rafmacalaba/data-use-mentions-extended huggingface dataset:
# Sheet2

**Table 8. Regions with highest and lowest levels of budget subsidies per capita. Data from 1995 budgets, in 1991 rubles**

|Lowest levels of Nat. economy|expenditures|Highest levels of Nat. econom|y expenditures|
|---|---|---|---|
|1 Aginskiy Buryatskiy AO|75.97|1 Magadanskaya oblast|2860.78|
|2 Adygeya republic|94.64|2 St.Petersburg|2104.11|
|3Kalmykia republic|95.94|3 Chukotskaya AO|1934.57|
|4 Chitinskaya oblast|123.96|4 Evenkiyskiy AO|1566.02|
|_5_Northern Osetia rep.|125.01|5 Moskovskaya oblast|1403.18|
|6 Dagestan republic|125.17|6 Yamalo-Nenetskaya rep.|1278.93|
|7 Mordovia republic|141.75|7 Koryakskiy auton. okrug|1201.57___|
|8 Vladimirskaya oblast|145.87|8 Sakha (Yakutia) republic|1157.37___|
|9 Ust-Ordynskiy Buryat. AO|149.79|9 Komi republic|701.09|
|10 Komi-PeTmyatskaya AO|153.09|10 Tyumenskaya oblast|695.51_|
|Lowest levels of Housing expe|nditures|Highest levels of Housing exp|enditures_<br>___|
|1 Kalmykia republic|46.85|1 Evenkiyskiy AO|1410.31|
|2 Aginskiy Buryatskiy AO|47.38|2 Chukotskaya AO<br>_|1369.66|
|3 Adygeya republic|56.41|3 Moskovskaya oblast|1128.76|
|4 Komi-Permyatskaya AO|58.20|4 St.Petersburg<br>_|1124.17|
|5 Mordovia republic|62.00|5 Yamalo-Nenetskaya rep.|1064.47|
|6 Chitinskaya oblast|68.84|6 Magadanskaya oblast|1050.71|
|7 Dagestan republic
)_|_(0.035)_|_(0.094)_|_(0.071)_|_(0.038)_|_(0.057)_|_(0.054)_|_(0.042)_|
|N<br>985|522|463|512|473|330|328|327|
|N Control<br>360|193|167|199|161|150|108|102|
|N Treatment 1<br>238|129|109|125|113|76|93|69|
|N Treatment 2<br>387|200|187|188|199|104|127|156|
|_R2_<br>_0.798_|_0.786_|_0.821_|_0.806_|_0.815_|_0.257_|_0.316_|_0.691_|

* p < 0.05, ** p < 0.01, *** p < 0.001. _Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level in parentheses. All figures are rounded to three decimal places. _Source:_ Own survey data, school year 2015/2016, own calculations.

38
private sector workers earned 5750 dongs per hour (see table 2). Our measure of earnings is the hourly compensation declared by the workers and includes the wages and benefits received from employers in cash and in kind.<sup>5</sup> The ability to account for workers’ benefits is crucial for the purpose of this study. Without benefits, public employees’ compensation would be much lower than the compensation of private sector employees (see table 2). Interestingly, wages, rather than benefits, drive the increase in the publicprivate pay gap. Finally, the last row of table 2 shows that the number of hours worked increased slightly more in the private sector, contributing to lower growth in hourly earnings compared with the public sector.<sup>6</sup>

|TABLE2. P|ublic and Privat<br>1|e Sector Earnin<br>993|gs in 1993 an|d 2006(Dongs,i<br>1998|n Nominal T<br>Ch|erms)<br>ange|
|---|---|---|---|---|---|---|
||Public|Private|Public|Private|Public|Private|
|Hourlyearnings|1,460|1,321|9,321|5,747|538%|335%|
|Hourlybenefits|315|81|1,203|414|282%|414%|
|Hours worked|1850|1621|2047|1892|11%|17%|

_Source:_ Author’s calculations based on VLSS 1993 and VHLSS 2006 data.

The distribution of earnings for public and private employees and their changes over time is displayed in figure 1. In 1993, the public and private sectors were difficult to distinguish whereas, in 2006, the public sector wage distribution clearly dominated the private sector distribution. Another striking change is the shape of the two distributions:
evaluation study consisted of a group of program participants and a control group that were selected on the basis of 2002 administrative data on training program clients.

