rafmacalaba/datause-encoder-data huggingface dataset:
cross-country comparisons will entail important qualitative components. Obviously, the findings from the estimates of child survival functions can be compared across countries. If we find effect ‘A’ to hold for all (or most) countries, then we can have quite some confidence in generalizing this result at least to other Sub-Saharan countries. Hence, one advantage of using a multi-country framework is the opportunity to generalize the results. Yet, if we can observe effect ‘A’ for all but one country (or a subset of countries) it would be interesting to find out why this is the case. In this paper, we will try to answer this type of question by resuming to a qualitative approach. Although the factors that cause effect ‘A’ to disappear for one of our countries could in principle be identified, quantified and tested statistically, many candidate factors will be difficult to observe (at least to quantify) and/or they are (quasi-) fixed for each country. Examples for such variables include the disease environment and the quality of public health centers. A qualitative approach however will possibly allow us to identify missing or context factors that can explain inter-country differences in the determinants of child mortality.

These considerations eventually motivate our country choices. The selected countries, typically the pairs, share some important characteristics, e.g. geographic and climatic conditions and/or income levels, but differ in other dimensions, e.g. infant mortality levels and changes therein, recent economic performance, or vaccination levels. Evidence from contexts that differ in unobserved or country-fixed characteristics – in our case from different SSA regions – enhances the opportunity to generalize the findings. If countries share many characteristics – as do the country pairs – this may allow isolating (unobserved, countryinvariant) factors that may explain the differences in child mortality and its determinants, respectively. Alternatively, countries can be grouped according to certain criteria, for example the existence of user fees, and difference between those groups examined.

Conducting detailed micro-econometric work on 16 country-year cases while trying to keep results comparable implies losing some country-specificity. For example, we use a common set of explanatory variables for the estimates of the survival function although we could have used more variables in some of the country-year cases. Although DHS data use a common questionnaire there are some differences in questionnaires and data availability both between countries and over time. In our view, the results that we discuss in the following suggest that a cross-country approach that keeps individual country cases identifiable is indeed an adequate methodological framework for the study of child mortality. While the biological determinants of child mortality are fairly similar among the selected countries, the differences in their relationship to socio-economic determinants (as well as the effect of these 15
motto wherever possible. But after Parisian leaders had painted the phrase on the walls of the Commune in 1870 (followed by “ou Mort”), the Third republic readopted it, despite the misgivings of many who thought it implied a leveling of society. Under German occupation, the government replaced the trilogy with another: “Work, Family, Fatherland.” Finally, the words reappeared in the 1946 and 1958 French Constitutions, and now have become the national motto.<sup>12</sup> What can be learned from this controversy? For many social scientists, particularly methodological individualists, the answer would be: Not much. From this standpoint, changing a motto might signal to supporters and rivals that a particular French faction has in fact achieved power, but the ideational content of the motto – the significance of using, say, “fraternité” rather than one of the proposed alternative terms for the sense of French collective understanding (e.g., “unité,” “force”) – is unlikely to be taken seriously as a causal factor in historical and political explanation. We question that working assumption. The burden of this paper is to develop an account of how shared meanings arise, and demonstrate how they constrain and enable certain forms of collective action.

Intersubjective meanings are concepts, arguments, beliefs and judgments that cannot be attributed to individuals; rather, they are the shared property of groups of human beings. The reason that they cannot be the property of a single individual is that they are not abstractions inside one’s head (as in, for instance, a picture one might have of heaven), but are rooted in the rules that govern social practices. They both arise from and are constitutive of coordinated social action, and would not exist in the form that they do were it not for the particular forms of coordination that prevail in a society.

