rafmacalaba/datause-encoder-data huggingface dataset:
economies and in over 140 languages, using randomly selected, nationally representative samples.<sup>10</sup>

# **3.2 Descriptive statistics**

We employ a representative sample of 143 economies and 147,692 individuals, aged 18 and older.<sup>11</sup> We use a binary variable (0/1) of responses to the question: “ _In the past 12 months, have you saved for old age?_ ” Figure 1 shows the percentage of adults who saved any money for old age during the last year by regional classification and Appendix Figure A1 presents a global map.<sup>12</sup> The weighted global figure for saving for old age around the world is 24.8%. The top performers are Thailand, in which 59.2% of the population saves for old age, Germany (55.1%), Malaysia (54.0%), Canada (51.7%), and New Zealand (51.5%). The bottom performers in terms of saving for old age are Niger (0.9%), Georgia (0.9%), the Republic of Yemen (1.4%), Jordan (1.9%), and Armenia (2.1%). The top performing region is OECD<sup>HI</sup> , though an interesting outlier is Greece amidst the debt-crisis, with 9.3% of the population saving for old age.

The bars in Figure 1 further distinguish between saving for old age and financial inclusion status, in terms of having an account at a bank or formal financial institution. It can be seen that 84.7% of the individuals who save for old age around the world are financially included, with the remaining 15.3% being financially excluded. The low figures in certain regions such as ECA and MENA could be indicative of the usage of alternative means of

> 10 The survey is conducted in the major languages of each country. The target population is the entire civilian, non-institutionalized population aged 15 and above. Data weighting is used to ensure a nationally representative sample for each economy. Final weights consist of the base sampling weight, which corrects for unequal probability of selection based on household size, and the poststratification weight, which corrects for sampling and nonresponse error. Poststratification weights use country-level population statistics on gender and age and, where reliable data are available, education or socioeconomic status. In some large economies, such as China and the Russian Federation, sample sizes of at least 4,000 are collected. Detailed country-level information about the data collection dates, sample sizes, excluded populations, and margins of error can be found at http://go.worldbank.org/IGRTPHK660

> 11 We do not exclude from the sample individuals older than 65 years old, following the evidence in Alessie et al. (1999), who find only some evidence of decumulation among the elderly, but only at an advanced age. The authors find that precautionary savings, bequest motives, and health are among the reasons why some elderly do not decumulate in the pattern predicted by the life-cycle model.

> 12 All summary statistics are weighted country averages.

8
in Productivity: The Role of Allocative Efficiency,” _American Economic Review_ , 103, 305– 334.

- Bento, P. and D. Restuccia (2016): “Misallocation, Establishment Size, and Productivity,” Tech. rep., National Bureau of Economic Research.

Bloom, N. (2009): “The Impact of Uncertainty Shocks,” _Econometrica_ , 77, 623–685.

- Buera, F. J. and R. N. Fattal-Jaef (2016): “The Dynamics of Development: Innovation and Reallocation,” _working paper_ .

- Buera, F. J., J. P. Kaboski, and Y. Shin (2011): “Finance and Development: A Tale of Two Sectors,” _American Economic Review_ , 101, 1964–2002.

- Buera, F. J., B. Moll, and Y. Shin (2013): “Well-Intended Policies,” _Review of Economic Dynamics_ , 16, 216–230.

- Cooper, R. W. and J. C. Haltiwanger (2006): “On the Nature of Capital Adjustment Costs,” _The Review of Economic Studies_ , 73, 611–633.

- David, J. M., H. A. Hopenhayn, and V. Venkateswaran (2016): “Information, Misallocation, and Aggregate Productivity,” _The Quarterly Journal of Economics_ , 131, 943–1005.

- David, J. M., L. Schmid, and D. Zeke (2018): “Risk-adjusted capital allocation and misallocation,” Tech. rep.

- David, J. M. and V. Venkateswaran (2017): “The Sources of Capital Misallocation,” Tech. rep., National Bureau of Economic Research.

- De Loecker, J. and F. Warzynski (2012): “Markups and Firm-Level Export Status,” _The American Economic Review_ , 102, 2437–2471.

- Gopinath, G., Ş.  Kalemli-Özcan,  L.  Karabarbounis,  and  C.  Villegas-Sanchez  (2017): “Capital Allocation and Productivity in South Europe,” _The Quarterly Journal of Economics_ , 132, 1915–1967.

