rafmacalaba/datause-encoder-data huggingface dataset:
# **Tables**

## **Table 1: Gaps in survey mean and national accounts means, across regions and income groups.**

_Panel A. Gap in levels between survey mean and HFCE mean_

|||**A**|**ll survey**|**s**|||**Consu**|**mption s**|**urveys**|||**In**|**come sur**|**veys**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Group**|**O**|**bs.**|**Ga**|**p**|**S.E.**|**O**|**bs.**|**Ga**|**p**|**S.E.**|**Ob**|**s.**|**Ga**|**p**|**S.E.**|
|All|2020|(156)|-0.202|***|0.018|947|(132)|-0.221|***|0.019|1073|(72)|-0.200|***|0.027|
|1980s|118|(57)|-0.149|***|0.030|52|(36)|-0.154|***|0.038|66|(30)|-0.166|***|0.037|
|1990s|358|(113)|-0.145|***|0.037|196|(92)|-0.161|***|0.044|162|(54)|-0.167|***|0.034|
|2000s|747|(140)|-0.244|***|0.017|368|(116)|-0.262|***|0.019|379|(58)|-0.242|***|0.021|
|2010s|792|(142)|-0.254|***|0.018|331|(116)|-0.287|***|0.019|461|(61)|-0.228|***|0.024|
|Low income|198|(50)|-0.129|***|0.037|189|(47)|-0.137|***|0.036|9|(7)|-0.055||0.129|
|Lower middle income|503|(87)|-0.232|***|0.026|338|(74)|-0.266|***|0.025|165|(23)|-0.117|*|0.068|
|Upper middle income|524|(55)|-0.326|***|0.025|238|(42)|-0.352|***|0.033|286|(27)|-0.337|***|0.030|
|High income|768|(42)|-0.216|***|0.023|168|(30)|-0.272|***|0.026|600|(41)|-0.209|***|0.024|
|East Asia & Pacific|160|(18)|-0.150|***|0.046|117|(14)|-0.170|**|0.058|43|(6)|-0.127|*|0.054|
|Europe & Central Asia|1072|(49)|-0.253|***|0.032|484|(47)|-0.275|***|0.033|588|(38)|-0.229|***|0.036|
|Latin America & Caribbean|392|(21)|-0.199|***|0.055|29|(6)|-0.316|***|0.074|363|(20)|-0.192|***|0.059|
|Middle East & North Africa|91|(14)|-0.160|***|0.048|65|(12)|-0.193|***|0.039|26|(3)|-0.034||0.166|
|North America|81|(2)|-0.189||0.119|32|(1)|-0.335||0.000|49|(2)|-0.175||0.105|
|South Asia|46|(7)|-0.245|**|0.080|45|(7)|-0.244|**|0.081||||||
|Sub-Saharan Africa|178|(45)|-0.174|***|0.034|175|(45)|-0.170|***|0.035|3|(2)|-0.276||0.064|
**Table 3a. Estimation results for Model 2 (household expenditures) with casualties as conflict indicator**

|||||**Casu**|**alties**||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|(1)<br>HH Weighted ‐<br>OLS<br>Log per capita<br>Consumption|(2)<br>HH Weighted ‐<br>OLS<br>Log per capita<br>Consumption|(3)<br>HH Weighted ‐<br>IV 2sls<br>Log per capita<br>Consumption|(4)<br>HH Weighted ‐<br>IV 2sls<br>Log per capita<br>Consumption|(5)<br>HH Weighted ‐<br>OLS<br>Log per capita<br>Consumption|(6)<br>HH Weighted ‐<br>OLS<br>Log per capita<br>Consumption|(7)<br>HH Weighted ‐<br>IV 2sls<br>Log per capita<br>Consumption|(8)<br>HH Weighted ‐<br>IV 2sls<br>Log per capita<br>Consumption|
|Demographics|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Education|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Location|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Opium Income District|No|Yes|No|Yes|No|Yes|No|Yes|
|Poverty Headcount District (2007)|No|No|No|No|Yes|Yes|Yes|Yes|
|Season Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Foreign Aid (USD per 1,000 inhabitant)|0.0143|0.0103|0.0120|0.00799|0.0493|0.0453|0.0466|0.0426|
||(0.0359)|(0.0355)|(0.0346)|(0.0342)|(0.0477)|(0.0491)|(0.0461)|(0.0475)|
|Casualties (per 1,000 inhabitant)|‐0.0193|‐0.0225|‐0.0197|‐0.0229|0.00172|‐0.00116|0.00112|‐0.00180|
||(0.0467)|(0.0463)|(0.0457)|(0.0453)|(0.0450)|(0.0439)|(0.0437)|(0.0426)|
|International Troops (per 1,000 inhabitant)|0.00301|0.00311|0.00303|0.00313|0.00167|0.00179|0.00169|0.00181|
||(0.00587)|(0.00585)|(0.00575)|(0.00574)|(0.00543)|(0.00542)|(0.00532)|(0.00530)|
|Afghan Troops (per 1,000 inhabitant)|0.00527***|0.00540***|0.00525***|0.00539***|0.00648***|0.00650***|0.00645***|0.00647***|
||(0.00185)|(0.00182)|(0.00180)|(0.00178)|(0.00212)|(0.00210)|(0.00207)|(0.00205)|
|Observations|18,221|18,221|18,221|18,221|18,211|18,211|18,211|18,211|
|Adjusted R‐squared|0.364|0.367|0.364|0.367|0.378|0.378|0.378|0.378|
|**Joint Significance (Prob > Test)**|**0.109**|**0.0862**|**0.0695**|**0.0505**|**0.0358**|**0.0357**|**0.0148**|**0.0147**|
|**Joint Significance(%)**|**89.1%**|**91.4%**|**93.1%**|**95.0%**|**96.4%**|**96.4%**|**98.5%**|**98.5%**|

