rafmacalaba/datause-encoder-data huggingface dataset:
# **[insert Table C.3; Figure C.3]**

# **_Bottom-up solution: Direct democracy_**

Since the classic Alesina et al. (2008) paper shows that procyclicality (associated with corruption) is a feature of democracies, we explore the role of democratic institutions on procyclicality. Taking advantage of the Varieties of Democracy (V-dem) database, we identify (i) whether the initiative is permitted at the subnational level, and or at the national level; and (ii) whether the popular referendum is permitted at the subnational level, and or at the national level. We focus on the period 2009-2015 (2016 not available yet in data). The correlations (Table C.4 and Figure C.4) show that both the initiative and the referendum are negatively correlated with fiscal procyclicality, but the effect seems stronger for the initiative rights. This finding echoes results of Matsusaka (2014) regarding the effects of initiative on fiscal congruence and fiscal conservatism in U.S. states. Hence, delegating more fiscal legislation to the citizens may be a tool to reduce the procyclical bias.

# **[insert Table C.4; Figure C.4]**

# **_Bottom-up solution: Spending and tax composition_**

The literature shows that different types of spending behave differently along the cycle. Ardanaz and Izquierdo (2017) show that current spending is more procyclical during upswings of the cycle, whereas capital spending is more procyclical during downturns. Hence, for policy making purposes and institutional design, it would be beneficial to limit the growth of current spending in the good times, while in bad times protecting the capital spending would be beneficial for stabilization. In this paper, we show that when spending is more rigid, it will be less procyclical during the good times, but less countercyclical during the downturns. The composition of the tax structure also indicates that VAT rates are less procyclical than the other types of taxes., hence adding a potential benefit to using this type of taxes for economic stabilization, along with the proposal of sales tax holidays during the Great Recession (Furman, 2018).

21
Appendix B) clearly show that reductions in Google search index for the term “restaurant” strongly traces out actual percentage changes in restaurant bookings for all seven countries. As shown in Table B.3 (in Appendix B) weekly Google search indices explain about 60 percent of the variations in actual restaurant reservations.

Finally, we use monthly retail data from the U.S. Census Bureau to assess the validity of our retail-related Google search terms (“shopping” and “mall”). We compare actual trends in monthly retail spending and Google search indices associated with retail services. Figure B.2 indicates that our search terms and associated indices indeed mimic the trends in actual monthly retail spending. Table B.4 (in Appendix B) shows that these indices are strong predictors of actual retail spending.

These pieces of evidence, along with previous validations in the literature, confirm that Google search data strongly capture actual consumer demand for the services that we investigate in this paper. This is particularly encouraging as traditional economic data are not available in a timely manner or with high frequency to inform immediate policy responses in times of emergency such as the COVID-19 pandemic.

# **3. Empirical Strategy**

The Google search data and COVID-19 data provide spatial and temporal (weekly) variations in the search intensity for services and COVID-19 cases and deaths, respectively. We exploit these dimensions of variation to identify the impacts of additional COVID-19 cases and deaths on demand for various services. We estimate the following fixed effects specification to quantify the impacts of COVID-19: where c stands for the demand for various services, as captured by Google search intensity index, for country _c_ and week _t_ , captures country fixed effects, C C c represents the number of DD confirmed COVID-19 cases for each country and week, cc stands for weekly time dummy, and c αα CC is an error term assumed to be uncorrelated with the number of COVID-19 cases/ deaths cc δδ εε conditional on government measures and country and week fixed effects. The country fixed effects 14
**Table 5: Meta-Analysis of Results in Tables 3 and 4**

