the distributional<br>methods. Our estimates of both the variance and distribution of treatment effects are sta-<br>tistically indistinguishable from the truth even when the strong assumptions are violat-<br>ed. However, it is not clear how sensitive the methods are to violations of their assumptions<br>in general, and we do not mean to suggest that the robustness of the estimates in our appli-<br>cation generalizes.<br>(2005): “Estimating treatment effects for discrete outcomes when re-<br>sponses to treatment vary: An application to Norwegian vocational rehabilitation programs”,<br>(2002): “Bootstrap tests for distributional treatment effects in instrumental variable models”,<br>(2002): “Instrumental variables estimation of quantile treatment effects”,<br>“Econometric evaluation of social programs, part III: Distributional treatment<br>effects, dynamic treatment effects, dynamic discrete choice, and general equilibrium policy evaluation”,<br>“A three-step method for choosing the number of bootstrap repeti-<br>tions”,<br>(1994): “Identification and estimation of local average treatment effects”,<br>(2011): “Does affirmative action lead to mismatch? A new<br>(2012): “Identifying distributional characteristics in random coefficients panel<br>(2002): “Recursive partitioning for heterogeneous causal effects”, arXiv:1504.01132.<br>(2014): “Inference on treatment effects after selection among high-<br>(2006): “What mean impacts miss: Distributional effects of welfare re-<br>— (2014): “Can variation in subgroups’ average treatment effects explain treatment effect heterogeneity?<br>(2014): “Experimental evidence on distributional effects of Head Start”,<br>"Non-parametric confidence bands in deconvolution<br>density estimation”,<br><!-- End of picture text -->
# **Table A4: Robustness of OLS Estimates of Wage Determinants to using Import Prices instead of Quantities, using Occupational versus Industry Offshoring Exposure, 1983-2002**

Dependent Variable: Log Wage

||Industry-Sp<br>|Offshoring<br>ecific Expos<br>|Measured by<br>ure,Manufactu<br>|ringOnly<br>|Occupat<br>|Offshoring<br>ion-Specific<br>|Measured by<br>Exposure,All<br>|Sectors<br>|
|---|---|---|---|---|---|---|---|---|
|Variable|All<br>Occupations|Most<br>Routine|Intermediate<br>Routine|Least<br>Routine|All<br>Occupations|Most<br>Routine|Intermediate<br>Routine|Least<br>Routine|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Lagged log of low income|-0.0106*|-0.0156***|-0.004|0.007|-0.0575***|-0.0633***|0.015|0.0705*|
|affiliate employment|(0.006)|(0.006)|(0.007)|(0.011)|(0.019)|(0.016)|(0.050)|(0.042)|
|Lagged log of high income|0.014|0.013|0.012|0.006|0.031|0.0529**|0.030|-0.094|
|affiliate employment|(0.01)|(0.01)|(0.01)|(0.01)|(0.03)|(0.03)|(0.06)|(0.08)|
|Lagged import prices|0.01|0.001|-0.005|0.0384*|0.01|0.0331***|0.01|0.0254**|
||(0.01)|(0.01)|(0.01)|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|
|Lagged export prices|0.09|0.05|0.05|0.101*|0.002|-0.001|-0.004|0.006|
||(0.06)|(0.09)|(0.06)|(0.05)|(0.00)|(0.00)|(0.00)|(0.01)|
|Number of observations|204,364|113,108|58,699|32,557|1,470,051|597,699|521,986|350,366|
|R-squared|0.44|0.34|0.38|0.35|0.50|0.40|0.54|0.41|

_Source_ : See Table 1.