Further illustrations from a variety of fields might be helpful. Bodily and mental health, for instance, are difficult to assess without a widely shared understanding of the normal range of physical and mental experience. The range of activities that a society typically expects its members to perform, and the attendant social judgments, constitute the primary standards against which one assesses one’s own (and others’) “health.” More concretely, they provide the benchmarks for evaluating the sufficiency and “healthiness” of a person’s physical attributes (e.g., anatomy, height, weight, range of motion) and mental experiences (e.g., a unified identity, self-control).<sup>13</sup> In a related way, knowledge claims, whether

> 12 See http://www.diplomatie.gouv.fr/en/france_159/institutions-and-politics_6814/the-symbols-of-the- - <u>republic_2002/liberty equality fraternity_1503.html</u>

> 13 See Brown (1995) and Payer (1996) on the social construction of medicine and medical concepts. See also Watters (2010) on the manner in which Western mental health professionals, via their ‘definitive’ _Diagnostic and Statistical Manual of Mental Disorders IV_ (DSM IV), have dramatically altered how the rest of the world comes to

6
# **3. Data and methodology**

# _Data_

Baseline data for the evaluation were collected in April-May 2005. A first follow-up survey was collected in July-August 2006 - nine months after households started receiving benefits. Due to implementation delays, the business grant had been disbursed only 2 to 3 months prior to this survey and vocational training courses has been selected but had not yet started. A second follow-up survey was collected between August 2008 and May 2009 (henceforth referred to as 2008). At this point, households had stopped receiving transfers and any related program benefits for an average of two years. As the first follow-up survey was done while some program activities were still under implementation, this paper primarily focuses on the second follow-up survey.

Thanks to thorough tracking procedures, attrition compared to the baseline was minimal, less than 2.4 percent in 2008, and is uncorrelated with treatment status (P-value = 0.54). Differences in attrition rates between treatment groups are also very small and not significant (P-value = 0.67). The sample includes 972 households eligible for the vocational training, 990 households eligible for the business grant, and 996 households in the control.<sup>7</sup> The surveys included comprehensive information on household socioeconomic status, including detailed modules for expenditures and economic activities.<sup>8</sup> The baseline survey included specific questions regarding households demand and preferences for potential programs that could help increase their income. It also included detailed modules on household asset endowments (durables, land, livestock), credit access, demographics, dwelling characteristics, education, and remoteness. We use this information, together with the information on baseline economic activities, to analyze the heterogeneity of program impacts. The second follow-up survey in addition included a labor market history module as well as a specific module on households’ nonagricultural self-employment activities, which together with the module on economic activities in the last 12 months is used to define the outcome variables.

Finally, we draw on the first follow-up survey to obtain a measure of how the program impacted attitudes towards the future early during program implementation. The sample size for the attitudes questions is lower (1519 households equally divided between productive investment grant, training and control households) as they were part of an additional questionnaire administered only to primary caregivers of

> 7 The analysis in this paper does not include the 964 households that only received the basic CCT.

> 8 The expenditure modules were taken from the 2001 Nicaragua Living Standards Measurement Study (LSMS) survey and include detailed information on various expenditure categories. For example, food expenditures include questions about 63 food items from actual expenditures, home production, and food consumed outside the home. The income module was designed specifically for this study with the objective to capture all the different economic activities household members are engaged in.

9
18 significant finding because while the price of coal is hardly regulated, district heating is still highly subsidized to consumers (Meyers, Schipper and Salay, 1994, p. 2-3; World Bank 1994). This implies that workers, pensioners, self-employed and social transfer recipients are benefitting much more from energy price controls than are other groups such as farmers or mixed households (Table 5). All groups but farmers and mixed households spend about 8% of their income on subsidized items proportionately, (and, by implication, receive that much, in terms of subsidy). Farmers and workers spend only 4% of their income. Subsidization of self-employed is particularly high, since their income is higher than other groups and they spend almost as much as pensioners, the topspending group. There are clearly also regional effects since urban areas use district heating while rural areas are more dependent on coal and wood. In sum, the subsidies benefit urban households.