- Guner, N.,  G. Ventura, and Y. Xu (2008): “Macroeconomic Implications of SizeDependent Policies,” _Review of Economic Dynamics_ , 11, 721–744.

- Hopenhayn, H. A. (2014): “Firms, Misallocation, and Aggregate Productivity: A Review,” _Annu. Rev. Econ._ , 6, 735–770.

17
factors, we need a panel data set to use a fixed-effects method to estimate the impacts of policies on private investments and outcomes of interest. In our case, we will use the thana level panel data to resolve the bias due to joint determination of program placement and outcomes of interest such as income and consumption.<sup>36</sup> Regarding the specific question of how to treat program placement, one problem with estimating equation (6.2) is that the direct impact of the agroclimatic variables  _j_ and  _j_ cannot be determined. We could estimate these effects if the policy investments ( _M_ ) were not a function of the unobserved agroclimatic variables,  _j_ . This would be the case if the observed set of agroclimatic variables  _j_ completely represented the set of local area endowments affecting placement of infrastructure and credit programs, and thus  _j_ could be treated as random. In this case, a random-effects estimation of equation (6.2) is valid. We conduct Wu-Hausman test, specifying the unobserved effect as the vector of local agroclimatic characteristics, to see whether the fixed-effects or random-effects specification is appropriate.

# <u>Effects of agroclimatic conditions on infrastructure, school, and credit interventions</u>

Following equation (6.2) described above, Table 11 presents the FE results for the effects of local agroclimatic conditions (interacted with year to obtain time-varying characteristics) on program placement. The Wu-Hausman tests indicate that the fixed-effects model is appropriate for explaining variation in these policies over time, and therefore only the fixedeffects estimates are reported. Table 11 shows that agroclimatic endowments explain as

> 36 Even if some policy variables are measured at the village level, public investments are truly made at the thana level.  Thus, if a particular thana receives funds for certain public investments, the village is likely to receive the same investment, and not the other way around.  Hence, a thana-level fixed-effect is appropriate to measure the impacts of public policies.

30
# **1. Introduction**

The HIV/AIDS epidemic in Sub-Saharan Africa has resulted in higher mortality rates among prime-age adults and sharp declines in life expectancy. The resulting “demographic hourglass” due to missing working-age adults has implications for a range of socioeconomic outcomes. These missing working-age adults were income earners who supported dependent children and, in some cases, their older parents. A World Bank (1997) report presents a detailed discussion of the direct and indirect ways in which an HIV/AIDS death may affect households and the means by which households might cope with these impacts.

Empirical research in this area in the African context focuses largely on the impact of HIV/AIDS-related deaths on the outcomes of orphaned or fostered children specifically, or households generally (see studies reviewed in Beegle and De Weerdt, 2008, and Naidu and Harris, 2005). There are far fewer studies on the impact of primeage deaths on older populations specifically. Qualitative studies suggest that the impact could be large for older persons (Knodel _et al._ 2003; Knodel and Landingham 2002). There are important aspects of direct and indirect effects of prime-age mortality specific to older adults (see discussion in du Guerny, 2002, and Messkoub, 2008). Surviving parents often bear the direct costs for ill adult children, including funeral and medical expenditures; indirect costs borne by the elderly include those associated with providing care to ill adult children, fostering-in of grandchildren, and the loss of remittances and income.<sup>2</sup> In addition to these socioeconomic outcomes, there are potential psycho-social costs associated with losing a child or other household members.

There may be important gender dimensions to these impacts on older adults, as well, with respect to the gender of the older adult as well as the gender of the deceased. Gender inequalities in education, income, and property ownership may be important for

> 2 The burden of caring for orphaned children is perhaps the most-often cited concern with respect to the elderly and HIV/AIDS epidemic (and rising prime-age mortality rates). Interestingly, even in countries in Africa with low HIV prevalence and/or where orphan rates are not increasing, grandparents are increasing becoming caregivers of children (rather than either a surviving parent or other relatives such as aunts/uncles) (Beegle _et al._ 2009). This fact suggests important changes in living arrangements that are not explained by HIV/AIDS, and which have implications for the well-being of the elderly.

