rafmacalaba/data-use-mentions-extended huggingface dataset:
within 5 percent of the actual levels in 11 of the 15 countries. However, in four countries the projections significantly overestimated employment levels and therefore underestimated job loss due to the crisis. This bias tends to be larger (i) for labor markets that were more disrupted by the pandemic as measured by the stringency of social distancing measures, changes in workplace mobility, and a measure of scale-up of cash transfer programs, and (ii) in the agriculture sector in comparison to the industry and service sectors.</mark>

- <mark>(ii) The simulations show declines in the per capita household income or consumption across the entire distribution. However, the impacts across the distribution varied across countries, with no clear pattern. In Sri Lanka and Brazil, for example, the simulations project larger shocks for poorer households. In Türkiye, the changes are similar across income groups. In the Philippines, the simulated reductions were largest for households in the middle three quintiles, while in South Africa, the simulated reductions were largest for the top three quintiles. When using employment projections based on elasticities instead of actual employment data, the projected income declines were smaller in all countries and quintiles.</mark>

- (iii) Actual 2020 micro data for Brazil shows that the simulations underestimated the magnitude of the shock throughout the distribution, especially for the wealthy, because they underestimated the earnings declines, and thus underestimated the increase in

4
more, while a more fortunate 10 percent have a PTR of 36 or lower. These inequities occur between districts and sub-district areas, but primarily occur within a single sub-district area ( _zone_ ): school PTRs within a single zone can vary by a factor of ten or more (Ministry of Education, 2021a).

This inequitable distribution of teachers contributes to Malawi’s poor levels of student retention and progression. More than 5 percent of students drop out in Grade 1,<sup>1</sup> with dropout rates rising to almost 7 percent in upper grades (Malawi Longitudinal School Survey, 2021). Fewer than two-thirds of students entering Grade 1 are still in school by Grade 5. Those students who remain in school frequently repeat grades:<sup>2</sup> more than one-third of Grade 1 students repeat, and repetition rates are above 18 percent in all grades (Ministry of Education, 2021b). Those students who do progress in school nevertheless achieve low learning outcomes: in Grade 2, fewer than 25 percent of students achieve minimum proficiency levels in the Early Grade Reading Assessment (EGRA) conducted by the United States Agency for International Development (USAID). At Grade 6, fewer than 25 percent of students achieve minimum proficiency levels in the Southern African Consortium for Monitoring Educational Quality (SACMEQ) assessment in Mathematics, placing Malawi near the bottom in the region.

The persistence of this poor distribution of teachers reflects weaknesses in Malawi’s systems of national- and district-level resource allocation. Until recently, the rules governing allocations were excessively broad, requiring only that teachers be sent to schools with a PTR above 60. With more than half of schools above this level, this introduced a high degree of discretion in allocations.<sup>3</sup> Teachers exploit this discretion, using formal and informal channels of influence to resist placements

> 1 Grades are known as Standards in Malawi, but will be referenced as Grades throughout this paper. Grades range from Grade 1-12.
**19.5**|10.9|
|Firms’ value lost due to power outages|% sales|7.0|**3.7**|1.6|
|Access to electricity|% population|46.1|**20.1**|59.9|
|Urban access to electricity|% population|79.4|**50.0**|85.2|
|Rural access to electricity|% population|28.0|**3.5**|31.8|
|Growth access to electricity|% population/year|2.4|**0.3**|1.5|
|Revenue collection|% billings|81.1|**96.5**|100.0|
|Distribution losses|% production|25.8|**12.0**|10.1|
|Cost recovery|% total cost|53.9|**39.1**|100.0|
|Total hidden costs as % of revenue|%|168.3|**93.3**|0.1|
|U.S. cents|**Zambia**|Predominantly<br>generatio|hydro<br>n<br>Oth|er developing regions|
|Power tariff (residential at 75 kWh)|**2.9**|10.3||5.0–10.0|
|Power tariff (commercial at 900 kWh)|**4.4**|11.7|||
|Power tariff (industrial at 50,000 kWh)|**2.9**|11.4|||

