rafmacalaba/datause-encoder-data huggingface dataset:
Borjas, George J. and Marta Tienda (eds) 1985. “Hispanic in the U.S. Economy.” New York: Academic Press.

Carliner, Geoffrey. 1980. “Wages, Earnings, and Hours of First, Second and Third Generation Males.” Economic Inquiry, 18(1): 87-102.

Chiswick, Barry R. 1978. “The Effect of Americanization of the Earnings of ForeignBorn Men.” Journal of Political Economy 86(5): 897-921.

Chiswick, Barry R. 1986. “Is the New Immigration Less Skilled Than the Old?” Journal of Labor Economics 4(2): 168-192.

Chiswick, Barry R., Yew Liang Lee and Paul W. Miller. 2005. “Longitudinal Analysis of Immigrant Occupational Mobility: A Test of the Immigrant Assimilation Hypothesis.” International Migration Review, 39(2): 332-53.

Deaton, Angus. 1985. “Panel Data from Time Series of Cross-Sections.” Journal of Econometrics, 30: 109-126.

DeFreitas, Gregory. 1980. “The Earnings of Immigrants in the American Labor Market.” Ph.D. dissertation, Columbia University.

Duleep, Harriet O. and Mark C. Regets. 1994 . “The Elusive Concept of Immigrant Quality: Evidence from 1970-1990.” Discussion Paper PRIP-UI-31, The Urban Institute, Washington, DC. Published in 2002 as IZA Discussion Paper No. 631.

Duleep, Harriet O. and Mark C. Regets. 1997 . “The Decline in Immigrant Entry Earnings: Less Transferable Skills or Lower Ability?” The Quarterly Review of Economics and Finance, 37 Special Issue: 189-208.

Duleep, Harriet O. and Mark C. Regets. 1999 . “Immigrants and Human-Capital Investment.” American Economic Review, 89(2): 182-191.

Duleep, Harriet O. and Daniel J. Dowhan. 2002. “Insight from Longitudinal Data and the Earnings Growth of U.S. Foreign-Born Men. Demography, 39(3): 485-506.

Friedberg, Rachel. 1992. “The Labor Market Assimilation of Immigrants in the United States: The Role of Age at Arrival.” Unpublished manuscript. Providence RI: Brown University.

Friedberg, Rachel. 2000. “You Can’t Take it with You: Immigrant Assimilation and Portability of Human Capital: Evidence from Israel.” Journal of Labor Economics, 18(2): 221251.

Funkhouser, Edward and Stephen J. Trejo. 1995. “The Labor Market Skills of Recent Male Immigrants: Evidence from the Current Population Survey.” Industrial and Labor Relations Review, 48(4): 792-811.

31
We use reported Colombian GDP in constant 2005 pesos as a proxy for licit aggregate income that includes both private as well as public income (RGDP ) in the following way We also use an estimate of aggregate real consumption in the Colombian economy, an observable quantity, obtained from National accounts We use a measure of the real stock of capital (KS) for the Colombian economy, for which more than one estimate is available. We use the measure of real aggregate capital stock in each period from the Departamento Administrativo Nacional de Estadísticas (DANE), Colombia’s National Bureau of Statistics Finally, for our calibration measure of laundered assets we use the real interest rate which can be obtained by using the Fisher identity which says that the real interest rate (RIR) plus one is equal to the nominal interest rate plus one divided by the in‡ation rate plus one, yielding the following measure All of the data used were collected by the Unidad de Información y Análisis Financiero (UIAF) from September 2013 to March 2014.<sup>18</sup> All macroeconomic time series come from o¢ cial sources, mainly from the Banco de la República of Colombia, National Bureau of Statistics DANE, National O¢ ce of Taxes and Customs DIAN, National Planning Department DNP and Treasury Department. Data on crimes and drugs come from the Ministry of Defense, National Police Department, National Institute of Prisons INPEC, United Nations O¢ ce on Drugs and Crime UNODC, Ministry of Justice and the District Attorney’s O¢ ce. Although the data were initially to be collected quarterly for the period 1999 to 2013, data limitations made this unfeasible. Hence, data were collected at an annual frequency for 1985 to 2013, which a¤ected the size of the time series. Not all of the data collected were used since the theoretical model guided the ...nal data necessary for the application of the estimation methods. Descriptive statistics for the variables used in the estimation are reported in Table 2.

