rafmacalaba/data-use-mentions-extended huggingface dataset:
Financial Regulation and Government Revenue regulation policy is introduced, and the magnitudes of its change (18 million USD) corresponds to the numbers presented in Section 4. Furthermore, because the GdM indicator is calculated using data from national accounts rather than bank level indicators, we believe this is a solid check of our main results.

First, we present the calculation of the GdM financial repression indicator. Then we show the evolution of this indicator between 2004 and 2013, and we discuss the parallel with the numbers shown in Section 4.

# **Calculating Financial Repression in Ethiopia**

In this subsection, we explain in detail the methodology adopted and refer to all the available data sources, which are published and available. To be consistent with GdM, we keep their coding for each variable. The starting point is _INTP_ $, which defines the total dollar interest payments in USD and is found in the _International Debt Statistics Database_ published by the World Bank. Secondly, _INTA_ $ indicates the change in interest arrears, as reported to the World Bank _Debtor Reporting System_ . With _DOD_ $ we indicate the average annual dollar debt outstanding, which is available through the _International Debt Statistics Database_ published by the World Bank.

At this stage, we can calculate the nominal effective dollar interest rate in year _t_ , _EFFINT_ $( _t_ ), where _DOD_ $( _t_ ) indicates dollar debt outstanding at the end of year _t_ , this is the first benchmark indicator to be used in our analysis.

In line with the previous section, we define _INTP_ to be the same as _INTP_ $, but in birr, taken as a percentage of the average domestic debt outstanding; this is available through the _Government Finance Statistics_ of the International Monetary Fund (IMF). Similarly _DOD_ refers to the total domestic debt outstanding and disbursed expressed in domestic currency, using the same source. Therefore, analogously to _EFFINT_ $( _t_ ), we can define _EFFINT_ ( _t_ ) as the nominal effective domestic 27
Capital Index (HCI) data set developed by the World Bank and its measure of Learning-Adjusted Years of Schooling (LAYS). The LAYS measure is made up of two components:

- Expected years of schooling. This measures the number of years of school a child born today can expect to obtain by age 18. It is based on age-specific enrollment rates between ages 4 and 17 and has a maximum value of 14.

- Harmonized measures of learning from global and regional learning assessments (Altinok, Angrist, and Patrinos 2018; Patrinos and Angrist 2018). Relative scores for learning are calculated for each country by dividing its harmonized score by 625, which corresponds to the “advanced” benchmark on the TIMSS and PIRLS learning assessments.

These two measures are multiplied together to arrive at the expected _learning-adjusted years of schooling_ (LAYS) we use as our measure of education outcomes (Filmer et al. 2018; Kraay 2018). For example, if a country had 10 years of expected years of schooling and an average test score of 500 (80 percent of 625), then its LAYS would be 8 years.

A crucial benefit of the LAYS measure is that it captures changes in both educational attainment and average learning outcomes. If a country maintains its average learning levels while increasing enrollment rates, our measure will increase since the average levels of learning of the population are now higher. If average learning levels increase because some children drop out of school and enrollments decrease as a result, the level of the LAYS will depend on the magnitude of those changes. Its limitations are mainly related to a lack of data in some countries.

We propose to measure spending as _total expenditure per child_ calculated as total spending in primary and secondary education divided by the total number of school-aged children in the country. This captures how much a country is spending on the population that it needs to serve (not just the population that it is currently serving). Thus, if a country is spending a lot on its students, but enrollment
, AFTA, the ASEAN-Japan FTA, the ASEANAustralia-New Zealand FTA and the P4 Agreement among Brunei Darussalam, Chile, Singapore and New Zealand).

