rafmacalaba/datause-encoder-data huggingface dataset:
Information on oil field locations and production were collated from multiple sources. Oil field boundaries for Syria and Iraq were obtained from [27] and [28]. The map lists the field name, operator, operational status, type, and utilization for each field. To supplement this data in Iraq, additional fields were digitized and added from IHS Global Exploration and Production Service’s map of Iraq. This map displays the oil and gas field boundaries, field name, field status, as well as oil and gas well points. Yearly field-level production data in barrels per day from 2012 to 2015 in Iraq and Syria were obtained from [27, 28, 24]. Production data was matched to the field locations using the field name.

A few restrictions were applied to the sample of included fields. Including Syria and Iraq, 45 fields with production data could be assigned to boundaries according to their name. First, to ensure differences in the geology and production technology between oil fields southern Iraq and Daesh-controlled fields did not bias the estimation, 16 oil fields in south Iraq were removed. South Iraq oil fields were defined as any field below the southern most flare in Syria, 34.64 degrees latitude. Removing south Iraq reduced the sample to 29 oil fields. One field (Miran West) were dropped for having no production data. In total, 28 fields outside of southern Iraq were matched and included data.

Second, two fields were dropped due to inconsistencies in the production data. Ain Zalah (West Butmah) oil field, was removed due to data reliability: oil output was reported invariant during the entire production history, while corresponding RH levels differed significantly. Likewise, output was reported invariant during all years with data at Bijeel, 2014-2015, yet RH varied substantially. Removing fields with inconsistent production data results in 26 oil fields. Last, four fields, Gebibe, Hamrin, Hawler, and Tawke, were dropped due to year to year changes in production that were not in proportion to the change in RH, indicating alterations in the technology or production process that would affect the stability of the oil to flaring relationship.

The final sample consists of 22 oil fields. Further, some individual years were missing 39
.4 <!-- Start of picture text --> Policy Research<br>Poverty Analysis and Policy<br><!-- End of picture text -->

**WPS 1080**

This paper-a product of the Pover;y and Social Policy Division, Africa Technical Department-is part of the output of the research project "Poverty and the Social Dimensions of Structural Adjustment in C6te d'Ivoire, 1985-88: A Policy-Oriented Analysis" (RPO 675-26). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Elena Vitanov, room J2-24 1, extension 38400 (January 1993, 21 pages).

Grootaert and Kanbur report on an exercise in economic statistics. They develop a regional price index for CBte d'Ivoire building on the strengths of two independent data sources: the C6te d'Ivoire Living Standards Survey (CILSS) and the International Comparisons Project (ICP).

Te CILSS collected detailed information on household incomes, spending, employment, and so on, but its coverage of prices left much to be desired. The ICP collected a wealth of information on prices across the country, but collected no information on household spending patterns **or** other socioeconomic data.

Grootaert and Kanbur bring together these two sources to produce a regional price index that they argue is superior te previous estimates based solely on the Living Standards Survey. The procedures they follow should be of interest to practitioners faced with similar data shortcomings, particularly when working on Africa.

They show this to be no mere statistical exercise. Using the new price index can have a significant effect on earlier evaluations of poverty in CBte d'lvoire. They also use the new price information to construct disaggregated indices by commodity category and by poverty group.

ThePolicy ResearchWorking PaperSeriesdisseminates thefmdings of work **under** way **in tieBank.** Anobjectiveof the series is to **get these** findings out quickly, even if presentations are less than **fully** polished. The **fmdings,** interpretations, and **conclusions** in these papers do not necessarily represent official Bank policy.

Produced by **the** PoUcy Research Dissamiation Center
Societies can get trapped into dysfunctional norms and behavior patterns. It is possible to change norms and to prime identities and mental models that change behavior. The _World Development Report_ 2015 provides dramatic examples. They include many to which we have referred here: using lockable boxes with savings books in order to increase individuals’ ability to act on their intentions to save for preventative health products; exposing individuals to soap operas with female characters with small families, which reduced birth rates; and using political reservations to expose villagers to local women leaders—thereby eliminating or reducing gender bias in evaluations of leaders and in parents’ aspirations for their teenage children.

