rafmacalaba/data-use-mentions-extended huggingface dataset:
these specifications produce results that are similar to the results of the linear weighted aggregation function.

Temperature is represented as the mean temperature in degrees Celsius, and precipitation is measured as the mean rainfall in millimeters per hour. Both temperature and precipitation data come from NASA’s Global Land Data Assimilation System Project (Beaudoing and Rodell 2020). Air pollution is measured by the concentration of ground-level fine particulate matter (PM2.5), in micrograms per cubic meter, with data obtained from the NASA Socioeconomic Data and Applications Center (Hammer et al. 2022). The Flood Severity Index is based on data from the Global Active Archive of Large Flood Events by the Dartmouth Flood Observatory at the University of Colorado (Brakenridge 2024).

# **4. Documenting the change**

<u>Figure 1</u> shows the changes in the willingness to pay more taxes to combat climate change between 2016 and 2023. The proportion of the population supporting higher taxes for that purpose declined in the 30 countries in our sample, from 36.6 to 30.7. This 5.9 percentage point (16.1 percent) decline resulted from an average drop of almost 12 percentage points for 20 countries in our sample (countries below the 45-degree line in <u>Figure 1) and an average increase of about 6.3 percent in 10</u> countries (countries above the 45-degree line).

The proportion of the population willing to pay part of their income or higher taxes to combat climate change declined among virtually all groups (Table 1) by more than 20 percent for men and only 12 percent for women. By age, the most significant decline was among 40 – 49 (23.9 percent) and 30 – 39 (17.9). Among respondents 70 and older, willingness to pay did not change between the two survey rounds. The differences between the two rounds exceeded 20 percent among people with upper-secondary education, post-secondary education, and bachelor’s degrees
# 5. Empirical Results

In this section, we discuss the summary statistics of various indexes of scheme functionality and their constituent indicators for Jalanidhi, KWA-BM, and other community managed schemes. This is followed by a discussion of the results from the PSM exercise.

## 5.1 _Summary Statistics_

Indicators relating to Water Availability and Reliability are based on data from household surveys aggregated at the scheme level. Around 50 percent of Jalandihi schemes and 60 percent of other community managed schemes are supplying water every day of the week. The number is much lower for the KWA schemes where only 27 percent of the schemes were reported supplying water daily. On average, water is available only for 3.7 days for Jalanidhi and other community managed schemes and for 3.2 days for traditional supply driven KWA schemes. Moreover, the supply was considered adequate for 63 percent of Jalanidhi schemes compared to 62 percent for other community managed schemes and 55 percent for KWA schemes. Regarding regularity of water supply, 36 percent of Jalanidhi schemes were reported supplying water irregularly compared to 43 percent of other community managed schemes and 41 percent of KWA schemes. The majority of the respondents from the community schemes found water pressure in the network adequate. Around 33 percent of Jalanidhi schemes and 38 percent of other community managed schemes were reported having low water pressure in the network. In contrast, beneficiaries of around 70 percent of the traditional supply driven schemes complained about low water pressure. Similarly, beneficiaries of around 70 percent of the traditional supply driven schemes reported facing water shortage during summer months compared to only 38 percent for other community managed schemes and 49 percent of Jalanidhi schemes. The community schemes also perform better in terms of quality of water supplied. Beneficiaries of more than 50 percent of the KWA schemes reported getting colored water compared to only 8 percent for Jalanidhi schemes and 13 percent for other community managed schemes.

So Jalanidhi and other community managed schemes perform better compared to traditional supply driven KWA schemes in terms of availability, adequacy and quality of water supply
# **Table A24: Cost of Corruption**

||**Tax yield per**<br>**declaration**<br>**(average)**|**% Counterfac-**<br>**tual increase**<br>**without**<br>**corruption**|**Tax yield per**<br>**declaration**<br>**(average)**|**% Counterfac-**<br>**tual increase**<br>**without**<br>**corruption**|
|---|---|---|---|---|
|**A. Before Delegated Rand**|**omization of Ins**|**pector Assignment**|||
||Declarations wit<br>excess inte|h signifcant<br>raction|All declar|ations|
|Actual tax yield|$11,423||$10,446||
|Counterfactual tax yield without corruption, based on:|||||
|internal reference price|$940|8.2%|$97|0.9%|
|internal reference price & measured weight|$1,468|12.8%|$152|1.5%|
|external reference price|$2,281|20.0%|$236|2.3%|
|external reference price & measured weight|$2,962|25.9%|$306|2.9%|
|third party valuation advice|$1,102|9.6%|$114|1.1%|
|**B. During Delegated Rand**|**omization of Ins**|**pector Assignment**|||
||
# 1. Introduction<sup>5</sup>