In Romania, eligibility for ALMP programs depended on being registered unemployed, having income less than 50% of the minimum wage, being unemployed due to layoffs, having been employed at least 6 months of the last 12 months or being a recent graduate from school or university. There were various types of training available to program participants, including a public service component, whereby local government and other eligible organizations could propose public projects with a maximum cost of up to $50,000 and hire ALMP participants to work on them. The analysis sample was comprised of individuals who entered the register not earlier than January 2001 and got off no later than December 2002.

Benus et al. (2005) evaluate the impacts of both the Russian and Romanian ALMP programs overall and separately by gender, using a propensity score methodology. The propensity score model is based on a fairly limited set of predictors of program participation, that include gender, age and education. Nonparticipants are people who applied for training but were not selected for it, so they would be expected to differ in some respects from participants. The outcomes of interest in the evaluation are the likelihood of being employed at the time of the follow-up survey, the likelihood of being employed at least once after the program, the likelihood of having a high salary, and the length of the current unemployment spell. The impact evaluation finds no significant effects of the ALMP programs in Russia, on the whole. In Romania, however, the program is found to have had a statistically significant impact for three of the four outcomes (the likelihood of employment, the likelihood of being employed at least once, and the level of wages were all higher among participants in Romania). Subgroup analyses reveal some gender, age, and education heterogeneity in the impacts for Romania. Re-training increased the probability of employment and decreased the wage for females.
Appendix 2: Variable Definitions and Data Sources

|Variable|Definition|Source|
|---|---|---|
|Headcount index|Headcount index is the percentage of population<br>living in households with consumption or income per<br>person below the poverty line ($1 per day). It is also<br>called poverty incidence.|PovcalNet|
|Poverty gap|Poverty gap is a ratio of the distance of mean<br>shortfall from the poverty line ($1 a day or less) and<br>the poverty line. It characterizes how far below the<br>’|PovcalNet|
||poverty line the average poors income lies.||
|Income of the poorest<br>quintile|Defined as the share of income earned by the poorest<br>quintile times average income divided by 0.2;<br>however, when the first quintile share is not|Dollar and Kraay<br>(2002),<br>WIDER database|
||available, it is assumed that the distribution of<br>income is lognormal, and the share of income<br>accruing to the poorest quintile is obtained as the 20<sup>th</sup><br>percentile of this distribution, using Gini coefficient.|PovcalNet|
|GDP per capita|Nominal gross domestic product divided by the size<br>of the population|IMF database|
|Private credit over GDP|Private credit by deposit money banks to GDP.|Financial Structure<br>Database (2007), and<br>International<br>Financial Statistics<br>(2007)|
|M3/GDP|Liquid liabilities of the financial system as a percent<br>of GDP.|WDI database|
|Property rights|An index measuring the ability of individuals to<br>accumulate private property, secured by clear laws<br>that are fully enforced by the
## **<mark>BHFS</mark>**

<mark>BHFS is a nationally representative survey of r</mark> egistered health facilities; it includes public facilities, NGO static clinics/hospitals, and private for-profit hospitals. The BHFS 2014 included 1,596 facilities,<sup>17</sup> while the <mark>BHFS 2017 included 1,600 f</mark> acilities.<sup>18</sup> The <mark>BHFS p</mark> rovides information on the availability of basic and essential health care services, and the readiness of health facilities for care provision. It uses standardized questionnaires from the service provision assessment (SPA) component of the Demographic and Health Surveys (DHS) Program. Specifically, it provides information on child health, maternal and newborn care and family planning. For each of these services, BHFS assesses whether necessary elements are present and functioning.

## **DHS**

DHS is a nationally-representative survey of households and ever-married women aged 15-49 years.<sup>12</sup> The DHS 2014 covered 17,300 households and 17,863 women,<sup>17</sup> while the 2017 survey covered 19,457 households and 20,127 women.<sup>12</sup> DHS collects information on health and living conditions of women and children, household assets and wealth status, socio-economic and demographic characteristics, maternal and child health status, nutrition, fertility, family planning, mortality and care seeking for maternal and child health. This study only considered the RMNCH-N care-seeking, perceptions and health outcomes for the most-recent childbirth and child health episode from each DHS survey.

