rafmacalaba/datause-encoder-data huggingface dataset:
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Lechene, V., Pendakur, K., & Wolf, A. (2019). _OLS estimation of the intra-household distribution of consumption_ (No. W19/19). IFS Working Papers.

Luttmer, E. F. (2005). Neighbors as negatives: Relative earnings and well-being. _The Quarterly journal of economics_ , _120_ (3), 963-1002.

Moatsos, M. (2016). Global absolute poverty: behind the veil of dollars. _Journal of Globalization and Development_ , _7_ (2).

Notten, G., & De Neubourg, C. (2011). Monitoring absolute and relative poverty:“Not enough” is not the same as “much less”. _Review of Income and Wealth_ , _57_ (2), 247-269.

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16
# Appendix A2: Do Reform Efforts Focus on Lagging Policy Areas?

The World Economic Forum’s Global Competitive Index (GCI) measures each country’s performance across the policy pillars through unit-less ‘progress scores’ on a scale of 0 to 100 (100 being the highest performance). Formally, each indicator is scaled according to the formula below: p = <u>, ,</u> R R <u>, ,</u> ∗100 R R R ii EE tt ii EE tt vvaav RR −wwR pptt ppR aaIIssRR ii,EE,tt We scale these progress scores into distance-to-frontier metrics as: ppR ppR ppssR RR RRIIttiiR ii,EE,tt −wwR pptt ppR aaIIssRRii,EE,tt p The GCI has all twelve indices for each of the six GCC countries from 2007 to 2017. This period is the only IIiippttaaIIssRR ttRR R RRIIttiiR ii,EE,tt = 100 − ppR ppR ppssR RRii,EE,tt The GCI has all twelve indices for each of the six GCC countries from 2007 to 2017. This period is the only time where the GCI scores are methodically consistent and comparable across years.

The final source, UNCTAD’s Export Concentration Ratio (also known as the Herfindahl-Hirschmann Index), measures a country’s export market concentration as follows: where Hc is the index for country _c_ , xi is the value of exports for product _i_ , X is the sum of export values across all products, and n is the number of products. We rescale the index so that 0 is the least concentrated portfolio while 100 is the most concentrated. This transformation allows us to include diversification as an additional ‘pillar’ to complement the GCI’s ten. We obtain for each GCC country for the years 2007 to 2017. Table A2.1 shows the average Distance to Frontier. Overall, the GCC performs well in Macroeconomic Environment, Health and Primary Education, and is underperforming in Diversification, Innovation and Technological Readiness.

**Table A2.1 Average Distance to Frontier (2007-2017)**

||**Bahrain**|**Kuwait**|**Oman**|**Qatar**|**Saudi**<br>**Arabia**|**UAE**|**GCC**<br>**Average**|
|---|---|---|---|---|---|---|---|
|**Diversification**|32.6|68.4|55.8|52.9|70.1|32.9|52.1|
|**Financial Market Development**|29.8|49.0|39.3|25.8|39.4|33.0|36.0|
|**Goods Market Efficiency**|24.7|51.3|35.0|18.8|31.2|13.4|29.1|
|**Health and Primary Education**|17.8|29.9|28.9|11.9|24.6|18.3|21.9|
|**Higher Education and Training**|36.4|52.8|48.1|30.1|37.9|30.4|39.3|
|**Infrastructure**|33.8|48.1|36.8|30.8|34.9|12.2|32.8|
|**Innovation**|67.9|75.4|64.4|36.3|53.0|45.5|57.1|
|**Institutions**|30.9|48.8|25.8|12.5|28.4|15.9|27.1|
|**Labor Market Efficiency**|40.2|55.2|48.2|30.0|50.6|27.8|42.0|
|**Macroeconomic Environment**|32.9|6.2|17.2|10.3|16.4|17.1|16.7|
|**Technological Readiness**|34.7|57.5|55.1|32.4|44.6|25.4|41.6|
|**Country Average across**<br>**Categories**|34.7|49.3|41.3|26.5|39.2|24.7|36.0|

