rafmacalaba/datause-encoder-data huggingface dataset:
previously estimated. The lower-bound elasticity tends to reflect income groups that would not change consumption patterns, such as rural residents or older people.

**Figure 2 – Cigarette Price Elasticities**

**Table 4 – Cigarette Price Elasticities and Income Deciles**

|_Price elasticity_|_Decile 1_|_Decile 2_|_Decile 3_|_Decile 4_|_Decile 5_|_Decile 6_|_Decile 7_|_Decile 8_|_Decile 9_|_Decile 10_|_Average_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Lower bound|-0.31|-0.19|-0.20|-0.14|-0.12|-0.12|-0.12|-0.05|-0.04|-0.06|-0.13|
|Medium bound|-0.51|-0.39|-0.40|-0.34|-0.32|-0.32|-0.32|-0.25|-0.24|-0.26|-0.33|
|Upper bound|-0.71|-0.59|-0.60|-0.54|-0.52|-0.52|-0.52|-0.45|-0.44|-0.46|-0.53|

_Source:_ Estimates based on data of the household budget surveys 2012–15.

Meanwhile, the upper-bound elasticity tends to reflect a longer-term scenario, echoing the effect the tobacco tax would have on younger people. After a few decades, these groups will be the majority of smokers. Thus, the total average effect of the price increase is more accurately approximated by the upper-bound price elasticity. To test the robustness of our calculation, we re-estimate elasticities eliminating different years from the sample. Results of such calculations are presented in Annex I. Annex II presents an assessment of the stability of the survey’s calculated deciles. We find that the income deciles are relatively stable in terms of demographic characteristics. In addition, we tested and confirmed that the elasticity estimations do not depend on a specific survey year, as the exclusion of different survey rounds from the estimation yields similar results.

The literature presents different views on the most accurate way to translate the reduction in tobacco consumption into the reduction of medical expenses. One argument is that tobacco price increases reduce consumption on average, but price increase does not necessarily lead to adult tobacco cessation in a similar way,[10] as some people may quit, while others may just reduce the number of cigarettes smoked per day. In addition, there are two two important points to underline. First, it has been shown that only quitting – and not smoking less -brings substantial positive effects on medical well-being[2,38-40]. Second, tobacco cessation has a stronger health effect for younger groups of the population[41]. This paper takes the approach that in the medium to long-term, all the reduction in consumption could be directly translated into tobacco cessation 9
improves the chances to escape unemployment and is associated with the shorter duration of job search. Thus, these two measures taken together point either to a growing and dynamic labor market, or to one that is shrinking and stagnant.

Second, job turnover shows the extent of reallocation of jobs across firms and is thus indicative of the intensity of enterprise restructuring. High job reallocation points to the labor market supporting industrial restructuring and productivity improvements. Low job reallocation, in turn, may point to constraints to restructuring, such as high firing and hiring costs, or low expected returns to firm creation. The degree of job reallocation has a bearing on overall economic efficiency and growth, which require shifting jobs away from declining, low-productivity firms and industries toward expanding ones.

How does job turnover in Moldova compare to that in other countries? The pattern of job creation and job destruction in _continuing_ firms in Moldova is similar to that in other low-employment transition economies, such as Bulgaria (Figure 2).<sup>12</sup> Its characteristic feature is a substantial gap between job creation and job destruction, which implies a fall in the employment level.<sup>13</sup> This pattern can be assessed against three different benchmarks: (i) a non-restructuring, stagnant economy (e.g. Croatia), (ii) a mature market economy (France), and (iii) a restructuring, dynamic economy (e.g. Lithuania). Lithuania is the most adequate point of reference: enterprise restructuring proceeds at a high pace, the labor market is dynamic, and most importantly, destruction of non-productive, old jobs in is followed by commensurate creation of new, presumably more productive jobs. High job turnover, such as that prevailing in Lithuania, may create problems of its own (spatial and skill mismatches and associated frictional and structural unemployment). However the underlying dynamic labor market is conducive to improvements in productivity and competitiveness, as well as to shorter and more

> 12 Continuing firms are firms which were active in two consecutive periods under investigation.

> 13 The fall in employment during the period 2000-2001 implied by the analysis of job creation and job destruction is greater than that shown in official publications (DSS, 2002).  This discrepancy reflects differences in sources of data and consequently definitions of employment, and the coverage of firms and workers.  Data presented here are derived from an employer based survey which covers firms registered as legal persons.  In contrast, the DSS reports employment data coming from the household based Labor Force Survey, which covers both formal and informal sector workers, including the self-employed, regardless of the nature of their job and their firm’s legal status.

