rafmacalaba/datause-encoder-data huggingface dataset:
Our results certainly offer suggestive support for the view that large, directed public investments can make a significant contribution to low-carbon development. However, we cannot discount the potential role of endogeneity in this exercise. The implied relationship between mass transit investment and CO2 emissions has no explicit endogeneity, because reducing CO2 emissions has not been a goal of mass transit investments until very recently. However, traffic congestion has been a target, with local air pollution as a correlate. Some element of endogeneity may therefore be present, because CO2 emissions and local air pollutants have common sources in vehicle traffic, heavy industry, power generation, and residential heating. At the same time, the heterogeneity of global cities makes it unclear whether simultaneity is an important consideration in this case. Our database includes cities with and without subways in 138 countries, with some subway installations dating back more than a century, in political and economic regimes as varied as the former Soviet Union, other COMECON countries, social democratic regimes in Western Europe, military and populist authoritarian regimes in Latin America, relatively laissez-faire regimes in the United States and Australia, and regimes in Asia that range from authoritarian in the Democratic People’s Republic of Korea and mainland China to relatively laissez-faire in Thailand and Taiwan, China. In light of these multiple, disparate factors, we believe that the exploratory results in Table 5 and Figure 13 offer reasonably strong evidence in favor of the public investment hypothesis. In future research, we hope to revisit this question in an econometric exercise with an explicit treatment of potential simultaneity.

# **7.  Future Research**<sup>**10**</sup>

The advent of satellite-based CO2 data has opened many research lines that could not be explored until the requisite information became available. In this section, we summarize some of the topics that have been identified in the course of our own research.

# **7.1 Quantifying CO2 Emissions**

Although satellite-based measures of CO2 concentration anomalies are extremely useful for comparative analyses, the policy community would undoubtedly benefit from conversion of concentration anomalies to physical estimates of CO2 emissions. As we note in the paper, recent research for a few cities has used OCO-2 observations to produce scaling factors (R) for rough adjustment of ODIAC-type emissions estimates (Oda et al. 2018). Future extensions of our econometric work could include the construction of sectoral R-factors that could be used to improve emissions inventories by identifying the sources of their discrepancies from satellitemeasured concentration anomalies. Hopefully, empirical research will ultimately depart from its continued dependence on emissions inventories by developing methods for direct conversion of satellite-measured CO2 anomalies to physical emissions estimates.

# **7.2 Potential Endogeneity Problems**

> 10 Our thinks to the reviewers of this paper for their useful thoughts about future research directions.

33
exclusive rights. One reason for higher disagreement around joint ownership — also related to local context — may be due to ambiguity around how “joint” ownership is interpreted/defined in lower-income contexts where documentation is limited (Jacobs and Kes, 2015).

**Table 3. Spousal Agreement/Discrepancies in Ownership and Rights of Agricultural Parcels in T5/IHPS**

||**(a)**|**Reported**<br>|**owner**<br>**W**<br>|**ship**<br>**ife**<br>|||**(b) E**|**conomi**<br>|**c owner**<br>**W**<br>|**ship**<br>**ife**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||**H**|**J**|**W**|**No**|||**H**|**J**|**W**|**No**|
||**H**|**14.0**|5.5|1.5|||**H**|**4.7**|2.1|0.2||
|**Husband**|**J**||**9.7**|5.2|7.9|**Husband**|**J**||**13.1**|1.4|18.0|
||**W**|||**22.7**|||**W**|||**9.0**||
||**No**||9.0||**24.6**||**No**||20.5||**31.1**|
|**Share**|**agre**|**e: 71%,**<br>**(c) Righ**<br>|**Share**<br>**t to sell**<br>**W**<br>|**disagree**<br>**ife**<br>|**: 29%**<br>|**Share a**|**gree: 5**<br>**(d)**|**7.9%,**<br>**Right t**<br>|**Share d**<br>**o beque**<br>**W**<br>|**isagree:**<br>**ath**<br>**ife**<br>|**42.1%**<br>|
|||**H**|**J**|**W**|**No**|||**H**|**J**|**W**|**No**|
||**H**|**21.1**|2.0|1.2|||**H**|**21.3**|3.2|1.6||
|**Husband**|**J**||**0.9**|1.3|4.9|**Husband**|**J**||**1.2**|2.1|5.5|
||**W**|||**19.6**|||**W**|||**20.2**||
||**No**||2.3||**46.7**||**No**||2.1||**42.9**|
|**Share agr**|**ee: 8**|**8.3%, S**|**hare di**|**sagree:**|**11.7%**|**Share ag**|**ree: 8**|**5.6%, S**|**hare di**|**sagree:**|**14.4%**|

<u>Notes: The findings are based on 1,719 parcel observations and the responses that were provided by 931 couples, about</u> 55.5 percent of which had more than one garden. H = Owned by Husband, J = Jointly Owned, W = Owned by Wife; “No” = Reported No Ownership or Rights.