Note: 100 is farthest away from the frontier 27
# **Table 1. Household characteristics common to the Census and the VLSS**

|||Question|number|
|---|---|---|---|
|Variable||1999|1998|
|name(s)|DescriptionofVariable|Census|VLSS|
|hhsize|Householdsize(numberofpeople)|PtI,Q4|SIA|
|pelderly|Proportionofelderlypeople (aged over60)inhousehold|PtI,Q4|S1A,Q2|
|pchild|Proportionofchildren(agedunder15)inhousehold|PtI,Q4|SIA,Q6|
|pfemale|Proportionoffemalesinhousehold|PtI,Q3|SIA,Q6|
|Iedchd_lto6|Highestlevelofeducation completedbyhead(lessthanprimaryschool<br>primaryschool,lowersecondaryschool, uppersecondaryschool<br>technicalorvocationtraining, collegediplomaoruniversitydegree)|PtI,Q11-13|S2A|
|Iedcsp_O|Dummyfornospouse|PtI,Q2|S1B,Q3|
|Iedcsplto6|Highestlevelofeducation completedbyspouse (lessthqnprimary,<br>primaryschool,lower secondary school,uppersecondaryschool<br>technicalorvocationtraining, college diplomaoruniversitydegree)|PtI,Q11-13|S2A|
|ethnic|Dummyforethnicminority head(notKinhorChinese)|PtI,Q4|SOA|
|loccup_1to7|Occupationofheadoverlast12months(political leaderormanager,<br>professionalor technicalworker,clerkorserviceworker, agriculture<br>non-farm enterprises,unskilledworker,not-working)|PtI,Q16|S4D|
|Ihouse1to3|Typeofhouse (permanent;semi-permanentor woodenframe,"simple"|PtIII,Q3|S6A,QI|
|htyplalto2|Housetypeinteracted withlivingarea(m2)|PtIII,4|S6C,Qla|
|electric|Householdwithelectricity|PtIII,Q7|S6B,Q33|
|Iwater_Ito3|Mainsourceofdrinking water (privateorpublictap,rainwaterand wel<br>riversandlakes)|PtIII,8|S6B,Q25|
|Itoilet_1to3|Typeoftoilet(flush,latrine/other,none)|PtIII,Q9|S6B,Q31|
|tv|DummyforTVownership|PtIII,QIO|S12C|
|radio|Dummyfor radioownership|PtIII,Ql1|S12C|
|ree7Ito7|Regionaldummies(7regions)|nage1|SOA|

Source: Questionnaires for 1998 VLSS and 1999 Population and Housing Census 6
This means that an unknown fraction of the subsidy targeted at ailing enterprises ends up in pockets of managers, corrupt public officials and the Mafia. The funds are mostly taken offshore, either draining the resource base of this economy, or coming back under the guise of off-shore investment to legalize ownership.

Given their central position in the nonpayments web and significant government ownership -- 35 percent of Gazprom, 52.5 percent of RAO UES -- Gazprom and RAO UES are obvious instruments for the government to dismantle nonpayments. A key strategy being used is to insist on a time table for increasing cash collections by the energy monopolies. The analysis here shows that this will work only if the EMs are permitted to disconnect nonpayers, which is now legally ambiguous under the existing Russian Civil Code; and if the government and all budget-funded entities make their own energy payments on time and in cash.<sup>32</sup> The importance of a credible disconnection policy is shown by two remarkable statistics: first, the railways have a much higher share of cash sales than either gas or electricity because they are not legally obliged to serve nonpaying civilian clients; second, casual observation shows that nuclear power stations have even lower cash collection ratios that conventional power stations. The reason: unlike conventional power stations, nuclear power stations cannot reduce the voltage without risking a crisis with the reactor. Thus, their disconnection threat is even less credible.