13
Policy Research Working Paper 8951

# **Abstract**

This paper presents the results of an impact evaluation of a performance-based financing pilot in rural areas of two regions of Tajikistan. Primary care facilities were given financial incentives conditional on general quality and the quantity provided of selected services related to reproductive, maternal and child health, and hypertension-related services. The study relies on a difference-in-difference design and large-scale household and facility-based surveys conducted before the launch of the pilot in 2015 and after three years of implementation. The performance-based financing pilot had positive impacts on quality of care. Significant impacts are measured on facility infrastructure, infection prevention and control standards, availability of equipment and medical supplies, provider competency, provider satisfaction, and even some elements of the content of care, measured through direct observations of provider-patient interactions. While the communities in the performance-based financing districts reported higher satisfaction with the local primary care facilities, and despite the improvements in quality, the findings suggest moderate effects on utilization: among the incentivized utilization indicators, only timely postnatal care and blood pressure measurements for adults were significantly impacted.

This paper is a product of the Development Research Group, Development Economics and the Health, Nutrition and Population Global Practice. 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 authors may be contacted at gshapira@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Appendix Table 1. Summary Statistics for Household Sample

|Variable Name|Mean|SD|
|---|---|---|
|Theta|0.299|0.21|
|Food expenditure share|0.486|0.12|
|Fuel and lighting expenditure share|0.087|0.04|
|Transportation fee expenditure share|0.055|0.05|
|Foot and clothing expenditure share<br>|0.061<br>|0.03<br>|
|Medical care expenditure share|0.043|0.06|
|Education expenditure share|0.027|0.05|
|Per capita total food caloric availability|1852.237|762.66|
|Per capita caloric availability from grains<br>|1180.939|510.03|
|Per capita caloric availability from vegetables and fruits|51.768|70.33|
|Per capita caloric availability from animal products|25.353|24.83|
|Per capita caloric availability from other sources<br>|600.725|485.03|
|Per equivalent adult expenditure (Rs.)|1743.577|1685.45|
|# of people in HH (members only)|7.468|3.61|
|Household highest no. of school years|8.08|5.15|
|Punjab|0.42|0.49|
|Sindh|0.249|0.43|
|NWFP|0.195|0.4|
|Rural|0.602|0.49|
|Household head age|45.135|13.49|
|Household head worked last month|0.829|0.38|
|Household head female|0.072|0.26|
|Household head married|0.919|0.27|
|Household head years of schooling|4.831|5.43|
|Agricultural land ownership|0.091|0.29|
|Ownership of buffalo/camel/horse/asses/ mules<br>|0.201|0.4|
|Ownership of cattle/sheep/goat|0.221|0.41|
|Ownership of fridge/freezer|0.346|0.48|
|Ownership of air conditioner|0.156|0.36|
|Ownership of cooking stove|0.324|0.47|
|Ownership of TV|0.485|0.5|
|Ownership of car/truck|0.047|0.21|
|Ownership of motorcycle|0.131|0.34|
|Computer ownership|0.055|0.23|
|Ratio of female people between 0 and 5 years old in HH|0.08|0.11|
|Ratio of male people between 0 and 5 years old in HH|0.083|0.12|
|Ratio of female people between 5 and 15 years old in HH|0.126|0.14|
|Ratio of male people between 5 and 15 years old in HH|0.136|0.14|
|Ratio of female people between 15 and 55 years old in HH|0.268|0.13|
|Ratio of male people between 15 and 55 years old in HH|0.239|0.14|
|Ratio of femalepeople older than 55years old in HH|0.032|0.07|
|Sample size|7,938||

30
Even for tangible assets, there is controversy and disagreement over the appropriate depreciation rates. The question becomes much more difficult for intangible assets, since there is no physical ̳wear and tear‘ to be observed. CHS admit that the rates of depreciation they assume are based on limited information. For better or worse, these rates have since been used in a number of CHS-type studies. In general, they are significantly higher than commonly-used rates of depreciation of tangible assets, reflecting the view that knowledge-intensive capital becomes obsolete relatively fast.

# **2.2 Methodology and data sources for Brazil estimation**

In measuring intangible investment in Brazil, we use the same categorization of intangibles as CHS, and to the extent possible, the closest Brazilian counterpart to the US data source originally used in CHS.<sup>13</sup> As in CHS, our preferred method is to build up the expenditure estimates from primary firm level survey data. The main surveys we use are the Annual Survey of Services (PAS), the Annual Survey of Industry (PIA), the Annual Survey of Commerce (PAC), the Annual Survey of Construction (PAIC) and the Survey of Technological Innovation (PINTEC), all conducted by the Brazilian Institute of Geography and Statistics (IBGE). Like CHS, in case firm survey data are not available, our preferred alternative is to use official data sources such as National Accounts data, and wage and employment data from the Ministry of Labor and Employment.