# **2.2 Variables**

Nineteen outcome variables (Table 2) related to service utilization, quality of care and nutrition status were selected from the DHS survey. Demand-side explanatory variables (or potential determinants) selected from the DHS were socio-economic and demographic characteristics and location of service seekers
others and by having to respond publicly. For the games we randomize: (i) the gender composition of the team i.e. all boys, all girls or mixedgender teams; and, (ii) the position that the adolescent responds in public. This allows us to estimate both group effects and position effects.<sup>49</sup> Individual private responses were first taken from each adolescent after they were randomly assigned to their team. Next, various conformity games were played in teams of 5 adolescents in public. The gender composition of peers that adolescent _i_ is grouped with and what their peers say in the positions in front of adolescent _i_ can be considered exogenous. We are interested in perceived normative pressures and whether _Choices_ adolescents are influenced by their peers in the same way as the control.

## **7.2.1 Gender Statements**

Tables 3A, 3B and 3C present the results for the gender statements where we observe social dynamics specific to gender attitudes. In Table 3A we examine group effects, in Table 3B position effects, and in Table 3C the direction of switching (egalitarian-discriminatory) from private to public. Our sample consists of all adolescents in the lab-in-the-field experiment.<sup>50</sup>

> 47Note sample size is different in Table 2 and Table A5 as A5 includes the panel of respondents.

> 48Table A9 compares effect sizes for endline survey data to the lab-in-the-field games data.

> 49The team members were randomly seated in a row to sequentially answer 3 gender attitudes statements and 2 line match games. In each subsequent game the position of the adolescent was re-randomized.

> 50The sample size for the experimental sessions is 1,000 respondents with 994 in the analysis as position was not recorded accurately for 6. With 3 different statements this makes 2,982 observations in Table 3A.

20
opportunities and women’s decision making power within households.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>, 119: 34-47.</mark>

- Mammen, K. and Paxson, Ch. (2000). “Women’s Work and Economic Development.” _Journal of Economic Perspectives_ , 14(4): 141–164.

- Marchionni, M., Gluzmann, P., Serrano, J. and Bustelo, M. (2019). “Participación laboral femenina: ¿qué explica las brechas entre países?” Inter-American Development Bank.

- Oaxaca, R. (1973). “Male-female wage differentials in urban labor markets.” _International Economic Review_ , 14(3): 693–709.

- Padilla-Romo, M. and Cabrera-Hernández, F. (2019). “Easing the constraints of motherhood: The effect of all-day schools on mother’s labor supply.” _Economic Inquiry_ , 57(2): 890-909.

- Sorsa, P., Mares, J., Didier, M., Guimaraes, C., Rabate, M., Tang, G. and Tuske, A. (2015). “Determinants of the low female labour force participation in India.” OCDE Economic Department Working Paper 1027.

- World Bank (2021). World Development Indicators. Retrieved April 2021. <u>https://datatopics.worldbank.org/world-development-indicators/</u>

20
and _M_ the underlying econometric model. The nowcast of quarterly GDP and the uncertainty of the nowcast conditioning on information set Ω _vj_ and the underlying model are given by: _M_ is a Dynamic Factor Model(DFM) estimated with a Kalman filter: where equation 47 is the measurement equation and equation 48 the transition equation. _xt|vj_ = ( _x_ 1 _t|vj , ..., xnt|vj_ )<sup>_′_</sup> is a vector of individual monthly data appropriately transformed to induce stationarity, _Ft_ common factors to be extracted, Λ the corresponding factor loadings, _ξt|vj_ = ( _ξ_ 1 _t|vj , ..., ξnt|vj_ )<sup>_′_</sup> a vector of the idiosyncratic component driven by variable-specific shocks and _ut_ a white noise shock to the common factors. I also assume that _ξt|vj_ are cross-sectionally orthogonal white noises, and they are also orthogonal to the common shocks _ut_ .

After the monthly factors, _Ft_ , are estimated for all months in the quarter, I proceed to obtain monthly GDP nowcasts based on these factors. The following regression is used to obtain coefficients in the nowcasting regression: where _F_<sup>ˆ</sup> 3 _k|vj_ is factor estimate at the end of the third month of a quarter. The GDP ˆ ˆ nowcast at the same point in time is then _y_ 3 _k|vj_ = _α_ + _β_<sup>ˆ</sup> _F_<sup>ˆ</sup> 3 _k|vj_ . GDP-Nowcasts at the end of the 1st and 2nd months can be obtained in a similar way, by projecting quarterly GDP on factor estimates at the end of the corresponding month.