There is growing evidence that factor misallocation has constrained firm performance in India. A large literature suggests that rigid industrial labor regulation reduces employment and productivity in firms. Besides labor, there has been rising interest in understanding the misallocation of land and capital, and their effects on the manufacturing sector. Some recent studies suggest that rigid land use regulation, such as land ceiling laws, have reduced productivity. Research on a related topic – the impact of policies promoting industrial parks and Special Economic Zones – is also growing.

Misallocation is important to study because it suggests that aggregate output could be increased by reallocating existing resources towards firms that can use them more productively. By one estimate, simply reallocating resources between existing Indian firms to match U.S. levels of efficiency in resource allocation could increase India’s aggregate productivity by 60%-80% (Hsieh and Klenow, 2009).

Factor misallocation could be one of the reasons for the absence of economic convergence between India’s states. The widening economic gap between Indian states has policy makers increasingly concerned.6 It is a puzzle, contradicting a basic paradigm in growth theory – the idea of convergence — and contrasting with the experience of the U.S. and Japan (cf. e.g., Barro and Sala-i-Martin, 1992; Caselli and Coleman, 2001). Since state governments have jurisdiction over many policy areas affecting factor markets, the design and implementation of factor market policies varies across states. It could be that some states remain poorer than others not because of a deficiency of resources, but because on an inefficient allocation of existing resources.

Despite the strong influence of state governments on factor market policies, there has been surprisingly little systematic analysis of how factor market misallocation and related policies vary across Indian states. This paper contributes to this policy-relevant research agenda by using Indian firm-level panel data from 1999-2014 to describe trends in factor market distortions across Indian states. We look at ‘permanent’
inference procedure for each of Hypotheses I-VI, as set out in Appendix Table A.1. We also included a small number of secondary tests based on different outcomes, samples, and/or specifications. In Section 4, we report results for every primary test; secondary tests are in Section 4 or in an appendix. To aid interpretation, we also include a small amount of supplementary analysis that was not discussed in the pre-analysis plan—e.g. impacts of advertised P4P on teacher attributes beyond observable skill and intrinsic motivation, and estimates from a teacher value added model—but are cautious and make clear when this is post-hoc.

# **3 Data**

The primary analyses make use of several distinct types of data. Conceptually, these trace out the causal chain from the advertisement intervention to a sequence of outcomes: that is, from the candidates’ application decisions, to the set (and attributes) of candidates hired into schools, to the learning outcomes that they deliver, and, finally, to the teachers’ decisions to remain in the schools. In this section, we describe the administrative, survey, and assessment data available for each of these steps in the causal chain. Our understanding of these data informs our choices of specification for analysis, as discussed in detail in the pre-analysis plan.

## **3.1 Applications**

Table 1 summarizes the applications for the newly advertised jobs, submitted in January 2016, across the six districts.<sup>19</sup> Of the 2,185 applications, 1,963 come from candidates with a TTC degree—we term these _qualified_ since a TTC degree is required for the placements at stake. In the table, we present TTC scores, genders, and ages—the other observed CV characteristics—for all qualified applicants. Besides these two demographic variables, TTC scores are the only consistently measured characteristics of all applicants.

The 2,185 applications come from 1,424 unique individuals, of whom 1
ROCKS database provides such regionally differentiated unit cost data for road maintenance activity.

Finally, to get a complete picture of infrastructure expenditure sources, the research also draws upon the World Bank’s Private Participation of Infrastructure (PPI) database that covers over 6,400 infrastructure projects in 137 middle- and low-income countries with private sector participation.

One big distinction between both the PPI database and the AidData’s Global Chinese Development Finance Dataset versus the others used in the analysis is that the latter provide actual executed expenditure, while the former only record financial commitments at the year of project closing, without recording how this is spent over time. Based on the assumption that infrastructure projects typically take five years from financial closing to complete, commitment data are converted to simulated disbursement data for greater compatibility with other sources. Similarly, AidData is also smoothened by taking five-year moving averages.