2
Table D.9: Heterogeneous Treatment Effects on Employment

|(1)<br>(2)|(3)|
|---|---|
|_Panel A: Heterogeneous Efects by Single Index Skill Measures_||
|Public treatment<br>0.052<br>0.052|0.054|
|(0.011)<br>(0.011)|(0.012)|
|_×_ Share top - share bottom terciles<br>0.019||
|(0.028)||
|_×_ PC1(Scores)<br>0.004||
|(0.025)||
|_×_ Earnings-weighted average of scores|-0.007<br>(0.029)|
|# observations<br>6607<br>6607|6603|
|# clusters<br>84<br>84|84|
|_Panel B: Heterogeneous Efects by Alternative Information Sources_||
|Public treatment<br>0.052<br>0.052|0.051|
|(0.011)<br>(0.012)|(0.012)|
|_×_ post-secondary education<br>-0.027<br>(0.028)||
|_×_ employed at baseline<br>-0.043<br>(0.032)<br>||
|_×_ <sup>ˆ</sup>Pr(Employed at endline | X)|-0.069<br>(0.028)|
|# observations<br>6607<br>6607|6607|
|# clusters<br>84<br>84|84|

Coefficients are from regressing each outcome on a vector of treatment assignments, displayed interaction terms, randomization block fixed effects, and prespecified baseline covariates (measured skills, self-reported skills, education, age, gender, employment, discount rate, risk aversion). Heteroskedasticity-robust standard errors shown in parentheses, clustering by treatment date. The measures used for interactions in Panel A and column 3 of Panel B are indicators for above-median values of the underlying indices. All measures in panels A and B are demeaned before being interacted with treatment, so the coefficient on the treatment indicator equals the average treatment effect. Pr(employed at endline |<sup>ˆ</sup> X) is estimated by regressing endline control group employment status on the baseline covariates listed above and predicting employment for all candidates. Prediction for control group candidates uses leave-one-out-estimation to avoid overfitting. PC1(Scores) is the first principal component of the skills. The earnings-weighted average of scores is the weighted average of the assessment results, with weights derived from a regression of control group earnings on assessment results.

56
24 Table 3 Social Marketing Prices (La U5t). by Method

||||Price In USS||||
|---|---|---|---|---|---|---|
|Region|Location (and Yoer)|Orals<br>to<br>_cyle)_|Condoms<br>Speraicide<br>pone<br>(docon<br>toor *olication)|Injectables|ORS&/<br>(one nacket)|IUD|
|Africa|Egypt (1966)|.24|.28<br>.35|||1.92|
||Ghana (1987)|.11|.21<br>.01||||
||Nigeria (1966)|.25|.60<br>.04||||
|Asia|Bangaldesh (1966)|.03..13|.0S_12_15<br>.01|.32|.09||
||aong tong (1936)|.32|1.03<br>1.03.1.28||||
||Indla (1985)|.12|.12-19||||
||Indonesia<br>(1967)||.46||||
||**Malaysia**(1986)|.32|.36|5.00|||
||Nepal<br>(1916)|.12|**.28**<br>.02||.05||
||Pakistan (1966)||.12||||
||Sri Lank& (k985)|.09,.13|.1S.5S<br>.32||||
||Taiwan. China (1986)|<br>.50|.3S||||
||Thailand (1985)|.19 .26 .33|.36,.46 .64||||
|Latin Ameriea|Barbados (1984)|1.26|3.00||||
|and the|Bolivia (1966)|.25|.25||||
|Caribbean|El Salvador (1963)|.40|1.36<br>.03||||
||**Guatemala**(1963)|.61|2.08<br>.09||||
||Honduras (1966)|.73|2.46||||
||Jamaica (1967)|.18|.44||||
||Mexico (1965)|.37|2.64,3.96,.516|1.40||4.35|
||Colombia (1966)|.20-.40|.46<br>.10||||
||(PROVANILIA)||||||

Ntoe: Two or **ore numbors** In one call denotes multiple brands.

# a. Oral rohydratlon Salts

Sources% Much of the above Information is provided courtesy of the Social Marketing Forum, which regularly iLsues price data and made some figures available prior to publication. Data for 1984 and 1985 **are** from **J.** D. SherriL et &L., PopulatLon Reports, Serics J-30 (July-August 1985). Other information **comes** from the 1987 qLestionnaire described In tho text.
9 We use the TDHS fielded between December 2003 and March 2004 by the Hacettepe Institute. The data were collected from a sample of 10,836 households, representative at the national level but also at the level of the five major regions of the country (the West, South, Central, North and East regions). Information on basic socioeconomic characteristics of the population was collected for all household members, and all ever-married women between 15 and 49 years-old also answered a detailed questionnaire on family background, demography and health. 8075 women provided such information.