In this framework, the nowcast and the uncertainty of the nowcast change as new data comes into the forecaster’s information set. The information content of the news contained in data vintage j+X can be measured as revision of the now33
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Benchmarking air transport indicators for Botswana and select other countries**<sup>**4**</sup>

|Country|Botswana|Namibia|Zambia|Zimbabwe|Mozambique|South Africa|
|---|---|---|---|---|---|---|
|Traffic (2007)|||||||
|Domestic seats (seats per year)|241,696|84,162|437,658|237,835|1,144,644|31,767,537|
|Seats for international travel within<br>Africa (seats per year)|435,708|877,812|1,459,766|1,109,986|582,836|6,314,557|
|Seats for intercontinental travel (seats<br>per year)|n.a.|242,736|113,217|182,585|91,637|7,707,063|
|Seats available per capita|0.357|0.574|0.168|0.118|0.087|0.954|
|Herfindahl-Hirschmann Index—air<br>transport market (%)|60.25|39.39|17.53|30.20|31.54|16.66|
|Quality|||||||
|Percent of seat-km in newer aircraft|100.0|79.0|63.8|71.4|57.0|83.8|
|Percent of seat-km in medium or<br>smaller aircraft|100.0|28.3|50.6|42.7|42.5|32.8|
|Percent of carriers passing IATA/IOSA<br>audit|0|100|0|33
benefit from the integration of these sectors. Our measures of upstream tariffs and FDI are, therefore, a form of Bartik instrument (in a reduced form setting) where IO weights reflect the differential exposure of downstream sectors to common shocks in upstream sectors. In this context, the main identification assumption is that input shares are exogenous to changes in TFPR, not to its level (Goldsmith-Pinkham et al. 2020). The use of IO table-based weights has several advantages. Weights are at the sector level and are based on both imported and domestic inputs. This reduces possible endogeneity concerns about productivity-related input adjustments at the firm level. Sectors are also aggregated at two-digit level (or higher level),<sup>13</sup> which limit input substitutability. In addition, the inclusion of both imported and domestic inputs when computing input shares ensures that weights reflect actual sector-level input requirements rather than tariff-induced input choices, which could bias results. Indeed, this latter concern is further mitigated by the use of predetermined fixed weights, i.e. before the tariff changes used in our estimations took place. When computing upstream FDI penetration, we separate manufacturing and service sectors.

# **_Endogeneity of trade policy and FDI penetration_**

> 13 For input tariffs we maintained the sector disaggregation of the original GTAP input-output table, consisting of 57 sectors. To compute upstream FDI, IO sectors were aggregated to match FDI data that were provided for 16 manufacturing sectors and 6 service sectors.

10
<u>OPERATOR:</u>_** When did you receive food assistance last time?

- Never received food assistance

- Less than a month ago

- Between 1 and 3 months ago

- More than 3 months ago

**Question 5:** **_<u>OPERATOR:</u>_** <u>If yes, in which form did you receive it? ......... [Select One]</u>

- in-kind

- Cash

- Vouchers

Was this assistance from WFP? ......... [Report Yes/No/I don’t know]

# **<u>Section 3: Food Consumption Score (FCS) Section</u>**

**Question 6: OPERATOR** How many members of your household were home in the last 7 days?

- ..................................................................................................  [Report the number of people]

# **Question 7: (Staples)**

**_<u>OPERATOR:</u>_** In the past 7 days, how many days did your household eat **main staple starches** , including **cereals, grains, tubers and/or roots** (such as potatoes, rice, pasta, bread, wheat flour, or other grains/cereals)? .....................
needs to be more expansive than focusing only on the cognitive, academic type skills measured by international tests.

The objective of our paper is threefold. Rather than focusing on cognitive academic skills alone, we focus on a more expanded notion of “skills.”<sup>3</sup> Our first objective is to develop a composite indicator of skills for developing countries in Asia like the European Skills Index (ESI) developed by the European Centre for the Development of Vocational Training (CEDEFOP).<sup>4</sup> The Asian Skills Index (ASI) intends to measure the performance of a similar group of Asian countries in terms of how effectively labor market skills from post-secondary education level and beyond are being formed (“skills formation”), how effectively such skills are utilized in labor markets (“skills activation”) and finally, how effectively labor market demand-supply mechanisms match skills to abilities (“skills matching”).