> 18 We thank the support of the Director of the UIAF Luis Edmundo Suárez and deputy director Javier Gutiérrez. We also thank especially Angela Hurtado from the UIAF for her superb assistance in constructing the data set used in this article. The UIAF did an excellent job in ...nding the best data available for the development of the empirical part of this project and assisted us in selecting the necessary information to construct the main time series required at the annual frequency and to obtain the relevant literature on asset laundering from national and international sources.

35
# **Table 9: Savings -- Intensive Margin**

||(1)|(2)|
|---|---|---|
||Saving Regularly<br>as per Financial<br>Plan|Log of Total<br>Deposits|
|Autonomous Trainer Selection (T1)|0.037**<br>|0.048*|
||(0.017)<br>|(0.027)|
|Fixed Trainer Selection (T2)|0.023<br>|-0.004|
||(0.019)<br>|(0.026)|
|R-squared|0.008<br>|0.506|
|Sample Size (N)|7885<br>|1191|
|Dependent Variable Mean in Control Group|<br>0.394<br>|18.637|
|Dependent Variable SD in Control Group||0.484|
|F-test (p-value): T1 = T2|0.450<br>|0.077|
|_Data Source_|_HH Surveys_|_Administrative_|

This table presents analysis for savings outcomes using both household survey data across two endline rounds and administrative data from SACCOs. All regressions control for survey round (or time) dummies and include stratification controls. Standard errors, clustered at the SACCO level, are reported in parentheses. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
**Table A3: Probability of remarriage**

||Probability<br>of<br>remarriage<br>widows<br>-Urban|Probability of<br>remarriage<br>divorcees<br>- Urban|Probability of<br>remarriage<br>widows<br>- Rural|Probability of<br>remarriage<br>divorcees<br>- Rural|
|---|---|---|---|---|
|_Ref: dissolution after age 40. _|||||
|Dissol. before age 25|0.476<sup>***</sup>|0.386<sup>***</sup>|0.488<sup>***</sup>|0.313<sup>***</sup>|
||(0.070)|(0.109)|(0.080)|(0.074)|
|Dissol. btw age 25-39|<br>0.308<sup>***</sup>|<br>0.249<sup>**</sup>|<br>0.267<sup>***</sup>|<br>0.256<sup>***</sup>|
||(0.036)|(0.106)|(0.048)|(0.081)|
|No children born in last<br>union|<br>-0.156|<br>0.046|<br>-0.133|<br>0.045|
||(0.104)|(0.101)|(0.103)|(0.083)|
|Had a son at time of|0.043|0.001|-0.057|-0.200<sup>***</sup>|
|dissolution|||||
||(0.049)|(0.073)|(0.051)|(0.058)|
|Number of marital|-0.043|0.048|0.018|-0.126<sup>**</sup>|
|dissolutions|||||
||(0.043)|(0.071)|(0.046)|(0.049)|
|Ever been to a French<br>school|<br>-0.012|<br>-0.185<sup>***</sup>|<br>0.051|<br>0.051|
||(0.057)|(0.063)|(0.080)|(0.082)|
|Polygamous father|<br>0.101<sup>**</sup>|<br>0.058|<br>0.089|<br>0.082|
||(0.046)|(0.068)|(0.058)|(0.070)|
|Fostered before age 15|<br>0.037|<br>0.120|<br>0.253<sup>***</sup>|<br>0.091|
|_Ref: Wolof/Lebou_|(0.064)|(0.083)|(0.072)|(0.096)|
|Serere|0.033|-0.039|0.042|0.131|
||(0.055)|(0.097)|(0.088)|(0.090)|
|Poular|<br>0.003|<br>-0.154<sup>*</sup>|<br>0.032|<br>0.088|
||(0.060)|(0.086)|(0.065)|(0.081)|
|Other ethnicity|<br>-0.083|<br>-0.057|<br>-0.026|<br>-0.061|
||(0.064)|(0.105)|(0.065)|(0.087)|
|Mean of dep. var|0.22|0.50|0.32|0.70|
|N|227|203|261|150|
|PseudoR2|0.323|0.092|0.227|0.314|