## RCEP, FTAAP and EU-IDN CEPA

RCEP and FTAAP scenarios include liberalization of tariffs and NTMs following Wignaraja et. al. (2015). Initial tariffs are based on the GTAP data whereas NTMs are provided by Petri and Plummer (2016). The (trade-weighted) ad valorem equivalent NTMs for the different scenarios are reported in Table 1. For the countries that are also CPTPP members we use the CPTPP tariff schedules from ITC and MAcMap (2015) and MAcMap (2016). In case of RCEP, the

> 6 For a detailed specification of the GIDD micro model see Osorio-Rodarte (2016).

> 7 For China, we replicate the growth projections of World Bank (2013).

7
before. The results for GRAVo and C ∗intlo are reported in Annex Table A6. The panel results following equation (3) are reported in Table 7. In there it also includes two sets of control variables: oo oo ECIPE’s data restrictiveness index, which again is split up between the CB and DR component; as well as the WEF’s Network Readiness Index. Both variables vary over time.

The table shows that the coefficient results are largely consistent with the ones found in Table 5 for the cross-section analysis. Yet, the significance for some coefficient outcomes is weaker, such under the conditional model for the CB component in column 5 (although still negative) and for the DR component in column 2, now coming out as insignificant. That said, the conditional model for the Digital+ sector for the CB component (column 1) is much stronger, as well as the negative coefficient outcome for the limited model which now is significant at the 5 percent level.

Table 7: Panel results following equation (3) using Data Restrictiveness Index as control

||**(1)**<br>**Digital+**|**(2)**<br>**Digital**|**(3)**<br>**Publishing**|**(4)**<br>**Telecom**|**(5)**<br>**IT & Info**|**(6)**<br>**Business**|
|---|---|---|---|---|---|---|
|MODEL**CB**OP|-0.307|0.524|-0.064|-0.223|1.369***|0.117|
||(0.122)|(0
revenue data from the Harvard University Library archives,13 as well as online data from national statistical offices and

> 10The variables include value added; compensation of employees; operating surplus of corporations; operating surplus of unincorporated enterprises; consumption of fixed capital; and indirect taxes net of subsidies.

> 11Specifically, 1968 SNA data always include compensation of employees and operating surplus of corporations, but do not disaggregate mixed income and operating surplus of the household sector (which are lumped into a single aggregate). We impute the split of mixed income vs. household operating surplus according to the split seen in the 2008 SNA at the time of switching (typically in the 1970s). We also follow the United Nations guidelines to stitch these series together (UN, 2018).

> 12Karabarbounis and Neiman (2014) restrict their sample to online UN SNA 2008 data, and to countries with at least 15 consecutive years of complete-case data.

> 13Lamont Library, Government Documents section.

12
1997b). For the 173 countries that have data on under-5 mortality and World Bank atlas method GNP per capita in US dollars (which is not purchasing-power-adjusted), the explanatory power of the regression which includes variables similar to those presented above, is about 90 percent. The coefficient on income in this auxiliary regression is -0.39 which is a smaller effect than we found. This is partly explained by the fact that non purchasing-power-adjusted estimates systematically underestimate the incomes of poorer countries which would bias the estimate towards zero. In addition, random measurement error may cause a bias towards zero. When using purchasing-power-adjusted GDP per capita (from the Penn World Tables database) as an instrument for GNP per capita with the set of countries included in our main analysis, over 92 percent of the variation is explained, and the coefficient rises (in absolute value) to -0.49.

Including the reported public spending on health as a share of GDP produces a point estimate of - 0.07 (t-statistic of .92) for the 1 19 countries for which the relationship can be estimated." _Cost of averting a death: Macro and medical estimates._ Overall, these regressions show that differences in public sector spending on health do not go far in explaining why some 27 The OLS regression has an R-squared on .9095 and estimates (t-statistics) for the sample of 175 countries: ln(u5) = -.387 In(GNPpc) + .008 **Fcmillit** + **.012** Gini - .001 Urb - .003 AcSaWa (13.2) (3.9) (2.4) (.64) (1.9) The regression which includes public sector health spending as a share of GDP has an R-squared of .9234 and estimates (t-statistics) for the samnple of 119 countries: In(uS) = -.412 In(GNPpc) + .008 Femillit + .014 Gini- .001 Urb **-.001
# **3. Findings**

Each of the three evaluation questions posed by the report is addressed by triangulating analysis obtained across data collection activities.