The motivation to develop such an index is threefold. First, the ASI considers the _entire skills system_ - ranging from how skills are formed, to how skills are activated, to how skills are ultimately deployed for economic growth. This is particularly important in the Asian context given that skills formation alone may not be the primary constraint since a significant pool of educated are unable to find productive employment. We also invoke the concept of deadweight loss for the education and skills development system due to a highly educated population with no jobs. It should be noted that a major difference between previous proxies for human capital such as “years of schooling” is that they essentially focus only on skills formation. But skills formation by itself is redundant if it is not activated through participation in the labor market and

> 3 The term “Skills” is used in this paper to broadly define the acquisition of marketable competencies and attributes as applicable beyond secondary education and encompassing tertiary education (both technical vocational education and training and higher education at university level).

> 4 - <u>
more subtle, although not least important, types of risks).

- iv. Since they provide credit to banks borrowers, particularly families, typically by issuing their own non-bank credit cards.

On the ground of potential spillover effects and to shed more transparency in this area, PCRs could include credit data from large non-bank creditors. Although they do not fall under the sphere of bank regulators and supervisors, preliminary lessons of the current crisis suggest that the scope of financial regulation should be widened to account for the operations of non-bank systemic players. These non-bank creditors may overstretch the repayment capacity of bank borrowers, making the banking sector vulnerable to their credit policies. This is of paramount importance if they are heavily intertwined with their activity as creditors, sponsors, investors or enhancers of their ABSs.

- **Macroprudential indicators** . In addition to serving the supervisors’ need to monitor an array of indicators that summarize bank’s financial and economic condition, indicators must also be constructed to monitor the stability of the overall financial system.

15
). We retain only the villages in the 55 sub-districts for which the proprietary data are available. Models are estimated on subsample of 414 villages covered in 2012-2013 HIES survey. The satellite indicators used as covariates include all publicly available and all proprietary indicators. Out-of-sample prediction accuracy (with the census) are conducted on the 946 villages not covered in the HIES. The weights for the HIES-based model are _V illage P opulation_ <u>1</u> _v_<sup>.Balancecorrectionreferstoadjustmentof</sup> � weights based on the predicted probability of the village being sampled in HIES ( _INHIESv_ ) based on a probit model with village sampled in HIES as the dependent variable and all LASSO-selected variables as independent variables. <u>1</u> The balanced corrected weights are �<sup>Population count predictions were obtained by mul-</sup> _V illageP opulationv XINHIESv_<sup>.</sup> tiplying density predictions by village-level area. The accuracy parameters for population predictions are calculated based on comparisons with actual census population and reported. The mean and median RE are mathematically the same for population density and population count predictions.

27
>products with higher-taxed products,<br>especially by unlicensed operators. All fuel<br>importers and distributors are required to<br>add a fixed proportion of a national marker<br>to all gasoline and diesel in an effort to<br>prevent adulteration and the sale of<br>smuggled fuel.|
|Uganda|**Pricing policy**: Fuel prices are deregulated.<br>**Information**: Uganda Bureau of Statistics posts detailed data on<br>price surveys every month, although no information is available by<br>company or filling station.|<br>**Supply**: Uganda was required to join the<br>Open Tender System in Kenya and did so<br>in Oct 2010. Transit through Kenya has<br>repeatedly led to supply problems.<br>**Shortages**: Uganda has experienced<br>numerous serious fuel shortages. The<br>factors in Kenya contributing to fuel<br>shortages in Uganda include disruption of<br>road and railway transit cargo, pipeline<br>leaks, pipeline and refinery outages, delays<br>in unloading of imported fuels, and slow<br>customs clearance. Ministry of Energy and<br>Mineral Development estimated that, in Feb<br>and Mar 2011, the gasoline shortfall was<br>32% and diesel shortfall 47%. LPG<br>shortages push up charcoal prices, and<br>diesel shortages reduce power generation<br>and result in load shedding, as in Oct 2010.<br>**Commercial malpractice and response**:<br>Smugglingof fuels notpayingtaxes is a|