rafmacalaba/datause-encoder-data huggingface dataset:
75 system.<sup>150</sup> The state also encourages enterprises to establish supplementary medical insurance for their employees, mainly to settle medical expenses not covered by mandatory medical insurance.

How, then, should well-known trade-offs in health care be addressed? In the case of poor countries, both ethical and efficiency concerns make a case for emphasizing preventive health services _and_ basic curative health services rather than sophisticated (specialized) curative health care. **(** Rich individuals may, of course, pay for sophisticated health care abroad, which would hardly create any impediments for the rest of the population in China.) Whereas preventive health care in developed countries is mainly an issue of individuals’ life style (smoking habits, diet, exercise etc.), in poor countries it is perhaps mainly an issue of sufficient nutrition, sanitation and effort to combat infectious diseases – areas where China has been quite successful. Indeed, the deficiencies in curative health care in China have not prevented a gradual improvement in broad health indicators in China, such as higher life expectancy. Perhaps health care has stronger effects on the _quality_ of individuals’ lives than on the length of life, which may largely be determined by the earlier mentioned examples of preventive health services.

With China’s rapidly ageing population, the need for institutionalized _old-age care_ is also bound to increase. Some state-owned enterprises still finance and organize such services for their previous employees – including housing, health care, recreational activities, and even funeral arrangements. However, as in the case of other human services, these responsibilities have gradually been shifted to municipalities, in particular in urban areas (Hussain, 2000b, pp. 67-68).<sup>151</sup> Nevertheless, as in most other countries, family members perform the bulk of old-age care. Naturally, this will become increasingly problematic when both spouses work in the open market.

The future health situation is, of course, also an issue of environmental problems such as air and water pollution. Indeed, not only research but also casual observation indicate that

> 150 The payroll tax that finances health insurance is currently 8 percent of the wage rate (OECD, 2005, Table 4:3). Social Policy Research Centre (2002, p. 9) estimates that it would have to be considerably higher than 10 percent in the future to avoid deficits.

> 151 There are also some developments in this direction in the countryside. An example is the attempt to follow up the traditional “five guarantees” system from the 1950s, designed to help the elderly (as well as the disabled and minors) without family support to obtain a certain minimum of food, clothing, housing, medical care, and proper burial.
edge lead to changes in behavioral practices; and behavioral improvements in turn spur child development. In this section, we further probe each aspect of this chain to demonstrate the link between knowledge and behavior, and to posit why there may be a lack of results on anthropometrics.

Many of the questions about knowledge translate directly into practices that we surveyed the women about.<sup>16</sup> For example, one knowledge item asks a mother how long she should exclusively breastfeed her newborn infant. The associated practice assesses how long she actually exclusively breastfed her newborn infant. We can assess if knowledge of the issue corresponds with actually implementing it.

Table 12 presents the results of running 7 such simple regressions, each taking a measurable action, and regressing if knowledge of the item was associated with having done it. This regression does not incorporate treatment status in any way, but rather is suggestive by providing the correlation between knowing something and the likelihood of practicing that behavior. As in table 5, we focus on the subset of women who had a new child after the baseline survey so that the behavioral practice questions correspond to when new knowledge may have especially been acted upon.

Column 3 indicates one of the strongest correlations between knowledge and behavior. Among women who know that the first milk should be given to the baby, there is a 27 percentage point increase in first breastfeeding the baby within one hour of birth. This is a 110% increase over the 24 percent of women who first breastfeed their baby within one hour despite not knowing that the first milk should be given to the baby. Though not causal, there is a clear correlation between knowledge and action. The other columns indicate similar patterns.

The correct timing on the practices seems to be especially important. Column 5 shows that there is a 30 percentage point (or 177%) increase in taking iron tablets for the suggested amount of time if a woman knows the correct recommendation. If we rather assess the correlation between this knowledge and taking iron tablets at all, the increase is still significant, but the magnitude is much smaller; knowledge of the correct timing to take iron tablets is associated with a 14 percentage point

> 16A knowledge item that does not directly correspond to an action is if a pregnant woman should eat more, less, or the same as before she became pregnant. Asking a woman about her eating practices while pregnant would be too broad of a question without using definitive numbers. This would more likely produce inaccurate responses so we did not ask such a question.

