rafmacalaba/data-use-mentions-extended huggingface dataset:
water, low levels of system use were evident. Moreover, 15 percent of farmers reported nonavailability of water from the JCLIS during the Rabi season as well (Table A.5 in Appendix).

**Table 2. Median and average hours of operation of JICS in the crop year 2020-21**

|**Season**|**Median hours of operation**|**Average hours of operation**|
|---|---|---|
|Kharif 2020|0|16.7|
|Rabi 2020-21|60|84.4|
|Summer 2021|27.5|90.8|
|**Total in 2020-21**|**93.5**|**191.8**|

_Source: Created by authors using primary data collected from system operators_ The average irrigation fee for solar pumps is Rs. 34/hour. Operators of three solar-powered systems reported that the irrigation fee had yet to be decided. Nine other operators also said that while the irrigation fee has been decided, fee collection was not yet underway. Because solar pumps have no variable costs, water user group members do not feel the need to collect any fees even if money may be required in the future for repair and maintenance. Irrigation fees for diesel pumps vary from Rs. 120 to Rs. 140 per hour. Though the discharge from solar pumps varies during the day and across the seasons with the intensity of the sunlight, the irrigation fees remain the same across the year and have been set on an hourly basis for all systems. Among those who cultivated in the given season, 71 percent of farmers reported paying an irrigation fee in Rabi 2021, 30 percent in Kharif 2020, and 66 percent in summer 2020.

35 of the 50 systems experienced no breakdowns. Of the 15 systems that did, the most common problems were related to leakage in the distribution system and glitches in the control box, the control panel,
- Bartik, A., M. Bertrand, F. Lin, J. Rothstein, M. Unrath. 2020b. “Measuring the labor market at the onset of the COVID-19 crisis.” NBER Working Paper 27613.

- Becker, Gary S. (1958). “Competition and democracy.” _Journal of Law and Econ_ . 1, 105-109.

- Benitez, J.A., C.J. Courtemanche, A. Yelowitz. 2020. “Racial and ethnic disparities in COVID-19: Evidence from Six Large Cities.” NBER Working Paper 27592.

- Bethune, Z.A., A. Korinek. 2020. “COVID-19 infection externalities: Trading off lives vs. livelihoods,” NBER Working Paper No. 27009.

- Borgonovi, F., E. Andrieu (2020). “Bowling together by bowling alone: Social capital and COVID-19.” Covid Economics 17(13), 73-96.

- Brzezinski, A., V. Kecht, D. Van Dijcke, A.L. Wright. (2020) “Belief in science influences physical distancing in response to COVID-19 lockdown policies.” University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020-56.

- Chetty, R., J.N. Friedman, N. Hendren, M. Stepner. 2020. “How did COVID-19 and stabilization policies affect spending and employment? A new real-time economic tracker based on private sector data.” NBER Working Paper No. 27431.

- Cronin, C.J., W.N. Evans. 2020. “Private precaution and public restrictions: What drives social distancing and industry foot traffic in the COVID-19 era?” NBER Working Paper 27531.

- Desmet, K., R. Wacziarg. 2020. “Understanding spatial variation in COVID-19 across the United States.” NBER Working Paper No. 27329

- Ding, W, R. Levine, C. Lin, W. Xie. 2020a. “Corporate immunity to the COVID-19 pandemic.
<!-- Start of picture text --> 300 60<br>All ships All ships<br>Small ships Small ships<br>250 Large ships 50 Large ships<br>200 40<br>150 30<br>100 20<br>50 10<br>0 0<br>0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.5 0.55 0.6 0.65 0.7 0.75 0.8<br>Strength of Onboard Security in 2011 Strength of Onboard Security in 2011<br>(a) Number of attacks (b) Number of hijacks<br>Number of Attacks in 2012 Number of Hijacks in 2012<br><!-- End of picture text --> Figure 10: Marginal effects of armed private maritime security Note: The graph plots the marginal effects of _κ_<sup>2011</sup> ( _S_ ) on the number of attacks and hijacks. The value of _k_<sup>2011</sup> varies between 0.5 and 1, while all other parameters of the model are set to their benchmark value. We have omitted the values of _k_<sup>2011</sup> greater than 0.8 because the number of attacks and hijacks are all zeros. The dashed vertical line indicates the baseline value of _k_<sup>2011</sup> = 0 _._ 75.

inated for every 4.6 private maritime security teams hired in 2011. To arrive at this interpretation, we carry out a simple calculation using the Suez canal traffic as a rough proxy for the traffic volume through the piracy-affected waters. In 2012, 14,765 large ships passed through the Suez canal.<sup>10</sup> A one percentage point increase in private maritime security hires (148 hires) leads to 32 fewer attacks (35.3 percent of the
All surveys in Morocco are based on a master sample based on the latest population census and all surveys use the same stratification structure. Based on the population census, the country is divided into sampling districts each including a fixed number of neighboring households. Each sampling district belongs to one exclusive stratified area. For urban areas, strata include the region, province, city size (large, medium and small) and type of housing (“lux”, “modern”, “old medina”, “new medina” and “clandestine”). For rural areas, the strata are regions and provinces. These are the stratas that apply to all surveys administered in Morocco and that are defined and updated with population censuses.