_Note_ : Robust standard errors reported in parentheses below coefficient estimates. These regressions are the same as Table 2 in the paper, with import and export price series data replacing the quantity measures used in our core specifications. Prices on imports are taken from data provided to the authors by Robert Lawrence at the 4-digit NAICS level, and converted to our consistent industry classification for the CPS MORG. Export price data is taken from the Bureau of Labor Statistics. Details are available in the data appendix. Sample sizes differ from Table 2 due to more limited price series data on imports and exports than on quantities.
Policy Research Working Paper 6220

# **Abstract**

Firms have various ways to cope with external risks. This paper analyzes the risk coping behavior that entails the smoothing of inputs (labor, raw materials, or capital). The theoretical framework shows that, if they face adjustment costs, firms prefer to smooth their inputs, especially if they expect a demand shock to be temporary. However, credit constrained firms will be adversely affected by the presence of liquidity constraints, and this will create a welfare loss due to incomplete smoothing. The authors estimate this behavior using a panel of Cambodian firms at the time of the 2008 global economic crisis. The survey shows that these firms were hard hit by the economic crisis between 2008 and 2009, with an average fall in demand (sales) of 30 percent. Based on the theoretical framework, the analysis can estimate the responsiveness of labor, capital, and raw materials input demand to demand shocks. It finds that firms try to smooth in particular if they believe the shock is temporary; in fact non-credit constrained firms reduce their inputs much less than firms that were credit constrained when the demand shock is expected to be temporary. The paper estimates that the welfare loss from incomplete smoothing due to credit constraints is many multiples of the adjustment costs of the firms that were not credit constrained. This has important policy implications about the role of financial sector development and regulations beyond the capital market. This micro analysis also has macro implications: if all firms expect a shock to be permanent, their combined limited smoothing of inputs will indeed make the shock more likely to be permanent.

This paper is a product of the Poverty Reduction and Economic Management Unit, East Asia and Pacific Region. 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://econ.worldbank.org. The authors may be contacted at sguimbert@worldbank.org and r.oostendorp@vu.nl.

_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
# 1. INTRODUCTION

After twenty years of collectivized agriculture,<sup>China's agricultural sector</sup> underwent a major reform during<sup>the last decade. A key element of the reform</sup> was the introduction of the "household<sup>responsibility system", which replaced the</sup> collective production mode by Individual<sup>farm household units. Households were</sup> given control over former commune<sup>land through Individual contracts (Initially for</sup> 3-5 years, but presently for 15<sup>years with even longer periods for tree crops).</sup> Asido from a commitment to provide<sup>certain prespecifled grain quotas at</sup> government-set prices, and the obligation<sup>to pay taxes, farmers now have the</sup> freedom to make cropping and<sup>Input decislons, and are allowed to retain any</sup> profits which they earn.

The Improved Incentives brought about<sup>by the Institutional reform, combined</sup> with higher prices for key agricultural<sup>products, Induced an unprecedented</sup> acceleration of agricultural growth<sup>In China. Between 1979 and 1984 the value</sup> of agricultural output (In constant<sup>prices) grew at an average of 7.5 percent per</sup> year, mostly due to Improved incentives<sup>(Lin, 1988; McMIllan et al., 1989). After</sup> 1984, the pace of growth has<sup>slackened considerably, essentially because the</sup> potential gains due to Improved<sup>Incentives under the reformed structure were</sup> exhausted. Further growth thus depentJs<sup>on the traditlonal sources of agricultural</sup> development, I.e., further Investment<sup>In physical capital, expanded material Input</sup> supplies, and technological change.

In this context, the analysis of factors<sup>affecting farm Investment In China</sup> Is of much policy relevance. National<sup>data indicate that along with the reform In</sup> production organization, public sector<sup>Investment In agriculture declined, In both</sup> absolute and relative terms.<sup>Agriculture's share In the government's capital</sup>
Lau, Lawrence J., Dean T. Jamison, and F. F. Louat. 1991. "Education and Productivity in Developing Countries: An Aggregate Production Function Approach." Policy Research Working Paper 612. Office of the Vice President, Development Economics, World Bank, Washington, D.C.

L6pez, Ram6n. 1998. "Growth and Stagnation in Natural Resource-rich Economies." Unpublished, University of Maryland, College Park.

Lucas, Robert E. 1993. "Making a Miracle." Econometrica, 61(2):251-72.

Middleton, John, Adrian Ziderman, and Arvil Van Adams. 1993. Skills for Productivity. New York: Oxford University Press.

Min, Wei-Fang, 1987. "The Impact of Vocational Education on Productivity in the Specific Institutional Context of China: A Case Study." Ph.D. dissertation, Stanford University.

