rafmacalaba/datause-encoder-data huggingface dataset:
handled in a qualitative dependent-variable framework, but some attributes such as effort may not be directly observable.

As observed on many occasions and, more recently in the report of the Sen, Stiglitz, Fitoussi Commission created by President Sarkozy, although it is possible to measure physical productivity (output per unit of physical input) in relation to private goods, measuring productivity in relation to many public goods and to services provided by the public sector remains a challenge (Stiglitz, Sen and Fitoussi 2009). We might find comparative data on the wages and labor needed to produce private and public goods, but what number of employees is appropriate for the production and provision of collective services, such as defense, maintenance of law and order, legislation and regulation, etc.? Since this problem is difficult to resolve, public sector accounts are based, in value terms, on the assumption that “the value of the final consumption of general government is taken to be equal to the value of the expenditures they incur on collective services” (UN 1993, paragraph 9.91, p.215).<sup>63</sup> Development is now almost universally synonymous with poverty reduction, and absolute poverty is recognized as the inability to achieve basic standards in nutrition, health, education, environment, and a voice in the decisions affecting poor people’s lives. The _2000/2001 World Development Report_ articulates the multifaceted dimensions of poverty, emphasizing that social development carries intrinsic value in addition to whatever instrumental value it may have. Signaling this recognition, the Millennium Development Goals (MDGs) were adopted in 2000 at the United Nations by the representatives of 191 countries. They commit the development community to work toward progress in the multiple dimensions of poverty reduction.

IDA is now much more focused on achieving the desired results in terms of poverty reduction than on inputs (measuring “success” in terms of dollars or bags of wheat given to the government). But the systematic measurement of outcomes raises a number of methodological and practical questions that are still not resolved.

> 63 A problem arises when measuring outcomes that are unobservable, e.g., improvements in governance. Proxies are used in the governance literature, including in the now famous governance index constructed by Kraay and Kaufmann—either quantitative proxies (e.g. the number of changes in government as a gauge of ‘political instability’) or qualitative variables (e.g. the subjective ranking of contract enforcement as a gauge of ‘institutional efficiency’).

51
Asher and Novosad (2020) estimate the economic impacts of India’s $40 billion national rural road construction program. Four years after road construction, the main effect of new feeder roads is to facilitate the movement of workers out of agriculture. However, there are no major changes in agricultural outcomes, income, or assets. Employment in village firms expands only slightly. Even with better market connections, remote areas may continue to lack economic opportunities. This paper suggests that even in a fast-growing economy such as India in the 2000s, rural growth is constrained by more than the poor state of transportation infrastructure. Instead of facilitating growth of village farms and firms, the main economic benefit of rural transportation infrastructure may be the connection of rural workers to new employment opportunities elsewhere.

Transportation improvements increase access to markets and play a critical role in facilitating the specialization of localities and the resulting concentration of industries to achieve higher economic growth. Blankespoor et al (2017) estimate the impacts of road improvements on local employment and specialization in Mexico for the period 1986 - 2014. A 10 percent increase in market access results in a 2.9 to 6.5 percent increase in employment, and a 13 percent increase in output specialization. The effects are heterogeneous across sectors, with employment in commerce and services benefiting more than manufacturing, from road improvements.

Upgrades in transport infrastructure can have a significant positive impact on firms' exports and thereby on employment. Martincus et al (2017) look at the impact of road infrastructure on international trade between 2003 and 2010 in Peru, where regions were exposed to an asymmetric infrastructure shock. Improved domestic road infrastructure translates into increasing firm exports, with a distance elasticity of exports of around 1.2. In addition, infrastructure driven exports accounted for 4 percent of the (net) new jobs that exports created during the study period.

Finally, human capital accumulation is also an important determinant of long-term productivity growth. However, access to roads can alter rural households’ opportunity costs of human capital investments. On the one hand, better transport infrastructure may reduce students’ travel costs to school, improving attendance. On the other hand, greater transport connectivity also exposes students to more immediate job opportunities, potentially incentivizing them to drop out of school to join the labor market. Li et al (2019) look at the impact of highway investments on educational choices in rural China. Using 2005 population census data, the authors find that highway access significantly reduces the enrollment rate of senior high schools in rural areas by as much as 9 percent, without affecting the enrollment rates for junior high and elementary schools. This negative effect is more significant on students in rural regions than in urban regions, indicating that better transport connectivity may improve the income of rural households in the 31
6

