rafmacalaba/datause-encoder-data huggingface dataset:
4. Data and Estimation Strategy 4.1. Data Demographic and Health Surveys (DHS) have been carried out in a number of developing countries as part of the Measure DHS project, a reputable USAID-funded project aimed essentially at providing detailed, reliable information on fertility, family planning, maternal and child health and mortality. The second and third DHS carried out in Nepal took place in 2001 and 2006, respectively, and are repeated cross-sections, not panel data. In 2001, six out of 257 sampling units had to be dropped from the sample for security reasons (Ministry of Health et al., 2002, p.6). But in 2006, conflict was nearly over everywhere, and so data collection could proceed without hindrance.

For each DHS, a national sample of households is first interviewed using a household questionnaire collecting usual individual demographic and education data, as well as household-level socio-economic information such as ownership of durable goods and housing materials. The demographic characteristics of individual members are then used to determine which members are eligible for individual interviews, namely all women aged 1549 (DHS 2006) or all ever-married women aged 15-49 (DHS 2001), as well as a subset of males aged 15-59. The data used for the education analysis comes from the household questionnaire, whereas that used for the marriage study comes from individual interviews with eligible women.

Summary statistics, by district conflict intensity and specification subsample, can be found in Table 3.

Another national dataset carried out in Nepal during the relevant period is the Nepalese Living Standard Survey (NLSS) of 2003/2004. Given the timing of the conflict, the DHS is generally more useful for the present analysis since data collection occurred later. As a consequence, the outcomes of interest (namely, education and marriage) are more likely to be realized for cohorts exposed to the conflict in 2006 than in 2003/2004. In addition, since the conflict was raging at the time of the NLSS data collection, there were more difficulties with data collection than in the 2006 DHS. Finally, we need to focus on specific cohorts to identify the effect of exposure to the conflict, and so the larger sample sizes provided by the DHS are particularly valuable. Nevertheless, one advantage of the NLSS over the DHS is that the NLSS collected data on the individual’s district of origin before migrating to the 11
[10] Krishna, Kala, Refik Erzan and Ling Hui Tan [1994], "Rent Sharing in the Multifiber Arrangement: Theory and Evidence from the U.S. Apparel Imports From Hong Kong," Review of International Economics, v.2., p.62-73 [11] Krishna, Kala and Ling Hui Tan [1998], Rags and Riches, Implementing Apparel Quotas under the Multifiber Arrangement, University of Michigan Press. [12] Maingot, Anthony P. [1994], The United States and The Caribbean: Challenges of an Asymmetrical Relationship,” London, Macmillan and Boulder, Westview, 1994. [13] Olarreaga, Marcelo and Caglar Ozden [fortcoming], "AGOA and Apparel: Who Captures the Tariff Rent in the Presence of Preferential Market Access?," World Economy. [14] Schiff, Maurice and Won Chang [2001], "Market Presence, Contestability, and the Terms-ofTrade Effects of Regional Integration," Journal of International Economics, vol.60, p.161-175. [15] Winters, L. Alan [1997], "Regionalism and the Rest of the World: the Irrelevance of the KempWan Theorem," Ozford Economic Papers,vol.49, p.228-234. [16] Winters, L. Alan and Won Chang [2000], "Regional Integration and Import Prices: An Empirical Investigation," Journal of International Economics vol.51, p.363-377. [17] Winters, L. Alan and Won Chang [2002], "How Regional Blocs affect Excluded Countries: The Price Effects of MERCOSUR," American Economic Review, vol. 92, p.889-904. 30
# **1. Introduction**

Azerbaijan has experienced appreciable economic growth over the last several years. Since 1998, by most macroeconomic indicators, it has enjoyed one of the highest rates of economic growth among the CIS countries. Between 1998 and 2005, average GDP growth rate is in double-digits (Figure 1). Macroeconomic stability, coupled with the recent boom in oil income and prudent management of the windfall, have contributed significantly to the improved economic performance. However, the lion’s share of business and economic activities of the country is concentrated in the capital city.

**Figure 1:** Azerbaijan’s economic growth has been impressive since 1998

<!-- Start of picture text --> Average GDP growth, 1998-2005<br>Uzbekistan<br>Ukraine<br>Tajikistan<br>Russia<br>Mongolia<br>Moldova<br>Kyrgyz Republic<br>Kazakhstan<br>Georgia<br>Belarus<br>Azerbaijan(oil)<br>Azerbaijan<br>Armenia<br>0 5 10 15 20<br>Real GDP growth<br><!-- End of picture text --> Source: Asian Development Bank; IMF; DDP, World Bank.