_Source:_ Eberhard and others, 2008 Derived from data downloadable at AICD on-line power utilities database http://www.infrastructureafrica.org/aicd/tools
<!-- Start of picture text --> Ireland U.K.<br>5<br>20 15<br>070614<br>101315<br>15 1 111289 1 67<br>0 08<br>10<br>1 1784<br>060719<br>09<br>101620<br>0 12 11 13 -5<br>08<br>09<br>20<br>-10 20<br>-10<br>-10 0 10 20 -10 -5 0 5<br>Reported % Chg. Real GDP Reported % Chg. Real GDP<br>Ireland UK<br>Reported Composite Difference Reported Composite Difference<br>2015 22.46 20.80 1.65<br>* 15.15 7.31<br>2020 -10.37 -9.96 -0.41<br>* -8.56 -1.81<br>Composite Estimate Composite Estimate<br><!-- End of picture text --> Figure 12: Multinationals.

**Multinationals.** In Figure 12, we finally turn to countries in the highest data quality grade A. At the top of the list in Table 7 was Ireland, which in 2015 reported an outlier 22.46% growth rate in GDP. This was due to the counting of intellectual property from multinational corporations attracted by relatively low tax rates. In this case, the reported figure causes us to pause and reflect whether GDP, which is inflated by such intangible capital, measures what we want it to: economic activity. The composite measure allows us to measure this more directly, as only economic activities cause NO2. Because Ireland is a grade A country, there is only a small weight on the composite estimate, so our initial estimate of 20.80% does not differ much from the reported figure. However, if we treat Ireland as
- **Debt flows:** Our key measure for debt flows (or borrowing) is commitments to public and publicly guaranteed borrowers (series code “DT.COM.DPPG.CD” in the most recent vintage), where public and publicly guaranteed borrowers are defined as above and commitments refer to the total amount of long-term external loans for which contracts were signed in a given year.

# **3.2 Measurement and interpretation**

By comparing debt statistics across different vintages in our database, we can measure both the amount of unreported debt in any given year and the timing of discovery or revelation of previously unreported debt. More formally, we rely on the following two measures that we construct for both the debt stock and debt flow series.

where _Debt_ is either the debt stock or flow value, reported in vintages _V_ and _v_ 0, with _V_ being the most recent vintage and _v_ 0 being the first vintage with a value for country _i_ and year _t_ . In other words, the amount of unreported or hidden debt in a given year is defined as the difference between the debt value when first published and the debt value in the most recently published vintage.

We define hidden debt revelations as the amount of hidden debt uncovered in a given vintage and derive it as the difference in debt values in vintage _v_ and vintage _v −_ 1, summed across all past years for which both vintages report a value for _Debti,t_ .

where _Debti,t_ is defined as above, with _t_ 0 being the first year available in both vintages _v_ and _v −_ 1, and _T_ being the most recent year available in the same two vintages.<sup>17</sup> By definition, the total amount of hidden debt equals the total amount of hidden debt revelations for any particular country.

**Interpretation:** As explained above, the data from the World Bank International Debt Statistics that we analyze is exclusively based on debtor reporting. It is not subject to
countries. We will examine the experience of all Latin American countries that have the complete set of data on growth and policies.

This framework builds on the existing cross-country growth literature that quantifies the effects of a variety of policies on long-term growth, after controlling for non-policy variables. Previous exercises in this literature in the same spirit include Barro and Lee's (1993) examination of winners and losers in economic growth; Easterly and Levine's (1995) examination of the low average growth in Africa; Bouton, KigueL and Jones' (1994) examination of the payoffs to reform in Africa; and Corbo and Rojas' (1993) and De Gregorio's (1992) examination of macroeconomic adjustment and growth in Latin America (see also the early treatment of Cardoso and Fishlow 1989). Use of panel data similar to ours in the literature is given in Barro and Sala-i-Martin (1995) and Caselli, Esquivel, and Lefort (1995). Our framework makes a new contribution to this literature by performing an evaluation of Latin America's reforms based on the _changes_ in growth performance associated with reforms, while explicitly addressing some of the econometric problems created by the first-difference specification.