Mincer, Jacob. 1962. "On the Job Training Costs: Returns and Some Implications." Journal of Political Economy 70(Supplement)(October) part 2:50-79.

. 1974. Schooling, Experience, and Earnings. New York: National Bureau of Economic Research.

Nelson, Richard R. and Howard Pack. 1996. "Firm Competencies, Technological Catch-up and the Asian Miracle." Paper presented at a conference in honor of Gustav Ranis, Yale University.

Nehru, Vikram, and Ashok Dhareshwar. 1993. " A New database on physical capital stock: Souces, methodology and results." Revista de Analysis Economico. 8(1):37-59.

Nehru, Vikram, Eric Swanson, and Ashok Dubey. 1994. " A new database on human capital stock: sources, methodology and results." Journal of Develoment Economics.

North, Douglass C. 1989. "Institutions and Economic Growth: An Historical Introduction." World Development 17(9): 1319-32.

. 1993. "The Ultimate Sources of Economic Growth." In Adam Szirmai, Bart Vanark, and Dirk Pilat, eds. Explaining Economic Growth: Essays in Honor of Angus Maddison Amsterdam: North Holland.

Padoan, Pier Carlo. 1996. "Trade and the Accumulation and Diffusion of Knowledge." Policy Research Working Paper 1679. International Economics Department International Trade Division, World Bank, Washington, D.C.

Patrinos, Harry A. 1994. "Notes on Education and Economic Growth: Theory and Evidence." Working paper, Human Development Department. World Bank.

Pritchett, Lant. 1996. "Where Has all the Education Gone?" Policy Research Working Paper 1581. Policy Research Department, Poverty and Human Resources Division, World Bank, Washington. D.C.

Psacharopoulos, G. 1984. ' The Contribution of Education to Economic Growth: International Comparisons." In J.W. Kendrick, ed., International Comparisons of Productivity and Causes of the Slowdown. Cambridge, Mass: Ballinger Publishing Co.

Psacharopoulos, George, and Ana-Maria Arriagada. 1986. "The educational attainment of the labor force: an international comparison." The World Bank, October 1986. Report No.: EDT38.

25
does not provide an assessment or analysis on how pollution overall, and in particular, air pollution, affects competitiveness and what could be policies and actions that could allow city leaders to manage the negative effects of pollution while still fostering increases in cities’ competitiveness.

In this work, we aim at taking a first step toward expanding the Competitive Cities framework to include considerations on pollution, by providing an exploratory analysis that brings together indicators of city competitiveness and air pollution, and through that elicit patterns that can help in providing a first step toward understanding the links between these two key issues.

We focus on a descriptive analysis for two main reasons. First, this type of analysis can provide some useful insights on the main characteristics that make a city competitive and how these characteristics relate to pollution. It can help understand the path that cities may take to achieve growth and how pollution can be a consequence and a cause in this journey to achieve competitiveness. In that sense, it can shed some light on the challenges a competitive city may face and how it can overcome them. Second, building on the argument that pollution interacts with city competitiveness, as both a consequence of growth and a cause affecting the city’s ability to become more competitive, a descriptive analysis can help to understand the stylized facts behind this relationship and conceptualize this interaction.

The paper is structured as follows. The next section briefly describes the data. Section three focuses on summarizing the Competitive Cities framework laid out in the World Bank Report (2016) and moves to focus on air pollution. It pinpoints to the most polluted cities across regions and how that characteristic relates to development and income levels. Section four looks more in detail at the interaction between pollution and competitiveness, paying attention to which types of cities are experiencing the challenge of fast grow and pollution, particularly in developing regions, and lays out a typology of cities bringing together the concepts of competitiveness and pollution.

# Description of the Data

For this analysis we combine three data sets. First, we use the same data set used by the Competitive Cities report (World Bank, 2016) which includes city‐level data from the Oxford Economics Dataset.<sup>3</sup> Oxford Economics Dataset is a global administrative data set that provides economic indicators and

> 3 We would like to thank the Competitive Cities Team for cleaning and preparing the Oxford Economics data set.