# Table 1 Annual Costs of Economic Regulation in the United States in 1988 (in Billions of 1988 Dollars)

|**_Regulated Sector_**|_Efficiency Costs_|_Transfers_|**_Sourcesb_**|
|---|---|---|---|
|International Trade|17.3|85.6-110.6|Hufbauer (1986)|
|Telecommunications|< 14.1|< 42.3a|Wenders (1987)|
|Agricultural Price<br>Supports|6.7|18.4|Gardner (1987)<br>l|
|Airline|3.8|7.7<br>_____<br>____<br>____<br>____|Morrison & Winston (1986,<br>1989)|
|Rail|2.3|6.8a|Winston (1985)|
|Postal Rates|na|4-12|President's Commission on<br>Privatization (1988)|
|Milk Marketing<br>Orders/Price Supports|0.4-0.9|0.9-3.5|Ippolito & Masson; Buxton &<br>Hammond_(reported in_<br>MacAvoy (1977))|
|Natural Gasc|0.2-0.4|_5.0_|Loury (1983)|
|Barge|0.2-0.3|0.6-0.9a|Litan & Nordhaus (1983)|
|Davis-Bacon Act|0.2a|_0.5_|Thiebolt (1975) (updated)|
|Credit|0.05-0.5|0.15-1.6a|Litan & Nordhaus (1983)|
|Ocean|0.05-0.08|0.15-0.22a|Jantscher (1975)|
|Trucking|od|0||
|Oil Price Controls|0|0||
|Cable TV|0|0||
|**Total**|**$45.3-46.5**|**$172.1-209.5**||

na not available

- a Figures estimated using 3:1 ratio of transfers to efficiency costs. b Indicates primary source of estimate.

c Cost of natural gas regulation expected to approach zero as all price controls are lifted. d If estimate is zero, federal regulation is assumed to be negligible.

Source: Hahn and Hird (1991)
Table 3. Descriptive Statistics: Controls

|||||Mean diff.|
|---|---|---|---|---|
||RAMV|Non-RAMV|P-value|Bonferroni P-value|
||(1)|(2)|(3)|(4)|
|_A. Demographics_|||||
|Female [=1]|0.504|0.588|0.000|0.000|
||(0.500)|(0.492)|||
|Age (years)|33.980|30.026|0.000|0.000|
||(10.252)|(11.546)|||
|Number of Venezuelan children|1.460|1.659|0.000|0.000|
||(1.497)|(1.489)|||
|Years of education before migration|13.513|12.908|0.000|0.000|
||(2.732)|(2.936)|||
|Migrated for health reasons|0.109<br>|0.096<br>|0.218|1.000|
||(0.312)|(0.295)|||
|Time in Colombia (months)|27.506|20.078|0.000|0.000|
||(10.949)|(8.873)|||
|Friends or family in Colombia before migration|0.702<br>|0.750<br>|0.001|0.022|
||(0.457)|(0.433)|||
|_B. Employment in Venezuela_|||||
|Ever worked in Venezuela [=1]|0.983|0.973|0.035|0.770|
||(0.129)|(0.163)|||
|Employed at private frm in Venezuela [=1]|0.610|0.584|0.126|1.000|
||(0.488)|(0.493)|||
|Employed with Government in Venezuela [=1]|0.150|0.154|0.707|1.000|
||(0.357)|(0.361)|||
|Self-employed or employee in Venezuela [=1]|0.188<br>(0.391)|0.172<br>(0.377)|0.208|1.000|
|Had a written contract in Venezuela [=1]|0.519|0.414|0.000|0.000|
||(0.500)|(0.493)|||
|Knew of job opportunity before migrating [=1]|0.343<br>(0.475)|0.346<br>(0.476)|0.867|1.000|
|Gap between last job and migration (months)|1.053|0.739|0.018|0.396|
||(4.397)|(3.321)|||
|_C. Housing Characteristics in Venezuela_|||||
|Had smartphone in Venezuela [=1]|0.647|0.505|0.000|0.000|
||(0.478)|(0.500)|||
|Had a dwelling in Venezuela [=1]|0.865|0.864|0.926|1.000|
||(0.341)|(0.342)|||
|Had electricity in Venezuela [=1]|0.994|0.994|0.766|1.000|
||(0.080)|(0.075)|||
|Had running water in Venezuela [=1]|0.876|0.854|0.054|1.000|
||(0.330)|(0.353)|||
|Had sewage in Venezuela [=1]|0.937|0.929|0.303|1.000|
||(0.242)|(0.257)|||
|Lived in Venezuela with parents or siblings [=1]|0.421|0.465|0.008|0.176|
||(0.494)|(0.499)|||
|Lived in Venezuela with partner or spouse [=1]|0.586|0.527|0.000|0.000|
||(0.493)|(0.499)|||
|Observations|1,709|1,939|3,648||

_Notes:_ The table reports the descriptive statistics of the controls included in all regressions.