For all surveys, the first stage of sampling consists of creating the Primary Sampling Units (PSUs) by joining neighboring sampling districts so that each PSU contains an approximately equal number of households. Neighboring PSUs are then joined in groups of five to create the sampling areas. Based on sampling theory, 20% of PSUs are sufficient to survey a country so that only one in five PSUs within each sampling area is selected. This procedure represents the first stage of the sampling process, applies to all surveys administered in Morocco and results in the set of PSUs to be used for selecting the households to interview.

In Morocco, sampling methods can differ across surveys depending on whether sampling is two stages or three stages. The 2001 NCSE followed a two- stages sampling while the 2007 NLSS and the quarterly LFSs followed a three stages sampling process. For the 2001 NCSE and in the first stage, 1,250 PSUs (710 urban and 540 rural) of approximately 300 households each were extracted from the initial set of PSUs based on the 1994 population census. In the second stage, twelve households per PSUs were extracted with a systematic extraction method.<sup>7</sup> For the 2007 NLSS and the quarterly LFSs, 1,848 PSUs (1,124 urban and 724 rural
European Commission (2007). _European Economy – Public Finance in EMU_ . European Commission, Brussels.

Guichard, S., M. Kennedy, E. Wurzel and C. André (2007), “What Promotes Fiscal Consolidation: OECD Country Experiences”, _OECD Economics Department Working Paper_ , No. 553 Gupta, S., E. Baldacci, B. Clements, and E.R. Tiongson. (2003). “What Sustains Fiscal Consolidations in Emerging Market Countries?” IMF Working Paper No. 03/224.

Gupta, S., B. Clements, E. Baldacci, and C. Mulas-Granados. (2004). “The Persistence of Fiscal Adjustments in Developing Countries.” _Applied Economic Letters_ : 1350–4851.

Kose, A., Kurlat, S. A., Ohnsorge, F. L., Sugawara, N. (2017). “A cross-country database of fiscal space.” Policy Research working paper; no. WPS 8157. Washington, D.C.: World Bank Group.

Lavigne, R., (2011). “The political and institutional determinants of fiscal adjustment: entering and exiting fiscal distress”. _European Journal of Political Economy_ , 27, 17-35.

Mattina, T. and V. Gunnarsson. (2007). “Budget Rigidity and Expenditure Efficiency in Slovenia”, IMF Working Paper, WP/07/131.

McDermott, C.J. and R.F. Wescott. (1996). “An Empirical Analysis of Fiscal Adjustment.” IMF Staff Papers No. 43, 725–53.

Mierau, J.O., Jong-A-Pin, R., de Haan, J., (2007). “Do political variables affect fiscal policy adjustment decisions? New empirical evidence.” _Public Choice_ 133, 297-319.

Mulas-Granados, C. (2003). “The Political and Economic Determinants of Budgetary Consolidation in Europe.” _European Political Economy Review_ 1(1): 015–039.
, but new entrants can be particularly dependent upon local infrastructure (e.g., established firms are better able to provision their own electricity if need be, which is quite common in India). Aghion et al. (2011) provide a theoretical model of this dependency. Many observers cite upgrading India‘s infrastructure as a critical step towards economic growth, and the Indian government has set aside substantial funds for this investment. The population census provides figures on the number of villages in a district which have telecommunications access, electricity access, paved roads, and access to safe drinking water. We calculate the percentage of villages that have infrastructure access within a district and sum across the four measures to create a continuous composite metric of infrastructure which ranges from zero (no infrastructure access) to four (full access to all four infrastructure components).<sup>5</sup>

> 5 In six districts (major cities) which were not further subdivided into separate geographic units, these indicators were not reported in the Census data. In these cases we assign the infrastructure access components as 100%. Our results are fully robust to excluding these major cities from the analysis sample.

12
).

Guyana Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1993 and source Illi, Code 2: _Employment office statistics_ and include persons in employment who are seeking a change of job or extra work and are therefore also registered at employment offices. Central Government employment data are taken from IMF Report No. SM/95/132 and relates to 1995. Non Central Government, Education and Health employment are taken from Guyana Public Sector Review of May 17, 1993 and relate to 1992. Both Education and Health employment are responsibility of Regional Administrations. State-owned enterprise employment is taken from IMF Report No. SM/95/132 and relates to 1995. Military employment data do not include personnel in paramilitary units, i.e., the Guyana People's Militia (1,500). Consolidated Central Government wages and salaries is from IMF Report No. SM/96/241 of September 24, 1996 and relates to 1993.