**Table 5: Energy expenditure as a percentage of total expenditure by social welfare group**

|Social Welfare<br>Group|Heat|Electricity|Gas|Major<br>energy<br>sources|Coal|Wood|Hot<br>water|All<br>energy'|
|---|---|---|---|---|---|---|---|---|
|Worker|2.7|3.0|1.9|7.6|_0.5_|0.1|0.8|8.8|
|Farmer||3.2|0.7|3.9|2.2|0.6||6.7|
|Mixed||2.9|1.2|4.1|2.0|0.4||6.4|
|Pensioner|2.5|3.7|2.2|8.4|2.6|0.3|0.4|**11.7**|
|Self-employed|2.1|3.4|2.5|7.7|0.3||0.6|8.9|
|Soc. Recipient|2.1|4.0|1.7|7.8|0.5|0.3|_0.5_|9.0|
|All|2.2|3.3|1.9|7.4|14|**0.2**|0.5|9.5|

a/ May not sum due to rounding. Note: Spent less than D.05% of budget. Source: Household budget survey.

While farmers and mixed households consumed much less district heat than most households, they also used more wood than the others. Interestingly, a large portion of the wood consumed was actually free; of the wood consumed by farmers and mixed households about two-thirds of it was free. Meyers, Shipper and Salay (1995 forthcoming) found that when the price of coal rose in 1990,
- 32 -

# Table 3.7: INDICATORS OF INCOME, URBANIZATION AND INDUSTRIALIZATION IN LAC COUNTRIES

|Countries|I<br>GNP p.c.<br>1985 US $ a)|ncome<br> <br>Share of Six<br>Lowest Deciles<br>b)|Urban<br>1980 Urban<br>Population<br>c)|ization<br> <br>In Cities<br> <br>500,000<br>d)|Industr<br> <br>Share of 1980<br>Labor Force<br>e)|ialization<br>Share of 1985<br>GNP<br>f)|
|---|---|---|---|---|---|---|
|Venezuela|2550|23|85|31|28|42|
|Argentina|2130|28|84|47|34|42|
|Panama|2100|18|50|21|18|18|
|,exico|2080|22|69|31|29|35|
|Uruguay|1650|NA|85|41|29|33|
|Brazil|1640|16|73|33|27|33|
|Chile|1430|NA|83|34|25|40|
|Colombia|1320|NA|67|24|24|30|
|Costa Rica|1300|25|45|28|23|29|
|Guatemala|1250|NA|41|15|17|NA|
|Paraguay|860|NA|41|26|21|25|
|Dom. Republic|790|NA|56|35|15|31|
|Bolivia|470|NA|44|14|20|25|
|13**LAC**Countrles|1677|**NA**|68|32|24|32|
|9IND**NW1r.**EOON.|11,810|37|**75**|35|**35**|**36**|