The rest of the paper is organized in three sections. Section II describes the empirical methodology. The results are described in Section Im, and the concluding section summarizes the implications of the exercise for the process of economic reform in Latin America.

**IL** Measuring the Average Effects of Reforms on Growth

# _1. Cross-section versus time series_

The existing empirical literature on growth has mainly used cross-country data without much consideration oftheir time-series dimension. In principle, it would be possible to apply the standard techniques to our problem by relying on cross-country growth regressions based on observations 3
# **Box 1 – Wealth Economics** **<u>and Mineral Depletion</u>**

Gross National Savings (GNS) measure the national capacity to finance domestic investment. It is computed at the sum of Gross National Income plus net transfers from abroad (Gross National Disposable Income, GNDI), minus consumption (CONS, public and private). Total domestic investment is financed by the sum of gross national savings and foreign savings.

## GNS = GNDI - CONS

Net National Savings (NNS) subtracts from Gross National Savings the consumption of fixed capital (CFC), to account for the fact that past domestic investments lose value over time (attrition, obsolescence). To maintain wealth unchanged, new investments need to be made every year to offset attrition and obsolescence.

NNS = GNS – CFC Adjusted Net Savings (ANS) accounts for several other factors: It adds to NNS education expenditures (EDU), not considered in national accounting as investment (as almost entirely taking the form of teachers’ salaries), even if it can be considered as an investment to build human capital. It subtracts to NNS the economic cost of environmental degradation (ENV), as well as the amount of natural resource depletion (NRD), including mineral depletion.

Quantitative methodologies have been established to compute and compare across countries the economic costs of environmental pollution, as well as the value of natural capital depleted (World Bank, 2011a). Mineral depletion (MD), on which this paper focuses, is computed as the present value of the future rents, divided by the number of years of extraction, T. (also known as the Real Wealth valuation method). World Development Indicators retain a discount rate of 4 percent across countries for present value calculation.

The rent at the period t, Rt, is measured as the difference between the output (quantities produced multiplied by world prices) and the market remuneration of factors (intermediate inputs, labor, and capital – see Annex
*References**

Avdiu, Besart and Moritz Meyer (2021) “How COVID19 changed the path of remittances in The Gambia”, _Africa Can End Poverty Blog_ <u>https://blogs.worldbank.org/africacan/how-covid19changed-path-remittances-gambia</u> Bah, Tijan, Moritz Meyer and Sering Touray (2021) “The Impact of COVID-19 on Labor Market Outcomes: Evidence from High Frequency Phone Survey from The Gambia”, Mimeo. World Bank.

19
rapidly on Java-Bali than in any of the other province groups (Table 8). They grew least rapidly in southern Sumatra and the other land abundant provinces.

The data point to problems experienced in the wake of large scale movement of labour into agriculture in the previous decade in southern Sumatra and land abundant provinces. The table also shows that the poorer densely/populated regions eastern

> 22 The employment elasticity implied for the manufacturing sector on Java from these figures was around 0.75, well above that recorded for the 1970s and early 1980s (around 0.5). 23 The rate of decline in agricultural employment may be overstated in the National Labour Force data for 1994. But the decline was so large that it suggests an important transformation was already underway.
# **C Results for all 20 countries**

Figure C1: Cost of living index across urban versus rural areas in 20 countries <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>1.4 1.4 1.4<br>1.2 1.2 1.2<br>1 1 1<br>.8 .8 .8<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: DOU: Degree of urbanization. DB: Dartboard. The cost-of-living index is prepared as a spatial deflator for each country in this study. It is normalized to 1 for each country. WorldPop 250m is used for both the DOU and DB methods.