We have shown that there is a wealth of evidence for behavioral changes—both natural and laboratory experiments—that is hard to reconcile within the model of the individual in standard economics (the rational actor model with fixed preferences), but which is consistent with our models of endogenous preferences and socially determined cognition.

Conventional economics provides a lens through which one can see the world, including human interactions. It stresses the importance of financial incentives and largely denies the relevance of altruism for understanding markets. By assuming that preferences are fixed, it assumes that policy interventions (including providing financial incentives) will not alter the nature of the individual, his identity, and his perception of others. Of course, well-trained economists do not deny the existence of these other effects, but suggest that they are the province of other social sciences.<sup>62</sup> This view of the world is itself a social construct—one that serves some interests but may have had adverse effects on others and on society as a whole. This worldview helps frame how we think about economic decisions and about society more broadly.

One central insight of behavioral economics is that framing matters. It affects individual behavior, collective decision-making, and the evolution of beliefs and culture. The standard economics framing of individuals and the determinants of their behavior has had profound effects on economic and social policy and the evolution of many societies.

Ideologies are often contested. In the last few years, the framing provided by conventional economics has become increasingly contested and the subject of debate. This paper can be viewed

> 62 The anthropologist Clifford Geertz (1973, p. 44) calls this the “stratigraphic approach.”  Our conclusion accords with his hypothesis that “we need to replace the ‘stratigraphic’ conception of the relations between the various aspects of human existence with a synthetic one; that is, one in which biological, psychological, sociological, and cultural factors can be treated as variables within unitary systems of analysis.”

59
36

# **Table VII. Probability of Democratization in Intervention Cases. Accounting for Selection Bias, Heckman Probit Estimation, 1961–96**

||**‘Lagged int**<br>**onset’**<br>|**ervention**<br>|**‘Lagged in**<br>|**tervention’**<br>|
|---|---|---|---|---|
|**Democratization Equation**|β<br>**(s.e.)**|**p-value**|β<br>**(s.e.)**|**p-value**|
|Democratic military intervention|0.43<br>(0.21)|0.038*|–0.092<br>(0.16)|0.569|
|Autocratic military intervention|0.013<br>(0.12)|0.908|–0.46<br>(0.19)|0.019|
|Number of non-governmental<br>organizations, logged|0.18<br>(0.082)|0.027*|0.18<br>(0.081)|0.027*|
|Post-Cold war period|0.10<br>(0.16)|0.521|0.076<br>(0.16)|0.633|
|Polity2, lagged|–0.042<br>(0.018)|0.020 *|–0.043<br>(0.018)|0.016 *|
|Polity2, lagged, squared|–0.011<br>(0.0030)|<0.0005*<br>**|–0.010<br>(0.0030)|<0.0005***|
|Proximity of democratization|0.11<br>(0.15)|0.487|0.10<br>(0.16)|0.514|
|Constant|–2.57<br>(0.38)||–2.32<br>(0.40)||
|**Selection equation (incidence**<br>**of all types of intervention)**|||||
|Number of non-governmental<br>organizations, logged|–0.17<br>(0.021)|<0.0005*<br>**|–0.17<br>(0.021)|<0.0005***|
|Post-Cold war period|–0.15<br>(0.060)|0.010**|–0.16<br>(0.060)|0.008**|
|Polity2, lagged|–0.0027<br>(0.0047)|0.572|–0.0023<br>(0.0047)|0.621|
|Polity2, lagged, squared|–0.0035<br>(0.00097)|<0.0005*<br>**|–0.0035<br>(0.00097)|<0.0005***|
|**Civil conflict, lagged**<br>No conflict<br>Minor<br>War|ref.gr.<br>0.65<br>(0.067)<br>0.81<br>(0.066)|ref.gr.<br><0.0005*<br>**<br><0.0005*|ref.gr.<br>0.65<br>(0.068)<br>0.82<br>(0.067)|ref.gr.<br><0.0005***<br><0.0005***|
|Constant|0.26<br>(0.12)||0.23<br>(0.12)||
|N(total)|3,822||3,822||
|Censored observations|2,804||2,804||
|Uncensored observations|1,018||1,018||
|ρ|0.24<br>(0.28)||0.19<br>(0.27)||
|LR test of independence of equa-<br>tions(ρ=0):<br>Loglikelihood|Х<sup>2</sup>(1)=0.74;<br>p-value = 0<br>–2286.21|<br>.39|Х<sup>2</sup>(1)=0.53;<br>p-value = 0|<br>.47|