Sources: Values In columns a), b), c) e) and b) derived from World<sup>Development Report 1987, World Bank</sup> no. ISBN 0-19-520563-4. Values in columns d calculated from UNCHS data Global Report 1986 Habitat. Values In column b) apply to different years.
|I aidel-<br>S- D<br>_____-<br>i<br>_______||_____||||||||.|.<br>_____|
|---|---|---|---|---|---|---|---|---|---|---|---|
|TheVorld at**WVork**<br>||||||||||||
|_Estimated as shares oflaborforce_<br>_in region_|||_____|_._||||||||
||Labor Force<br>|Unemployed|Workers in|Workers in<br>|Workers in|Wage|Non-wage|Wage|Non-wage|Wage|Non-wage|
|______|_____<br>_<br>(ages 15-64)||agriculture|industry|services|agriculture|agriculture|industry|industry|services|services|
|._______<br>|_(%)_|__(%)_|**(%/)**|_(%)_<br>_|<br>_(%)_|_(%)_|_(%)_|(/'o)|_(%)_<br>-|- **(O/°)**<br>-|_(e)_|
|Woild|100|4.9|43.4|19.4|<br>32.3|3.5|40.0|11.4|8.0|20.9|11.4|
|Illhi**nconie**econonles<br>|100|8.3|4.4|27.4|59.8|1.7|<br>2.7|24.4|3.0|51 2|8.6|
|MiddleIncome economies<br>|100|6.9|28.6|25.0|39.5|7.3|21.3|_19.1_|**5.8**|27.0|12.6|
|Lorincone<br>**economles**|**100**|3.0|60.6|14.7|<br>21.7|**2.2**|58.4|**4.4**|10.3|10.1|11.7|
|East**Asia**and Paciflc<br>|**100**|2.8|56.3|17.5|<br>23.4|**2.5**|53.8|**6.3**|**11.1**|**12.2**|<br>11.3|
|Furo,pe and Central Asia<br>|100|7.9|22.1|32.7|37.3|4.6|17.5|27.0|**5.7**|30.0|7.3|
|**Latin**America and Caribbean<br>|100|**5.4**|21.6|24.0|49.0|**9.0**|12.7|**18.6**|**_5.5_**|33.7|15.3|
|MiddleEast and North**Aftice**<br>|**100**|13.7|**28.7**|20.2|37.4|6.0|22.7|14.8|**_5.4_**|27.1|**10.4**|
|**O.E.C.D.**<br>|**100**|8.3|4.5|27.4|59.8|**1.7**|<br>2.8|24.4|3.0|51.2|8.6|
|**South**Asia<br> <br>|100|2.9|60.5|14.3|<br>22.4|**2.2**|**58.3**|**4.3**|9.9|9.5|12.9|
|.Sub-Sahiaran<br>Africa|100|6.0|60.7|8.7|24.6|**5.6**|**55.1**|3.3|**5.4**|**9.4**|15.3|

40
|||||-0.64||||7.23||||-5.91|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_cons|156.080***|157.799***|159.920***|160.072***|156.375***|157.339***|160.144***|157.525***|149.958***|151.608***|150.735***|152.247***|
||212.26|169.96|135.99|131.1|145.86|115.15|75.85|72.14|72.02|67.68|60.41|60.94|
|R Adj sq.|0.196|0.202|0.203|0.203|0.303|0.307|0.309|0.319|0.026|0.032|0.033|0.039|
|N|10961|10961|10961|10961|5143|5143|5143|5143|5818|5818|5818|5818|

The regression also includes oblast dummies, dummies for different types of schools and evening schools. The full table can be found in excel tables which can be made available upon request. *** means significant at the 1% level, ** at the 5 % level and * at the 10 percent level.T statistics are based on robust standard errors for the OLS specifications.

**_Table 8b –Ukrainian History, Mean Score_**

|||To<br>|tal<br>|||Ur<br>|ban<br>|||Ru<br>|ral<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|# students|0.006***|0.005***|0.006***|0.006***|0.005***|0.005***|0.008***|0.007***|0.001|0.001|0.012***|0.013***|
||12.67|11.97|4.28|4.3|10.76|10.59|5.02|4.53|0.95|0.79|2.73|2.9|
|(# students)<sup>2</sup>|||0|0|||-0.000*|0|||-0.000***|-0.000***|
||||-0.43|-0.46|||-1.86|-1.48|||-2.82|-2.95|
|Student/Teacher|-0.224***|-0.208***|-0.288**|-0.287**|-0.130**|-0.145**|-0.333*|-0.340*|-0.09|-0.082|-0.178|-0.16|
||-4.83|-4.54|-2.3|-2.29|-2.24|-2.54|-1.9|-1.93|-1.05|-0.97|-0.76|-0.68|
|(Stud./Teacher)<sup>2</sup>|||0.005|0.005|||0.008|0.009|||0|-0.001|
||||1.14|1.13|||1.31|1.35|||0.02|-0.1|
|Class Size|-0.095***|-0.082***|-0.410***|-0.410***|-0.031|-0.016|-0.452***|-0.446***|-0.083***|-0.086***|-0.332***|-0.333***|
||-5.34|-4.6|-5.51|-5.5|-1.29|-0.64|-3.18|-3.15|-3.07|-3.19|-3.08|-3.08|
|(Class Size)<sup>2</sup>|||0.009***|0.009***|||0.011***|0.010***|||0.007**|0.007**|
||||5.16|5.16|||3.4|3.37|||2.47|2.44|
|Urban|1.641***|1.847***|1.983***|1.994***|||||||||
||6.65|7.35|7.4|7.37|||||||||
|Gender Comp.||-0.017***|-0.017***|-0.017***||-0.029***|-0.030***|-0.032***||-0.014**|-0.014**|-0.012*|
|||-3.09|-3.12|-3.06||-3.01|-3.09|-3.29||-2.02|-2.02|-1.82|
|Unemployment rate<br>2009||0.034|0.028|0.029||-0.031|-0.026|-0.027||0.011|0.013|0.024|