The impact of these developments on distribution of wealth and poverty remains largely unclear, however. Although the government of Azerbaijan undertakes a regular annual household survey, the resulting data have been considered by many as unreliable for measuring poverty due to extremely low inequality estimates in the Azeri survey data. The Azerbaijan Household Income and Expenditure Survey (HIES) data, which are the primary source of economic and social information for poverty analysis in the country, exhibit extremely low consumption inequalities. For instance, the measures of inequality such as the Gini coefficient of consumption expenditures—the commonly used summary measure of inequality—are unusually low. Azerbaijan’s per capita consumption Gini coefficients estimable from the HIES data are low—by 1
Table 3: Descriptive Statistics for Father’s Labor-Supply in Albania (2002 - 2012)

|Variables<br>|Observations|Mean|Standard<br>Deviation|Min|Max|
|---|---|---|---|---|---|
|**Father’s Labor Supply**||||||
|Worked in last 7 days|5496|0.787|(0.409)|0|1|
|Worked of-farm|5496|0.393|(0.488)|0|1|
|Worked on-farm|5496|0.236|(0.424)|0|1|
|Self-employed|5496|0.197|(0.398)|0|1|
|Second occupation|4566|0.058|(0.234)|0|1|
|Hours/week (total)|4566|34.23|(23.06)|0|112|
|Hours/week (of-farm)|4566|18.50|(23.43)|0|99|
|Hours/week (on-farm)|4566|9.10|(18.71)|0|99|
|Hours/week (self)|4566|7.63|(19.09)|0|112|
|Monthly labor income (old leks)|4566|191841.9|(333208.7)|0|12000000|
|**Other characteristics of fathers**||||||
|Age|5737|39.92|(5.855)|22|64|
|Age at frst birth|5737|28.33|(4.177)|15|58|
|Years of education|5541|11.08|(3.426)|0|20|
|Rural|5737|0.478|(0.499)|0|1|
|Household size|5737|5.210|(1.424)|1|16|
|Extended family|5737|0.364|(0.481)|0|1|
|Marital status (married)|5737|0.998|(0.032)|0|1|
|Muslims|5737|0.798|(0.401)|0|1|
|Catholics|5737|0.082|(0.275)|0|1|
|Orthodox|5737|0.069|(0.254)|0|1|
|Coastal region|5737|0.262|(0.440)|0|1|
|Mountain region|5737|0.285|(0.451)|0|1|
|Tirana region|5737|0.164|(0.370)|0|1|

_Notes:_ The data used are the 2002 LSMS, the 2005 LSMS, the 2008-09 DHS and the 2012 LSMS 36
# **Appendix. Details on Parameter Calculations and Data Sources**

_Mileage_ . We consider four modes of passenger transportation in the GCMA: auto (including taxi), private microbus, public bus, and rail. Other modes like minibuses and the Haliopolis tram account for a very small share of passenger mileage and are excluded from our analysis.

According to IAPT (2007) the average person in the GCMA travelled 1,344 miles in 2002, where modal shares were auto (28.1 percent), microbus (32.2 percent), public bus (30.0 percent) and rail (9.7 percent).

We were also able to calculate modal shares for non-rail travel for 2005 based on estimates of the number of vehicles, average daily trips per vehicle, average trip length, and average vehicle occupancy obtained by pooling data from Central Statistical Office ( ̳CAPMAS‘), Cairo Transportation Authority, and the Ministry of Finance<sup>16</sup> . Assuming the same share for rail travel as in 2002, this alternative data suggests modal shares for auto (24.4 percent), microbus (33.8 percent), and public bus (32.1 percent). In Table 1, we split the difference between these two estimates, assuming the same overall per capita mileage as in IAPT (2007). The resulting modal shares are also consistent with figures in NKCL and KEI (2008). Vehicle mileage data were estimated using average daily trip per vehicle and average trip length. These information together vehicle occupancy were obtained from NKCL and KEI (2008) and personal communication with Cairo Transportation Authority.

_Fuel economy_ . Fuel economy for different vehicle types was taken from the IAPT (2007) database.