# **Honduras**

Unemployment data are drawn from CIA Factbook 1995 and relate to 1992. Data on paid employment in nonagricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1992. Data relating to Central Government Employment is drawn from Country Economic Memorandum/PA of November 17, 1994 (A-3). Central Government employment does not include military or military police. Data relating to education (teachers), military and local government are from Public Sector Modernization Technical Assistance credit (PSMTAC) of January 19,1996. Data on military employment include conscripts (13,200), but do not include personnel in paramilitary units, i.e., the Public Security Forces (5,500), under the authority of the Ministry of Public Security and Defense.

Wages and salaries as percent of GDP is taken from WB Report No. T-6740-HO of January 19, 1996 -Technical Annex for Public Sector Modernization Technical Assistance Credit.

**Jamaica**

Data on paid employment in non-agricultural activities
requiring more disciplined fiscal governance frameworks, and the fact that as more countries adopt these rules, they are also becoming the _de facto benchmark_ for fiscal policy.

Our paper fills a gap in the literature by exploring how the effects of fiscal rules develop over time and how these effects depend on the conditions under which the rules are adopted. Using a large sample of 108 countries, the results confirm that fiscal rules have positive effects on primary balances, though these effects take time to materialize.

32
the different representativeness of the various sampling subpopulations and the changes in household composition over time. Section 4 presents improvements in field operations and data collection to facilitate the implementation of the proposed methodology. Section 5 briefly introduces the Living Standards Measurement Study (LSMS), defining its objectives and key aspects of the quality of the estimates. Section 6 illustrates the empirical application of the proposed methodology to the LSMS-ISA data, using the 2009, 2013 and 2015 waves of the Uganda National Panel Survey as a case study. Section 7 summarizes the main findings and concludes.

# 2. FORMAL DEFINITION OF CROSS-SECTIONAL AND LONGITUDINAL POPULATIONS

2.1. Populations observed at different points in time is . We represent tt tt tt Let UU be the population of NN individuals of a given country at the current time t. UUt = tt tt,1 tt,ii tt,MM partitioned into MM∑ . subpopulations of households denoted as UU , ... , UU , ... , UU tt tt,ii tt,ii tt the set of households as HHMMtt = {1, ... , ii, ... , MM }. The household UU has NN individuals and NN ii=1 tt,ii as cross-sectional populations of individuals and households since NN they refer to a specific point in time. tt tt We can refer to UU and HH To analyze the dynamics of cross-sectional populations in a longitudinal setting from an initial longitudinal-populations . For the sake of simplification, we first introduce a population not partitioned in households. Thus, point in time tt<sup>∗</sup> , to the current time tt, we introduce the concept of individuals. The longitudinal-population identification is complicated by both the scope of study and the longitudinal observation's tt←tt<sup>∗</sup> tt←tt<sup>∗</sup> operability over time (Helliot et al. 2009). The most common definitions are the intersection-let UU denote the longitudinal population of NN . tt
# References

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vignette responses amongst the regressors commonly used to explain subjective welfare. However, the effects were neither very strong nor consistent across different vignettes.

In their second and third tests, BHR explored the extent of bias due to the aforementioned frame of reference effect by embedding vignette rankings by households in standard regressions for subjective welfare. BHR did this in two ways: they included vignettes among the covariates and, to address endogeneity concerns with this approach, BHR also recalibrated the selfassessments of welfare to accord with the heterogeneity in scales revealed by the vignette responses. The striking finding of these tests is that the bias is negligible with respect to the ―income effect‖ on subjective welfare as well as other covariates. Based on this study, the concerns that past uses of subjective economic welfare data are compromised by systematic differences in the meaning given to the scales used appear to be unwarranted.

A third test by BHR involved estimating an explicit model of the heterogeneity in scales, whereby the thresholds are modeled as functions of covariates, assuming that the structure of this heterogeneity is common between ―own welfare‖ and the vignettes.<sup>34</sup> As usual, there is assumed to be a continuous latent variable for the respondents’ self-rated welfare, but now the thresholds are explicit functions of a vector of explanatory variables. The extra information on the vignette responses provides the basis for identification, under the assumption that the thresholds for selfassessed welfare are determined identically as for these thresholds in the vignette responses. This is a natural assumption to make. Without the vignettes, identification would only be possible under questionable assumptions about the nonlinearity of the functional forms involved (as discussed in King et al., 2004). Thus BHR were able to model determinants of the thresholds separately to those of the latent continuous variable for subjective welfare.