||(1)<br>Table3|(2)<br>(3)<br>Table3<br>Table3|(4)<br>Table4|(5)<br>Table4|(6)<br>Table4|
|---|---|---|---|---|---|
|DummyFE regression|<br>-13.085|<br> <br>-13.085<br>-13.085|<br>-0.231|<br>-0.234|<br>-0.231|
||(3.011)***|(2.845)***<br>(2.924)***|<br>(0.076)***|(0.072)***|<br>(0.074)***|
|Dummysize =20-49|0.274|-0.130<br>0.164|0.293|0.260|0.292|
||(3.559)|(3.348)<br>(3.398)|(0.107)**|(0.102)**|(0.103)**|
|Dummysize = 50-249|-0.540|-1.229<br>-0.728|0.220|0.165|0.218|
||(3.308)|(3.140)<br>(3.155)|(0.082)**|(0.089)*|(0.081)**|
|Dummysize =250-499|-2.677|-2.719<br>-2.688|0.076|0.072|0.076|
||(3.336)|(3.143)<br>(3.236)|(0.057)|(0.055)|(0.055)|
|No. of observations|-0.000|-0.000<br>-0.000|-0.000|0.000|-0.000|
||(0.000)|(0.000)<br>(0.000)|(0.000)|(0.000)|(0.000)|
|DummyAustria|4.834||0.184|||
||(0.029)***||(0.001)***|||
|DummyBelgium|21.193||0.925|||
||(0.035)***||(0.001)***|||
|DummyChile|<br>10.692||<br>0.255|||
||(0.042)***||(0.002)***|||
|DummyChina|12.152<br>||0.681<br>|||
||(2.294)***||(0.075)***|||
|DummyColombia|12.839||0.293|||
||(0.064)***||(0.002)***|||
|DummyDenmark|14.354||0.379|||
||(0.034)***||(0.001)***|||
|DummyEast Germany|12.444||-0.080|||
||(0.090)***||(0.003)***|||
|DummyFrance|11.482||0.332|||
||(0.507)***||(0.018)***|||
|DummyIreland|7.186||-0.235|||
||(0.031)***||(0.001)***|||
|DummyItaly|10.401||0.179|||
||(0.291)***||(0.010)***|||
|DummySpain|9.646||0.418|||
||(0.005)***||(0.000)***|||
|DummySweden|-2.888||0.051|||
||(0.039)***<br>||(0.001)***<br>|||
|DummyUK|0.995||-0.127|||
||(0.075)***||(0.003)***|||
|DummyWest Germany|9.385||-0.063|||
||(0.532)***||(0.019)***|||
|Dummyestablishment level data||-0.600<br>3.203||-0.212|-0.147|
|||(2.982)<br>(2.633)||(0.120)*|(0.106)|
|Averageyear||-0.320<br>-3.032||-0.011|-0.074|
|||(0.259)<br>(1.028)**||(0.012)|(0.052)|
|GDP||0.000|||0.000|
|||(0.000)**|||(0.000)|
|GDPper capita||0.000|||-0.000|
|||(0.000)|||(0.000)**|
|Openness||0.164|||0.004|
|||(0.073)**|||(0.004)|
|Ease of doingbusiness||11.777|||0.917|
|||(13.120)|||(0.563)|
|Government effectiveness||21.202|||1.146|
|||(8.862)**|||(0.562)*|
|Regulatory quality||-5.260|||-0.496|
|||(5.331)|||(0.286)|
|Observations|120|120<br>120|120|120|120|
|R-squared|0.50|0.29<br>0.37|0.62|0.20|0.41|
|Robust standard errors in parentheses<br>Regression includes constant term|* significant at|10%; ** significant at 5%; **|* significant at|1%||

46
<!-- Start of picture text --> ye Leo a ge fo:<br>£ y’ my Ve<br>(ele ae<br>es +wr ri BAe ~y Cote civoire 1<br>@ ceerp15.000)» a<br>BE tar pons t eibne’- te alll poei<br>elise<br>oo-23 .' a i }<br>Mi ww BUCA ne LXae<br>Pe “s LeaN/<br>mie tae aa ke<br>i ss0-207 Qe F<br><!-- End of picture text --> <!-- Start of picture text --> ye ¥ =<br>v Cee |<br>(735a<br>ps:<br>8 Re pantky Cote civeire<br>fe<br>@ cower (15,000) Boere Weis€' ¥ weLat<br>Exiting firma :<br>. ral BUCHANAN Rue “har<br>| iced <<ax ACY<br>—" crminse A<br>MB esse Orage i<br><!-- End of picture text -->
- 34 -

**Breusch T. and A. Pagan** (1980) A Lagrange Multiplier Test and its Applications to Model Specification in Econometrics., Review of Economic Studies, 47: 239-53.