# **4. Results**

# **4.1 (Individual-Level) Ownership of and Rights to Agricultural Land**

Table 4 presents a summary table of the estimates of the coefficient 1 from equation 1 (i.e. the effect of following the business-as-usual approach to respondent selection vis-à-vis conducting ββ individual interviews) – derived from the regressions that are estimated on the whole, and separately for male and female sub-samples, and for a range of ownership and rights constructs as the dependent variables. We show that under the business-as-usual approach, the levels of exclusive reported and economic ownership of agricultural land are higher among men, while the levels of joint reported and economic ownership of agricultural land decline among women. The effect on the joint reported ownership among men is also negative, albeit significant only at the 10 percent level. On the other hand, for the rights to sell and bequeath, the business-as-usual approach to respondent selection leads to an increase for both men and women, driven by a positive effect on reporting of joint rights (as reflected above in Table 2). Given the survey treatment effects on 20
# **Table A19: Heterogeneity in Differential Treatment**

|Before deleg|ated rando|mization of i|nspector assign|ment||
|---|---|---|---|---|---|
|**Dependent variable:**|**Time**|**Fraud**|∆**log value**|∆**log tax**|**Hyp. tax**<br>**revenue**<br>**losses**|
||(1)|(2)|(3)|(4)|(5)|
|Excess interaction share|-1.974**|0.023|-0.011|-0.100|-0.199|
||(0.922)|(0.079)|(0.036)|(0.065)|(0.208)|
|Tax rate|0.913***|0.186***|0.048***|0.040**|0.095|
||(0.211)|(0.043)|(0.010)|(0.019)|(0.081)|
|Excess interaction share*Tax rate|-0.100|-0.862**|-0.196|0.040|1.698**|
||(2.644)|(0.342)|(0.121)|(0.209)|(0.675)|
|Declaration characteristics|Yes|Yes|Yes|Yes|Yes|
|Inspector fxed efects|Yes|Yes|Yes|Yes|Yes|
|Broker fxed efects|Yes|Yes|Yes|Yes|Yes|
|Source country fxed efects|Yes|Yes|Yes|Yes|Yes|
|HS2-product fxed efects|Yes|Yes|Yes|Yes|Yes|
|Month-year fxed efects|Yes|Yes|Yes|Yes|Yes|
|Observations|41,121|44,522|44,434|40,471|44,497|
|R-squared|0.318|0.214|0.152|0.132|0.211|

_Notes:_ Standard errors clustered two-way by inspector and by broker presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. _Excess interaction share_ is the difference between the share of given broker’s declarations handled by an inspector in a given semester and the hypothetical share she would be expected to handle if the allocation of declarations to inspectors were random conditional on their productivity, as prescribed by official rules, calculated using calibration methods (as explained in Section 4). Declarations characteristics include the tax rate, the risk score, a dummy for the red channel, the share of value accounted for by differentiated products, a dummy indicating whether the declaration was mixed, and a dummy indicating the declaration was subject to valuation advice. "Observations" refers to the number of non-singleton observations. OLS estimation is used. The sample covers the period January 1, 2015 to November 17, 2017 (i.e., before the delegated randomization intervention).