Figure C2: Share of household heads working in agriculture across urban versus rural areas <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Share of household heads working in agriculture (%) Share of household heads working in agriculture (%) Share of household heads working in agriculture (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Each boxplot shows the share of household heads working in agriculture over different geographic areas in 19 countries. WorldPop 250m is used for the DOU and DB methods.

71
|-235.406|(379.792)|
||Major illness|||49.144|(148.420)|
||P-valueforpre-transfer income|||0.551||
|**_Children Char_**|**_acteristics_**|||||
||Age|45.188|(33.500)|44.621|(33.803)|
||Age square/100|-48.652|(34.362)|-47.573|(34.734)|
||Oldest son|269.309|(358.486)|260.596|(351.774)|
|_Demographics_|Youngest son|13.740|(211.908)|20.508|(224.390)|
||Daughter|-207.249|(126.633)|-226.639*|(133.484)|
||Married|-26.839|(339.553)|-32.229|(323.768)|
||#ofchildren(age<16)|98.688|(78.154)|102.906|(78.549)|
||_Education (Illiterates omitted)_|||||
||<br>Primary|-145.349|(170.049)|-154.430|(172.322)|
|_SES_|Middle School|-5.779|(187.153)|-5.304|(186.350)|
||High school and above|859.219***|(264.286)|839.743***|(277.622)|
||P-valueforSES|0.008||0.013||
|CountyDummie|s|Yes||Yes||
|Observations||2,190||2
of 11 years starting from year-end 2005, capturing the peak valuations in 2007, the ensuing recession, the ongoing recovery, the kick-start of growth, and an already apparent slowdown.

The capital markets in Southeast Asia are classified as frontier, which means that the availability of data is very limited. This paper uses end-of-quarter data for the calendar year, with the initial quarter starting on December 31, 2005 and the data set ending on December 31, 2016. In this sense, the study uses data with a starting point _ex post_ to the establishment of the frontier capital markets and initial transitional years, which were marked by frequent transaction failures. More so, the study captures approximate data for the full economic cycle, thus lowering the seasonality bias in the results. The data is sourced from Bloomberg, Eurostat, the European Commission Directorate-General for harmonized data, and the central banks and national statistics agencies in the countries under study.

In this empirical research, the model variables are chosen by the common times series variables included in the studies of capital markets performance, the popularity and frequency in research, the state of the underlying Southeastern European economies, and the pervasiveness of the indicators. The other factors of choice are the simplicity in accounting and the availability of the data. Given that prices in capital markets are influenced by company-specific, industryspecific, and external sociopolitical impacts that are not controlled for in this paper, the focus of this research is to measure, _ceteris paribus_ , the implied relationship with the selected macroeconomic indicators. This research addresses the limitation of the omitted variables error by broadening the sample data set in conducting a cross-country panel study and utilizing the advancements in the PMG methodology, which have improved the reliability of results. The dependent variables that were chosen are the end-of-quarter values of the individual stock exchange indexes. The independent variables, on the other hand, are the end-of-quarter values of a set of chosen macroeconomic indicators.

Among the chosen independent variables, the economic rationale establishes that countries with higher GDP per capita tend to
|

4 focus has been the absence of election data in a larger sample of countries. The Database on Political Institutions has election dates (month and year) and election winners and losers for more than 150 countries since 1975. Both legislative and executive elections are included, as is the composition of governments both before and after elections.

Checks and balances: stability versus decisiveness. The contribution of political institutions to economic policy outcomes is another important area of research. For example, divided governments or checks and balances are thought to have conflicting influences on economic development. One line of research suggests that they encourage budget deficits and delay reactions to crisis. Another line of work finds that they increase the predictability and restraint with which governments regulate firms and citizens. Research on the first topic (e.g., by Roubini and Sachs) focuses on a selected group of OECD countries. Research on the second issue has relied on case studies (for example, North and Weingast, 1989, study the introduction of institutional checks following the Glorious Revolution in England), or on subjective data, such as the extent of “executive constraints” in a country (e.g., Keefer and Knack, 1997). The DPI includes several objective measure of checks and balances that permit the impact of this institutional feature of countries to be systematically analyzed, and the tradeoffs between stability and decisiveness to be investigated.