Robust standard errors (Province) in parentheses *** p<0.01, ** p<0.05, * p<0.1 _Note:_ Standard errors are clustered at the province level are reported below the estimated coefficients. Statistically significant coefficients at the 10, 5, and 1 percent level are indicated by one, two, and three asterisks, respectively. Joint significance tests are performed for the four conflict indicators. That is an F test for the OLS specifications and chi2 for the IV 2LS. The preferred estimation is highlighted in yellow. HH stands for households, OLS for ordinary least squares, and IV 2SLS for instrumental variables two-stage least squares.

17
<!-- Start of picture text --> &@ India:Marriagege ageag<br>0 +<br>-~)<br>Ew<br>5<br>c<br>2ae<br>a<br>5<br>gy<br>W 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+— Quintile2 +——— Quintile 3<br>+—— Quintile4 +——— Quintile 5<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> .<br>[i]% Bangladesh:Marriage age<br>ot<br>a<br>6<br>Ew<br>6<br>55°<br>o<br>=<br>Byn<br>i 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995<br>Year of birth<br>+—— Quintile2 +—— Quintile 3<br>'— Quintie4 +——— Quintile 5<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> 7 Pakistan:Marriage age<br>gs<br>7<br>eC9<br>go<br>ao<br>G<br>8:<br>by<br>g!<br>wi Fe ee eee oO eee i aiay, (el 1980 1885 1880. 1885<br>+—S Quinte 2 +—— Quintile 3<br>+—— Quintile 4 +——— Quintile 5<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text --> <!-- Start of picture text --> g Nepal:Marriage age<br>ot<br>Le]<br>&<br>N<br>§Lo<br>5<br>a<br>=<br>c<br>m<br>3 t<br>Po4940 1945 1950 1955 1960 Year1965 of1970birth 1975 1980 1985 1990 1995<br>+— Quintile2 +—— Quintile 3<br>+— Quintile 4 ——— Quintile 5<br>Generated by regressing marriage age on wealth, urban, higher education and interactions with<br>year-of-birth and state fixed effects. Standard errors clustered by state.<br><!-- End of picture text -->
53

# **Table 30. Brazil: emission standards for alcohol and gasoline-powered motor vehicles**

|Type of<br>Emission|Effective<br>Date|Remarks|CO|Emission L<br>(g/km)<br>HC|imits<br>NOx|
|---|---|---|---|---|---|
|Exhaust|Jan. 1st, 88|Brand new vehicle<br>configurations|24.0|2.1|2.0|
||Jan. 1st, 89|50% of sales is the<br>minimum required|24.0|2.1|2.0|
||Jan. 1st, 90|100% of sales except<br>light duty trucks|24.0|2.1|2.0|
||Jan. 1st, 92|Only light duty trucks|24.0|2.1|2.0|
||Jan. 1st, 92|100% sales except<br>light duty trucks|12.0|1.2|1.4|
||Jan. 1st, 97|All light duty vehicles|2.0|0.3|0.6|
|Evaporative|Jan. 1st, 90|All light duty vehicles|-|(g/test)<br>6.0|-|
|Crankcase|Jan. 1st, 88|All light duty vehicles|emissio<br>engine o|n shall be nil<br>perating con|under any<br>dition|

