rafmacalaba/datause-encoder-data huggingface dataset:
9 abroad than at home. Equity issuance costs in Chile compare favorably to Brazil and Mexico but debt issuance costs are the highest in Chile. The high cost of issuing debt comes from the imposition of a stamp duty on issuances. While other sources of issuance costs are similar across countries, neither Brazil nor Mexico levy this stamp tax. For a US$100 million issue for instance, the stamp tax adds 25bps to the yield the company pays to raise debt capital. In addition, there is a clear threshold for credit ratings evident in debt outstanding as seen in Figure 9. Nearly all bond issues in Chile are indeed rated A- or above. This is likely due to the importance of pension funds as bond investors (they hold the largest amount of corporate debt), and result directly from the minimum rating requirements imposed on their investments. Insurance companies have grown their holdings of corporate debt dramatically in the last few years, but remain small players compared to pension funds. In any case, their rating requirements are quite similar. Due to these cost, rating and liquidity considerations, the minimum size for a bond issue in Chile is estimated by market practitioners at US$45 million for a domestic bond issue, and at US$200 million for an international debt issue. Costs do not appear to create a minimum size for equity issuances. In Mexico, the minimum size for a domestic bond issue is estimated at US$18 million by local banks although there has been many smaller issues. In Brazil, 18 percent of all issues between 2001 and 2003 were below US$50 million.

**Figure 8:  The Cost of Issuing Debt and Equity in Chile, Brazil and Mexico (2004)**

<!-- Start of picture text --> Issuance costs by instruments Issuance cost breakdown in Chile<br>(US$ 100 million issue) (issue of US$ 100 million)<br>Other<br>3.5% Stamp tax<br>4.5% Regulatory<br>3.0%<br>4.0% Legal costs<br>3.5% 2.5% Investment Banking<br>3.0%<br>2.0%<br>2.5%<br>2.0% 1.5%<br>1.5%<br>1.0% 1.0%<br>International debt<br>0.5%<br>Domestic equity 0.5%<br>0.0%<br>Domestic debt<br>Chile 0.0%<br>Brazil<br>Mexico Domestic debt Domestic equity International<br>debt<br>Source: Sara Zervos (2004)<br><!-- End of picture text -->
32

|Subcomponent|Open defecation (%)|1 if no defecation facility used and 0<br>otherwise|
|---|---|---|
|Subcomponent|Self-reported handwashing with<br>soap/ash (%)|1 if respondent washed their hands with<br>soap/ash at least once since previous day<br>0 Otherwise|
|Subcomponent|Frequency of latrine cleaning over past<br>2 weeks|Number of times the latrine has been<br>cleaned in thepast 2 weeks|
|Subcomponent|Water pot is clean and covered (%)|1 if the pot has clean water and is<br>covered<br>0 Otherwise.<br>This indicator is measured via actual<br>observations|
|Subcomponent|Water treated for consumption, any<br>method (%)|1 if drinking water stored in household is<br>treated with any product/method for safe<br>consumption<br>0 otherwise|
|**Secondary**<br>**Outcome**|**Life satisfaction & self-esteem index**|Summary index of 11 questions<br>The 11 questions include the (1) life<br>satisfaction question as defined by the<br>World Values Survey and (2) the 10<br>questions as defined in Rosenberg's Self-<br>Esteem Scale.|
|Subcomponent|Life satisfaction (WVS)|All things considered, on a scale of 1 to<br>10, how satisfied are you with your life as<br>a whole?<br>1 means completely dissatisfied<br>10 means completelysatisfied|
|Subcomponent|Feel I am person of worth (Rosenberg)|I feel I am a person of worth, at least on<br>an equal plane with others.<br>Tell me to what extent you: Strongly<br>agree, Agree, Disagree, or Strongly<br>disagree with this statement aboutyou.|
|Subcomponent|Feel that I have good qualities<br>(Rosenberg)|I feel that I have a number of good<br>qualities.<br>Tell me to what extent you: Strongly<br>agree, Agree, Disagree, or Strongly<br>disagree with this statement aboutyou.|
|Subcomponent|Inclined to feel I am a failure<br>(Rosenberg)|All in all, I am inclined to feel that I am a<br>failure.<br>Tell me to what extent you: Strongly<br>agree, Agree, Disagree, or Strongly<br>disagree with this statement aboutyou.|
disaggregated micro-survey data often suffers from sample selection bias because electricity expenditures are only selectively observed for those households choosing to report.