37
35 Kindig, D. A., C. H. Taylor. 1985. "Growth in the International Physician Supply. 1950 through 1979." Journal of the American Medical Association 253:3129-32.

- Kinston, W. 1983. "Hospital Organization and Structure and its Effect on Inter-professional Behavior and the Delivery of Care." Social Science and Medicine 17:1159-70.

- Klein, R. 1984. "The Politics of Ideology vs. the Reality of Politics: The Case of Britain's National Health Service in the 1980s." Milbank Memorial Fund Quarterly/Health and Society 62:82-109.

- Kovner, A. R., D. Neuhauser. 1983. lntroduction. In Kovner, A. R., D. Neuhauser (eds). Health Services ManaRement: Readings and Commentary (Second Edition). Ann Arbor, MI: Health Administration Press, pp. 3-18.

- Kuhl, I. K. 1977. The Executive Role in Health Service Delivery Organization. Washington: Association of University Programs in Health Administration.

- Lawrence, P., J. Lorsch. 1967. Organization and Environment Managing Differentiation and Integration. Boston: Harvard Business School Press.

- Leatt, P., V. Mickevicius, J. Barnsley, E. Vayda, J. I. Williams. 1983. Hospital Medical Staff Organization: An Annotated Bibliography. Ottawa: Canadian Hospital Association.

- Longest, B. B. 1980. "A Conceptual Framework for Understanding the Multihospital Management Strategy." Health Care Management Review Winter:17-24.

- Longest, B. B. 1981. "An External Dependence Perspective of Organizational Strategy and Structure: The Community Hospital Case." Hospital and Health Services Administration 26:50-69.

- Mechanic, D. 1975. "Ideology, Medical Technology, and Health Care Organization in Modern Nations." American Journal of Public Health 65:241-47.

- Miles, R. E., C. C. Snow, J. Pfeffer. 1982. "Organization-environment: Concepts and Issues. In Spirn, S., D. W. Benfer (eds). Issues in Health Care Management Rockville, MD: Aspen, pp. 421-38.

- Mintzberg, H. 1975. "The Manager's Job: Folklore and Fact. Harvard Business Review 53:49-61.

- Mintzberg, H. 1979. The Structuring of Organizations. New Jersey: Prentice-Hall, Inc.

- Mintzberg, H. 1981. "Organizational Design: Fashion or Fit." Harvard Business Review January-February: 1088-116.
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main issue may indeed be the need to better manage traffic to address safety and environmental concerns. This is largely being done, slowly but surely, as the concern for pollution has become a worldwide policy item and as, at least in the developing world, the concern with the lack of safety associated with unmanaged competition resulting from excessive liberalizations is now leading governments to consider hybrid solutions.

From the viewpoint of freight transport, the story emerging from the 1990s reforms is somewhat more complex. Most infrastructure privatizations have followed a price cap type of regulatory regime, at least in developing countries. Because these caps have seldom been binding and the competition with the trucking industry has been quite strong, it is unlikely that the average users may have been penalized by the reform. The main residual concern may come from captive shippers (this is discussed later in the paper). As for quality, there is mixed evidence. Visible quality has generally improved (punctuality, safety, ...). Less visible (environment) quality has improved but not as much as expected. Highly visible accidents in the UK have provided highly mediatized (but hardly scientific) evidence of the problem and similar stories are emerging in developing countries. A recent study of the use of under-investment in safety by Brazilian railways operators confirms this [Estache _et al._ (2004c)].