Source: [Szwarc and Branco 1987, pp. 24]
<!-- Start of picture text --> Manufacturing employment in India<br>°<br>Tat2S10<br>=<br>=<br>=<br>Ba<br>ie}<br>~<br>1990 1995 ee 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> te Manufacturing employment in India<br>a”<br>7v NL ~ 2 * ~~<br>N ~“ = ~~<br>E a ~s woe<br>£0 vr °<br>eu®5 oe/<br>Ke)—<br>&<br>1990 1995 2000 2005 2010<br>ear<br>—+*— Formalsector ——#-—- Informal sector<br><!-- End of picture text --> <!-- Start of picture text --> Manufacturing employment in India<br>wo<br>a<br>c<br>2<br>=——c eo ie --=s--<br>= eek<br>S a- TT<br>ae a oe 8<br>uw ll ee Oe et<br>Oo<br>1990 1995 2000 2005 2010<br>Year<br>“-@--~ Formal tradables — ®— - Formal non-tradables<br>—-—#—- Informal tradables ——*— Informal non-tradables<br><!-- End of picture text --> <!-- Start of picture text --> Co)<br>Manufacturing firms in India<br>oO<br>= _-*<br>== “7<br>g= . ~*~ -_--7 _--7 -~- ~~." ae<br>=S) 77<br>-<br>TEN “7<br>1990 1995 2000 2005 2010<br>ae<br>“-@-- Formal tradables — @— - Formal non-tradables<br>—-—@-—- Informal tradables ——*— Informal non-tradables<br><!-- End of picture text -->
Table 3. Effect of displacement on gender norms around reproductive health

|||Ap|Attitude<br>prove con|s<br>trac|||Use &|Behavior<br>decide|s<br>contra||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Displaced|-0.01|-0.01|-0.01|-0.00|0.00|-0.05***|-0.03*|-0.03*|-0.03*|-0.05**|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|(0.03)|
|**Household characteri**|**stics**||||||||||
|Children under 5||-0.00|-0.00|0.00|-0.00||-0.04**|-0.04*|-0.04**|-0.02|
|||(0.01)|(0.01)|(0.01)|(0.01)||(0.02)|(0.02)|(0.02)|(0.02)|
|Female headed||0.01**|0.01**|0.01**|0.01||0.10***|0.11***|0.11***|0.10***|
|||(0.01)|(0.01)|(0.01)|(0.01)||(0.03)|(0.03)|(0.02)|(0.03)|
|**Wealth quintile**|||||||||||
|Poor||0.02**|0.02|0.02|0.00||0.05*|0.05*|0.04|0.00|
|||(0.01)|(0.01)|(0.01)|(0.01)||(0.03)|(0.03)|(0.03)|(0.03)|
|Middle||0.03***|0.02**|0.02*|0.01||0.02|0.02|-0.01|-0.00|
|||(0.01)|(0.01)|(0.01)|(0.01)||(0.03)|(0.03)|(0.03)|(0.03)|
|Rich||0.03***|0.03***|0.02**|0.01||0.01|0.01|-0.03|-0.04|
|||(0.01)|(0.01)|(0.01)|(0.01)||(0.03)|(0.03)|(0.03)|(0.04)|
|Richest||0.03***|0.03***|0.03**|0.01||-0.00|0.01|-0.04|-0.07*|
|||(0.01)|(0.01)|(0.01)|(0.01)||(0.03)|(0.03)|(0.04)|(0.04)|
|**Context**|||||||||||
|Reference network|||0.16**|0.09|0.13*|||-0.00|-0.03|-0.03|
||||(0.07)|(0.06)|(0.07)|||(0.04)|(0.04)|(0.04)|
|Royalties (ln)|||||-0.00|||||0.00|
||||||(0.00)|||||(0.00)|
|Social investment (%|exp)||||-0.00<br>(0.00)|||||-0.00<br>(0.00)|
|Constant|0.98***|0.96***|0.80***|0.88***|0.91***|0.23***|0.21***|0.21***|0.21***|0.50**|
||(0.00)|(0.01)|(0.06)|(0.06)|(0.07)|(0.01)|(0.02)|(0.02)|(0.04)|(0.22)|
|Observations|18,850|18,875|18,840|18,840|11,866|18,850|18,875|18,271|18,271|11,516|
|Number ofgroups|4,749|4,749|4,741|4,741|3,665|4,749|4,749|4,621|4,621|3,508|
|Department FE|No|No|No|Yes|Yes|No|No|No|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Source_ : Author based on DHS 2005/2010/2015. _Note:_ Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Reference group for wealth quintile is poorest.