Note: Logit models, marginal effects shown. Col 1 and 3: Sample of ever-widowed women; Col 2 and 4: Sample of ever-divorced women.<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1,<sup>+</sup> _p_ < 0.12. Source: Authors’ estimations using PSF1.

50
project nature of the CDM; however, they cannot represent the economy-wide impacts resulting from market interaction and income effects.

CGE models, on the other hand, contain a comprehensive representation of market interactions through price- and income-responsive supply and demand reactions. Beyond price-induced structural change in production and consumption, CGE models can quantify efficiency implications and distributional impacts of policy measures. The CDM is usually represented in CGE models as an integrated emissions-trading system (IET) where developing countries receive emissions permits in proportion to their business-as-usual levels and engage in trade with industrialized countries (see, e.g., Manne and Richels, 1999; Bernstein et al., 1999; MacCracken, 1999; Klepper and Peterson, 2005; Burniaux et al., 2009). By non-arbitrage, the CDM leads to a full equalization of marginal abatement costs (at the level of the endogenous permit price) across all sectors in the industrialized and developing countries that are covered by the IET.

Representing the CDM as an IET has, however, three major shortcomings. First, it may overstate the cost-savings potential of the CDM. As a more discrete, project-based mechanism the CDM is unlikely to capture all the cost-effective abatement options in production and consumption that would be ideally realized under an IET. Springer (2003) therefore describes this modeling approach as an optimistic version of the CDM. Addressing this issue, the EMF-16 impact study of the Kyoto Protocol (Weyant and Hill, 1999; see also Bernstein et al., 1999, and MacCracken, 1999) constrains the sale of CDM credits to 15% of the permit sales that would result under a full global emissions trading scheme. However, while such percentage-constraints are meant to capture the difficulties in the implementation of the CDM, they remain highly subjective (Manne and Richels, 1999).<sup>4</sup> Second, the CDM representation as an IET is inconsistent with the incentive structure of the CDM. In particular, firms undertaking CDM projects are compensated for employing less emissions-intensive but more expensive production technologies through the sale of certified emission reductions – socalled CERs. In contrast, in an IET, firms face an emissions price on their inputs which increases their costs, while the revenues from CERs accrue to the region's representative agent or government.

4 Another more political than technical reasons for constraining the trade of emissions allowances between Annex I and non-Annex I countries is to represent the supplementarity requirement laid out in the Marrakech Accords to the Kyoto Protocol. Those accords state that the use of each of the flexible mechanisms (ETS, CDM, JI) shall be supplemental to domestic action and that domestic action shall thus constitute a significant effort made by each Party included in Annex I to meet its quantified emission limitation and reduction commitments. Supplementarity constraints are also part of various national climate policy strategies such as the EU Climate Action and Renewable Energy Package (EU, 2008).