47
Table 2: Firm-Level Databases: List of Countries and Data Sources

|**Country**|**Database**|**Description**|
|---|---|---|
|Ethiopia|Central Statistical Agency<br>(CSA): Large and Medium<br>Scale Manufacturing and<br>Electricity Industries Survey|Census of frms employing<br>more than 10 workers, 2011|
|Ghana|Ghanaian Statistical Service<br>(GSS), National Industrial<br>Census|Census of more than 10<br>workers, 2003|
|Kenya|Kenya National Bureau of<br>Statistics (KNBS)- Census of<br>Industrial Sector|Census of all formal frms,<br>2010|
|Bulgaria, Belgium, Finland,<br>France, Hungary, Italy, Latvia,<br>Portugal, Romania, Spain|Amadeus|(see note below for selection<br>criteria)|
|Chile|ENIA (Encuesta Nacional<br>Industrial Annual), 2013|Census of all industrial frms<br>with 10 or more workers|
|El Salvador|EAM (Encuesta Anual<br>Manufacturera)|Census of all industrial frms<br>with 10 or more workers|
|India|ASI (Annual Survey of<br>Industry), 2004–2005|Census of all industrial frms<br>with 10 or more workers in<br>case of power usage; 20 or<br>more workers without power<br>usage|
|Colombia|EAM (Encuesta Anual<br>Manufacturera)|Census of all industrial frms<br>with 10 or more workers|
|Malaysia|Census of Manufacturing<br>Sector|Census of all industrial frms<br>with no size cutof but<br>restricts analysis to 10<br>workers or more|
|Peru|National Economic Census|Census of all frms with no<br>size cutof but restricts<br>analysis to 10 workers or<br>more|

Note: The table shows selection criteria for countries in the Amadeus database. Keep in sample if 1) the ratio of aggregate manufacturing employment in Amadeus to aggregate manufacturing employment in Eurostat is greater than or equal to 80 percent, and 2) the ratio of manufacturing employment between Amadeus and Eurostat in each bin of Eurostat’s size distribution above the worker threshold (10–20, 20–50, 50–250, 250+) is also greater than or equal to 80 percent.

72
Table 5: Estimated Effect of Fuel Subsidies on Extensive Margin Measures of Fishing Activity

||||Dep|endent va|riable:|||
|---|---|---|---|---|---|---|---|
||1|_{_Fishing|hours _>_ 0|_}_|1_{_Ent|ered_}_|1_{_Exited_}_|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Conventional|0.554|291.095|-0.006|-0.015|0.104|0.431|0.071|
||(0.100)|(0.117)|(0.017)|(0.018)|(0.043)|(0.231)|(0.133)|
|Bias-Corrected|0.696|429.920|0.001|-0.021|0.188|0.531|0.117|
||(0.100)|(0.117)|(0.017)|(0.018)|(0.043)|(0.231)|(0.133)|
|Robust|0.696|429.920|0.001|-0.021|0.188|0.531|0.117|
||(0.137)|(0.135)|(0.021)|(0.021)|(0.121)|(0.281)|(0.163)|
|Bandwidth|0.199|0.148|0.310|0.270|0.199|0.271|0.259|
|N|1,500|1,020|8,472|6,912|79|89|87|
|Binding vessels|X|X|||X|X|X|
|Non-binding vessels|||X|X||||
|Impose bandwidth|X||X||X|||
|Mean dep. var.|0.700|0.686|0.803|0.809|0.316|0.562|0.165|

Notes: All regressions use default options from the rdrobust package and control for gear fixed effects, gross tonnage, length in meters, and engine horsepower. Mean dep. var. is the mean dependent variable within the bandwidth. In Columns 1 to 4, the unit of observation is a vessel-month-year, we exclude vessels built on or after 2016, the dependent variable is an indicator for positive fishing hours, and we cluster standard errors at the vessel-level. In Columns 5 to 7, the unit of observation is a vessel and standard errors are heteroskedasticityrobust nearest neighbor. The dependent variables in Column 5 to 7 are indicators that equal 1 for vessels built on or after 2016, if the first year we observe a vessel in the GFW data is not 2016, and if the last year we observe a vessel in the GFW data is not 2020. In Columns 1, 3, and 5, the bandwidth is from Columns 1, 2, and 1 (again) of Table 3, respectively. In Columns 2, 4, 6, and 7, the bandwidth selection procedure is mean-squared error optimal.