Although there is very limited information on earnings or consumption, the TDHS (like other DHS surveys elsewhere) collected reasonably detailed data on certain durable goods owned by households, on housing conditions, and on access to amenities. The TDHS survey also contains information on a set of circumstance variables for the sample of ever-married women, namely the region where they were born, the type of area of the place of birth (rural or urban), the levels of education of both the mother and father, the respondent’s mother tongue, and the number of siblings<sup>11</sup> .

A Household Budget Survey (HBS) was also collected in Turkey in 2003. This survey collected information on basic individual and household characteristics from a nationally representative sample of about 8,500 households. It is the staple survey for assessing the distribution of household consumption expenditures, and thus contains a reasonably detailed questionnaire on that topic, which provides the most reliable estimates of current living conditions for Turkish households. Although the 2003 HBS lacks information on a number of important circumstance variables, it does contain information on durables owned, housing conditions, and access to amenities, comparable to that available in the DHS.

This survey configuration permits two alternative methods to circumvent the missing data problem for measuring inequality of opportunity. The first method is to construct a household “wealth index” on the basis of information contained in the TDHS alone. Wealth indices constructed from DHS information on the ownership of durable

> 11 Region was classified into three broad regions: West, Center, and East; the type of area of birth place into rural or urban according to whether the respondent considered it as a village or sub-district or not; parental education into four categories: no education or unknown level, primary, secondary, and higher education; mother tongue into Turkish or another language; and number of siblings into: less than 3, 4 to 5, 6 to 8, 9 or more.
**Table 3. Overall Deficit, Debts, and Indicative Compliance with Rules** Percent of GDP

||2007|2008|2009|2010|2011|2012|2013|2014|2015|2016|2017|
|---|---|---|---|---|---|---|---|---|---|---|---|
|_Albania_||||||||||||
|Deficit|3.5|5.6|7.1|3.1|3.5|3.4|5.1|6.0|4.9|1.8|2.0|
|Debt|53.5|55.1|59.7|57.7|59.4|62.1|70.4|72.0|72.7|72.3|71.8|
|_Bosnia and Herzegovina_||||||||||||
|Deficit|-1.3|-8.6|5.3|3.1|1.6|2.6|2.2|2.0|-0.7|-1.2|-2.6|
|Debt|19.6|20.2|26.7|31.7|34.3|38.0|39.1|43.1|43.2|43.3|38.0|
|_Kosovo_||||||||||||
|Deficit|-7.3|0.2|-1.3|1.1|1.1|2.2|3.1|2.6|2.0|1.4|1.2|
|Debt|0.0|0.0|6.1|5.9|5.3|8.1|9.0|10.6|12.8|14.4|16.3|
|_North Macedonia_||||||||||||
|Deficit|-0.6|1.1|2.7|2.4|2.6|3.9|4.0|4.2|3.4|2.7|2.8|
|Debt|25.8|23.0|26.2|27.2|32.0|38.3|40.3|45.8|46.6|48.8|47.8|
|_Montenegro_||||||||||||
|Deficit|-6.2|0.3|5.3|3.6|5.1|5.3|5.2|3.1|7.3|2.8|5.6|
|Debt|31.4|32.3|41.6|50.7|57.2|65.4|66.0|67.1|73.7|71.4|70.0|
|_Serbia_||||||||||||
|Deficit|1.7|2.4|4.1|4.4|4.6|6.8|5.3|6.2|3.4|1.2|-1.1|
|Debt|28.4|25.2|31.0|39.6|43.0|54.0|56.5|65.4|70.6|68.6|60.1|

Sources: World Bank Staff estimates of the overall deficit and public and publicly guaranteed debt from the February 2019 database compiled for the Regular Economic Report for the Western Balkans.

Note: The assessments for Albania’s and Serbia’s deficit rules rely also on estimates of real rates of economic growth from the same database. Albania’s growth rate in 2017 was below the threshold that triggers the maximum-deficit rule, so that cell is not shaded.

# **_How well known are the rules?_**

**One factor that is likely to affect compliance is whether the rules are widely understood** . As noted earlier, national fiscal rules are more likely to work when noncompliance is politically embarrassing, and this in turn is more likely if the rules are well understood, at least among a group of politicians, journalists, academics, think-tank analysts, businesspeople, and others interested in public finances.