4
**Table 2: Effects of Improved Stoves on Child Height-for-Age**

**Dep. Var.: Height-for-age (z-score std. deviations)**

||**Intent to**<br>|**Treat Effe**<br>|**cts (ITT)**<br>|**Sto**<br>|**ve Use Eff**<br>|**ects**<br>|
|---|---|---|---|---|---|---|
|**Model Specification**|**OLS**<br>**(1)**|**RE**<br>**(2)**|**FE**<br>**(3)**|**IV**<br>**(4)**|**IV-RE**<br>**(5)**|**IV-FE**<br>**(6)**|
|Treatment X Baseline Age < 36 mo.|0.277*|0.367**|0.383**||||
||(0.153)|(0.148)|(0.168)||||
|Treatment X Baseline Age ≥ 36 mo.|-0.013<br>(0.140)|0.024<br>(0.135)|||||
|Stove Use X Baseline Age < 36 mo.||||0.320*<br>(0.172)|0.425**<br>(0.170)|0.447**<br>(0.195)|
|Stove Use X Baseline Age ≥ 36 mo.||||-0.016<br>(0.155)|0.026<br>(0.153)||
|Additional controls|yes|yes|yes|yes|yes|yes|
|Household random effects||yes|||yes||
|Household fixed effects|||yes|||yes|
|Observations|1,152|1,152|1,152|1,152|1,152|1,152|

Notes: Table reports intent to treat estimates of stove offers in columns 1-3 and instrumental variable effects of stove use in columns 4-6. Columns 2 and 5 include household level random effects and columns 3 and 6 include household fixed effects. Dependent variable is child height-for-age (z-scores), standardized according to the CDC growth tables. Sample is all children ages 15 and under. All models include a cubic polynomial in age in months, sex, mother's height and age, and father's age. Household level controls are omitted from regressions with household fixed effects, and include household size and demographic composition, age, education and sex of the household head, dwelling characteristics, community fixed effects, and a composite wealth index. Standard errors clustered at the household level in parentheses. ** p<0.05, * p<0.10 Column 1 presents treatment estimates for both those children exposed while in the sensitive period and older children. The estimates suggest being in a household that was offered a _Mirt_ stove while in the sensitive period of growth is associated with a 0.277 standard deviation increase in predicted height-for-age relative to the control children of the same age. This is a sizeable effect and similar in scale to the estimated benefits of improved cookstoves on birth outcomes such as gestational age and birth weight (e.g. Alexander et al., 2018), suggesting part of the mechanism may work through in-utero exposure.

The underlying physiology predicts there should be no impact on older children in the treatment group. The point estimate of -0.013 standard deviation (p-value = 0.92) points to this conclusion, as treatment children older than 36 months at the baseline saw no benefit in terms of their height-for-age at follow-up compared to children in the control group. This is in-line with established literatures in nutrition and epidemiology, and it supports the experimental design of our study as it 15
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ICT use, which could be an indicator of skill-biased technical change and increasing pressures towards wage inequality.

Where: ∆ _NW /NBi_ =Difference in the number of white-collar/number of blue-collar workers for firm _i_ between _t_ 0 and _t_ 1 ∆ _WW /WBi_ =Difference in the wage of white-collar/wage of blue-collar workers for firm _i_ between _t_ 0 and _t_ 1 These equations are estimated throughout this paper using different specifications and different econometric methods to test the robustness of our results.

# **3.2 Instrumental variables**

ICT adoption could be endogenous to the mix and demand for each type of labor. Therefore, we adopt an instrumental variable approach following Iacovone et al. (2016), where the first stage is defined as follows: ∆ _ICTi,j,s_ = _β_ 0 + _φ_ 1 _ICTint,j ∗ Average elevations_ + _φ_ 2 _ICTint,j ∗ CV_ ( _locality elevation_ ) _s_ + _β_ 1 _xi_ + _vi_ (5)

## Where:

∆ _ICTi,j,s,t_ = Change in ICT use of firm _i_ from sector _j_ in municipality _s_ between _t_ 0 and _t_ 1 _ICTint,j_ =ICT intensity of sector _j_ in the US _Average elevations, t_ =Average elevation of municipality _s_ _CV_ ( _locality elevation_ ) _s_ =Coefficient of variation localities elevation for municipality _s_ Similarly to Iacovone et al. (2016), we construct our instruments using the ICT-intensity classification from the Appendix of Bloom et al. (2012), which is based on the revision made by O’Mahony and 6
17 first group of involuntarily excluded cannot be a target of financial sector policy, the other three groups demand different responses from policy makers.