27
experimental data could open up new avenues for reducing frictional mismatches between the supply and demand for workforce skills. Behavioral-economics analysis could help policy makers better understand job seekers’ motivations and more effectively mitigate skills mismatches in the labor market. The analysis presented below examines two aspects of job-seeking strategies that can be used to guide policy making in India: (i) the number of job categories that applicants apply to and (ii) the timing of their applications.

# **_Application of the Analysis to Babajob Data_**

Job seekers may not necessarily apply for jobs that utilize the skill sets that they have acquired through their education or training. Employer demand and the personal circumstances of the individual can both influence a job seeker’s behavior. Those facing chronic unemployment or a serious financial constraint may adopt various coping strategies (Lazarus 1991; Lazarus and Folkman 1984; Liu, Huang and Wang 2014). For example, educated job seekers may choose to take lower-skilled jobs. However, their less-educated counterparts often face more limited employment prospects.

Figure 7 shows the proportion of job seekers who applied to only one occupational category on Babajob. Job seekers looking for managerial and professional-level positions were generally more likely to apply for jobs across multiple occupational categories.<sup>10</sup> Only 31 percent of applicants for managerial positions applied exclusively for jobs in one occupational category, while 26 percent of applicants for professional positions focused on a single category. This suggests that more-skilled job seekers have a greater degree of employment flexibility and are more willing to apply for diverse jobs. Among non-professional job seekers, some types of workers tended to restrict themselves to one specific job category, while others applied to jobs across various categories. About 50 percent of service workers, sales workers and plant operators and assemblers applied exclusively to jobs in one occupational category. This may indicate that workers in these categories are attempting to leverage a particular skill set or previous work experience. For example, 52 percent of beauticians and 53 percent of drivers applied to only one occupational category. However, skilled agricultural workers, workers in elementary occupations,<sup>11</sup> and construction and craft workers were more likely to apply to multiple occupation categories. Workers in these categories may have less-specialized skills and could be more sensitive to wages, employment terms or other factors besides job type. Clericalsupport applicants tended to have an especially high degree of flexibility in terms of the employment categories to which they applied.

> 10 This refers to the 26 occupational categories used by Babajob.

> 11 Elementary occupations consist of simple and routine tasks which mainly require the use of hand-held tools and often some physical effort.

17
Although _Sri Lanka_ has a lower level of inequality among the unitary countries, it is more unequal than India. Its weighted CV is almost four times that of the US. Sri Lanka saw a fall in regional inequality in 1991, after which it has steadily increased. Inadequate infrastructure development in rural areas, the concentration of industry close to the main ports and the airport, and the poor performance of the agricultural sector have led to an unequal distribution of the benefits of economic growth between regions. Because of its proximity to the port and airport, Western province is home to 85% of industry and generates over 40% of GDP, while predominantly agricultural areas such as North-western and Uva provinces remain backward due to the stagnation in coconut and domestic agriculture (Source: EIU Country Profile)

**Figure 4.2c: Regional disparity trends in unitary countries**

<!-- Start of picture text --> 0.45<br>0.4~~~~~~~~~~~~~~~~~~~,<br>0.35 X-<br>a 0.25 ,., . -@<br>,- 0.2 <<br>0.12 ',5'<br>0.1<br>0.05 *- 4.,7 ,7*X;t.-. 9<br>1978 1979 1980 1981 1982 1983 1984 1985 198861987 1988 1989 1990 1991 1992 1993 1994 1995 1998 1997 1998 1999<br>Year<br>-- Chile China '~China (new) ~Indonesia +Philippines | Romania<br>-Sri Lanka -Thailand Uzbekistan Vietnam<br><!-- End of picture text -->

_Vietnam_ has seen a dramnatic rise in regional inequality in the 1l990s. The weighted CV has almost doubled during this period. There was a big spurt in inequality from 1990 to 1992, after which it increase at a slower pace till _1995._ 1995 to 1997 has seen another big increase. In 1990, 25
<!-- Start of picture text --> Share of DPF lending in total Bank lending<br>IDA/Blend IBRD<br>o<br>t<br>N<br>O39 29 8 PaRee sh S@ 8 8 Beas BBE FTE<br>238 Se FF RRR 288 Sse & FRR<br>$e22282x & S§ Bs§ § Bs&§ & wvR Sze9h& 28 S<ggekBe8BsR RB<br>ME > sO SD SCM) s£F FI<br><!-- End of picture text -->
**Table 1. Estimated Economic Potential for Mitigation in 2030 under Alternative Price Scenarios, Giga tons of carbon dioxide equivalent** **<u>(GtCO2e)</u>**

|Mitigation Sources|Low prices|High prices|
|---|---|---|
|Global|15.80|31.10|
|OECD countries|4.90|7.40|
|Transition economies|1.80|2.80|
|Developing countries|8.30|16.80|
|Developing country sector|||
|Energy supply|1.30|2.70|
|Transport|0.15|0.15|
|Buildings|2.70|3.30|
|Industry|1.60|3.80|
|Agriculture|1.60|4.50|
|Forestry|0.75|3.00|
|Waste|0.20|0.70|

Source: Table 11.3 in chapter 11 of IPCC Working Group III [24] 14
second sample is at the individual level (children) and the dependent variable is the school enrollment of children age 5 through 15. The size of this sample is 17,696.