79
<u>http://www.unaids.org/en/media/unaids/contentassets/documents/unaidspublication/2010/JC2034_UNAIDS_Str ategy_en.pdf</u> UNAIDS (2011) World AIDS Day report 2011: How to get to zero: Faster, Smarter, Better. Available on: <u>http://www.unaids.org/en/media/unaids/contentassets/documents/unaidspublication/2011/JC2216_WorldAIDSd ay_report_2011_en.pdf</u> UNAIDS (2012) World AIDS Day report 2012: Results 2012. Available at: <u>http://www.unaids.org/en/media/unaids/contentassets/documents/epidemiology/2012/gr2012/JC2434_WorldAI DSday_results_en.pdf</u> Underwood, L., Thomas, J., Williams, T. and Thieba, A. (2007) The effectiveness of interventions for people with common mental health problems on employment outcomes: a systematic rapid evidence assessment. In: Research Evidence in Education Library. London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London. Available at: http://eppi.ioe.ac.uk/cms/Default.aspx?tabid=2315 Valentine, J. C, Biglan, A., Boruch, R. F Castro, F. G., Collins, L. M., Flay, B. R., Kellam, S., Mościcki, E. K., Schinke, S. P. (2011), Replication in Prevention Science, Prevention Science, 12:103–117, DOI 10.1007/s11121-011-0217-6 <mark>Vojtkova, M., Snilstveit, B., Bhavsar, A., Hobrados, J.G., Thomae, M., Thomas, S. and Waddington, H.W., (2011) Gap Map of Systematic Reviews: HIV/AIDS programmes in low and middle income countries. International Initiative for Impact Evaluation. Available at: http://www.3ieimpact.org/media/filer/2012/06/12/hiv_gap_map.pdf</mark> Waddington, H., White, H., Snilstveit, B., Hombrados, J.G., Vojtkova, M., Davies, P., Bhavsar, A., Eyers, J., Koehlmoos, T.P., Petticrew, M., Valentine, J.C. and Tugwell, P.(2012) How to do a good systematic review of effects in international development: a tool kit, Journal of Development Effectiveness, 4 (3): 359-387.

White, H. and Waddington, H., (2012), Why do we care about evidence synthesis? An introduction to the special issue, Journal of Development Effectiveness, 4 (3): 359-387.

24
# **1. Introduction**

Solar energy has experienced an impressive technological shift. While early solar technologies consisted of small-scale photovoltaic (PV) cells, recent technologies are represented by solar concentrated power (CSP) and also by large-scale PV systems that feed into electricity grids. The costs of solar energy technologies have dropped substantially over the last 30 years. For example, the cost of high power band solar modules has decreased from about $27,000/kW in 1982 to about $4,000/kW in 2006; the installed cost of a PV system declined from $16,000/kW in 1992 to around $6,000/kW in 2008 (IEA-PVPS, 2007; Solarbuzz, 2006, Lazard 2009). The rapid expansion of the solar energy market can be attributed to a number of supportive policy instruments, the increased volatility of fossil fuel prices and the environmental externalities of fossil fuels, particularly greenhouse gas (GHG) emissions.

Theoretically, solar energy has resource potential that far exceeds the entire global energy demand (Kurokawa et al. 2007; EPIA, 2007). Despite this technical potential and the recent growth of the market, the contribution of solar energy to the global energy supply mix is still negligible (IEA, 2009). This study attempts to address why the role of solar energy in meeting the global energy supply mix continues to be so a small. What are the key barriers that prevented large-scale deployment of solar energy in the national energy systems? What types of policy instruments have been introduced to boost the solar energy markets? Have these policies produced desired results? If not, what type of new policy instruments would be needed?

A number of studies, including Arvizu et al. (2011), have addressed various issues related to solar energy. This study presents a synthesis review of existing literature as well as presents economic analysis to examine competitiveness solar energy with fossil energy counterparts. Our study shows that despite a large drop in capital costs and an increase in fossil fuel prices, solar energy technologies are not yet competitive with conventional technologies for electricity production. The economic competitiveness of these technologies does not improve much even when the environmental externalities of fossil fuels are taken into consideration. Besides the economic disadvantage, solar energy technologies face a number of technological, financial and institutional barriers that further constrain their large-scale deployment. Policy instruments introduced to address these barriers include feed in tariffs (FIT), tax credits, capital subsidies and grants, renewable energy portfolio standards (RPS) with specified standards for solar energy, public investments and other financial incentives. While FIT played an instrumental role in 2
describe changes in outlook due to the pandemic. The specific GDP measure that we use is the annual percent changes of constant price gross domestic product, where the base year is country-specific, according to WEO.

**Labor market projections.** To relate our findings to other labor market projections on the economic impact of COVID-19, we use data from the International Labour Organization’s (ILO’s) ILOSTAT, the employment-to-population ratio in percent, the number of persons who are employed as a percent of the total working age population, based on country labor force surveys, restricted to the group ages 15 years or older. The quarterly data for quarter 1 and quarter 2 of 2020 allow us to describe the change in employment at similar time points to the HFPS measure of the labor market.

# **4 Results**

## **4.1 Disruptions to Work in the High-Frequency Phone Surveys**

We start by examining the impact of the pandemic on work stoppages in different countries. As discussed in Section 3.2, we count a respondent as having stopped work if she was working pre-pandemic but no longer working the week preceding the interview. Figure 1 shows the share of respondents who report stopping work during the pandemic by country.