**Catalan M., G. Impavido and A. R. Musalem** (2000) Contractual Savings or Stock Markets Development: Which Leads? World Bank Policy Research Working Paper No 2421.

**Chernow R.** (1990) The House of Morgan: An American Banking Dynasty and the Rise of Modem Finance., New York: Simon & Schuster Inc.

**Davidson R. and J. G. MacKinnon** (1993) Estimation and Inference in Econometrics., Oxford University Press, New York.

**Davis E. P.** (1995) Pension funds. Retirement Income Security and Capital Markets. An International Perspective. Oxford University Press, New York.

**DemirguiV-Kunt A. and V. Maksimovic** (1996) Stock Market Development and Financing Choices of Firms., The World Bank Economic Review, 10(2).

**DemirkdV-Kunt A. and R. Levine** (1996) Stock Markets, Corporate Finance, and Economic Growth: An Overview., The World Bank Economic Review, 10(2).

**Elias J.J., G. Impavido and A.R. Musalem** (2000) Contractual Savings and Government Debt Maturity Structure, forthcoming.

**Feldstein M.** (1974) Social Security, Induced Retirement and Aggregate Capital Accumulation., Journal of Political Economy 82(5): 905-26.

**Feldstein M.** (1996) The Missing Piece in Policy Analysis: Social Security Reform., American Economic Review, 86(2): 1-14.

**Feldstein** M. **S.** (1978) Do Private Pensions Increase National Savings?, Journal of Public Economics, 10(3): 277-93.

**Hausman J.** A. (1978) Specification Tests in Econometrics., Econometrica 46: 125171.

**Holzmann R.** (1997) Pension Reform, Financial Market Development and Economic Growth-Preliminary Evidence for Chile?, IMF Staff Papers.

**Hubbard R.** G. (1986) Pension Wealth and Individual Saving: Some New Evidence., Journal of Money, Credit and Banking, 18: 167-78.

**IMF** International Financial Statistics.

**Impavido G.** (1998) Institutional Investors, Stock Markets and Firms' Information Disclosure., University of Warwick Working Paper No 305.
# **6 Annex: Detailed RPSR Indices**

- A. Global reform index

The standard package of reforms prescribed by international donors in the 1990s included four principal components: restructuring (vertical and horizontal unbundling of power utilities); private sector participation; creation of an independent regulator; and competition in power generation.

In order to aggregate across the four dimensions of power sector reform considered in this study, a simple Power Sector Reform Index is constructed. The index gives each country a score on an interval of 0 to 100 on each dimension of power sector reform. The scores are based on giving equal weight to each step on each dimension of the reform continuum (see tabulation below). The average of the four 0-100 scores is used to provide an overall summary of the extent of reform.

|**Regulation**|No regu<br>= 0|lator<br> <br>Regulator<br>= 100|
|---|---|---|
|**Restructuring**|Vertically integrated<br>= 0|Partial vertical<br>unbundling<br>= 33<br>Full vertical<br>unbundling<br>= 67<br>Vertical & horizontal<br>unbundling = 100|
|**Competition**|Monopoly<br>= 0|IPPs<br>= 25<br>Single Buyer<br>Model<br>= 50<br>Bilateral<br>Contracts<br>= 75<br>Competitive<br>market<br>= 100|
|**Private Sector**<br>**Participation**|0.5*(Percent<br>0.5*(Percent|age of generation capacity with private sector participation)<br>+<br>age of distribution utilities withprivate sectorparticipation)|

## B. Generation and transmission planning index for Pakistan and comparators, 2015

||**India**|**Pakistan**|**International**<br>**benchmark**|
|---|---|---|---|
|**Generation Planning**|**57%**|**29%**|**56%**|
|Countryhas ageneration masterplan|||94%|
|Countryhas an overall energy plan|||65%|
|Competent entityis responsible forproducingtheplan|||88%|
|Inter-governmental committee oversees theplanningunit|||29%|
|Powergeneration systemplan is mandatory|||19%|
|Plan leads to timelyinitiation ofprocurement|||38%|
|Planning process is transparent andparticipatory|||59%|
|**Transmission Planning**|**75%**|**25%**|**72%**|
|Competent entityis responsible forproducingtheplan|||100%|
|Explicitlylinked topowergenerationplans|||88%|
|Plan is mandatory|||29%|
|Planning process is transparent andparticipatory|||71%|