# **Enterprise Behavior and** NCS<sup>33</sup>

According to the analytical framework and macro-micro policy links presented earlier: (i) nonpayments (arrears and NCS) by enterprises would increase with high real interest rates and the real appreciation of the ruble (as the trends in Table 1 showed); (ii) there would be a rising need for enterprise bailouts and subsidies as part of implicit social protection policy (Tables 4 and A-1); and (iii), public debt would grow to finance the subsidies, a tendency reinforced by (a) the low inflation target and (b) attempts by the energy monopolies to pass on the costs of the subsidies they transmit by becoming "tax delinquents" (Tables 2 and 5). A macroeconomic crash would eventually result. This can be summed up as follows:

# Enterprise Nonpayments = f (macroeconomic stringency, micro soft budgets).

In keeping with the above formalism and the systemic view of nonpayments presented here, enterprise nonpayments has abated after the August 1998 meltdown. This is largely due to the

> **32** See The World Bank (1999) and Pinto, Drebentsov and Morozov (2000).

> **33** For a historical, evolutionary account of barter and other NCS in Russia, see Woodruff (1999). For an account of how nonpayments over 1995-98 differs qualitatively and quantitatively from that in the Soviet era, see OECD (2000). See also Ledeneva and Seabright (1998).
# **[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]**

C16b_maternityactuallength2 How many weeks of maternity leave (paid or unpaid) did you take? **[If don’t know then put “Don’t Know”] [If refused then put “Refused”]**

**[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]** C16c_maternityactualpaid2 How many weeks of those weeks were paid?

**[If don’t know then put “Don’t Know”=888] [If refused then put “Refused”=999]**

à **Go to C18**

# **[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]**

C17a_maternity2 Would your employer have allowed you to take maternity leave from this previous MAIN job if you needed it?

1. Yes

2. No à **Go to C18** 888. Don’t know à **Go to C18** 999. Refused

# **[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]**

C17b_maternitylength2 How many weeks of maternity leave (paid or unpaid) would your employer have allowed you to take if you needed it?

**[If as needed then put “As needed”] [If don’t know then put “Don’t Know”=888]**

**[If refused then put “Refused”=999]**

# **[ONLY ASK IF RESPONDENT IS FEMALE; FOR MALES SKIP TO C18]**

C17c_maternitypaid2 For how many of those weeks would your employer have paid you?

# **[If don’t know then put “Don’t Know”=888]**

**[If refused then put “Refused”=999]**

C18_overtime2 Did your employer give you extra pay for overtime work in this previous MAIN job?

1. Yes

2. No 3. Do not work overtime 888. Don’t know 999. Refused C19_bonus2 Did your employer offer any bonus payments in this previous MAIN job?

1. Yes 2. No 888. Don’t know 999. Refused

5
POLICY RESEARCH WORKING PAPER 1711

# Summary findings

C6te d'Ivoire is one of the few cotntries in Sub-Saharan Africa that conducted household surveys between I _985_ and 1995. These surveys make it possible to trace changes in urban and rural poverty and changes in poverty arnong different socioeconomic groups.

During the decade surveyed, the country experienced a major recession. Economic recovery began only with the devaluation of the CFA franc in January 1994. Poverty increased substantially during the recession and did not diminish in the year after devaluation, in part because rnuch of the increase in export crop prices, especially for cocoa, was taxed away.

Moreover, the effects of the recession and devaluation were not uniform. Between 1988 and 1993, urban poverty increased faster than rural poverty did, though mean expenditures in urban areas remained substantially ,ibove mean expenditures in rural areas. And food crop farmers apparently suffered more than export crop farmers. These trends persisted after devaluation.

But exploring poverty trends across regions and socioeconomic groups raises questions about the patterns of change and the reliability of the data and the methods used to derive poverty estimates. Secondary data cast some doubt on certain survey findings, suggesting that they may be more an artifact of the survey data than a reflection of real economic trends.

These problems are not unique to C6te d'Ivoire. Results from surveys in Uganda and to a lesser extent Ghana have also raised issues of comparability.

The ability to draw reliable conclusions from time series data is crucial for our understanding of how policy reform affects poverty. Far more attention must be paid to comparison issues in designing surveys and analyzing data. Attention must also be paid to developing better regional and temporal price indices, if reliable time series data are to be generated for Sub-Saharan Africa.