_Fuel prices_ . The gasoline price and subsidy were obtained from the Ministry of Finance (Personal Communication, April 2010), averaging over octane 80 and 90.

_Transit fares and operating costs_ . Personal communication with Cairo Transport Authority.

_Local pollution damages_ . We are unaware of any local study of pollution damages from road vehicles in Cairo. We therefore extrapolate damage estimates from Mexico City where, like Cairo, natural factors are especially conducive to pollution formation.

Parry and Timilsina (2010) assumed a pollution damage estimate of 90 cents per gallon of gasoline (in year 2005 dollars) for the Mexico City metropolitan area. This was based on pooling evidence from a local study and an estimate they extrapolated from Los Angeles, after making some

> 16 Most of these data were obtained through personal communication with various officials of these organizations.

30
Table 4. Smoking-Attributable Direct Medical Expenditures, 2016

|_Country_|_National_<br>_NCU(Million)_|_aggregate_<br>_PPP(Million)_|_Per adult (_<br>_NCU_|_+15 yr)_<br>_PPP_|_Per smoker (_<br>_NCU_|_+15 yr)_<br>_PPP_|
|---|---|---|---|---|---|---|
|Bangladesh|28,105|881|243|<br>8|1,055|33|
|Bosnia and Herzegovina|188|236|62|<br>78|160|201|
|Chile|271,209|596|19,061|<br>42|50,427|111|
|Indonesia|18,794,853|3,769|99,491|<br>20|252,515|51|
|Moldova|896|131|299|<br>44|1,237|181|
|Russian Federation|403,028|16,202|3,376|<br>136|8,590|345|
|South Africa|20,207|3,218|509|<br>81|2,509|400|
|Ukraine|16,296|2,757|427|<br>72|1,478|250|

_Sources:_ Adapted from Goodchild, Nargis, and Tursan d’Espaignet 2018 and Fuchs and Matytsin 2018. Population, smoking rates, consumer price index and PPP conversion factors are taken from WDI (World Development Indicators) (database), World Bank, Washington, DC, http://data.worldbank.org/products/wdi. _Note:_ PPP = purchasing power parity exchange rate. NCU = national currency units. The consumer price index is used to account for national price changes, 2012–16.

# **5. Results**

To analyze the distributional effects of an increase in tobacco taxes, each component of the model is estimated separately under three different price shock scenarios—price rises of 25 percent, 50 percent, and 100 percent—and elasticity assumptions. The results, based on a medium-bound elasticity scenario, are discussed in the sections below. The lower- and upperbound scenarios are simulated as 0.2 below and above the base (medium-bound) scenario; the results under these assumptions are available upon request.

## **a. Tobacco price increase**

Figure 3 shows the effects of the different price shocks on cigarettes for each country and income group in the sample. As prices rise, consumers must allocate greater shares of their budgets to continue to purchase the same quantity of tobacco. Allocating larger shares of their consumption expenditures to tobacco would be particularly difficult among lower-income households. Hence, the direct price effect constitutes a welfare loss, and it is often expected to be regressive. The results in the eight countries analyzed, however, show that substantial responsiveness to price changes mediates a complete pass-through scenario.

While the effect is an income loss under most scenarios and for most households, the distribution is not necessarily regressive. In the case of large price shocks (100 percent), some lower-income groups effectively stop consuming cigarettes and free up resources for other consumption needs. In some case, such as Indonesia, mainly the middle class is affected by the direct price effect.