16
of time invariance of the predictors used in the models. Therefore, the objective of the first part of this paper is to validate the LASSO-PMM method when we have comparable data across countries, weighting generalizability over specificity.

# _3.2 Application to harmonized cross-section data_

After performing the validation on panel data, we apply the LASSO-PMM method to harmonized cross-section data. We employ the Global Monitoring Database (GMD) which gathers income and/or expenditure household surveys across the world. Raw surveys routinely conducted by national statistical offices in each country are compiled by the GMD project that is hosted and maintained by the World Bank. Most of the value added of this exercise, besides the compilation of a large number of household surveys, is the additional data processing and cleaning within the GMD initiative that harmonizes data sources to be comparable not only across countries but also within countries over time. The GMD database is the main input for the well-known _PovcalNet_ online tool<sup>16</sup> to monitor global poverty and inequality.<sup>17</sup> The benefits of using the GMD data in terms of population coverage are undeniable, since they contain more than 1,000 surveys in 156 countries.<sup>18</sup> However, with greater coverage, some specificity is lost. The number of surveys and variables that can be harmonized in order to be plausibly compared across and within countries is limited. Therefore, a set of dimensions have been selected and harmonized by the GMD project. These dimensions are: welfare (nominal, real, and in PPP terms); demographics (household size, gender, relation to household head, and marital status); education (school attendance, education level, and literacy); location (either subnational regions or rural-urban areas); some basic assets (landline phone, cellular phone, and computer);

> 16 <u>http://iresearch.worldbank.org/PovcalNet/povOnDemand.aspx</u> 17 At the time of writing, our results are drawn from the July 2020 version of the GMD database. 18 In this paper we use surveys in 43 countries for which two round of cross-sectional data exist.

17
35 Figure 10: Effect on financial protection index of changing definition of catastrophic spending <!-- Start of picture text --> SV ESPCZEHRVKN<br>EST<br>AREHUN<br>URY<br>MWI BIHRUSBRA<br>MLI<br>SEN BGD LVAMARECUDOMPRYMUS<br>GTMMYS<br>UKR<br>ZMB ZAF<br>ZWE<br>SWZ CIV MRT TUNKAZ<br>NAM BFA KENCOG LKAMEX<br>PAK CHN<br>GHA<br>NPL<br>TCD<br>VNM<br>IND<br>GEO<br>COM<br>ETH PHL<br>LAO<br>-.6 -.5 -.4 -.3 -.2 -.1 0<br>Difference at $1.25 a day<br>0<br>-.1<br>-.2<br>-.3<br>-.4<br>Difference at $5.00 a day<br>-.5<br>-.6<br><!-- End of picture text -->
14 Table 1. The Scope of the Administered Protection in Uruguay: 1981-1989

|StOCk of measures|1981|1982|1983|1 1984|j1985|1986|1987|| 1988|1989|
|---|---|---|---|---|---|---|---|---|---|
|||<br>A: N|<br>umber of|<br>general p|<br>roducts c|overed oth|er than tc<br>|y jiCa<br>||
|RefrenCe prica|8|34|70|95|89|70|62|_54_|5|
|Minimum export||||12|18|20|25|40|40|
|'Tranaferted"<br>goods**[a)**|22|46|46|47|47|46|36|_3S_|_35_|
|Subtotal|30|80|116|154|154|136|123|129|129|
|||B: Text<br>|ile and ap<br>|parel cate<br>|gories co<br>|vered =b<br>||||
|Yarns|177|177|177|177|177|177|177|177|177|
|Fabrics|105|1OS|105|105|105|_105_|105|_105_|105|
|Other textiles|129|129|129|129|129|129|129|104|82|
|Subtotal|411|411|411|411|411|411|411|386|364|
|Al**product item**|441|491|527|565|565|547|_534_|_s5s_|493|

Source: CERES, 1989 Note: [a) Goods initially under referenee price and progresively trAnsfrred to the minimum eXPOIt price system. [b) Number of NADI item under the RP/MEP systm.