Panel (a) of Figure 1 groups countries by region, highlighting EAP countries in blue, ECA countries in red, LAC countries in green, MENA countries in yellow, and SSA countries in purple. Taking a simple average across countries, 34% of respondents reported stopping work. The average across countries in our data is 21% in the EAP region, 29% in the ECA region, 48% in the LAC region, 45% in the MENA region and 26% in the SSA region. (We note that the set of countries in our data is not representative of regions.) There is significant variation, even within regions. For example, within the LAC region, at the lower end 30% stopped working in Chile and 36% in Costa Rica, while at the higher end 59% stopped 17
**<u>Table 7: Characteristics associated with total hours worked in the previous week for female workers</u>**

|||Female Yout|h||Female Adul|ts|
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|Weekly earnings|1.86<br>[4.54]***|||1.08<br>[5.39]***|||
|Actual or predicted weekly<br>|||1.66<br>|||1.05<br>|
|earnings|||[4.02]***|||[5.30]***|
|||-9.36|-9.22||-7.22|-7.02|
|Own-account workers||<br>[9.12]***|<br>[8.98]***||<br>[9.31]***|<br>[8.99]***|
|||1274|1259||1012|993|
|Unpaid family workers||-.<br>[12.17]***|-.<br>[12.01]***||-.<br>[12.61]***|-.<br>[12.27]***|
|||-5.96|-5.85||-3.22|-3.03|
|Casual wage workers||[5.56]***|[5.43]***||[4.07]***|[3.79]***|
|Age|2.43|1.42|1.5|0.1|0.43|0.38|
||[1.09]|[1.18]|[1.25]|[0.36]|[3.45]***|[3.08]***|
|Age –squared|-0.06|-0.03|-0.03|0|-0.01|-0.01|
||[1.02]|[0.98]|[1.07]|[0.60]|[3.90]***|[3.73]***|
|Married|-2.47|-1.68|-1.62|-2.04|-1.18|-1.35|
||[2.01]**|[2.66]***|[2.55]**|[2.58]**|[2.45]**|[2.79]***|
|Had training|1.15|-0.56|-1.15|1.75|2.88|2.68|
||[0.51]|[0.32]|[0.65]|[0.96]|[1.93]*|[1.79]*|
|Below primary education|-5.31|-0.65|-0.54|-1.49|-3.19|-3.58|
||[2.66]***|[0.57]|[0.47]|[0.70]|[3.15]***|[3.53]***|
|Primary education|<br>-3.9|<br>-1.4|<br>-1.45|<br>-2.95|<br>0.21|<br>0.12|
||[3.12]***|[2.14]**|[2.24]**|[2.27]**|[0.35]|[0.20]|
|Middle|-5.39|-0.83|-1.66|-1.07|-1.39|-1.8|
||[3.05]***|[0.91]|[1.80]*|[0.65]|[1.35]|[1.73]*|
|Matric|<br>-7.02|<br>-3.66|<br>-3.8|<br>-4.57|<br>-3.36|<br>-4.48|
||[4.97]***|[4.28]***|[4.45]***|[4.18]***|[4.16]***|[5.41]***|
|Inter|-9.22|-6.23|-6.72|-4.96|-2.76|-3.77|
||[5.29]***|[5.12]***|[5.49]***|[4.00]***|[2.70]***|[3.60]***|
|Bachelors or Higher|-12.2|-8.6|-10.01|-7.75|-3.85|-6.36|
||[6.85]***|[6.58]***|[7.42]***|[7.41]***|[4.89]***|[6.99]***|
||5.66|2.86|2.8|2.63|0.55|0.47|
|Migration within 10 years|[3.63]***|[2.91]***|[2.86]***|[2.44]**|[0.79]|[0.67]|
|Manufacturing|-5.51|-1.33|-1.31|-8.37|-4.04|-3.93|
||[4.50]***|[1.94]*|[1.91]*|[8.76]***|[7.33]***|[7.12]***|
|Construction|1.83|5.39|5.77|1.7|5.38|4.6|
||[0.59]|[1.94]*|[2.04]**|[0.66]|[2.28]**|[1.91]*|
|Wholesale and retail|<br>5.82|<br>10.77|<br>10.69|<br>0.82|<br>9.07|<br>9.07|
||<br>[1.79]*|<br>[6.04]***|<br>[6.01]***|<br>[0.24]|<br>[10.06]***|<br>[10.04]***|
|Transportation|9.18|14.02|13.35|10.89|13.15|12.99|
||[2.82]***|[5.23]***|[4.99]***|[3.93]***|[5.41]***|[5.35]***|
|Service|<br>0.46|<br>-0.21|<br>-0.1|<br>-1.63|<br>-0.85|<br>-0.67|
||[0.31]|[0.19]|[0.09]|[1.76]*|[1.12]|[0.88]|
|Other industry|2.99|6.32|5.06|3.98|7.4|6.38|