Page 13
|RY-Power Sector|Joern Thorsten Huenteler|
|---|---|
|RY-Power Sector|Joern Thorsten Huenteler|
|RY-Power Sector|Joern Thorsten Huenteler|
|RY-Power Sector|Joern Thorsten Huenteler|
|RY-Power Sector|Joern Thorsten Huenteler|
|RY-Power Sector|Joern Thorsten Huenteler|
|RY-Power Sector|Joern Thorsten Huenteler|
|RY-Power Sector|Joern Thorsten Huenteler|
|RY-Power Sector|Joern Thorsten Huenteler|
|RY-Power Sector|Joern Thorsten Huenteler|
|RY-Power Sector|Joern Thorsten Huenteler|
|RY-Power Sector|Joern Thorsten Huenteler|
|RY-Power Sector|Joern Thorsten Huenteler|
|RY-Power Sector|Joern Thorsten Huenteler|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Democratic Republic of Congo EmergencySocial Action Project|Maurizia Tovo|
|Education and TrainingSector Improvement Program - ETSIP|Milan Vodopivec|
|Education and TrainingSector Improvement Program - ETSIP|Milan Vodopivec|
|Education and TrainingSector Improvement Program - ETSIP|Milan Vodopivec|
|Education and TrainingSector Improvement Program - ETSIP|Milan Vodopivec|
|Rural Water Supplyand Sanitation Project|Xavier Chauvot De Beauchene|
|Rural Water Supplyand Sanitation Project|Xavier Chauvot De Beauchene|
|Rural Water Supplyand Sanitation Project|Xavier Chauvot De Beauchene|
|Rural Water Supplyand Sanitation Project|Xavier Chauvot De Beauchene|
|Rural Water Supplyand Sanitation Project|Xavier Chauvot De Beauchene|
|Rural Water Supplyand Sanitation Project|Xavier Chauvot De Beauchene|
|Rural Water Supplyand Sanitation Project|Xavier Chauvot De Beauchene|
|Rural Water Supplyand Sanitation Project|Xavier Chauvot De Beauchene|
|Rural Water Supplyand Sanitation Project|Xavier Chauvot De Beauchene|
|Rural Water Supplyand Sanitation Project|Xavier Chauvot De Beauchene|
|Rural Water Supplyand Sanitation Project|Xavier Chauvot De Beauchene|
Table 4 is the regression equivalent of the figures which shows that the tendencies in the graphs are typically not statistically significant. For construction permits for instance the lowest slope is 1.22 and falls to .010 and -.018 in the upper ranges of the DB data. However, the F-test on the spline terms fails to reject that 1.22 and .010 are different because the standard errors on each coefficient are so large—the data cannot reject that 1.22 is equal to zero (t-stat is only 1.84) so cannot reject it is equal to .01.

**Table 4:  The spread of “favored” and “disfavored” firms (90**<sup>**th**</sup> **less 10**<sup>**th**</sup> **or 75**<sup>**th**</sup> **less 25**<sup>**th**</sup> **) is associated with increases in the DB days at best at low levels of DB**

||Imp|orts|Constr<br>Per|uction<br>mit|Operati|ng License|
|---|---|---|---|---|---|---|
||90-10|75-25|90-10|75-25|90-10|75-25|
|Constant|7.887|1.221|-50.446|27.821|48.182|22.750|
|(t-stat)|1.056|0.350|-0.515|0.766|2.712|3.630|
|Slope over lowest tercile of DB|0.616|0.422|1.228|0.119|0.743|-0.005|
|(t-stat)|1.179|1.732|1.849|0.482|0.640|-0.012|
|Slope over middle tercile of DB|0.287|0.074|0.010|0.195|-0.210|-0.015|
|data(t-stat)|0.371|0.204|0.010|0.524|-0.118|-0.024|
|Slope over upper tercile of DB|0.126|0.012|-0.018|-0.027|0.105|0.036|
|data(t-stat)|0.289|0.059|-0.029|-0.119|0.121|0.116|
|N|88|88|72|72|87|87|
|R2|0.088|0.081|0.076|0.055|0.011|0.004|
|F-test of spline terms|0.545|0.203|0.166|0.435|0.855|0.963|
|Outliers deleted|Yes|Yes|Yes|Yes|Yes|Yes|

# **VI. Comparing changes in Doing Business with changes in ES**

The results shown so far are based on cross-national comparisons in which we have matched the DB indicator to the year of the ES survey. DB has been updated annually since its introduction in 2003. However, the ES have been repeated in only a few countries, generally with a 3-5 year lag. This section uses only those countries for which an ES was fielded, there was then a formal change in the regulation that manifests as a reduction in the formal time expected for the requirement to be met, and another ES fielded.

The frequency of reforms varies across the 3 indicators. Reforms were more frequent for getting goods through customs and getting operating licenses. However, there are only 3 instances of reforms of construction permits enacted between Enterprise Surveys.

24
# **2. Context**

Financial markets in Colombia have been transformed recently by the appearance of new providers and new intermediation channels such as agent and mobile banking. Many of these new actors, products and channels have appeared to provide financial services to certain segments of the population that have been traditionally excluded from the financial system.