- - This paper a product of the Macroeconomics and Growth Division, Policy Research Department is part of a larger effort in the department to understand the link between economic policy change and poverty. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rebecca Martin, room N1i-059, telephone 202-473-1320, fax 202-522-3518, Internet address rmartinl@worldbank.org. January 1997. (77 pages)

_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 paperare entirely those of the author. They do notnecessarily represent the view ofthe WorldBank, itsExecutive Directors, orthecountries [they represent._ Produced by the Policy Research Dissemination Center
IaIble ) Adillinistirative costs ill relittion to arrears rates, 1984

||(Grameenbank|Smnall<br>farmers'<br>development<br>programnme<br>Nepal|Average of 38<br>third world<br>sclhemes|
|---|---|---|---|
|Admini stn at ive||||
|costs as a % of||||
|total lent out|12.3|7.6|4.68|
|Arreairs rate|1.6|11.7|39.00|

Sources: Arrears rates. Table 3a abo"s and World Bank<sup>[1984], Annex 12;</sup> administrative costs from World Bank [1094], Annex 13. Hossain<sup>[1985] page</sup> 72, and Agricultural Development Bank of Nopal [1986b). Cited in Mosley<sup>and</sup> Dahal (1987:53).

Table Comparison of indicators of internal efficiency

||1977/78|1978/79|1979/80|1980/81|1981/82|<sup>1982/83</sup>|<sup>1983/84</sup>|<sup>1984/85</sup>|
|---|---|---|---|---|---|---|---|---|
|(1) Arrears rate (%)|||||||||
|SFDP, NepaL|4.5|16.2|25.1|26.6|io.8|13.0|13.6|17.8|
|Grameen Bank<br>(a)<br>Bangladesh<br>(b)|..<br>..|.<br>..|..<br>..|..<br>16.9|..<br>13.5|4.2<br>11.9|1.6<br>11.7|1.4|
|(ii)<br>Profitability<br>(%)|||||||||
|SFDP, Nepal|..|..|..|..|..|..|||
|Grameen<br>Bank<br>Bangladesh|..<br>..|..<br>..|..<br>..|..<br>-6.4|-9.5|-3.1|-10.2|17.6|

Notes: Arrears rate for a give period is Amount overdue (total amount due) x 100 for repayment The arrears rate is equat to 100% minus the repayment rate.<sup>For the Grarmeen</sup> Bank arrears statistics are pubtished in two forms: (a) the proportion of loan recipients who had not paid back their entire loan after one year (the standard term of a Grameen Bank toan); (b) the standard<sup>arrears rate which is described as a</sup> "regularity of repayment index.

Sources: For SFDP: Agricultural Development Bank of Nepal (1986b), TabLe IV. For Grameen Bank: Hussain 1984, Table 111 .14 and 111.15. Supplemented by Grameen Bank. Annual Report 1985. Dhaka 1986 cited in Mosley and Dahal (1987:51).
# **Appendix B1: Description of Variables**

|**Category: Independent**<br>**perception variables**|**Variable code**|**Exact question**|**Answer categories**|
|---|---|---|---|
|Satisfaction with Standard of<br>Living|Wp30|Are you satisfied or dissatisfied<br>with your standard of living, all the<br>things you can buy and do?|1 Yes<br>2 No|
|Satisfaction with Standard of|Wp40|Have there been times in the past|1 Yes|
|Living||twelve months when you did not|2 No|
|(Index construction)||have enough money to buy food<br>that you or your family needed?||
|Satisfaction with Standard of|Wp43|Have there been times in the past|1 Yes|
|Living||twelve months when you did not|2 No|
|<br>(Index construction)||<br>have enough money to provide<br>adequate shelter or housing for<br>you and your family?||
|Satisfaction with Standard of<br>Living|Index_fs|<br>Construction of variable wp40 and<br>wp43|Not applicable|
|(Alternative specification)||||
|Satisfaction with freedom TO<br>CHOOSE LIFE|Wp134|Are you satisfied or dissatisfied<br>with your freedom to choose what<br>you do with your life?|1 Yes<br>2 No|
|Satisfaction with civil freedom|Wp143|<br>Do you have confidence in the|1 Yes|
|(Alternative specification)||Quality and Integrity of the<br>Media?|2 No|
|Perceptions about Corruption|Wp145|Is corruption widespread within<br>business?|1 Yes<br>2 No|
|Perceptions about Corruption|Wp146|Is corruption widespread within<br>government?|1 Yes<br>2 No|
|Perceptions about Corruption|Wp6267|Do you think the level of<br>corruption in this country is lower,<br>about the same or higher than it<br>|1 Same or lower<br>2 Higher|
|||was 5 years ago?||
|Cronyism|Wp128|Can people in this country get<br>ahead by working hard or not?|1 Yes<br>2 No|
|Quality of jobs|Wp133|<br>Are you satisfied or dissatisfied<br>with efforts to increase the<br>number of quality jobs?|1 Yes<br>2 No|
|Quality of jobs|Wp89|<br>Thinking about the job situation in|1 Good time|
|<br>(Alternative specification)||<br>the city or area where you live<br>today, would you say that it is now<br>a good time or a bad time to find a<br>job?|2 Bad time|
|Satisfaction with education|Wp93|<br>In the city or area where you live,<br>are you satisfied or dissatisfied<br>with the education system or the<br>schools?|1 Approve<br>2 Disapprove|