Indeed, even though Turkey has near universal access to electricity, 4,051 households (27.7%) in the dataset did not report electricity expenditure. Furthermore, poorer households were more likely to have non-positive electricity expenditures (Table 2), suggesting that the sample selection is not random. Suppose that households are endowed with observable and unobservable characteristics, sample selection bias would arise if unobservable characteristics affecting reporting or non-reporting decisions are correlated with unobservable characteristics affecting consumption behavior. On the other hand, if non-reporting behavior and electricity consumption are correlated purely through the observables, we can control for this by including the appropriate observable variables in the demand equation.

I use the Heckman selection model to test for the selection bias. For the first stage regression, one needs variables that affect the chances of not reporting electricity expenditures but not the level of electricity consumption (variables that are not in the consumption equation). The selection variables used are the education level of the household head, whether the household head had a permanent job during the survey month, and whether the household head had health insurance. Education of the household head is putatively not included in the electricity demand estimation, but may plausibly affect household response to survey questions. Similarly, the job status and the possession of health insurance of the household head may not directly affect electricity consumption, but may affect the reporting decision.<sup>11</sup> In the results section, I present estimates of Equation (1) using an ordinary least square (OLS) model where I limit the observations to households who reported electricity consumption. I also present estimates from the selection model. I found the two models yield nearly identical results. Furthermore, both Wald and likelihood ratio tests<sup>12</sup> suggest that the unobserved variables influencing the reporting decision are statistically independent of unobserved factors of the electricity demand. Therefore, sample selection can be ignored conditional on observable household characteristics.

# **IV. Data and Descriptive Analysis**

# _Data_

The model is estimated using data from the Turkish HBS in 2008 and 2009. The HBS is conducted annually by the Turkish Statistical Institute to collect information on household socioeconomic status, living standards, income, and consumption expenditures. The survey is a

> 11 A United Nations Development Programme (UNDP) field study on four provinces (Istanbul in both sides of the Bosporus, Cankiri, Kars and Urfa) (Bagdadiogl etc. 2009) suggests that households who did not pay electricity bills during the survey month due to arrears or illegal connections would not report electricity expenditure to the HBS.

> 12 See Yamagata (2004) for a discussion on the performance of different tests for sample selection bias.

9
<!-- Start of picture text --> Figure 23: Guyana’s Seat Capacities and Direct Airport Pairs with the Rest of the World<br> 180 4<br> 160<br> 140 3<br> 120<br> 100<br>2<br> 80<br> 60<br> 40 1<br> 20<br> - 0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Year<br>Estimated Seats Airport Pairs<br>Estimated Seats, thousands Extra-Regional Airports Served<br><!-- End of picture text --> _Source: Authors’ calculation based on data by Diio (2014)._ After a decline between 2003 and 2005, Suriname has experienced some recent growth in air traffic, in excess of 6 percent annually since 2005, due to one route with Curaçao, which has grown by a phenomenally high 37.6% annually between 2008 and 2013 (Table 20 and Figure 24). The route with Brazil has been growing at 11.1% annually since 2008 (the route started in 2005, and the longer term growth rate has been very high at 14.9% annually). The route with French Guyana has been intermittent, with no service reported in 2008, or between 2010 and 2011.

**<u>Table 20: Traffic between Suriname and the Rest of the World</u>**

|**Country**|**Est. Seats**<br>**2013**|**Share**|**Annual**<br>**Growth**<br>**01-13**|**Annual**<br>**Growth**<br>**08-13**|
|---|---|---|---|---|
|Netherlands|289,172|65.6%|3.2%|0.3%|
|Curaçao|96,824|22.0%|6.1%|37.6%|
|Brazil|32,760|7.4%||11.1%|
|Aruba|13,377|3.0%|||
|French Guiana|8,736|2.0%|-6.0%||
|**Total**|**440,869**|**100.0%**|**3.6%**|**6.4%**|

_Source: Authors’ calculation based on data by Diio (2014)._

**Figure 24: Suriname’s Seat Capacity and Direct Airport Pairs with the Rest of the World**

<!-- Start of picture text --> 600 7<br> 500 6<br>5<br> 400<br>4<br> 300<br>3<br> 200<br>2<br> 100 1<br> - 0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>Year<br>Estimated Seats Airport Pairs<br>Estimated Seats, thousands Extra-Regional Airports Served<br><!-- End of picture text --> _Source: Authors’ calculation based on data by Diio (2014)._ 53
_Inference_ , 97:113–139.

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- Anselin, L. and Le Gallo, J. (2006). Interpolation of air quality measures in hedonic house price models: spatial aspects. _Spatial Economic Analysis_ , 1:31– 52.