systems. In other words, the apparently low coverage mirrors the extra protection granted to domestic goods under RP or MEP.which introduces a systematic bias underestimating the importance of these mechanisms. The base level apparent tariff for the sample of goods under the RP and MEP systems examined in this secton varies from _50_ percent to 58 percent on average fbr 1986, and from 39 percent to 45 percent on average for 1989. That is higher than the 29 percent average apparent tariff imposed on all goods imported by Uruguay in 1986'. As a result, the measurement of administered protection based on the number or percentage of affected imports underestimates the real impact of reference price 7As of April 1989, nominlI tariffs in Uruguay ranged from 0 percent to _45_ percent.
Jorgenson and Vu (2010) projections based on econometric estimates for 122 economies over the 1990 - 2008 period (see Figure B.1, panel c).

# B.1.4 Population, Preferences, and Welfare

## B.1.4.1 Population

We assume that the population, Π _t,_ follows logistic (Verhulst) model with declining growth rates over time: where Π0 is level of population in 2004 _,_ Π _T_ is the limiting population in 2104 _,_ and _π_ is the population growth rate. Compared to standard exponential growth assumption the logistic model provides a better t to demographic projections, and has been recently adapted in the economic literature (Guerrini 2006, Bucci and Guerrini 2009, Guerrini 2010). Data on population in 2004 are from GTAP v.7 database. The estimate of limiting population is from United Nations Department of Economic and Social Aairs Population Division (2011). The logistic growth rate of population is calibrated to match United Nations Department of Economic and Social Aairs Population Division (2011) demographic projections (see Figure B.1, panel a).

## B.1.4.2 Preferences

The parameters _αq_ , _βq_ and _<u>yq</u>_ dening the varying marginal budget shares of goods and services and the subsistence level of consumption _q_ in the consumers' total real expenditures in equation (A.30), are estimated by maximum likelihood based on the cross-section of 113 countries and regions listed in GTAP v.7 database as described in Craneld et al. (2003) and Yu et al. (2004). The parameters _αq_ and _βq_ were subsequently calibrated to match the allocation of land resources in 2004, while keeping the key ratio<sup>_α_</sup> _βq_<sup>_<u>q</u>_deningtheincomeand</sup> price elasticities constant.

We assume that the coecient of relative risk aversion parameter _γ_ is equal to 2, the value that has been commonly used in the economic modeling of climate change (Weitzman 2010, Pindyck 2011).

19
127

||1975|1980|1985|
|---|---|---|---|
|5 M E1 Salvador<br>|8.50|7.33|3.77|
|5M Guatemala||-||
|5 L Guyana<br>|6.95|3.16|-|
|SLHaiti|||-|
|5 X Honduras|15.82<br>|-|-|
|5 M Jamaica<br>|10.80<br>|-<br>||
|5MNexico<br>|24.69|19.33|11.83|
|5MNicaragua|27.97|7.36|-|
|5M Panama|12.39|14.29|17.38|
|5 M Paraguay<br>|20.68|19.59|-|
|5MPeru||||
|5 M Suriname|16.26|--||
|5MTrinidadandTobago|-|17.29|-|
|5MUruguay|45.82|49.24|49.51|
|5 M Venezuela|8.35|10.97|10.46|
|6 Eastern Europe|23.20|11.75|19.02|
|(M)|23.20|11.75|19.02|
|6M Bulgaria|-|-|-|
|6MCzechoslovakia|-|||
|6nGerman Democratic Republic||-|-|
|6 XHungary||-|27.94|
|6M Poland||||
|6MRomania<br>|12.61|15.45|22.13|
|6M Yugoslavia|33.78|8.05|6.99|

Note: The Regional averages are only for the group of countries that have data for that year; L - low income; M - middle income.