In summary, broad access to financial services is characterized by an absence of price and non-price barriers in the use of financial services. The distinction between access and use suggests that not only it is important to collect information on the use of financial services, but also on barriers to access, in order to identify boundaries and causes of exclusion. These barriers, by excluding large parts of the population from access to finance, are likely to play an important part in perpetuating inequality and limiting economic opportunities for the poor.

# **_4.2.  Use versus Depth_**

An exact statistic on the number of households that use formal financial services can only be inferred from household surveys. However, only around 34 countries have household surveys containing this information and researchers have therefore turned to proxy indicators to estimate the share of population using financial services.

Beck, Demirguc-Kunt and Martinez Peria (2007a) compile loan and deposit account data through surveys of bank regulators for a cross-section of countries and document the large variation in these indicators across countries. While in Austria there are three deposit accounts for every inhabitant, there are only 14 for 1,000 inhabitants in Madagascar. While in Greece there is almost one loan account for every inhabitant, there are only four for every 1,000 inhabitants in Albania. The ratio of deposit and loan accounts per capita increases with income, although the average deposit or loan account balance relative to income per capita decreases with income, indicating that poor people and smaller enterprises are better able to make use of these services in more developed countries. Still there is great variation among developing countries. For example in Bolivia, the average loan amount is 28 times GDP per capita, while it is only a third of GDP per capita in
# **The impact of gas flaring on child health in Nigeria**

Omoniyi Babatunde Alimi<sup>1,2</sup> and John Gibson<sup>2</sup>

## **JEL codes: Q53, I10, I18**

**Keywords: Gas Flaring; Infant and Child Health; Stunting; Disease Incidence; Oil and Gas production**

> 1 Africa Fellow, Office of the Chief Economist World Bank Africa Fellowship Programme (Education Global Practice).

> 2 School of Accounting, Finance and Economics, University of Waikato, Hamilton, New Zealand
presented here is specific to Stata, the general problem of untested parallel streams is more pervasive. Tests of econometric software must be brought back into accord with the methods of using that software. Future software evaluators would do researchers and developers a service by including not only standards of “random” number generation in their assessments, but also tests of parallel random streams.

# **References**

- Busso, M., J. DiNardo, and J. McCrary (2011): “New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators,” _mimeo_ , University of California, Berkeley.

- Compagner, A. (1995): “Operational conditions for random-number generation,” _Physical Review E_ , 52(5), 5634–5645.

- Keeling, K. B., and R. J. Pavur (2007): “A comparative study of the reliability of nine statistical packages,” _Computational Statistics and Data Analysis_ , 51(8), 3811–3831.

- Knuth, D. E. (1998): _The art of computer programming_ . Addison-Wesley, Reading, MA, 3 edn.

- Law, A. M., and M. G. McComas (1994): “Simulation software for communications networks: the state of the art,” _IEEE Communications Magazine_ , 32(3), 44–50.

- L’Ecuyer, P. (2001): “Software for uniform random number generation: distinguishing the good and the bad,” in _Proceedings of the 2001 Winter Simulation Conference_ , ed. by B. A. Peters, J. S. Smith, D. J. Medeiros, and M. W. Rohrer, pp. 95–105.

- L’Ecuyer, P., and R. Simard (2007): “TestU01: A C Library for Empirical Testing of Random Number Generators,” _ACM Transactions on Mathematical Software_ , 33(4), article 22.

- Marsaglia, G. (1968): “Random numbers fall mainly in the planes,” _Proceedings of the National Academy of Sciences_ , 61(1), 25–28.