Electoral rules, policy maker incentives, and corruption and fiscal policy. Another set of theories in political economy predict that pork barrel spending and corruption will vary systematically both with the number of decision makers (Rasmusen and Ramseyer, 1994) and with the electoral rules by which decision makers are selected (Cox and McCubbins, forthcoming). Tests of these theories are made difficult by the absence of cross country data on both of these institutional dimensions. This data set goes some distance in remedying this
0.304|0.247|0.268|
|Sample Size|522|893|1,494|376|661|1,162|672|1,183|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). We correct for inconsistencies in 'ever had sex' across rounds. 'Age at First Sex' is defined for those that had ever had sex. 'Older Partner' is defined as having a partner who is 5 years older or more in the past 12 months. 'Condom Use' is defined as using a condom at last sex with most recent sexual partner. Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
26

# **Annex 1: Survey Descriptions and Expenditure Findings**

The survey descriptions and annual household expenditures for capita are shown in Table A.1. Expenditures in U.S. dollars are computed by converting the expenditures in the local currency to U.S. dollars using the official exchange rate averaged over the survey months first, and then adjusted to 2022 U.S. dollars using the U.S. GDP deflator applicable midway through the survey period. The dollar conversion of annual per capita expenditures should be treated with caution because not all countries had deregulated foreign exchange. For example, Sudan had multiple exchange rates at the time of the survey, which in turn were much more over-valued than the parallel market rate (IMF 2014, 2016).

**Table A.1  Household surveys analyzed**

|**Country**|**Name of the survey**|**Survey**<br>**start date**|**Survey**<br>**end date**|**An**<br>**expe**<br>**Urban**|**nual per c**<br>**nditure, 2**<br>**Rural**|**apita**<br>**022 US$**<br>**National**|
|---|---|---|---|---|---|---|
|**Angola**|Inquérito Combinado de Despesas, Receitas e Emprego em Angola<br>2018/2019|Mar 2018|Feb 2019|$1,837|$720|$1,385|
|**Botswana**|Botswana Multi-Topic Household Survey 2015/16|Nov 2015|Oct 2016|$3,107|$1,797|$2,654|
the region. If the overseas spouse is the husband, the stay-behind spouse might or might not designate herself as the household head in his absence. This can underestimate poverty among “true” FHHs, where the female head does not rely on others for support, since some of the self-declared female heads or main income-earners are in fact temporary designees while the main income-contributing spouses are overseas. In the surveys for all years, remittances are major sources of income for self-reported FHHs, consisting for example of 68% of the consumption per capita in Egypt, and 37% of the consumption per capita in Jordan (Appendix B, Tables B.1 and B.4, all years columns). However, the surveys lump together domestic and overseas remittances and do not allow any further breakdown or provide information on the amount of overseas remittances. The surveys do not identify the relations between the remitters and the households, which complicates matters as such remittances might be alimony or in-kind support.

9
in both dimensions, i.e., . The measure proposed by Alkire and Foster (2007) goes further in introducing an extra parameter, such that a household is

> 10 The headcount index has a number of well-known limitations, and there is a large literature on alternative measures; Atkinson (1987) lists a number of these. But the headcount index is all we need for the present expository purpose.

> 11 Higher-order measures can also be defined, such as based on the Watts (1968) index (see Chakravarty et al., 2008) and the Foster, Greer and Thorbecke (1984) measure (Alkire and Foster, 2008). Tsui (2002) provides a general formulation of subgroup-consistent poverty measures based on deprivation aggregation. However, I will confine attention to the simple form in (2).

9