Between 2011 and 2015, the number of financial access points increased substantially. Nowadays, all municipalities have at least one financial access point available. This is explained by the spread of banking agencies throughout Colombia, increasing from around 19.938 in 2012 to 95.000 in 2015.

Today, however, the percentage of individuals with savings accounts or credits with the financial system remains low with respect to international standards. In the case of savings accounts, which are usually the first products that link Colombian consumers to the financial system, only 50.5% of the population had one in 2015. This percentage, which is clearly below the average for OECD countries, may show that the country has yet important barriers to overcome in order to enhance growth in household savings. It is worth mentioning that many households still use informal ways to save. Twenty-four percent of respondents of a survey conducted by Banca de las Oportunidades and the Financial Superintendence of Colombia in 2015 reported participating in Rotating Savings and Credit Unions. In addition, only 26.2% of the population reported having a loan from a financial system while 33.0% reported having informal loans.

Although efforts to promote financial inclusion have shown some success, many challenges remain, especially in rural municipalities. In particular, dispersed rural population do not have access to financial products that meet their needs. Colombia enacted Law 1735 of 2014 for example to encourage the design and implementation of electronic payment services that should ease access of the rural population to the financial system.

This new financial landscape in Colombia has improved the relevance of the consumer protection and financial education of the population in a context where an increasing percentage of the population is having access to the financial system.

In this sense, Colombia has enacted laws and decrees to strengthen the regulatory environment for promoting consumer protection and financial education.

Law 1328 of 2009, and later Law 1748 of 2014 established the rules and principles for the transparency and disclosure of the financial information in order to improve the financial consumer protection.<sup>7</sup> Mandatory information to be provided includes the terms and conditions of the contracts,

> 7 Other regulation related to consumer protection include Decree 2555 of 2010, Decree 4809 of 2011 and Decree 1854 of 2015 as well as the SFC ́s Circular Básica Jurídica Part I, Title III and Circular Externa 029 of 2014.

4
**Table I7: Relationship between food security measures and weight-for-age z-scores (Uganda): Expanded Controls Version**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|VARIABLES||Weig|ht-for-age z-s|cores||
|1-SSEXR|0.044|||||
||(0.034)|||||
|FCS||0.084***||||
|||(0.031)||||
|DDS|||-0.017|||
||||(0.036)|||
|PC expenditure||||0.075*<br>(0.040)||
|Food Share|||||-0.035|
||||||(0.030)|
|Child is female|0.008|0.000|0.006|0.009|0.008|
||(0.057)|(0.056)|(0.057)|(0.057)|(0.057)|
|Age of Child (in months)|-0.010|-0.009|-0.010|-0.010|-0.010|
||(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|
|Age Squared|0.000|0.000|0.000|0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Household size|0.008|0.004|0.005|0.012|0.008|
||(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|
|Share if kids under 5|-0.601**|-0.655**|-0.591**|-0.612**|-0.595**|
||(0.281)|(0.280)|(0.279)|(0.280)|(0.281)|
|Female-headed HH|0.006|0.021|0.008|0.018|0.005|
||(0.079)|(0.079)|(0.079)|(0.080)|(0.079)|
|_HH Head Education: Reference_|_category - No_|_formal educati_|_on_|||
|Less than primary|0.072|0.079|0.096|0.096|0.093|
||(0.097)|(0.096)|(0.098)|(0.096)|(0.096)|
|Completed primary|0.136|0.132|0.157|0.156|0.151|
||(0.109)|(0.109)|(0.110)|(0.108)|(0.108)|
|Completed O-levels|0.095|0.084|0.127|0.121|0.125|
||(0.135)|(0.134)|(0.136)|(0.132)|(0.133)|
|Completed A-levels|-0.229|-0.238|-0.220|-0.187|-0.218|
||(0.244)|(0.240)|(0.247)|(0.248)|(0.248)|
|Technical college/University|0.257|0.244|0.297*|0.268|0.290*|
||(0.167)|(0.168)|(0.169)|(0.165)|(0.166)|
|_Mother's Education_||||||
|Less than primary|-0.047|-0.060|-0.047|-0.055|-0.055|
||(0.076)|(0.076)|(0.076)|(0.077)|(0.076)|
|Completedprimary|0.058|0.045|0.068|0.048|0.054|

111