Turning to economic opportunities, Columns (2)-(5) in Table 4 show that displacement is associated with more traditional attitudes around women in the domestic sphere. Depending on the specification, displacement reduces the probability of disagreeing with the statement ‘a woman’s main role is family caregiving and cooking’ by 6-8 percentage points. This might be explained by the fact that social expectations that assign women to the domestic sphere can make them ‘protective’ of the unpaid care space, attaching value to their leadership of it and being resistant to 18
# **Mind the Gap**

## **Disparities in Assessments of Living Standards Using National Accounts and Household Surveys**

Espen Beer Prydz<sup>aϯ</sup> , Dean Jolliffe<sup>ab</sup> , Umar Serajuddin<sup>a</sup> _Keywords_ : National Accounts Systems, Household Income and Expenditure Surveys, Poverty, Inequality.

_JEL Codes_ : I3, I32, E31, F01.

_Acknowledgements:_ The authors appreciate the feedback from Daniel Mahler, Christoph Lakner, Marshall Reinsdorf and Xavier Mancero, as well as conference participants at the Special IARIW-World Bank Conference, Washington, DC November 7-8, 2019. The authors gratefully acknowledge financial support from the UK Government through the Knowledge for Change Program and the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Program. They also thank for financial support the Republic of Korea, acting through the Korea Development Institute School of Public Policy and Management (KDIS) and its Knowledge Partnership with the World Bank. The analysis in this paper would not be possible without data from national accounts and household surveys, and publicly available compilations by international agencies made available from the World Development Indicators database, PovcalNet and others. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of any of the funders, author affiliations, or the World Bank Group and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

ϯ Corresponding author. Email addresses: eprydz@worldbank.org (E. B. Prydz), userajuddin@worldbank.org (U. Serajuddin), djolliffe@worldbank.org (D. ~~Jolliffe)~~ a The World Bank, Development Economics Data Group (DECDG) b Other affiliations include, adjunct faculty at Johns Hopkins University School of Advanced International Studies (SAIS), Research Fellow with the Institute for the Study of Labor (IZA), and Fellow of the Global Labor Organization (GLO).
# **2.  Background: Resource Sector in DRC**

With a GNP per capita of US$380 in 2014<sup>3</sup> , DRC is a low-income country endowed with vast natural resource wealth. It has huge mineral resources, including copper, cobalt, zinc, diamond, gold, and coltan. Its copper reserves are estimated to be as large as 70 million tons. Copper reserves of Katanga are the second largest in the world after Chile’s. Its cobalt and zinc reserves are estimated to 5 million tons and 6 million tons, respectively. The country has the largest diamond reserves which represent nearly 25 percent of the world total. Major reserves of gold, cobalt, rare earths, cassiterite, and columbite-tantalite (coltan) are located in the eastern parts of the country. The latter two are in high demand by global electronic goods manufacturers. 70 percent of the world’s coltan production is in DRC.

DRC has a huge potential to generate hydroelectric power, with an estimated capacity of up to 40 GW (100,000 MW) and could provide electricity to the whole continent. The energy sector has the potential to yield outsize and transformative returns for the country’s economic development and broader southern African region as a whole. DRC has the world’s second largest tropical forest endowment estimated at 148 million ha and the second largest carbon sink in the world. It represents over 60 percent of the total forest area in the Congo basin.

The country is also endowed with over 80 million ha of fertile, arable land with abundant water resources that is capable of supporting immense agricultural activities. DRS’s

> 3Using  Atlas method, for details see <u>http://econ.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:20452009~menuPK:641 33156~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html</u>