## **QUESTION 1: HOW FREQUENTLY IS VALUE FOR MONEY ANALYSIS INCORPORATED INTO PUBLISHED IMPACT EVALUATIONS?**

We estimate that 18.9 percent of the World Bank’s impact evaluations include any kind of VFM analysis, while the predicted proportion in the 3ie data set is 14.1 percent. Our prediction indicates that the 3ie sample has a somewhat lower, yet not statistically different, proportion of VFM studies compared to the sample of World Bank IEs.

The estimated percentage of IEs with any kind of VFM analysis in the 3ie sample has not changed much over time. The estimated percentage of IEs that include VFM was:

- 16.8 percent of IEs published prior to 2004;

- 11 percent of IEs published between 2005 and 2008; and

- 14.5 percent of IEs published between 2009 and 2012.

A majority of the World Bank’s IEs with efficiency analysis —around 80 percent— conducted a CBA or CEA, and the remaining conducted a general efficiency analysis.

The quality of cost ingredients reporting was mixed in the sample of 30 World Bank IEs. About 13 percent of the CEAs and CBAs listed only the main cost components, whereas just over half listed several cost ingredients; and the remaining third simply reported a Cost estimate without detailing much of what went into it. About 40 percent of World Bank IEs classified as category II (those with any kind of VFM) reported the cost of a program in relation to the number of beneficiaries—a common metric used in VFM analyses.

Fewer than 20 percent of CEA and CBA studies reported on unit cost per impact. These percentages are very low, considering that they are included in impact evaluations where at least one measure of impact is supposed to be very available to be used in these comparisons.

We find that just 25 percent of the CEAs and CBAs reported opportunity costs.
provide limited evidence on how household consumption, poverty and inequality were impacted at the start of the lockdowns<sup>12</sup> Second, we exclude districts that are covered by the NSS consumption survey but not by the CPHS to obtain geographical consistency in our analysis. The excluded non-overlapping districts represent about 4.8 percent of the country’s population in 2011. Third, in an effort to approximate the NSS’ 30-day uniform recall period, we retain item-wise household expenditures for the month preceding the CPHS survey and ignore values that are reported with a lag of two to four months. Fourth, we construct a harmonized basket of items across the two surveys. Expenditures on loan repayments, insurance premiums and household’s private transfers to emigrated members are discarded from the CPHS -- while expenditures on durables, household appliances, etc.

> 12All references CPHS consumption years in this paper refer to the financial year starting April to March. That is, CPHS 2015 refers to the corresponding months in 2015-16.

10
and Canada, and Brazil’s dynamics were discussed above. To understand the dynamics of Argentina, it is necessary to take into account two things. First, the globalization dynamics are measured with respect to a base year, in this case 1990.<sup>15</sup> Second, Argentina started from a low level of trade integration and despite a fragile economic situation in the 2000s, its exports grew significantly. According to the World Bank’s WDI data, Argentina experienced a period of sustained export growth in which exports grew from US27 _billiontoUS_ 97 billion between 2003 and 2011. Albornoz et al. (2018) studies the period 2003-2011 and explains that while the currency depreciated with the economic crisis

> 13See table (A5) for details on World Bank’s regions classification.

> 14The data section discusses the data in more detail.

> 15The empirical strategy presented in this paper, in line with the literature, is very demanding in terms of fixed effects and does not allow estimating international trade integration in levels since all the years cannot be included due to multicollinearity issues. See Bergstrand et al. (2015), Baier et al. (2019) and Yotov (2022a) for more details on this issue.

15
19,1995 and relates to 1993. Education data are supplied by Herbert Bergmann AF3PH and are for 1995. Education employment is made up of 6,530 teachers, of which 4,412 are for elementary schools, and 2,118 for secondary schools.