Anselin, L. and Lozano-Gracia, N. (2007a). Errors in variables and spatial effects in hedonic house price models of ambient air quality. _Empirical Economics_ (forthcoming).

Anselin, L. and Lozano-Gracia, N. (2007b). Exploratory spatial data analysis of the housing markets in Bhopal and Bangalore. mimeo. World Bank.

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Beron, K.J., Hanson, Y., Murdoch, J.C. and Thayer, M.A. (2004). Hedonic price functions and spatial dependence: Implications for the demand for urban air quality. In Anselin, L., Florax, R.J., and Rey, S.J., editors, _Advances in Spatial Econometrics: Methodology, Tools and Applications,_ pages 267—281. Springer-Verlag, Berlin.

Crane, R., Daniere, A., and Harwood, S. (1997). The contribution of environmental amenities to low-income housing: a comparative study of Bangkok and Jakarta. Urban Studies, 34(9):1495–1512.

Deichmann, U., Lall, S.V., Suri, A., and Rajoria, P. (2003). Information-based instruments for improved urban management. In Policy Research Working Paper 3002. World Bank: Washington, D.C.

Earnhart, D. (2002), Combining Revealed and Stated Data to Examine Housing Decisions Using Discrete Choice Analysis, _Journal of Urban Economics_ , 51(1), 143-169.

- Florax, R.J.G.M, H. Folmer and S. Rey (2003). Specification Searches in Spatial Econometrics: The Relevance of Hendry’s Methodology. _Regional Science and Urban Economics_ 33, 557579.

- Follain, J.R. and Jimenez, E. (1985). The Demand for Housing Characteristics in Developing Countries. _Urban Studies_ , 15, 77-107

Greene, W. H. (2003). _Econometric Analysis_ , Fifth Edition. Prentice Hall, Upper Saddle River, NJ.

Ioannides, Y.M. (2002), Residential Neighborhood Effects, _Regional Science and Urban Economics_ . , 32(2):145--165.

- Kelejian, H. H. and Prucha, I. R. (2007). HAC estimation in a spatial framework. _Journal of Econometrics_ . 140(1), 131-154

- Kim, C. W., Phipps, T., and Anselin, L. (2003). Measuring the benefits of air quality improvement: a spatial hedonic approach. _Journal of Environmental Economics and Management_ , 45, 24–39.

- Knight, J. R., Herrin, W. E., and Balihuta, A. M. (2004). Housing prices and maturing real estate markets: Evidence from Uganda. _Journal of Real Estate Finance and Economics_ , 28(1), 5– 18.

- Lall, S. V. and Deichmann, U. (2006a), Fiscal and Distributional Implications of Property Tax Reforms in Indian Cities, _Economic and Political Weekly_ , July 22, 3209-3220.
# APPENDIX 1: Creatizig a Consistent Data Base

Given the nature of the RMSM-X models, it is necessary to assemble the macroeconomic statistics of Turkey into a flow-offunds accounting framework. This nc. only maps the budget constraints of the various sectors into actual data but also describes the characteristics of the Turkish economy in detail. The methodology is based on the fact that each source of funds for one sector is also a use or resource for another. This system of double entry accounting portrays income, expenditure, investment, savings and financing in such a way that consistency among sectors is assured.

The theoretical sections of the paper include a description of the economic sectors and markets that we need to incorporate into the model. This theoretical framework determines the data requirements. In spite of its adaptation to the Turkish case we were likely to encounter two data problems: (i) inconsistency, and (ii) incompleteness. The first problem arises from the fact that similar data are collected by different agents or institutions in the economy. Resolving the problem can only be done by means of a qualitative judgement on the data sources. Timeliness of the availability of data is also a concern. The second problem needs to be solved through assumptions about the missing data.

Creating a consistent data set is an iterative process. At the outset it is difficult to see which original data set and which assumptions will lead to a "better" consistency of the data. Hence, it is often necessary to revise assumptions, collect
_Fiscal Crisis in Central Europe_ _25_ less profitable than SOEs, this pattern seems to point to a better ability of private entrepreneurs to hide their profits through creative accounting (evasion on turnover tax is in fact more difficult to accomplish).

# V. CONCLUSIONS

The main message of this paper, namely that the fiscal crisis in central Europe was mainly attributable--in the early 1990s--to increasing social expenditures, rather than to the collapse in enterprise protitability, is not intended to minimize the importance of the continued reform of the tax system and of tax administration. In fact, the data on private sector participation in taxation in Poland that we show suggest that that sector is likely to be a less willing complier with the requirements of the fiscal system. To the extent that privatization advances, maintaining an adequate buoyancy for the tax system might become more problematic, unless tax reform continues with vigor.