Overall, the bottom line may be provided by a perception index collected by the World Competitiveness Report. This index gives a sense of what commercial users of transport services feel about the overall quality of infrastructure services. Figure 1 reports the data for each sector and a grouping of countries which allows a double comparison. First, transport is clearly the worst rated of all sectors. Second, this is true across country groups. This suggests that the reforms in the transport sector have in general not been able to generate the level of satisfaction observed for other sectors.

17
# **References**

Agarwal, Bina, 1997. "Gender, Environment, and Poverty Interlinks: Regional Variations and Temporal Shifts in Rural ILdia, 1971-91," World Devel mn, 25(1):23-52.

Ahuja, Vinod, and Deon Filmer, 1996. "Educational Attainment in Developing Countries: New Estimates and Projections Disaggregated by Gender," Journal of Educational Planning and,,Administration, Vol3 ,4:229-254.

Arnold, Fred, 1992. "Sex preference and its Demographic and Health Implications," In al Family Planning Perspectivs, 18:93-101.

Bruno, Michael, Martin Ra,vallion, and Lyn Squire, 1996. "Equity and Growth in Developing Countries," Policy Research Working Paper No. 1563, The World Bank. Washington, DC.

Coale, Ansley J, Paul Demeny, and Barbara Vaughn, 1983. Regional Model Life Tables and Stable oulations, New York: Academic Pres.

Das Gupta, Monica, 1987. "Selective Discrimination against Female Children in Rural Punjab," Poulation and Develo nt Review, 13(1):77-100.

Easterly, William, 1997. " Life During Growth," mimeo, Development Research Group, The World Bank, Washington, DC.

Filmer, Deon, and Lant Pritchett, 1997. "Child Mortality and Public Spending on Health: How Much does Money Matter?" mimeo, Development Research Group, The World Bank, Washington, DC.

Folbre, Nancy, 1984. "Market Opportunities, Genetic Endowments, and Intrafamily Resource Distribution: Child Survival in Rural India: Comment," American Economic Review, 74(3):518-520.

Government of India, 1994. Economic Survey, Ministry of Finance, Economic Division.

Hill, Kenneth, and Dawn M. Upchurch, 1995, "Gender Differences in Child Health: Evidence from the Demographic and Health Surveys," PpuIation and Develoment Review, 21(1): 127151.

King, Elizabeth M, and M Anne Hill, 1993. Women's Education in Developing Countries: Barriers, Benefits and Polie. Baltimore MD.: Johns Hopkins Press.

Klasen, Stephan, 1996. "Nutrition, Health and Mortality in Sub-Saharan Africa: Is There a Gender Bias," Journal of I)evelopment Studies, 32(6):913-932, 944-948.

28
# **Table A.7. The effect of expectations, RLMS 1994-2017**

|_Main variables_<br>**Pooled Ordered Logit**|**BUC**|
|---|---|
|Log of household income<br>0.623***<br>_(0.011)_|0.406***<br>_(0.079)_|
|Log of household size<br>-0.404***<br>_(0.022)_|-0.166***<br>_(0.034)_|
|Children*Log of household size<br>0.050***<br>_(0.008)_|0.020**<br>_(0.010)_|
|_Expectations in T-1 period (base – “Nothing will change”)_<br>You will live worse<br>-0.509***<br>_(0.016)_<br>You will live better<br>0.448***<br>_(0.013)_|-0.139***<br>_(0.017)_<br>0.152***<br>_(0.014)_|
|_Number of observations_<br>237,395|712,448|
|_Log pseudolikelihood_<br>-400,970|-263,550|

**Note:** Robust standard errors are in parentheses, controlling for two-way clustering (i.e., at the individual for the POL model and at the household-wave level for the BUC models). All regressions include the same controls as in Table 1.

42