GDP and Wages and Salaries estimates are taken from IMF Background Paper No. SM/95/242 of September 19,1995 and relate to 1995.

Gambia Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1987. It reflects data gathered through Establishment surveys, that is, data on the number of workers on establishment payrolls. This in tum may result in an underestimation of employment.

Central, Non-Central, Education and Health employment are taken from IMF Report No SM/95/238 of September 18, 1995, Statistical Annex and relate to 1994/95.

Ghana Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1991. It reflects data gathered through Establishment surveys, that is, data on the number of workers on establishment payrolls. This in tum may result in an underestimation of employment.

Central Government employment corresponds to 'Civil Service organizations". Local Govemment corresponds to Metropolitan, municipal and district assemblies. The number for Education Service excludes 18,000 trainees. Education is believed to continue to be paid by the central government since recurrent expenditures authority has not yet been devolved to local authorities. Health employment data include people working for the Ministry of Health and people working in Health for subvented agencies. No information is available regarding administration staff in both these areas; accordingly, data on health probably overstates the number of medical personnel and understates that of central government personnel. Military employment does not include paramilitary personnel in the people's militia (5,000 part-time force with police duties), or the Presidential Guard (one infantry battalion). State-owned Enterprise employment is taken from IMF Staff country Report No. 96/69 of August
or both of the trading countries are landlocked, and _bordercd_ is the dummy variable for common border. The right-hand side of the specification is identical to the one Frankel and Romer (1999) use. We use bilateral trade flows from the COMTRADE database, converted to the 3-digit ISIC Revision 3 classification. To estimate the gravity equation, the bilateral trade flows _Xicd_ are averaged over the period 1980-2007. This allows to smooth out 24
Policy Research Working Paper 7665

# **Abstract**

This paper uses a large national household panel from 1999/2000 and 2007/08 to analyze the short-term effects of India’s Mahatma Gandhi National Rural Employment Guarantee Scheme on wages, labor supply, agricultural labor use, and productivity. The scheme prompted a 10-point wage increase and higher labor supply to nonagricultural casual work and agricultural self-employment. Program-induced drops in hired labor demand were more than outweighed by more intensive use of family labor, machinery, fertilizer, and diversification to crops with higher risk-return profiles, especially by small farmers. Although the aggregate productivity effects were modest, total employment generated by the program (but not employment in irrigation-related activities) significantly increased productivity, suggesting alleviation of liquidity constraints and implicit insurance provision rather than quality of works undertaken as a main channel for program-induced productivity effects.

This paper is a product of the Poverty and Inequality Team and Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at ssingh12@worldbank.org or kdeininger@worldbank.org.

_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 paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
seem to be unique to our country context. Importantly, though, such issues by themselves do not necessarily explain why our intervention significantly _decreased_ grit-interest for treated

> 22 We cannot conclusively dispel the role of these factors. However, when translating the Short Grit Scale into Macedonian or Albanian language, we applied back and forth translation to make sure that there are no language translation problems (either in general or with respect to specific terms). Moreover, the Short Grit Scale (and all other instruments) was piloted in the country and with a random set of students in the targeted age group. The pilots also included qualitative discussions where none of the issues above came up. This gives us confidence in the correct application of the scale.

> 23 We used the 2017 round of the Development Bank for Latin America (CAF) annual household survey of sociodemographic information (N=10,687), which is representative of the adult population of major cities in Latin America. The 2017 round added the Short Grit Scale to the questionnaire (CAF, 2017).