68
POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA TABLE A4 — PPP RATIOS REGRESSED ON GDP PER CAPITA AND REGIONAL BLOCS, 50 COUNTRIES

||Excluding s<br>only|upermarket<br>items|Excluding<br>garments & fo|branded<br>otwear items|Excluding pr<br>nonalcoholic b|emium food<br>everages items|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||_ln_<br>(Normalized<br>PPP Ratio)|_ln_<br>(Normalized<br>PPP Ratio)|_ln_<br>(Normalized<br>PPP Ratio)|_ln_<br>(Normalized<br>PPP Ratio)|_ln_<br>(Normalized<br>PPP Ratio)|_ln_<br>(Normalized<br>PPP Ratio)|
|_ln_(GDP per<br>i|0.00398***|0.00287*|-0.0000484|-0.000130|0.00280**|0.00280*|
|capta,|||||||
|<br>2011 PPP)|(0.00112)|(0.00116)|(0.000399)|(0.000407)|(0.000991)|(0.00122)|
|Economic||0.00104||-0.000210||0.000513|
|Community Of||(0.00262)||(0.00131)||(0.00272)|
|West African|||||||
|States (ECOWAS)|||||||
|Economic||-0.00345||-0.00118||-0.00305|
|Community of||(0.00311)||(0.00140)||(0.00326)|
|Central African<br>States (ECCAS)|||||||
|Southern African||0.00798**||0.00137||0.00469|
|Development||(0.00258)||(0.00147)||(0.00241)|
|Community<br>(SADC)|||||||
|Arab Maghreb||0.00535||-0.00113||-0.00569*|
|Union (UMA)||(0.00563)||(0.00221)||(0.00264)|
|Constant|4.573***|4.580***|0.000387|0.00103|-0.0224**|-0.0228*|
||(0.00880)|(0.00874)|(0.00310)|(0.00291)|(0.00781)|(0.00953)|
|N|50|50|50|50|50|50|
|R<sup>2</sup>|0.207|0.397|0.000|0.099|0.150|0.334|
|Adjusted R<sup>2</sup>|0.191|0.328|-0.021|-0.003|0.133|0.258|
|Root MSE|0.00800|0.00728|0.00294|0.00291|0.00683|0.00632|

Base dummy group: Common Market for Eastern and Southern Africa (COMESA) Robust standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 _Source_ : Authors’ calculations. GDP per capita (2011 PPP adjusted) from the World Bank World Development Indicators (WDI).

19
- Portes, A. (1998): “Social Capital: Its Origins and Applications in Modern Sociology,” _Annual Review of Sociology_ , 24, 1–24.

- Riley, E. (2022): “Resisting social pressure in the household using mobile money: Experimental evidence on microenterprise investment in Uganda,” Tech. rep., Working Paper, University of Oxford.

- Rosenzweig, M. R. (1988): “Risk, Implicit Contracts and the Family in Rural Areas of Low-Income Countries,” _The Economic Journal of Nepal_ , 98, 1148–1170.

- Rosenzweig, M. R. and O. Stark (1989): “Consumption Smoothing, Migration, and Marriage: Evidence from Rural India,” _The Journal of Political Economy_ , 97, 905–926.

- Schaner, S. (2015): “Do opposites detract? Intrahousehold preference heterogeneity and inefficient strategic savings,” _American Economic Journal: Applied Economics_ , 7, 135–74.

- Scott, J. C. (1976): _The Moral Economy of the Peasant: Rebellion and Subsistence in Southeast Asia._ , Yale University Press.

- Somville, V. (2011): “Daily Collectors, Public Good Provision and Private Consumption: Theory and Evidence from Urban Benin,” Tech. Rep. 1106.

- Squires, M. (2021): “Kinship Taxation as an Impediment to Growth: Experimental Evidence from Kenyan Microenterprises,” _Unpublished Manuscript_ .