The paper, however, also sends a cautionary message regarding the possibility of financing further increases in expenditures. On a net basis, the decrease in revenues that has accompanied the economic transformation is limited. All three countries examnined in this paper appear to already have levels of taxation that are higher than comparators at similar per-capita income levels, and approximate or surpass those of western Europe. While no iron law exists here, society at large may not be willing to provide the resources that would be required to support the current level of expenditures or to extend it even further.

these data overall simply suggest a widespread phenomenon of profits hiding and, in general, tax cheating in the private sector.
**Table 1: Argentina, 2006. Production and value added Structure as percentage of totals**

|**Sectors**|**Value**<br>**Added**|**Domestic**<br>**Intermediate**<br>**Consumption**|**Imported**<br>**Intermediate**<br>**Consumption**|**Labor**|**Capital**|**Gross**<br>**Output**|
|---|---|---|---|---|---|---|
|Soy|3.00|1.66|0.20|1.25|4.73|2.27|
|Maize|0.52|0.31|0.04|0.19|0.73|0.41|
|Sugar cane|0.14|0.04|0.00|0.06|0.24|0.09|
|Rest of agriculture|4.64|2.38|0.28|1.65|6.21|3.43|
|Petroleum and Mining|5.61|2.47|0.91|1.82|7.98|4.01|
|Soy oil|0.45|1.19|0.30|0.17|0.21|0.75|
|Other vegetable oils|0.13|0.35|0.09|0.07|0.09|0.22|
|Sugar|0.12|0.30|0.08|0.09|0.11|0.19|
|Rest of Food, Beverages and Tobacco|5.89|15.02|3.75|4.11|5.13|9.67|
|Textiles, Leather, Wood, Paper & Editing|3.28|8.25|9.68|2.61|2.89|5.76|
|Gasoline|0.35|0.59|0.28|0.06|0.39|0.45|
|Diesel|0.68|1.20|0.69|0.14|0.87|0.91|
|Biodiesel|0.0037|0.0095|0.0022|0.0009|0.0056|0.0059|
|Bioethanol|0.0001|0.0001|0.00004|0.00002|0.0001|0.0001|
|Refineries: other products|0.90|1.96|0.16|0.21|1.29|1.31|
|Other manufacturing industries|12.13|16.41|59.22|9.02|9.66|16.60|
|Transport|6.19|6.29|11.54|6.38|5.77|6.53|
|Trade, construction & Services|55.97|41.57|12.77|72.15|53.68|47.40|
|Totals|100.00|100.00|100.00|100.00|100.00|100.00|

Source: Own elaboration based on INDEC Biodiesel is a small sector according to 2006 data, but is growing steadily. Though there were many investment projects ongoing, there was no production of bioethanol yet for that year<sup>5</sup> . Hence, to perform simulations considering future production of this product, we have included a “latent” industry of bioethanol (see Table 1) in which the proportions of value added, labor and capital costs, and intermediate costs are coincident with those of the current available bioethanol technology. This “virtual” or latent sector is ready to grow if the simulations give the proper incentives.

Table 2 shows the intermediate transactions as percentage of total intermediate costs for the sector included for the specific purposes of this analysis. The biodiesel and bioethanol cost structures are based on own estimates.

Regarding the demand side, domestic consumer groups are divided by income decile, the government, one foreign consumer and one foreign producer. The small open economy assumption is adopted, implying that Argentina is a price taker in the international markets.

> 5 As of 2006; there were some bioethanol plants already in operation and several announced investments.

7
from their responses to their own business cycles. In response to adverse global shocks, such as an increase in the VIX, inflows to banks and corporates decline, while domestic banks and corporates invest less abroad, decreasing their outflows. Sovereigns do not respond to such global shocks on average.<sup>7</sup> Several papers document that gross flows respond systematically to changes in global conditions.<sup>8</sup> Our results are consistent with these works and explain that another potential source responsible for the co-movement of capital inflows and outflows might be the response of both banking and corporate sector inflows and outflows to global shocks in all countries.

These four facts stand in contrast to standard international macroeconomic models, which treat domestic and foreign investors symmetrically. The evidence we provide helps to discriminate among several classes of models. As we explain in section 5, our findings are consistent with models with financial shocks and financial frictions and not with models with only productivity shocks and/or asymmetric information and sovereign risk as the sole sources of friction.

The rest of the paper is organized as follows: Section 2 describes the construction and coverage of our data; Section 3 illustrates descriptive patterns; Section 4 presents the results from our empirical analysis; Section 5 discusses the theoretical implications and Section 6 concludes.