This approach revealed similar results on the factors influencing subjective welfare, but also threw new light on the heterogeneity in scales. In particular, BHR found that poorer households tend to have a narrower range in
86 In addition to a lifeline rate for residential consumers consuming less than 100 kWh a month, a second category of a lower lifeline rate for those consuming less than 50 kWh a month was introduced in January 2014 (IMF 2014m; <u>http://www.steg.com.tn).</u> The prices of petroleum products were lowered in 2009, raised twice in 2010, and then once a year in 2012, 2013, and 2014. Against the backdrop of the political turmoil in Tunisia and sweeping across the rest of North Africa, no price adjustments were made in 2011. None of the individual price adjustments exceeded 10 percent; the highest increase was 8.6 percent for diesel fuel in December 2010.

In January 2014, the government introduced a new pricing formula, which would raise domestic prices by TD 0.1 (US$0.06)/liter if international prices increase by DT 10 (US$6) per barrel—TD 0.063 (US$0.04)/liter—in a quarter. The authorities had planned 3‐percent increases in March and September 2015 for the prices of petroleum products, and 7‐percent increases for the electricity tariffs of medium and low‐voltage consumers in January 2015, but the electricity tariff increases were canceled. Because world oil prices had fallen far below the prices assumed in estimating subsidies, petroleum product price increases were not implemented, although the prices of kerosene and LPG for household use remained heavily subsidized. In December 2015, the Ministry of Industry, Energy, and Mines stated that the government would start applying a new automatic pricing mechanism in January 2016, with the first fuel price adjustments to be applied in July 2016 (Kapitalis 2015).

An audit of energy firms presented to the council of ministers in August 2014 found a complex web of cross‐subsidies. Among them is a large subsidy historically provided through an artificially low price of crude oil sold by the national oil company to
between one grade and the next. Again, this is consistent with what has been reported using data from other countries (see Kane and Staiger 2008; Jacob, Lefgren, and Sims 2010; Rothstein 2010; and Andrabi et al., 2011). These estimates combine to give a cumulative effect of 0.123 SD.

As seen from Table 8, the magnitudes and patterns of the estimated correlation coefficients are consistent with the assumptions made in Section 4.3, namely the correlation in teacher content knowledge between any two grades is positive, ρ�,�� �0 for all g and g′; and (ii) the further apart any two grades, the lower is the correlation in teacher content knowledge across grades; i.e., ρ�,�� is decreasing in |g�g′|. We can therefore interpret the sum of the reduced form coefficients, β<sup>�</sup> � �β<sup>�</sup> �, as a lower bound on the cumulative effect. Moreover, this lower bound is fairly tight: Adjusting the structural estimates for the fact that student test scores are measured halfway through year 4, so that CE��α�0.5 �α∑���� γ<sup>�</sup> �0.094, we see that this number is 13% larger than the sum of the reduced form coefficients, β<sup>�</sup> � �β<sup>�</sup> � �0.083 (Table 4, column 5).

Second, as discussed in Section 4.4, introducing previous teacher test scores as a regressor not only reduces the overall bias, but also shifts a disproportionate amount of the remainder on the corresponding coefficient: The bias in β<sup>�</sup> � is 0.015 or 50% of the true αγ. In contrast, the bias in β
upside in interest rates). And to make matters even worse, bank supervisors and regulators often times lacked the appropriate information to readily monitor the developments unfolding in the market place.

Credit reporting systems (CRSs) are important institutions in credit markets in that they provide a complete picture of an individual or firm’s creditworthiness. Besides being important for credit risk management, the availability of high quality and timely information on borrowers’ behavior is important for other reasons as well, such as developing a “credit culture.” A credit culture facilitates access to credit as it addresses the fundamental problem of credit markets: asymmetric information between borrowers and lenders, which leads to adverse selection and moral hazard. This problem is reduced as credit reporting systems capture current and historical lending and payment information. The more accurate, complete and timely information that is available in the credit system, the better the lenders are able to use credit data to grant credits and monitor their portfolios. One of the most important institutional elements supporting a well-functioning credit market is credit reporting firms. They are able to provide rapid access to accurate, reliable, and standardized information on potential borrowers and are of critical importance in determining risk exposure.<sup>5</sup>

> 4 The guidelines that sound financial regulation should observe have been assembled in Brunnermeier _et al_ (2009).

> 5 The benefits of the sharing of positive and negative information and thus of complete credit reporting systems are supported by profuse empirical evidence: see Jappelli and Pagano (1999), Barron and Staten (2003) and Majnoni _et al_ (2004). For theoretical developments see Pagano and Padilla (1993) and Brown and Zehnder (2005).

3