- Stack, C. B. (1974): _All Our Kin: Strategies for Survival in a Black Community_ , Harper and Row.

- Tam, P., B. S. Yamey, et al. (1957): _The Economics of Under-Developed Countries_ , University of Chicago Press.

- Townsend, R. M. (1994): “Risk and Insurance in Village India,” _Econometrica: Journal of the Econometric Society_ , 62, 539–591.

- Wherry, F. F., K. S. Seefeldt, and A. S. Alvarez (2019): “To Lend or Not to Lend to Friends and Kin: Awkwardness, Obfuscation, and Negative Reciprocity,” _Social Forces_ , 98, 753–793.

- World Bank (2018): “Cashew Value-Chain Competitiveness Project: Project Appraisal Document,” Tech. rep., The World Bank Group, Washington, DC.

- ——— (2020): “World Development Report 2020: Trading for Development in the Age of

- Global Value Chains,” Tech. rep., The World Bank Group, Washington, DC.

45
|**Table 5: distribu**<br>**Subsidy**|**tive impacts of diffe**<br>**Distributive**<br>**impact (Ω)**|**rent supply side subsi**<br>**Error of inclusion**|**dies in Mexico City**<br>**Error of exclusion**|
|---|---|---|---|
|Metro|1.00|52%|68%|
|RTP(Buses)|1.14|45%|68%|
|STE (Trolleys)|0.96|54%|68%|

_Source_ : Flynn (2007).

The three subsidies analyzed by Flynn (2007) are supply side subsidies and with the exception of the RTP subsidy —which is mildly progressive— these subsidies are neutral in terms of their distributive impact. The high errors of exclusion point to the relatively low use of these transport modes by the poor. In fact, the ‘colectivos’ (small buses) are the most popular transport mode for the poor, but this mode does not receive any subsidies.

# _Santiago, Chile_

Another recent study for Santiago, Chile, shows similar results (Gómez-Lobo, 2007) regarding supply side subsidies and some types of demand subsidies. Table 6 presents the results for different subsidy policies.<sup>22</sup>

**<u>Table 6: Distributional impacts of transport subsidies in Santiago, Chile</u>**

|**Subsidy**|**Quasi-Gini**<br>**coefficient**|**Error of inclusion**|**Error of exclusion**|
|---|---|---|---|
|Student preferential<br>pass Buses|-0.16 (benefit)<br>-0.11 (funding)|50.6%|70.0%|
|Student preferential<br>fareMetro|0.13 (benefit)<br>0.28 (funding)|75.5%|97.2%|
|Metro<br>investment<br>grant|0.27|77.7%|88.7%|
|Direct transfer|-0.34|36.5%|51.6%|

_Source_ : Gómez-Lobo (2007a).

The student preferential fares in buses and metro are funded through cross subsidies. Thus the distributive impact of these policies must be analyzed both from the benefit and the funding side. In the case of buses, the distribution of benefits is mildly progressive. However, the funding of the subsidy is also mildly “progressive” in the sense that poorer households pay a disproportionate amount of the tax needed to fund the subsidy. These counteracting effects almost cancel each other out, making this categorical subsidy basically neutral. As discussed in Gómez-Lobo (2007a), this subsidy takes money from poor and rich households without student members and gives it to rich and poor households alike that have student members. In fact, 70% of poor households do not receive any benefit from this scheme.

> 22 These results were obtained using data prior to the Transantiago reform of 2005-2007. Thus, they may not be representative of the current situation in Santiago.

37
48 Table **10** above illustrates the typ of data **IEC** staff is expected to culP from various Bank and Fund documents to support the estimation of an alternative conversion factor that gives some recognition to price-measure restrictions.

# Deviations of "Trade Tax-Effective" Exchange Rates

**68.** Table **11** below shows the distribution of the deviations of the "trade tax-effective" rate from the official rate. The distribution sh(,wn here is very similar to that of total duties imposed on trade in goods and nonfactor services shown earlier in Table **8.** In Table **11,** some **30** percent of developing countries for which data are available show deviations greater than **10** percent in the "trade tax-effective" conversion factor; similarly, Table **8** shows that the "implicit tariff rate" of **10** percent or greater is imposed in some **30** percent of developing countries.