(*) Significant at 0.10 level * Significant at 0.05 level

** Significant at 0.01 level
from a new survey that was conducted in 58 investment promotion agencies between February and May 2002.4 At the outset, it is worth underscoring that our research should be viewed as a first attempt to fill the existing empirical gap and has obvious limitations. The most important is that we have been only able to examine empirically the relationship between promotion and foreign direct investment at one point in time, specifically the year 2001. Unfortunately, data are simply not available for additional years. We believe that it nonetheless provides some answers to four sets of questions that should help IPA managers and policy makers to develop a better understanding of the conditions -both external and internal to the agency-- that influence the effectiveness of promotion:

- How does the amount of spending on investment promotion affect its effectiveness? Does an agency need to exceed a minimum level to have any effect on international investors?

- To what extent does the business environment or the country's characteristics affect the effectiveness of investment promotion? Does the quality of the general business environment matter?

- Does the effectiveness of investment promotion vary according to the functions or activities on which it focuses? Should an IPA devote more resources toward policy advocacy or image building?

- Is the effectiveness of investment promotion influenced by different agencies' characteristics such as their structure, mandate, sources of funding, and institutional relationships?

The paper proceeds as follows. In Section II we begin by presenting a small analytical model through which we explain the behavior of an investment promotion agency. Section Em[ discusses the selection of variables as well as the main sources of data used for the application of the above model. Section IV presents the main empirical results

> 4The questionnaire was sent via email or fax to 114 agencies, both from developing and industrial countries. We received responses from 75 agencies, yielding a response rate of 66%. The rate was higher in Latin America and in Eastern and Central Europe where it reached 86% and 71%, respectively. A description of the **main** responses can be found in the FIAS website (www.fias.net).

4
Note: This table reports the effect of FDI shock on labor productivity growth in three broad sectors and GDP per capita growth. Lagged city characteristics include population, average age, share of Han ethnicity, log GDP per capita and average years of schooling. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

The above results suggest that manufacturing FDI inflows in China propelled local structural transformation primarily through demand-side effects during the study period. This is consistent with the “Vent for surplus” theory, which argues that trade and FDI liberalization will provide a vent for the output of surplus resources, which is labor in China’s case (Fu and Balasubramanyam, 2005). Many foreign firms that entered China during the study period (1990-2005) were export-oriented, booming exports raised labor demand tremendously in the industrial sectors and absorbed excess labor from the agriculture sector. Rising income levels and business linkages boosted demand for services as well, services GDP and employment grew rapidly consequently. Supply-side effect through enhancing productivity is not apparent in the industry sector.

# 5.2 Mechanism for school enrollment

Many previous studies document that foreign firms often pay a wage premium relative to domestic firms, and the wage premium is particularly high for skilled workers (Aitken et al 1996; Bandick 2011; Lipsey and Sjöholm 2004; Sjöholm and Lipsey 2006; Heyman et al. 2007). There are multiple reasons why foreign companies tend to pay a wage premium: first, foreign firms might try to prevent technological spillovers through labor turnover by paying higher wages (Fosfuri et al. 2001); second, foreign firms are more productive and can benefit from rent-sharing with their global headquarters (Budd et al. 2005); thirdly, the wage premium can be a compensation for higher labor demand volatility and lower job security (Fabbri et al. 2003, Bernard and Sjöholm 2003).