**To shed some light on the extent of public awareness of fiscal rules, the World Bank conducted an online survey on the subject in December 2018 (see Appendix)** . In all, there were 197 responses to the survey, which asked 19 questions. The respondents were from all the countries of the region, though representation varied from country to country, and were from many backgrounds, including the media, business, academia, the public sector, and elsewhere (Figure 2). The survey was not intended to be representative of the populations of the countries: those who responded were probably more likely than the average person to be interested in

16
|Irrigation and Drainage Project|Xiaokai Li|
|---|---|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|Irrigation and Drainage Project|Xiaokai Li|
|MAURITANIA IDF: Strengtheningthe Public Procurement Fu|nction Allan Rotman|
|MAURITANIA IDF: Strengtheningthe Public Procurement Fu|nction Allan Rotman|
|MAURITANIA IDF: Strengtheningthe Public Procurement Fu|nction Allan Rotman|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Second State Statistical System Development Project|Buyant Erdene Khaltarkhuu|
|Africa EmergencyLocust Project|Salimata D. Follea|
|Africa EmergencyLocust Project|Salimata D. Follea|
# **2.6** **<u>The panel dimension of our data</u>**

We are also interested in changes of a given country-decile over time. Hence, the panel dimension of our data is crucial. Table 2 counts the number of countries by the number of benchmark years for which a country appears in the data. For example, for 58 countries (out of a total of 162), mostly mature economies and Latin American countries, we have the complete panel. When we consider changes between two benchmark years, we do not require observations in the years in between, i.e. there could be gaps in the panel. For 63 countries, we can thus consider changes between 1988 and 2008. As a robustness check, we also consider the period 1993 to 2008, for which we have 90 countries and particularly improve the regional coverage of Sub-Saharan Africa and Russia/Central Asia/South-East Europe.

|**Table 2: Panel summary st**|**atistics:**|<br>**Numb**|<br>**er of**|<br>**count**|<br>**ries b**|**y pa**|<br>**nel duration**||
|---|---|---|---|---|---|---|---|---|
|||**No. o**|**f ben**|**chma**|**rkye**|**ars**|<br>**No. of countrie**|<br>**s with data in...**|
|**Regions**|**Total**|**1**|**2**|**3**|**4**|**5 **|**1988 & 2008**|**1993 & 2008**|
|**World**|162|22|24|27|31|58|63|90|
|**Mature economies**|39|0|2|2|10|25|29|34|
|**China**|2|0|0|0|0|2|2|2|
|**India**|2|0|0|0|0|2|2|2|
|**Other Asia**|19|2|5|1|3|8|8|11|
|**M. East & N. Africa**|11|4|1|1|3|2|2|3|
|**Sub-Saharan Africa**|43|11|9|14|5|4|4|16|
|**L. America & Caribbean**|26|4|3|0|5|14|15|17|
|**Russia, C. Asia, SE Europe**<br>_Notes: The last two column_|20<br>_s allowf _|1<br>_orgap_|4<br>_s in th_|9<br>_epan_|5<br>_el._|1|1|5|

23
||Table 7: Poli|tical Decentralization Variables|
|---|---|---|
|Name|Type|Definition<br>|
|LG legislative elec.<br>.<br>tion|App. contin-<br>.<br>uous: 0-1|Final value: average over all fiers considered, for each tier:<br>1 - whole council is directly elected; 0.5 - council is partly<br>elected, partly appointed, council is elected indirectly, LG<br>.<br>a<br>.<br>are treated asymmetrically; 0 - council is appointed, or does<br>t<br>exist<br>:<br>.<br>Final value: average over all tiers considered;<br>for each|
|LG executive elece<br>tion<br>.<br>Direct democracy|App. contin-<br>vous: 0-1<br>.<br>Discrete:<br>(),<br>0.95.05.<br>1<br>“oe|tier:<br>1 - mayor is directly elected; 0.5 - mayor is indirectly<br>elected, does not exist, coexistwith an appointed executive,<br>LG are treated asymmetrically; 0 - major is appointed|<br>1 - obligatory referendum in case of certain gov't decisions<br>(prescribed by law or constitution); 0.5 - obligatory public<br>approval in case ofcertain gov't decisions (public hearings,<br>_.<br>.<br>-.<br>citizen assemblies); 0.25 - lez. provisions for other forms<br>of citizen participation (civil councils, open LG sessions,<br>possibility to submit petition or initiate referendum); 0 -<br>noleg.provisionsfordirectdemocracy|

Source: Authors’ calculations based upon data sources reported in Annex Table Al.