Exploratory data analysis is complemented with selective use of econometric techniques. To explore the relationship between infrastructure spending and GDP over time, with a view to assessing the extent of cyclicality, a fixed effects panel data model is estimated and further complemented with a panel vector autoregression (PVAR) analysis. In addition, the efficiency of infrastructure spending is assessed using Data Envelopment Analysis. The details of these techniques are discussed in the relevant sections.

> 5 Care was taken to establish whether the institutional responsibility for maintaining primary and secondary road networks in any given country was consistent with the corresponding level of the budget. Road network lengths were averaged over time to address some problematic fluctuations in the data over time.

5
|||||
|Land Area (Hectare)|4.986|4.775|5.358|-0.583|
||(4.210)|(3.390)|(5.458)|(0.012)|
|Land Value (Billion RMB)|1.307|1.328|1.286|0.042|
||(1.172)|(1.074)|(0.956)|(0.113)|
|Land Value (% of Market|0.573|0.579|0.583|-0.004|
|Capitalization)|(0.377)|(0.345)|(0.447)|(-0.057)|
|**Panel B: Cost of Equity**|||||
|Historical Annual Stock Market|-0.350|-0.321|-0.423|-0.102|
|Return|(0.130)|(0.134)|(0.085)|(-1.460)|
|Δ Historical Annual Stock Market|0.032|-0.029|0.190|0.219|
|Return|(0.220)|(0.203)|(0.188)|(1.241)|
|Dividend / Total sales|0.038|0.049|0.009|0.0406|
||(0.161)|(0.189)|(0.019)|(0.74)|
|**Panel C: Financial Constraints**|||||
|Size-Age Measure|1.019|0.779|1.43|-0.651|
||(3.808)|(1.691)|(6.001)|
---|---|
|**Poverty Rate in 1991/92**|38.6|28.1|28.7|40.8|
|**Poverty Rate in 2000/01**|35.6|17.6|25.8|38.6|
|**Change in Poverty**|-3.0|-10.5|-2.9|-2.2|
|**Breakdown in levels**|||||
|**Growth Component**|-4.6|-16.3|-4.4|-2.6|
|**Redistribution Component**|1.1|9.8|1.5|-0.4|
|**Residual**|0.6|-4.0|0.0|0.9|
|**Breakdown in percentages**|||||
|**Growth Component**|155%|156%|154%|119%|
|**Redistribution Component**|-35%|-93%|-53%|20%|
|**Residual**|-20%|38%|-1%|-39%|

Notes: Decompositions were calculated using the approach of Datt and Ravallion (1992). The analysis shown here uses 1991/92 as the base year for the decomposition.

# **3.2 Sectoral Decomposition of Changes in Poverty**

Another way to break down the overall change in poverty is by considering the contribution of changes in poverty in each sector to the national change. Following Huppi and Ravallion (1991), the national change in the headcount rate can be attributed to a combination of changes within each sector and shifts in population between sectors. There is also a small portion of the change that is due to an interaction effect between
|Actual discharge from vessel|LKCMB|2021-12-05-21.09.51.000000|Full|
|---|---|---|---|
|Actual loaded on vessel|LKCMB|2021-12-09-03.26.28.000000|Full|
|Actual vessel departure|LKCMB|2021-12-09-07.18.00.000000|Full|
|Actual vessel arrival|INVTZ|2021-12-15-12.09.00.000000|Full|
|Actual discharge from vessel|INVTZ|2021-12-16-05.28.00.000000|Full|
|Actualgate out|INVTZ|2021-12-22-06.11.00.000000|Full|
|Actualgate in|NPBRG|2022-02-03-09.15.00.000000|Full|
|Actualgate out|NPBRG|2022-02-17-18.16.00.000000|Full|
|Actualgate in|NPBRG|2022-02-18-09.52.00.000000|Empty|