4
# <u>Table 1: Characteristics of Students in</u> _<u>Licence Appliquée</u>_ <u>and Entrepreneurship Track</u>

|||Number of<br>students enrolled|Number of<br>applicants to|Alication rate|Distribution<br>among students in|Distribution|
|---|---|---|---|---|---|---|
|||in "Licence<br>Appliquée"|entrepreneurship<br>track|pp|"licence<br>appliquée"|among applicants|
||Female|12,539|1,129|9.0%|67.1%|66.3%|
|Gender|Male|6,143|573|9.3%|32.9%|33.7%|
||Gafsa|1,060|304|28.7%|5.7%|17.9%|
||Monastir|1,935|316|16.3%|10.4%|18.6%|
||Sfax|2,005|284|14.2%|10.7%|16.7%|
||Jendouba|1,550|216|13.9%|8.3%|12.7%|
|ty|Ez-Zitouna|93|10|10.8%|0.5%|0.6%|
|ersi|Kairouan|1,237|109|8.8%|6.6%|6.4%|
|niv|Carthage|2,012|120|6.0%|10.8%|7.0%|
|U|Gabès|1,798|108|6.0%|9.6%|6.3%|
||Sousse|2,351|141|6.0%|12.6%|8.3%|
||Tunis|1,010|61|6.0%|5.4%|3.6%|
||Tunis El-Manar|1,787|22|1.2%|9.6%|1.3%|
||Manouba|1,659|11|0.7%|8.9%|0.6%|
||Total|18,682|1,702|9.1%|100%|100%|

_Source_ : Observatoire National de l’Emploi et des Qualifications.

30
**Figure 3. 15  Reasons for not requesting a loan from the informal sector**

<!-- Start of picture text --> Other 1.5%1.5%<br>High cost of credit 2.0%5.4%<br>Lack of collatera/guarantees 2.6%2.7%<br>No w ork/job 4.3% 12.3%<br>Not enough income 7.2% 16.0%<br>Prefers ow n resources 15.2%16.4%<br>Don't like to get into debt 22.2%22.4%<br>Didn't need 26.6% 41.5%<br>0% 10% 20% 30% 40% 50%<br>Banked Unbanked<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_

# **3.5 THE COSTS OF BEING UNBANKED**

29. The survey does not itemize costs of informal and formal financial services. Further analysis is encouraged in this regard. Conceptually, the costs paid for financial exclusion by the unbanked include:

- The cost of informal savings in terms of insecurity, values lost to inflation and to interest charges from lay away plans, or of outright losses in lottery programs.

-

- The costs of cashing checks at CCO’s

-

- The costs of remittances

- The costs of lost IVA returns (i.e. 2% of all purchases which are returned through bank accounts)

- The costs of paying bills – time lost traveling to utility companies to make payments in person

- The costs of informal loans, averaging 10% per month or 150% annually, versus 15- 27% annual interest rates for bank loans and 36% for credit cards.

# **3.6 REASONS FOR BEING UNBANKED**

30. In answer to the question “what is your main reason for not having a bank account?” the majority of the unbanked pointed to their lack of resources. The percentages of persons stating they lacked resources 59
# **1. Introduction**

Irrespective of how one perceives the pros and cons of water sector reform, it is widely agreed that empirical evidence on household demand can generate critical inputs into water sector reform, including but not limited to the design of pro‐poor contracts with public or private operators to deliver safe and reliable water to the public.

Because there is often no other pre‐existing data on consumer demand for service quality improvements, the policy community usually relies on stated preference surveys (also called willingness‐to‐pay [WTP] surveys) to understand and estimate consumer demand. One potential problem from such surveys is that demand can be over‐estimated because of the hypothetical nature of the survey. Such an error can trigger a spiral of activities leading to a negative impact on public and private investments in the water sector – i.e., lower than expected demand hurts the financial viability of water utilities, which results in insufficient revenues, which causes inadequate maintenance and poor service, which further lowers demand for service, and so on. We will argue that such over‐estimates arise because of misinterpretation of concepts ( _e.g._ , WTP) and incomplete and inappropriate use of the data from such surveys.

We present a case‐study from Sri Lanka, where we surveyed approximately 1,800 households in 2003, to clarify concepts and offer an operational interpretation the use of WTP surveys to estimate coverage of piped water connections _ex ante_ . In the early 2000s, the Government of Sri Lanka decided to engage private sector operators to manage water and sewerage services in two separate service areas: one in the town of Negombo, and one stretching along the coastal strip from the town of Kalutara to Galle. Since then, the government has abandoned the idea of setting up a public‐private partnership in these two areas. This paper is part of a series of investigations to determine how pilot private sector transactions (forming part of the overall water sector reform strategy) could be designed in such a manner that they would benefit the poor.