Other welfare includes social security and welfare, housing and community development, and recreational, cultural and religious affairs and services as classified by the IMF Government Financial Statistics Yearbook. Source: INF Government Financial Statistics: Consolidated Central Government Expenditure.
in these estimates depending on which urban center is specifically of interest due to differences between regions in our simulated frequency of urban power system disruptions and because of the disparate sizes of the economies of urban centers. As a reminder, our estimates for GDP are based on 2015 GDP figures and no assumptions regarding economic growth are made for the economies modeled here. Because the estimated frequency of urban power system disruptions is a primary driver of these cost estimates, there are again substantial uncertainties in our estimates that result from the variation in climate model simulations and the assumed socioeconomic pathway (Table 3 and Table 5). In fact, in some cases, these uncertainties result in our 90 percent confidence interval spanning about half an order of magnitude, as is the case for Tirana.

Despite the large ranges of the estimated cost of disruptions due to extreme cooling demands, increases in the extreme cooling demands are unlikely to be a primary driver of lost economic activity in many parts of Southeast Europe given the fragile state of the electricity system that currently exists. For example, rolling blackouts have recently been introduced in Kosovo (including in the capital of Pristina) due to lack of supply, which has been blamed on lack of investment.<sup>3</sup> In the near-term, the power supply of many countries in Southeast Europe may be more threatened by aging coal infrastructure (resulting from a lack of investments in maintenance and rehabilitation) and drought due to large dependence on coal and hydropower generation. For example, as of 2020, it was estimated that Kosovo depended on coal for over 94% of electricity generation, while it was estimated that Albania, Montenegro, Bosnia and Herzegovina, Serbia, and North Macedonia relied on hydropower for over 99%, over 42%, over 27%, over 25%, and over 23% of electricity production, respectively.<sup>4</sup>

> 3 https://www.bloomberg.com/news/articles/2022-08-26/europe-energy-crisis-kosovolearns-to-live-with-rolling-power-blackoutsagain?utm_source=website&utm_medium=share&utm_campaign=copy

> 4 https://www.iea.org/countries/kosovo, https://www.iea.org/countries/albania, https://www.iea.org/countries/montenegro, https://www.iea.org/countries/bosnia-andherzegovina, https://www.iea.org/countries/serbia, https://www.iea.org/countries/north-macedonia

17
First, for each quantile q, we estimate the vector of quantile regression coe ffi cients (returns), b<sup>i</sup> (q), using the data from Ri. Second, using covariates from Rj and vector of coeffi cients estimated for Ri, we estimate the predicted consumption expenditure as y<sup>p</sup> (q) = Zj b<sup>i</sup> (q) where Zj is the matrix of covariates in Rj. For each quantile q, this generates Nj fi tted values where N is the size of sample for Rj. Third, we select randomly 100 elements of y<sup>p</sup> (q) for each q and stack them into a vector y<sup>p∗</sup> . This y<sup>p∗</sup> is then used to construct the counter-factual distribution. Now the gap between the qth quantile of LPRCE of the Ri and Rj can be decomposed as: Since the counter-factual distribution F (y<sup>p∗</sup> ) provides the distribution of LRPCE that would have prevailed if returns to covariates in Rj had been the same as in Ri, the fi rst term on the right hand side measures the contribution of the di ff erence in returns to the Ri − Rj gap at the qth quantile. This is known as the returns e ff ect. The second term on the right hand side, the covariate e ff ect, thus measures the contribution of the di ff erent values of covariates to the Ri − Rj gap at the qth quantile. We generated the con fi dence intervals of these e ff ects by randomly re-sampling of the Ri data at the fi rst step of the estimation.

# 3 Data

The main data source for our empirical analysis is the Household Expenditure Survey (HIES) 2000 and 2005 of Bangladesh which were carried out by the Bangladesh Bureau of Statistics with assistance from the World Bank. The surveys utilized a nearly identical three-stage strati fi ed sampling strategy to select a nationally representative sample of the households. The questionnaires for the two rounds are also nearly identical. The HIES 2000 covers 7440 households in 442 primary sampling units (psus). The sample size for the HIES 2005 is 10,080 households in 504 psus.