10
# **<u>Table A10 : Impact on  other measures of business performance</u>**

||(1)<br>**Inverse**<br>**hyperbolic of**<br>**Total sales in**<br>**the last day**<sup>**αβ**</sup>|(2)<br>**Inverse**<br>**hyperbolic of**<br>**Total sales in**<br>**the last**<br>**week**<sup>**αβ**</sup>|(3)<br>**Inverse**<br>**hyperbolic of**<br>**Last month**<br>**profit**<sup>**αβ**</sup>|(4)<br>**Above the 95**<sup>**th**</sup><br>**percentile of**<br>**the control**<br>**group weekly**<br>**sales**<br>**distribution**|(5)<br>**Above the 95**<sup>**th**</sup><br>**percentile of**<br>**the control**<br>**group profit**<br>**distribution**|(6)<br>**Hired**<br>**someone in**<br>**the last 6**<br>**months**|(7)<br>**Number of**<br>**hours worked**<br>**in the business**<br>**last week by**<br>**the owner**|
|---|---|---|---|---|---|---|---|
|**1st stage: impact of treatment allocation:**||||||||
|Group1 X year1 (b1)|0.298|‐0.073|0.135|‐0.041***|‐0.002|0.000|‐7.30***|
||(0.345)|(0.327)|(0.302)|(0.015)|(0.015)|(0.044)|(1.98)|
|Group2 X year1 (b2)|0.454**|0.160|‐0.073|0.003|0.008|0.004|‐2.85**|
||(0.210)|(0.190)|(0.189)|(0.009)|(0.009)|(0.027)|(1.14)|
|Group3 X year1 (b3)|0.023|0.018|‐0.038|0.000|0.005|0.055**|‐4.12***|
||(0.188)|(0.175)|(0.172)|(0.009)|(0.009)|(0.024)|(1.06)|
|Group1 X year2 (c1)|‐0.972***|‐0.294|‐0.243|0.009|0.006|0.017|1.17|
||(0.368)|(0.326)|(0.310)|(0.019)|(0.018)|(0.028)|(1.75)|
|Group2 X year2 (c2)|‐0.212|‐0.423**|‐0.492**|0.017*|‐0.001|‐0.016|0.89|
||(0.218)|(0.204)|(0.199)|(0.010)|(0.010)|(0.017)|(1.06)|
|Group3 X year2 (c3)|‐0.165|‐0.193|‐0.258|0.008|0.001|0.009|1.84*|
||(0.192)|(0.178)|(0.176)|(0.009)|(0.010)|(0.016)|(0.98)|
|Observations|5,918|6,043|5,874|5,408|5,410|4,081|5,422|
|Mean Dep. var in control year1|6.646|9.753|9.727|0.046|0.049|0.226|61.75|
|Mean Dep. var in control year2|6.292|9.509|9.303|0.056|0.053|0.118|66.7|
|Adjusted R‐squared|0.125|0.138|0.123|0.131|0.148|0.077|0.112|
|_Test for impact constant..._||||||||
|...accross treatments, year1 (b1=b2=b3)|0.166|0.728|0.825|0.024|0.839|0.232|0.129|
|...accross treatments, year 2 (c1=c2=c3)|0.106|0.585|0.538|0.701|0.946|0.344|0.704|
|Coef. are jointly 0 (b1=b2=b3=c1=c2=c3=0)|0.002|0.052|0.105|0.038|0.974|0.226|0.000|
|**(IV) impact of Formalization:**||||||||
|Formalization instrumented by 1st stage|‐0.156|‐0.545|‐1.027|0.028|0.019|0.081|‐7.35*|
|treatment variables|(0.832)|(0.803)|(0.814)|(0.035)|(0.036)|(0.066)|(4.36)|
|P‐values|0.851|0.498|0.208|0.411|0.596|0.220|0.092|
|Sharpened two‐stageq‐values<sup>μ</sup>|1|1|1|1|1|1|1|

Note : Panel data from midline and endline surveys in 2015 and 2016. All regressions are controlling for strata dummies (dummies for each triplet). Standard errors (in parentheses) are clustered at the firm level. **α:** top‐coded at the 99th percentile. **Β:** controling for baseline value. **μ** : Sharpened two‐ stage q‐values as described in Anderson (2008) ***, **, * indicate statistical significance at 1, 5 and 10% 51