Table **11:** Distribution of Deviations of "Trade Tax-Effective" Rate from Official Rate

|Range**(%**|**)1975**|**1976**|**1977**|**1978**|**1979**|**1980**|**1981**|**1982**|**1983**|1984|**1985**|**1986**|**M87**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**0.0-1.0**|**1**|**1**|**1**|**1**|**1**|**1**|**1**|**3**|2|2|2|**3**|**6**|
|**_1.1-5.0_**|**17**|21|**17**|**18**|**18**|**19**|20|22|**19**|**19**|**17**|14|**6**|
|**5.1-10.0**|**32**|**_35_**|**33**|**30**|**30**|**31**|**39**|29|**33**|**32**|**35**|**27**|**10**|
|**_10.1-15.0_**|**_15_**|16|**16**|**18**|**15**|**17**|**10**|12|**11**|**_15_**|**8**|**8**|**7**|
|**15.1-20.0**|**9**|**6**|**_5_**|**_6_**|**7**|**7**|**_5_**|**_5_**|**3**|**3**|4|**1**|**0**|
|**20.1-25.0**|2|**1**|**_5_**|2|2|**1**|**0**|**1**|**1**|**3**|**3**|**1**|**0**|
|_2r.1-30.0_|**0**<br>|**0**<br>|**1**|**3**|**1**|**0**|**0**|**0**|**0**|**0**|**0**|2|1|
|>|**1**|**1**|**1**|**1**|2|**1**|**1**|**1**|**1**|**1**|**1**|**1**|**0**|
|Total:|**77**|**81**|**79**|**79**|**76**|**77**|**76**|**73**|**70**|**75**|**70**|**57**|**30**|
Figure 4: Developing World Poverty Forecasts <!-- Start of picture text --> Author<br>IB Chandy et al. (2013)<br>— 8 Edward & Sumner (2013)<br>. BB international Futures v7.04<br>. BB Ravallion (2013)<br>RA : §§) World Bank (forthcoming)<br>_ Sx—<br>= 800M ‘ =<br>3<br>oOo:<br>=al .<br>= 600M :<br>400M <=<br>200M<br>OM<br>2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030<br>Year<br><!-- End of picture text -->

# Developing world poverty forecasts from authors referred to in this analysis.
this effect is not statistically different from zero. Likewise, we find no evidence of a RD effect on QAT takers in QAT 7. In QAT 8, we find a significant, negative RD effect on the number of QAT takers—the finding is however not robust to setting the bandwidth to twice the optimal bandwidth for the estimation.<sup>17</sup> The evidence collectively suggests that, despite their size, group-based teacher bonuses do not provide sufficiently strong incentives for marginal nonqualifiers to raise either QAT performance or participation in order to increase their chances for qualifying for bonuses in the next award round. It is plausible that given that PEF has revised the bonus eligibility criteria for every award round (without communicating the revisions well in advance), it may have generated uncertainty on the applicable criteria for the next award round, discouraging the effort of marginal disqualifiers to raise learning and test participation. Independent of this conjecture, it is plausible that gains in learning and test participation by marginal qualifiers may have been masked by gains in the corresponding measures among marginal nonqualifiers. Estimation issues may also be relevant: the inability to detect any effects at the composite score cutoff could be due to inadequate statistical power.

# **8. Conclusion**

Low student learning is a consistent finding in much of the developing world and the search for solutions is an active research and public policy agenda. This paper uses a relatively unique dataset of five semiannual rounds of standardized test data to characterize and explain the shortterm evolution of student learning in primary and secondary schools. The data are collected as part of the quality assurance system for a public-private partnership program which offers public subsidies conditional on minimum learning levels to low-cost private schools in Pakistan. Apart from a large positive distributional shift in learning between the first two test rounds, the learning distributions over test rounds show little progressive movement. School-level panel regressions essentially show that pre-program learning levels and the level of the school are associated with the level and evolution of conditional mean learning. Variance component decomposition shows that between 60–70% of the cross-sectional student-level test score variation is attributable to between-student variation, with no discernible trend in either the level or component shares of

> 17 The effect remains statistically significant when the selected bandwidth is set at half the optimal bandwidth.

37