13
contributions|701<br>|0.0|Yes|713|102%|
|Health insurance contributions|61,204|3.6|Yes|52,869|86%|
|Other contributions|15,496<br>|0.9|No|||
|Other revenue<br>Sources: Eurostat, GUS, and own estimates based on HBS 20<br>1/ We assume that the share of VAT paid by households is e<br>consumption according National Accounts and total gross v|100,299<br>14.<br>quivalent to t<br>alue added in|5.8<br>he ratio<br>the econ|No<br>between to<br>omy.|tal house|hold|

Similarly, capital gains and investment income, such as dividends, interest and proceeds from sale of shares, are subject to withholding tax at a flat rate of 19%. Income from renting property can be either included in global income, or individuals can choose the flat tax on revenues of 8.5%. Households can deduct costs of obtaining income, a basic tax-free allowance of PLN 3,000 per

> 5 If a self-employed person decides to use the flat tax, they may not file jointly with their spouse or deduct child tax credit.

5
co/noticias/mas-de-1-millon-825-mil-venezolanosestarian-radicados-en-colombia (April 22, 2021).

- Mutz, Diana C (2011), Population-Based Survey Experiments _Population-Based Survey Experiments_ . Princeton: Princeton University Press.

- Ratkovic, Marc (2021), “Subgroup Analysis: Pitfalls, Promise, and Honesty.” In _Advances in Experimental Political Science_ , eds. James Druckman and Donald P Green. Cambridge University Press, 271–88.

- Rodon, Toni, and Marc Sanjaume-Calvet (2020), “How Fair Is It? An Experimental Study of Perceived Fairness of Distributive Policies.” _Journal of Politics_ 82(1): 384–91.

- Rodrigues Chatruc, Marisol, and Sandra Rozo (2021), _Attitudes Towards Migrants during Crisis Times_ . https://www.iza.org/publications/dp/14319/attitudes-towards-migrantsduring-crisis-times.

- Rozo, Sandra V., and Juan F. Vargas (2021), “Brothers or Invaders? How Crisis-Driven Migrants Shape Voting Behavior.” _Journal of Development Economics_ 150: 102636.

- Ruiz, Isabel, and Carlos Vargas-Silva (2016), “The Labour Market Consequences of Hosting Refugees.” _Journal of Economic Geography_ 16(3): 667–94).

- ———. (2018), “Differences in Labour Market Outcomes between Natives, Refugees and Other Migrants in the UK.” _Journal of Economic Geography_ 18(4): 855–85).

- ———. (2021), “The Impacts of Refugee Repatriation on Receiving Communities.” _Journal of Economic Geography_ 21(2): 169–94.

- Santamaria, Julieth (2020), _“When a Stranger Shall Sojourn with Thee”: The Impact of the Venezuelan Exodus on Colombian Labor Markets_ .

- Scheve, Kenneth F., and Matthew J. Slaughter (2001), “Labor Market Competition and

30
Most of the data in this report are drawn from the International Bank Credit Analysis Croup (IBCA) sources, although even here, the data is constrained by the factors just listed. 6/ Table 1 shows the exposure of the banks, by country of origin, to individual Latin American states. Clearly the degree of exposure in these highly indebted cotntries is likely to influence the banks' attitudes to provisioning. Table 1 reveals the dominant position of the US banks. Exposures are, however, also significant in the cases of the UK an, - anese banks, becoming less significant in the case of banks in France, Germany and Canada, and much less significant in the case of those in Switzerland and Italy. (Exposure relative to capital would be a better measure but crosscountry comparisons of bank capital are difficult.) Given that the costs of provisioning will be positively related to the extent of exposure, it might be anticipated that banks in the countries where exposure is less would tend to favor rather higher percentage provisions (related to the values of their loans to individual or groups of developing countries) than would banks in countries where exposure is greater. In the latter group of countries the absolute costs of provisioning will be higher. This, in turn, suggests that the extent of provisioning will be more affected by the ability of the banks to make such provisions, than by an unbiased assessment of risk. However it needs to be noted that U.S. Banks also hold a larger overall portfolio of loans, with the result that their relative exposure may not be high by international standards. For examp±e, it is reported that at the end of 1986 6. The report, however, draws not only on published statistics but also on a rdnge of interviews conducted with bankers and officials in London, New York and Washington.