# **3.2. Construction of women’s relative decision-making power (θw)**

There are eight questions in the PSLM survey regarding women’s decision making about their education, employment, use of birth control, having more children, and household food, clothing, medical treatment, and recreation expenditures for married women age 15 through 49. The answers to these questions are broadly categorized as ―woman decides alone,‖ ―household head or husband decides alone,‖ ―household head or husband and woman decide jointly,‖ and ―other family members decide.‖ A woman is considered to have decision-making power on a particular issue if she jointly or by herself makes the decision (equal to 1), since on at least some issues, such as birth control or number of children, women do not have to have the sole decisionmaking power. However, I have also done the calculations by assigning ―jointly making the decision‖ a weight of 0.5 or 0, and the results are very similar and available upon request. Similar scales are also applied to the influential male household members. The results on distribution of women’s decision-making power are quite striking (see Table 1). For decisions on women’s own education, only 18% participated in this decision-making process (alone or in consultation with a male household member), but 86% of men in households participated in this decision (made the decision alone or in consultation with the woman). The results are very similar for decisions on women’s employment. Women have more to say about expenditures on food and clothing, but men still play the major decision-making role on those and other expenditures, particularly medical and recreation expenditures. For decisions on birth control and having more children, most men and women participated in the decision-making process. For 8
O.A.M.D.G.

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**Glaeser Edward and Jesse Shapiro** : “Cities and Warfare: the Impact of Terrorism on Urban Form.” Harvard Institute of Economic Research, December 2001

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**Greene William** : LIMDEP, Econometric Software, _Plainview_ NY, 1997

**Greene William** : “Econometric Analysis”, 4<sup>th</sup> Edition, _Prentice Hall_ , 2001

**Grogger, Jeffrey** : "Local Violence and Educational Attainment." _Journal of Human Resources_ 32(4): 659-682, 1997

**Guiso Luigi and Tullio Jappelli:** “Household Portfolios in the United Kingdom”, in _Guiso L, Haliassos M and T Jappelli, ed., “Household Portfolios”_ , MIT 2002

**Guiso Luigi, Jappelli T and Daniele Terlizzese:** “Income Risk, Borrowing Constraints and Portfolio Choice”, _The American Economic Review_ , Vol. 86, Issue 1, 158172 _”_ , March 1996

**Heckman James** : “Sample Selection Bias as a Specification Error”, _Econometrica_ , Vol. 47, 153-161, 1979

**Heckman James, Ichimura Hide and Petra Todd: “** Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme”, _Review of Economic Studies 64_ , pp. 605-654

**Kossoudji S, Mueller E** : “The Economic and Demographic Status of Female-Headed Households in Rural Botswana”, _Economic Development and Cultural Change_ , Vol. 31, No. 4 (Jul., 1983), pp. 831-859

**Krueger Alan and Jitka Maleckova** : “Education, poverty, political violence and terrorism: is there a causal connection?”, _NBER Working Paper 9074_ , July 2002

**Levitt Steven and Mauricio Rubio** : “"Understanding Colombia's Crime Situation and the Institutional Reforms Required to Alleviate the Problem," in: _Alberto Alesina (ed.) ‘Institutional Reforms in Colombia’_ , Harvard University, mimeo, 2000.

**Londoño JL and R Guerrero:** ”Violencia en America Latina, Epidemiología y Costos”, Interamerican Development Bank, August 1999

45
# **Chart 7B: Uphill Flows of Sophisticated Exports and Per Capita GDP, 1986, 1996, and 2005 (Controlling for other factors)**

<!-- Start of picture text --> 7 8 9 10 11 12<br>Log per capita GDP (in PPP constant $)<br>1986 1996 2005<br>10.4<br>10.2<br>10<br>9.8<br>9.6<br>Uphill flows of sophisticated exports<br>9.4<br><!-- End of picture text --> This chart is the same as Chart 7A above, except that it includes for each year for which the relationship is plotted, controls for area, population and remoteness (all in log terms).

Remoteness (due to Berthelon and Freund (2008)) is measured as j≠k, where D is distance and there are k foreign countries.

25