Note: 1. Absolute t statistics in bracket. 2. * significant at 10%; ** significant at 5%; *** significant at 1%. 3. Other variables in the model but not reported include: household size, household demographic structure, household head information, and province dummies.

28
Capacity to deliver public goods has also been measured using proxies for governance, such as “absenteeism.” Chaudhary et al. (2006) reported from surveys in which enumerators made unannounced visits to primary schools and health clinics in Bangladesh, Ecuador, India, Indonesia, Peru and Uganda. On average, they found that across countries about 19 percent of the teachers and 35 percent of the health workers were absent. Within India, Muralidharan et al. (2011) estimated that doctor absenteeism rates ranged from 30 percent in Madhya Pradesh to over 67 percent in Bihar. Kremer et al. (2005) estimated teacher absenteeism rates varied from 15 percent in Maharashtra to 42 percent in Jharkhand, with higher absenteeism in poor states. Muralidharan et al (2017) surveyed schools across 1297 villages in India. They found that 23.6% of teachers were absent during unannounced school visits; they estimate the salary cost of unauthorized teacher absence to be $1.5 billion per year. The link between absenteeism and governance was found to be strong, with Kremer et al. (2005, p. 664) stating that “moving from a district with no inspections in the past three months to one where every school has been inspected in the past three months was associated with a seven-percentage point lower level of teacher absence (equivalent to nearly 30 percent of the level of absence observed in the data).” Muralidharan et al (2017) found that increases in the frequency of schools monitoring was strongly correlated with lower teacher absence.

The link between attendance and outcomes (student performance and health indicators) was also found to be strong.<sup>3</sup> For example, Duflo, Hana, and Ryan (2012) show that through the use of effective monitoring (time-stamped photos) and monetary incentives, teacher attendance can be improved. Furthermore, improved teacher attendance results in improved student performance.

> 3 The causal chain connecting “incentives” to “lower absenteeism” to “better outcomes” also relies on the competence of the service delivery agent. For example, the mere presence of an untrained (or otherwise incompetent) teacher may not translate into better grades. See for example, Das, Hammer, and Leonard (2008) and Pandey, Goyal, and Sundararaman (2010).

4
We construct loans and deposits by converting all reported LBS data (including restricted and confidential parts) into a bilateral country-to-country format. The LBS data are collected in a format where banks in each reporting country record their loans to and deposits from every counterparty country (all sectors). We transform this bank-to-country format into a country-to-country format by using mirror data available from the reported liabilities (Brei and von Peter 2018). In the latter format, deposits and loans from country _i_ to country _j_ need to include all sectors. The claims held by banks on country _i_ represent bank lending to all sectors (banks and non-banks) in every other country _j_ . In addition, what non-banks in country _i_ deposit with banks in country _j_ also represent claims of country _i_ on country _j_ and they can be inferred from liabilities that banks in country _j_ report owing to nonbanks in country _i_ . These mirror liabilities are reliable: banks know in which country the holders of bank deposits reside (unlike for holders of traded securities). Note that interbank positions appear twice whenever source and destination are both reporting countries. In those instances, we select the larger of two reported values _._<sup>25</sup> The use of mirror data has the benefit of maximizing coverage. Loans and deposits are available whenever the source or destination country reports information (recall the China example in subsection 2.2 in the main text). Despite the enhanced coverage thanks to mirror data, coverage remains incomplete when neither the source nor destination reports to the BIS. Incomplete coverage mainly afflicts the South-to-South block, although it has improved over time as more South countries have begun to report.

The treatment of unreported values in the loans and deposits data set is handled as follows. Bilateral positions between any two non-reporting countries are unobserved and thus treated as

> 25 Taking the maximum value addresses the general issue that incentives and reporting systems make underreporting more prevalent than overreporting. For instance, the set of reporting banks (‘‘internationally active banks”) is generally smaller than that on the counterparty side (‘‘all banks”) and taking the larger value gets closer to the ideal of including all banks on both sides.

32
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