3
# _California Emission Standards_

Among the states in the U.S., California tends to be the leader in imposing increasingly stringent environmental regulations. In 1989, the California Air Resources Board (CARB), in response to severe air pollution problems in Los Angeles and other major cities in California, established stringent, technology-forcing vehicle emission standards to be phased in between the period of 1994 and 2003 (Faiz et. al., 1996). As California began to regulate vehicle emissions earlier than the Federal government, it is treated differently than the other states when it comes to providing a free hand to adopt its own unique vehicle emissions control program. Under the Clean Air Act in 1970, California is allowed to set its own emissions standards (ECMT, 2000). The LEV II regulations, which were formally adopted on 5 August 1998 and came into operation on 27 November 1999, are the current standards for California (See table 3 & 4) (CONCAWE, 2006).

**Table 3. LEV II Exhaust Emissions Standards-Light and Medium Duty Vehicles**

[All Private cars & Light Duty Trucks < 8500 lb GVW]

|**Category**|||**50,000 mile**|**s**|||
|---|---|---|---|---|---|---|
|||**NMOG**|**CO**|**NOx**|**PM**|**HCHO**|
|LEV|0.075||3.4|0.05|-|0.015|
|ULEV|0.04||1.7|0.05|-|0.008|
|SULEV|-||-|-|-|-|
||||**120,000 mi**|**les**|||
|||**NMOG**|**CO**|**NOx**|**PM**|**HCHO**|
|LEV|0.090||4.2|0.07|0.01|0.018|
|ULEV|0.055||2.1|0.07|0.01|0.011|
|SULEV|0.010||1.0|0.02|0.01|0.004|

*Limits are for intermediate life of 5 yrs or 50,000 or full useful life of 10, 0000 miles or 10 years Source: CONCAWE, 2006

18
|Grenada Hurricane Ivan EmergencyProject|Niels B. Holm-Nielsen|
|---|---|
|Grenada Hurricane Ivan EmergencyProject|Niels B. Holm-Nielsen|
|Grenada Hurricane Ivan EmergencyProject|Niels B. Holm-Nielsen|
|Grenada Hurricane Ivan EmergencyProject|Niels B. Holm-Nielsen|
|Grenada Hurricane Ivan EmergencyProject|Niels B. Holm-Nielsen|
|Grenada Hurricane Ivan EmergencyProject|Niels B. Holm-Nielsen|
|Grenada Hurricane Ivan EmergencyProject|Niels B. Holm-Nielsen|
|Grenada Hurricane Ivan EmergencyProject|Niels B. Holm-Nielsen|
|ID ECONOMIC BENEFITS OF COMMUNITY INVESTMENT PROJECT|Sri Kuntari|
|SUPPORT OF THE ACCOUNTANCY PROFESSION(TF53981)|Bella Diallo|
|SUPPORT OF THE ACCOUNTANCY PROFESSION(TF53981)|Bella Diallo|
|SUPPORT OF THE ACCOUNTANCY PROFESSION(TF53981)|Bella Diallo|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|DRC - Agriculture Rehabilitation and RecoverySupport|Nora Kaoues|
|National Agricultural Innovation Project|William B. Magrath|
|National Agricultural Innovation Project|William B. Magrath|
|National Agricultural Innovation Project|William B. Magrath|
|National Agricultural Innovation Project|William B. Magrath|
|National Agricultural Innovation Project|William B. Magrath|
|National Agricultural Innovation Project|William B. Magrath|
|National Agricultural Innovation Project|William B. Magrath|
|National Agricultural Innovation Project|William B. Magrath|
|National Agricultural Innovation Project|William B. Magrath|
|National Agricultural Innovation Project|William B. Magrath|
|National Agricultural Innovation Project|William B. Magrath|
|National Agricultural Innovation Project|William B. Magrath|
|National Agricultural Innovation Project|William B. Magrath|
<!-- Start of picture text --> Lorenz curve for accessibility - Nairobi<br>1<br>____ matatus<br>0.9 Gini=0.36<br>8 | ___ cars<br>= 0.8 Gini=0.29<br>5 — 45° degree line<br>t 0.7<br>fe)<br>Qa<br>20.6<br>2<br>20.5<br>on<br>3<br>© 0.4<br>Oo<br>©<br>6 0.3<br>2<br>£ 0.2<br>7)<br>0.1 ‘<br>0<br>0 0.2 0.4 0.6 0.8 1<br>Share of population<br><!-- End of picture text -->
sampled girl from our target population. Part I collects information on the household roster, dwelling characteristics, household assets and durables, consumption (food and non-food), household access to safety nets, and shocks (economic, health, and otherwise) experienced by the household. In Part II, the core respondent provides further information about her family background, her education and labor market participation, her health, her dating patterns, sexual behavior, marital expectations, knowledge of HIV/AIDS, her social networks, as well as her own consumption of girl-specific goods (such as soaps, mobile phone airtime, clothing, braids, sodas and alcoholic drinks, etc.). This paper utilizes baseline and follow-up data to analyze the one-year impact of the program on the marital status, childbearing, and the detailed sexual behavior for the program participants. The first round of the survey was administered from October 2007 to February 2008, and the second wave from October 2008 to February 2009.