What is the best way to estimate demand for a good or a service? The analyst usually will rely on people’s revealed preferences (RP) for the commodity or service, using behavioral data to 2
We also use a set of control variables in our estimations. An important aspect to control for when analyzing electoral cycles is the degree of political competition. The political competition is likely to affect the incentives of incumbents with regard to public spending around election time (Block <u>2001). To measure political competition, we use the</u> <mark>index produced by the Polity5 project (Center</mark> <u><mark>for Systemic Peace 2020). This index measures the degree of political competition in a country,</mark></u> <mark>independently of the political regime. It ranges from 1 (political competition is suppressed) to 10 (political competition is institutionalized and electoral).</mark> Other aspects of governance may also affect the relationship between electoral cycles and budget outcomes. We use three indicators from the World Bank’s Worldwide Governance Indicators (WGI) (World Bank 2022b). The voice and accountability index measures the responsiveness of governments to their citizens. This index is a composite of several indicators on democracy, electoral processes, accountability of public officials, rights, reliability of government budget documents, transparency in policy making, the freedoms citizens have, and citizens’ trust in their government. The second indicator provides a measure of government effectiveness across countries. This index includes measures of the quality, coverage, and citizen satisfaction with public goods and services (roads, public transport, electricity, health and education services, water and sanitation, and bureaucratic quality). The third indicator captures the extent to which citizens and firms trust and abide by the rule of law. It includes perceptions of the quality of contract enforcement, the protection of property rights, confidence in the police and the courts, and the likelihood of crime and violence. The three indexes are normalized to mean zero and a standard deviation of one in each year. Higher values correspond to more advanced democratic institutions in a country.

Other variables used in the analysis are the level of real GDP per capita, from Eurostat and World Development Indicators (World Bank 2022a), and the size of the informal sector, sourced from the Informal Economy Database produced by the World Bank (Elgin and others 2021). Table 1 summarizes the descriptive statistics.

> cycle depends on the outcomes of previous elections (Alesina and Passarelli 2019). By using an exogenous variation in the electoral cycle, we avoid the bias driven by these effects.

9
**Appendix 1 Table 8: School-level Principal Components Analysis of Level 1 Variables**

||Eigen-<br>Sum|Diff-<br>mary Stati|stics|Coefficients|for first fiv|e principal c|omponents|||
|---|---|---|---|---|---|---|---|---|---|
||value|erence|Prop.|Variable|Comp1|Comp2|Comp3|Comp4|Comp5|
|Comp1|**3.90**|2.18|0.32|**SDI variables**||||||
|Comp2|**1.71**|0.45|0.14|Share of time teaching and learning|-|-|-|-|-|
|Comp3|**1.26**|0.22|0.11|Good Teacher Demeanor|0.22|**0.52**|-0.05|0.11|0.29|
|Comp4|**1.05**|0.06|0.09|Good Pedagogical Practice|0.25|**0.49**|-0.09|0.14|-0.08|
|Comp5|0.99|0.19|0.08|Classroom environment|0.04|-0.08|**0.43**|0.22|**0.82**|
|Comp6|0.80|0.16|0.07|Materials and infrastructure|0.06|-0.11|**0.64**|**0.43**|**-0.33**|
|Comp7|0.64|0.08|0.05|Use of materials|0.21|**0.51**|0.09|-0.11|-0.05|
|Comp8|0.56|0.15|0.05|**Stallings variables**||||||
|Comp9|0.42|0.04|0.03|Share of time teaching and learning|-|-|-|-|-|
|Comp10|0.38|0.21|0.03|Materials and infrastructure|-|-|-|-|-|
|||||Classroom environment|-|-|-|-|-|
|||||**CLASS variables**||||||
|||||Emotional Support|**0.42**|-0.28|-0.19|0.06|0.10|
|||||Classroom Organisation|**0.31**|-0.05|0.18|0.20|**-0.29**|
|||||Instructional Support|**0.43**|-0.24|-0.20|0.04|0.08|
|||||Student Engagement|**0.43**|-0.24|-0.19|0.08|0.04|
|||||**Teach variables**||||||
|||||Share of time teaching and learning|-|-|-|-|-|
|||||Classroom culture|0.22|0.05|**0.44**|**-0.41**|-0.10|
|||||Instruction|**0.32**|0.03|0.01|0.11|-0.14|
|||||Socioemotional Skills|0.22|-0.10|0.23|**-0.70**|0.06|

Note: In this analysis, share of time teaching and learning and redundant variables are excluded from the principal components analysis. Eigen values greater than 1 and component loadings greater than 0.3 (in absolute value) are in bold.

A1-10