_Table 4: Rules that consistent events must follow (TradeLens dataset)_

|**Prior event**|**Prior location**|**Event**|**Next location**|**Posterior event**|
|---|---|---|---|---|
|Vessel arrival|same|'Actual discharge from vessel'|same|Gate out<br>Rail departure<br>Loaded on vessels<br>Barge departure|
|Gate in<br>Rail arrival<br>Discharge from vessel<br>Barge arrival|same|'Actual loaded on vessel'|same|Vessel departure|
|Vessel departure|previous|'Actual vessel arrival'|same|Discharge from vessel|
|Loaded on vessel|same|'Actual vessel departure'|next|Vessel arrival|
|Gate out|previous|'Actual gate in'|same|Gate out<br>Rail departure<br>Loaded on vessel|
|Gate in<br>Discharge from vessel<br>Rail
), along with the confidence intervals. Results indicate that we do not reject the null that the GMD means are the same as the online</mark>

> 22 Clustering usually provides more conservative standard error estimates compared to robust standard errors. Still, we face the problem of the small number of clusters biasing our standard error estimates downwards because the number of regions in each country is low and this could lead to over-rejection of the null hypothesis (Cameron and Miller 2015). The ideal way to proceed is to make corrections by estimating the Wild cluster bootstrap- _t_ statistics which would provide more conservative standard errors. This downward bias will however not hurt us because, ideally, we are hoping that the null hypothesis of GMD and online survey mean equivalence is not rejected. But if it is not rejected with clustered standard errors, it will not be rejected with downward adjustments using the Wild method either.

16
**Figure 3. Labor supply by educational level**

(a) Women <!-- Start of picture text --> Labour supply by educational level (women)<br>Never attended 1 Primary education .8824 Vocational education<br>.6512<br>.3488<br>.1176<br>General education Secondary general education Secondary special education<br>.8311 .7059 .61<br>.1689 .2941 .39<br>0 1<br>Higher education Postgraduate student 1<br>.5517 .4483<br>0 1 0 1<br>lsupply<br>Graphs by Level of completed education (Q28)<br>1<br>.5<br>0<br>1<br>.5<br>Density 0<br>1<br>.5<br>0<br><!-- End of picture text -->

# (b) Men

<!-- Start of picture text --> Labour supply by educational level (men)<br>Never attended Primary education Vocational education<br>.875 .7742<br>.6286<br>.3714<br>.125 .2258<br>General education Secondary general education Secondary special education<br>.7961<br>.4958 .5042 .3724 .6276<br>.2039<br>0 1 0 1<br>Higher education<br>.5934<br>.4066<br>0 1<br>lsupply<br>Graphs by Level of completed education (Q28)<br>1<br>.5<br>0<br>1<br>.5<br>Density 0<br>1<br>.5<br>0<br><!-- End of picture text --> _Source: own elaboration on the ILO School-to-work transition survey
Duckworth, A. L., and Seligman, M. E. P. (2006). Self-discipline gives girls the edge: Gender in selfdiscipline, grades, and achievement test scores. _Journal of Educational Psychology, 98_ (1):198– 208. https://doi.org/10.1037/0022-0663.98.1.198 Duckworth, A. L., and Seligman, M. E. P. (2006). Self-discipline gives girls the edge: Gender in selfdiscipline, grades, and achievement test scores. _Journal of Educational Psychology, 98_ (1):198– 208. https://doi.org/10.1037/0022-0663.98.1.198 Duckworth, A. and Yeager, D. (2015). Measurement Matters: Assessing Personal Qualities Other Than Cognitive Ability for Educational Purposes. _Educational Researcher, 44_ (4): 237–251.

<mark>Duflo, E., Dupas, P., & Kremer, M. (2011). Peer effects, teacher incentives, and the impact of tracking: Evidence from a randomized evaluation in Kenya.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>101</mark>_ <mark>(5), 1739-74.</mark> <mark>Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., and Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta‐analysis of school‐based universal interventions.</mark> _<mark>Child development</mark>_ <mark>,</mark> _<mark>82</mark>_ <mark>(1): 405-432.</mark> D'Zurilla, T.
Nevertheless, the unpredictability of monsoon rains left colonial India highly exposed to famines, given its dependence on rain-fed agriculture. By reducing internal transportation costs, the arrival of the railroad networks could potentially allow food to be moved around the country as a means of improving food security. Burgess and Donaldson (2010) investigate the issue of how trade changes the weather-to-death relationship, by employing a colonial era Indian district database for the period 1875 to 1919. They show that the arrival of railroads in Indian districts dramatically limited the ability of rainfall shocks to cause famines in the colonial era. On average, before the arrival of railroads, local rainfall shortages led to a significant rise in the index of famine intensity. But after a district gained railroad access, the effect of local rainfall shortages on famine intensity was significantly muted. Burgess and Donaldson (2010) suggest that investments in transportation infrastructure like railroads that enable internal trade can play an important part in improving food security.