30
identical, but close, to the corresponding value of 0.29 for ρ based on the actual panel.<sup>13</sup> We will return in the next section to more discussion on estimation results using this value of ρ from the IHDS panel.

Table 1 compares point estimates of IHDS consumption dynamics in the IHDS true panel and the synthetic. We use data from the 2011/12 second survey round ( _x_ i2) as the base year for predictions. See the Appendix for detailed estimation results. We calculate the bootstrap standard errors by bootstrapping (yij, xij) from its empirical distribution function (1,000 times), adjusting for the complex survey design of the IHDS (including stratification, cluster sample, and population weights), and applying the estimated parameters for equation [1] from the original samples.

We provide two ways to evaluate the goodness-of-fit for estimation results. First, we consider the precision of the synthetic panel point estimates by enumerating the number of times they fall within the 95 percent confidence interval (CI) around those from the actual panels. A more demanding test is to consider similar statistics on a narrower band of one standard error around the actual panel estimates. Table 1 reveals that of the 15 possible cells, the synthetic panel method produces estimates that fall within the 95 percent CI of the true panel estimates in 10 cases (and in half of those, the estimates fall within one standard error). Even in those cases where the estimates fall outside the 95 percent CI, scrutiny of the estimates reveals that they are actually quite close. For example, while 21 percent of the population in the actual panel was estimated to be vulnerable in both 2004/05 and 2011/12, the corresponding estimate in the synthetic panel was 19.3 percent.<sup>14</sup> Similarly 18.6 percent of the population in

> 13 Monte Carlo simulations provided in Dang and Lanjouw (2013) suggest that estimates on poverty dynamics based on synthetic panels remain rather robust even where ρ changes its values over a range of up to 40 percent. The estimated values of ρ for the periods 1987/88-1993/94, 1993/94-2004/05, and 2004/05-2011/12 using the NSS data are respectively 0.74, 0.57, and 0.57.

> 14 In Table 1 we apply a vulnerability line that is twice the poverty line of INR 486. As our focus was on probing the validity of the synthetic panel method, we chose not to also apply the method for estimating a vulnerability

18
8 When estimating equation (2) without any of the controls included, is positive and statistically significant for both females and males in the completed years of education regression and positive and statistically significant for females only in the primary education completion regression (full results are available upon request).

9 The education data used in this paper come from the DHS household datasets. Information on date of arrival at the present location was only collected in individual interviews with women aged 15– 49 years. The same exclusion could not be implemented for the 2001 DHS because individuals listed on the household roster cannot be matched to their mothers.

10 In the 2001 DHS, 44.4 percent of five-year-olds, 24.6 percent of 14-year-olds, and 13.6 percent of 15-year-olds were enrolled in primary schooling.

11 According to the data provided by INSEC, 76 schools were destroyed.

12 See the appendix for some insights based on self-reported measures of female empowerment available in the DHS.

28