# **5. Program Impacts**

# **5.1. Balance and Attrition**

Before examining the short-term impacts of the CCT program on sexual behavior, it is important to first confirm that our randomization, with respect to key outcomes and controls, was successful. Table 1a provides some basic summary statistics from baseline data. Table 1b shows the success of the randomization. As per our research design, we always compare treatment and control groups for dropouts and schoolgirls at baseline separately, and hence the equality of means at baseline is also examined within each of these two important sub-groups. Across the thirteen variables that are most pertinent for this paper, there are no significant differences at baseline between the treatment and 14
non-Mekong South, together with mollusks, crabs, mussels, scallops, and clams. Saltwater aquaculture involves mostly grouper and cobia. These observations establish that catfish is only produced in the Mekong region.

Even within the Mekong region, there is considerable heterogeneity in the composition of aquaculture production. While landlocked provinces specialize in freshwater aquaculture, coastal provinces tend to be more heavily engaged in brackish and saltwater aquaculture. Also, suitable river conditions for catfish farming are more prevalent in some provinces than in others. To see why, Figure 2 displays a map of the Mekong area and its provinces. Some Mekong provinces (Kien Giang, Ca Mau, Bac Lieu, Soc Trang, Tra Vinh, and Ben Tre) have extensive marine coastlines. Instead, the provinces of An Giang, Can Tho, Dong Thap, Vinh Long, Long An, and Tien Giang are mostly landlocked. The Mekong river, where catfish thrives, flows down from Cambodia and enters Vietnam at the border between An Giang and Dong Thap. The river then divides into the Hau branch, which crosses the Can Tho province, and the Tien branch, which crosses Tien Giang and Vinh Long provinces. The Mekong finally empties into the sea mostly in the provinces of Soc Trang and Tra Vinh. Clearly, the catfish habitat is concentrated in the provinces more heavily touched by the Mekong River.

Table 2 includes information on aquaculture production for each province in the Mekong region.<sup>10</sup> Columns 1 and 4 show the share of freshwater aquaculture in total aquaculture output in 2002 and 2003. In Dong Thap, An Giang, Vinh Long, and Can Tho, 100 percent of the aquaculture production is freshwater aquaculture. The share of freshwater aquaculture is much smaller in coastal provinces, where brackish and marine fishing is more relevant (columns 2 and 5). In particular, shrimp is prevalent in Bac Lieu, Ca Mau, and Kieng Giang, which are located on the Southernmost tip of Vietnam (columns 3 and 6). This confirms that landlocked provinces tend to be much more specialized in freshwater aquaculture than coastal provinces. Further, column 7 displays information on production levels of catfish in 2003, calculated from data on total catfish production in the Mekong region, as well as 10 Data have been gathered from difference sources, which include the Ministry of Fisheries ( `www. fistenet.gov.vn` ) and seafood industry magazines such as Seafood from Vietnam Magazine ( `www. seafoodfromvietnam.com.vn` ) and World of Pangasius ( `www.worldofpangasius.com.vn` ).

10