Each of the surveys collected a wealth of information on many aspects of the living standards including detailed household level expenditure, demographics, employment, education, 10
**Table 4: Robotics, product complexity and initial quality gap**

|||Comple|xity||Complexitya|ndQualityG|ap|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)<br>(4)|(5)|(6)|(7)|(8)|
|Outcome:||Quali|ty||Qu|ality||
||OL|S|IV|O|LS||IV|
|Robots|0.024**||0.118***|0.006||0.145***||
||(0.011)||(0.038)|(0.011)||(0.028)||
|Robots * Complexity|0.064***||0.024|0.003||-0.057***||
||(0.024)||(0.022)|(0.017)||(0.019)||
|Robots * Quality Gap||||**0.065*****<br>**(0.005)**||**0.113*****<br>**(0.008)**||
|Robots – Developing||0.039**|0.245***||0.004||0.171***|
|||(0.019)|(0.081)||(0.016)||(0.057)|
|Robots – Developing * Complexity||0.086***|0.072**||0.014||-0.016|
|||(0.031)|(0.029)||(0.021)||(0.020)|
|Robots – Developing * Quality Gap|||||**0.071*****||**0.099*****|
||||||**(0.006)**||**(0.008)**|
|Robots – Developed||0.008|0.048*||0.010||0.138***|
|||(0.010)|(0.026)||(0.012)||(0.030)|
|Robots – Developed * Complexity||0.005|-0.039||-0.032*||-0.107***|
|||(0.018)|(0.029)||(0.018)||(0.035)|
|Robots – Developed * Quality Gap|||||**0.052*****||**0.129*****|
||||||**(0.007)**||**(0.016)**|
|Kleibergen-Paap F statistic|||58.3<br>16.4|||40.4|11.1|
|# Observations|897,260|897,260|897,260<br>897,260|897,260|897,260|897,260|897,260|

_Note: Quality gap reflects the initial distance from the “quality frontier. The quality frontier is defined within a given product as the top 10% highest exporters quality. The quality gap is defined as the difference between export quality and the frontier export quality in the first year. Product complexity is measured following Levchenko (2007) using US IO tables in 1997. Robots reflects log robot stock. The instrument reflects robot diffusion in (initial) foreign export partners, in other world regions, defined using 2000 export flows. Quality refers log quality estimated via Khandelwal et al (2013). Country income status is defined by the World Bank (2000). Controls include (log) employment and (log) real value added per worker at the country-industry-year level. The first stage has been omitted for brevity. The regressions also includeexporter*10-digit product and exporter*year fixed effects. Robust standard errors clustered at the exporter-industry level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1._ 30
across the board), and the increased spread in the explanatory variable (i.e., women’s share of establishments can increase or decrease much faster than the employment share). The economic magnitudes in Panel B, however, are very comparable. These estimates are precisely measured, with a 10% increase in the share of unorganized sector businesses owned by women being correlated with a 0.1% increase in the share of the unorganized sector overall in the district.

The relationships measured in Table 4 and Figure 3 could be due to reverse causality (e.g., places that could not transition well into more formal sector employment drew women into local business ownership) or omitted factors. To provide some additional assurance in the patterns and our interpretation of them, Table 5 reports instrument variable estimations. We instrument for the actual changes in the share of unorganized sector businesses owned by women with the share change that would have been expected due to the industry mix initially present in the district and the national rates by industry of female ownership. Using the 1994 industry distribution, we first calculate the expected rate of women-owned businesses in the district based upon national rates by industry of women’s ownership in 1994. We then calculate the expected rate in 2005 by combining the 1994 industry distribution (held fixed at its initial level) with the national rates by industry of women’s ownership in 2005. The changes in these expected values for districts serve as the instrument for actual changes observed.

The first stage is quite strong with a coefficient of 0.68 (0.13) and F-statistic of 27.2. Table 5 shows that the second stage results are somewhat stronger than those estimated in least squares, although one would generally accept that the least squares coefficients are not statistically different. One possible reason for the potential downward bias in the least squares estimations is that the surveys possess measurement error with respect to the changes in femaleownership shares, but other explanations may exist. The overall message of Table 5 is that the relationship that we identify at the aggregate level and in district changes continues to hold with basic instrumental variable corrections.

# **5. Comparison with Other Opportunities**

The above empirical analyses support the overall notion that increases in women’s businessownership are associated with a more persistent unorganized sector. Given that the majority of these new businesses are household-based and of small scale, this link is intuitive (even if its 14