Individuals might be motivated to improve their educational credentials to join foreign firms. Foreign firms indeed are more likely to hire individuals with higher educational attainment. After controlling for gender, age and age squared, marital status, ethnic minority dummy, industry and city fixed effects, the probability of being employed by a foreign firm is 2.6 percentage points higher for college graduates, and 5.1 percentage points higher for people with at least a high school diploma.

To see whether there is a foreign wage premium in China and whether it is higher for skilled workers, we use the 2005 mini census data to run a simple cross-section regression below: lnincomepic = αForeignpic + βSkilledpic + γForeign× Skilledpic + Xpic′ ρ+ μi + θc + εpic (4) lnincomepic represents monthly income in RMB yuan of person p in industry i, city c in natural logarithm. Foreign is a dummy variable that takes the value of 1 if the person is employed in a foreign25
indication of planned reform, not necessarily an ex-post measure of actual reform. During the course of a loan, plans may change. Some reforms are scrapped, others are altered. In Pakistan's FSAL, for example, a major component was the restructuring of many stateowned banks. Upon implementation, the Government decided that its restructuring efforts would be less effective than bank privatizations. Although the Bank later applauded the shift in emphasis,8 the OED classification indicates that the Pakistan FSAL attempted restructuring but not privatization. Of course, that classification may be partially accurate in that, although Pakistan's was a 1989 FSAL, little privatization had occurred as of 1996.9 The episode underscores two problems with the reform data -- although planned reform is differen **t** han actual reform, and substantial reform is different than minor reform, those differences are sometimes difficul **t** o capture in simple dummy variables which are based largely on the expressed intentions of the parties to these loans. That said, to the extent that expressed intentions are reliable indicators of ensuing reform and that the Bank's loan documentation adequately summarizes those intentions, the measurement error associated with the OED variables should not be debilitating. Indeed, given the judgments that ex-post reform variables would have entailed (What form did privatization take? Was the re-capitalization sufficient? Were prudential regulations 8 World Bank, "Pakistan FSAL: Project Completion Report," March 16, 1995, pp. 6-11.

9 More generally, the privatization policy category identified by OED is broadly defined and does not permit one to determine what form eventual privatizations took. Data on what assets were sold to whom and at what price were not available in a systematic format, although OED's project audit reports did provide good information in some cases.

6
- [60] Renkow, Mitch, Daniel G. Hallstrom and Daniel D. Karanja. 2004. “Rural infrastructure, transactions costs and market participation in Kenya.” _Journal of Development Economics_ 73(1): 349-367.

- [61] Rijkers, Bob, Mans S ̈oderbom and Josef L. Loening. 2010. “A rural-urban comparison of manufacturing enterprise performance in Ethiopia.” _World Development_ 38(9): 12781296.

- [62] Rosenzweig, Mark R. and Oded Stark. 1989. “Consumption smoothing, migration, and marriage: evidence from rural India.” _Journal of Political Economy_ 97(4): 905-926.

- [63] Rothenberg, Alex. 2012. “Transport infrastructure and firm location choice in equilibrium: evidence from Indonesia’s highways.” Working Paper.

- [64] Ruist, Joakim and Arne Bigsten. 2013. “Wage effects of labour migration with international capital mobility.” World Economy 36(1): 31-47.

- [65] Shiferaw, Admasu, Mans S ̈oderbom, Eyerusalem Siba and Getnet Alemu. 2013. “Road infrastructure and enterprise dynamics in Ethiopia.” Working paper.

- [66] Suen, Wing. 2000. “Estimating the effects of immigration in one city.” _Journal of Population Economics_ 13(1): 99-112.

- [67] Tybout, James R. 2000. “Manufacturing firms in developing countries: how well do they do, and why?” _Journal of Economic Literature_ 38(1): 11-44.