> 7Rey (2013) uses quarterly BOP data and shows that across all geographic regions, portfolio equity, portfolio debt, and other investment debt are all negatively correlated with the VIX. Nier, Sedik, and Mondino (2014) and Forbes and Warnock (2012) find similar results to Rey.

> 8See Forbes and Warnock (2012), Milesi-Ferretti and Tille (2011), Cerutti, Claessens, and Puy (2015), Broner et al. (2013), J. Caballero (2016), Obstfeld (2012), Cat ̃ao and Milesi-Ferretti (2014), Borio and Disyatat (2011), Lane (2013), Cerutti, Claessens, and Rose (2018).

9
_For labor or for divorce?_

# **For Labor or for Divorce?**

# **Unilateral Divorce Laws and Women’s Labor Outcomes**<sup>**1**</sup>

NAJAT EL MEKKAOUI<sup>2</sup> , SARA LOUKILI<sup>3</sup> , YEGANEH FOUROUHESHFAR<sup>4</sup> , NADA EISSA<sup>5</sup>

**JEL-Codes** : J12 ; J16 ; J22 ; D10 ; D13.

**Keywords** : Unilateral divorce, Labor force participation, MENA region, Morocco, Jordan, Egypt, Mothers, Intra-household bargaining.

> 1 The authors are grateful and thank Nelly Elmallakh, Nazmul Chaudhury  and Daniel Halim  for their helpful suggestions and comments. This work was supported by the MENA Chief Economist Office under the labor and gender research programs (TTLs: Nelly Elmallakh and Nazmul Chaudhury). It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author(s) may be contacted at <u>najat.el-mekkaoui@dauphine.psl.eu or sara.loukili@dauphine.eu.</u>

> 2 Université Paris- Dauphine PSL LEDa DIAL IRD, EMANES and ERF.

> 3 Université Paris-Dauphine PSL, LEDa DIAL IRD, EMANES.

> 4 The Euro-Mediterranean Economists Association (EMEA), EMANES and LEDa DIAL IRD.

> 5 Georgetown University, ERF, and the National Bureau of Economic Research (NBER).
# **APPENDIX C: ITT RESULTS**

Table C1: ITT Estimates of the Effect of Temporary Incentives on Timing of 1<sup>st</sup> Prenatal Visit

||(1)|(2)|(3)|
|---|---|---|---|
||Intervention<br>Period|Post‐Intervention<br>Period I<br>(Jan 2011 – March<br>2012)|<br>Post‐Intervention<br>Period II<br>(April – Dec 2012)|
|**A. Weeks Pregnant at 1**<sup>**st**</sup>**Prena**|**tal Visit**|||
|Treatment|‐1.39**|‐1.59**|‐2.47**|
||(0.67)|(0.73)|(1.02)|
|Large Sample_p_‐value|0.04|0.03|0.02|
|Wild Bootstrapped_p_‐value|0.09|0.03|0.03|
|Control Group Mean|17.80|17.90|20.10|
|Sample Size|769|1296|710|
|**B. First Prenatal Visit Before W**|<br>**eek 13 of Pregnancy**<br>|,<br>|<br>|
|Treatment|0.10***|0.08**|0.08**|
||(0.04)|(0.04)|(0.04)|
|Large Sample_p_‐value|0.01|0.02|0.04|
|Wild Bootstrapped_p_‐value|0.03|0.05|0.08|
|Control Group Mean|0.31|0.34|0.27|
|Sample Size|769|1,296|710|

Notes: This table reports ITT estimates of the treatment effect of the modified fee schedule on indicators of the timing of the 1<sup>st</sup> prenatal visit. The LATE estimates are reported in Table 4. The differences are estimated from OLS regressions of the dependent variable on an indicator for clinic treatment random assignment. The _p_ ‐ values are for 2‐sided hypothesis tests of the null that the difference is equal to zero. We present both the _p_ ‐ value computed for large samples and a Wild bootstrapped _p_ ‐value that is robust in samples with small numbers of clusters (Cameron et al. 2008). Our Wild bootstrap procedure assigns symmetric weights and equal probability after re‐sampling residuals (Davidson and Flachaire 2008) and uses 999 replications. Column (1) reports the results for the sample observed in an 8‐month intervention period (May 2010 – December 2010). Column (2) reports the results for the sample observed in the 15‐month period following the end of the intervention (January 2011 – March 2012). Column (3) reports the results for the 9‐month period after the change in the coding of the first prenatal visit (April 2012 – December 2012). Standard errors are in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

50