Chen et al (2016) study the heterogeneous effects of inter- and intra-city transportation infrastructure on the growth of 219 Chinese cities for the period 1999 - 2012. The long-term contribution of inter-city infrastructure in the form of highways or railways accounts for 6 percent of city growth, that of intra-city public transit being 2 percent. The core cities are found to benefit more from these infrastructures than peripheral cities. The impacts of transport infrastructures on economic growth are heterogeneous across regions, with more productive impacts in the west. Policy-wise, cities seem to benefit from intra-city transit in the short run, but most of the long-term benefits are brought by inter-city transportation facilities. In addition, infrastructure investments are most productive in core cities at the expense of peripheral cities. It is important to consider these trade-offs between regional inequality and efficiency while designing investment strategies.

**Table 14: Survey of selected papers on the development impact of railroads for freight**

|**Author**|**Level of**<br>**Analysis**|**
in their place to fetch the water, fetch water themselves on motorbikes, or they would pay others who have motorbikes to fetch water.

“ _The lack of water puts stress on men. It will force you to put in the effort because you can’t live without water. It will force me to put more effort as a man (...) Sometimes it forces us to distribute roles because as men, we mostly let our wives go for water. So it reaches a point where the water that we depend on that is nearer has dried up so it forces you to take responsibilities as a man so that the wife remains at home to do other household chores as you go look for water and bring it to the house_ ”. -Man, In-depth interview It is also the case that the decision regarding water collection and source choice is intertwined with decisions on water expenditures, which is often a decision made by older men. One woman explains: _“[The] decision [of where to collect the water] is ours, all of us, not just myself, if I say I can do that by myself I would be lying, because I don’t make money, and I will have to talk to my husband if the water is from far, I can tell him I don’t want to fetch the water from the dams today so please give money for the motorbikes to fetch the water if you have 250 [shillings], or if you have 200 shillings please give it to me and I will fetch it myself from the tanks..._ " So while men report not participating in the decision about the water source used for drinking, their decision on water expenditures determines which source can be used.

The quantitative data confirms that while women are more likely than men to make the decisions about the water source for drinking, the decisions are often made jointly. But it varies by household structure. When the husband has more than one wife,
organized as follows. Section 2 introduces the rationale for the analysis and describes the conceptual framework that defines and provides the structure of the Global BOS database. Section 3 presents the main data sources used. Section 4 describes the methodology to identify SOEs and create the Global BOS data set using ORBIS and additional data sources. Section 5 describes the structure

> 6 Some empirical efforts to estimate models using firm-level data in ORBIS have documented the low number of observations in ORBIS in Australia, Chile, Iceland, Mexico, New Zealand, Israel, among others.

> 7 A number of SOEs in the Global BOS database would not have been identified if some of these data sources had not been used.

5
**Implementation** : It is relatively straightforward to introduce legislative changes that require the publication of a call for tenders, including all relevant conditions for bidding, for all or nearly all tenders. The implementation of such requirements could be closely monitored using real-time public procurement data and the dashboard advocated earlier.

**5. Improve budgeting practices to limit end-of-year inefficiencies.** We documented a sizable increase in corruption risks at the end of each year (quarterly CRI jumps in the fourth quarter of most years since 2011). This is most likely due to pressures to spend allocated budgets at the end of the financial year, otherwise next year’s allocation will be lower (this is the so-called ‘December fever’). The need for spending the remaining budget quickly increases the room for corruption and inefficient spending.

**Recommendation** : Introduce more flexible budgeting rules limiting end-of-year rushed spending.

**Implementation** : There is a range of good budgetary practices for limiting end-of-year spending inefficiencies. Allowing carry-over of funds from the end of one fiscal year to the next represents a simple adjustment that can cushion at least some of the detrimental effects. Moreover, allowing organizational units to retain some of the savings made during the financial year could greatly dampen motivations for rushed spending at the end of the year. More fundamentally, abandoning incremental budgeting practices for alternatives such as performance or program budgeting could largely eliminate the documented inefficiencies and heightened corruption risks.

37