- [68] Wei, Yingqi, Xiaming Liu, David Parker and Kirit Vaidya. 1998. “The regional distribution of foreign direct investment in China.” _Regional Studies_ 33(9): 857-867.

42
<!-- Start of picture text --> Policy, Research, and External Affairs 1<br>L Education a d Employment<br>WPS 464<br><!-- End of picture text --> This paper a produc,tof the Education and Emrloyment Division, Population and Human Resources Department- is part of a larger effort in PRE to assess the impact of women's education on productivity and family welfare. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Valerie Charles, room S6-228, extension 33651 (17 pages with tables).

A recent focus on decisionmaking within the household (rather than by the individual) has opened a new field of research into the economics of maniage and the family.

Recent research indicates that in the United States, at least, a wife's education has a positive effect on a husband's earning capacity - a focused instance of the economic benefits of (particularly nonmarket) association. Even if education did not get women jobs or improve their ability to function as housewives and mothers, it is not wasted.

Drawing on data from the Israel Labor Mlobility Survey, Neuman and Ziderman found that the wife's educationa **l** evel increased a husband's eaniings in Israel's primary sector (in which workers have good jobs, with good pay, security, and fringe benefits) - but not in the secondary sector (in which workers have lowpaying, unstable, generally unattractive jobs).

These new findings are consistent with the general implications of the dual labor market model.

Such cross-productive effects may be different in Qe type of dual labor market that exists in Israel.

**T'he** PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research. and Extemal AffairsComplex. Anobjective oftheseries is toget these findings outquickly. even if presentations are less than fullypolished. **The** findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.
5 THE INTENSIVE MARGIN IN TRADE and Pareto models. In contrast, Melitz and Redding (2015) show that the formula proposed in Arkolakis et al. (2012) to compute welfare changes given changes in trade shares (their formula for “ex-post welfare evaluation”) is no longer accurate if the Pareto productivity distribution is bounded from above. We find Melitz-Pareto a good approximation because variation in the trade elasticity is much smaller in our full Melitz-lognormal model estimated on the EDD data than in their symmetric Melitz model with a truncated Pareto distribution calibrated to match the relative size of exporting and non-exporting U.S. firms.

To recap, this paper makes several contributions to the literature. First, we use the EDD to establish a new stylized fact, namely that between 40% and 60% of the variation in exports across country pairs takes place along the intensive margin, with this margin being important all along the firm-size distribution. Second, we show that the Melitz-Pareto model cannot match this fact, even allowing for ad hoc fixed trade costs, multi-product firms and granularity. Third, we show that a lognormal firm productivity distribution generates a positive role for the intensive margin as required by the data. Fourth, we use likelihood methods and the EDD to estimate a Melitz model with a lognormal distribution for productivity plus idiosyncratic demand shocks and fixed costs. Finally, we extend the exact hat algebra approach to a generalized Melitz model and use it with the estimated Melitz-lognormal model to explore counterfactual trade flow and welfare implications relative to those of the Melitz-Pareto model.

The rest of the paper is organized as follows. Section 2 describes the EDD data and document the importance of the extensive and intensive margins in accounting for cross-country variation in exports. Section 3 contrasts the predictions of the Melitz-Pareto model (with a continuum of single product firms, multi-product firms or a finite number of firms) to the EDD facts. Section 4 shows how the implications of the Melitz model change when we drop the Pareto assumption and instead assume that the firm productivity distribution is lognormal. Section 5 describes the quantitative impact of trade cost shocks estimated based on “exact hat algebra.” Section 6 concludes.

# **2. The Intensive Margin in the Data**

## **The Exporter Dynamics Database**

We use the Exporter Dynamics Database (EDD) described in Fernandes et al. (2016) to study the intensive and extensive margins of trade. The EDD is based on firm-level customs data covering the universe of export transactions provided by customs agencies from 59 countries (53 developing and 6 developed countries). For each country, the raw firm-level customs data con-