Since many of our estimates are based on firm surveys which do not capture the government and household sectors, for the sake of consistency we have excluded government and household investment when calculating aggregates for all investment categories. This is largely consistent with CHS, who focus on business intangible investment.

Reflecting differences in data availability across the US and Brazil, some of our sources differ significantly from the CHS original, though they attempt to measure the same underlying economic phenomenon. The Appendix lists our source for every intangible asset class and compares it to its CHS counterpart.

> 13 In some cases where comparable data sources are simply not available for Brazil, we use the closest counterpart to the source used by Maranno et al. (2007) to estimate intangible assets in the UK. Since Maranno et al. also follow the CHS methodology, their departures from CHS are the second-best option and closest in sprit to the original.

10
The weak correlation between the two variables also holds when the five categories of food insecurity are grouped as either (i) moderate or better, or (ii) high or very high vulnerability to food insecurity (Figure 6). When total agricultural spending (not in per capita terms) and vulnerability categories are compared, results remain very similar, thus confirming that scale considerations do not appear to drive these patterns (Figure 7).

**Figure 6. Per Capita Agricultural Expenditure and Probability of Being in Each VAM Category Grouping**

<!-- Start of picture text --> Total PC AGR Expenditure and Prob of being in each VAM cat<br>Prob VAM 1, 2, 3 Prob VAM 4 or 5<br>.5 .6 .7 .8 .9 1 .5 .6 .7 .8 .9<br>low07 high07<br>PC exp_2007 Fitted values PC exp_2007 Fitted values<br>600 600<br>400 400<br>PC exp_2007<br>200 200<br>0 0<br><!-- End of picture text --> Source: Authors from World Bank (2010) APER database. Note: each figure depicts the association between the estimated probability of vulnerability across municipalities pertaining to the respective vulnerability group, that is, low (categories 1 to 3) and high (categories 4 and 5)

**Figure 7. Total Agricultural Expenditure and Probability of Being in Each VAM Category**

<!-- Start of picture text --> Tot Agr Exp and prob of being in each VAM cat<br>Prob of VAM 1, 2, 3 Prob of VAM 4 or 5<br>.5 .6 .7 .8 .9 1 .5 .6 .7 .8 .9<br>low07 high07<br>Tot Exp_2007 Fitted values Tot Expl_2007 Fitted values<br>15000 15000<br>10000 10000<br>Tot Exp_2007<br>5000 5000<br>0 0<br><!-- End of picture text --> Source: Authors from World Bank (2010) APER database.

10
A reverse causality between the _Welfare Ratio_ or _Labor_ and the _Roads_ issue may also mar the estimates. The localization choices of a household alongside a road and the means to access roads are highly endogenous decisions respective to the poverty status of the household. Our data do not permit us to control for the localization choices. However we argue that reverse causality in terms of means to access the road is not the phenomenon that drives endogeneity in our data. Whatever the poverty group, the main mean of access to a tarred road is walking. More convincing is the fact that the respective shares for the use of a care are 36.07% for the poor-households group against only 20.50% for the non-poor group.

We use an instrumental variable approach to solve the endogeneity of road localization. A road may be constructed in a given region because this is characterized by some economic potential that in turn shapes the poverty situation of the region and then the poverty status of households living in that region. An appropriate instrument has to explain why a given household benefits from a satisfying access to tarred roads but it should not be correlated with the level of consumption expenditures of this household.

Our first instrument is the density<sup>18</sup> of fixed and mobile "Gendarmeries" companies at the province level. We follow the macro literature that considers military spending as “the only plainly exogenous major influence on the economy” (Hall, 1986; 1990). The argument in our context is that the decision to settle a gendarmerie station should be totally exogenous from any other consideration than the sovereign defense mission accruing to the state. In fact governments have to provide protection to their citizens, whatever their wealth, religion or community... and the regions they live in. But the presence of a "gendarmerie" station requires a developed road network to ensure more effective police interventions. Households in areas with many "gendarmerie" stations per km2 should then face a better access to roads in the sense that the road density should be higher. We obtained the data on "gendarmerie" stations at the district level from the French Embassy in Yaoundé and used the list of the 612 sampled clusters in ECAM II to construct our instrument.

Our second instrument is the tarred road density by province lagged one year (in 2000). The current road access is in fact determined by the previous prevailing network density. A higher

> 18 For each province: Number of "Gendarmeries" companies over the surface area.

17
# **Introduction**

Recent literature surveys document that the potential gains from liberalization of trade in communications, finance, transport, business and other services are large (Hoekman, 2006; Deardorff and Stern, 2007). For many countries the potential gains are significantly larger than those that could be derived from further liberalization of goods trade. Moreover, even exploiting the opportunities arising from goods trade liberalization will require better services. For example, Indian horticultural producers receive only one-sixth of the price that consumers pay because of inefficient storage, transport and distribution (Mattoo, Mishra and Narain, 2007).

Notwithstanding the prospective gains from liberalization, WTO negotiations under the General Agreement on Trade in Services (GATS) have been going nowhere. The best market access offers made in the Doha Round do not even reflect the liberalization that countries have already implemented (Adlung and Roy, 2005). As a result, there is a serious risk of the GATS becoming irrelevant – increasingly lagging behind developments in service technology, regulation and business practice.

After five years of fruitless negotiations on services, in 2006 WTO members launched an effort to complement the bilateral request-offer process with a plurilateral or “collective” approach. This involved subsets of the WTO membership seeking to agree to a common “minimum” set of policy commitments for a given sector. While arguably a step forward, we argue in this paper that for there to be a reasonable prospect of achieving significant increases in the coverage and depth of services commitments by WTO Members, more attention is needed for the regulatory context in which services liberalization takes place. In particular, regulators must be reassured that GATS commitments will not deprive them of the freedom to regulate; that liberalization will not be prematurely thrust upon countries with weak regulatory institutions; and that liberalization will be supported by international cooperation.

Providing credible assurances that GATS commitments will only deprive regulators of the freedom to discriminate, and not limit their freedom in any other way, is necessary but not sufficient for a significant expansion in GATS commitments to occur. As, if not more, important is creating an effective multilateral mechanism to provide regulatory assistance to countries that need it. Dedicated assistance to improve regulatory 1
Given the larger number of remarried widows and the fact that 60 percent of current widows identify themselves as heads of households, while a majority of the rest reside either with their parents or brothers, the following analysis assumes that widows who accepted the levirate are classified as currently married by the DHS.

Table 5 shows the sample of women interviewed in the 2006 DHS by urban-rural sector and marital status. Around 16 percent of this sample have been widowed or divorced at some time, with a rate that is higher in urban than rural areas. Out of 14,583 surveyed women between 15 and 49 years of age, near to 5 percent (655) are previously widowed but currently remarried (430 or 2.95%), or currently widowed (225 or 1.54%). Many more have ever been divorced (12%) and most of these have remarried.<sup>18</sup> The data also reveal that in rural (urban) areas 57 (74) percent of once married women have no co-wives while this is true only for 26 (31) percent and 44 (62) percent of remarriages of prior widows and divorcees, respectively. An examination of the spousal rank of women in polygamous unions confirms that wives who have previously been married are less likely to be ranked as first wife. In urban Mali, 44 percent of once married women are first wives compared to 6 and 11 percent for previously widowed and divorced wives, respectively. In rural areas, the figures are 41 percent for first marriages versus 16 and 23 percent, for those whose first husband passed away or who have divorced.

In sum then, remarriages are much more likely to be into polygamous unions as lower ranked ─ and hence lower status ─ wives.

The 2006 DHS also contains information on a number of individual welfare indicators for women as well as for their children. The first of these I examine is women‘s body mass index (BMI), a measure of health. Low BMI may reflect heightened stress and undernourishment.<sup>19</sup> In addition to the level of BMI, one can look at the share of women whose BMI is greater than the underweight cut-off of 1900.<sup>20</sup> Other indicators available from the DHS relate to whether women have dependent children ─ defined as children for whom they have primary responsibility ─ and whether a mother has dependent children living without her in a different

> 18  Table 5 presents the population weighted percentages which turn out not to be significantly different from the sample percentages quoted in the text.

> 19  BMI is equal to weight/(height/100)2. An individual is widely considered overweight if his or her BMI is larger than 25; and underweight if it is less than 18.5.  (Note that the DHS multiplies BMI by 100 to ensure precision, and I follow their practice.)

> 20  I use 1900 rather than the more common 1850 cutoff benchmark because there are few data points at higher ages for some groups of women resulting in high volatility in the non-parametric regression lines used later.

17
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