rafmacalaba/datause-encoder-data huggingface dataset:
the compulsory tariff caused a major improvement. Acquisition costs declined as a percentage of gross premiums in 2002.

Most claims are reportedly settled without long delays. However, some claims suffer from long delays and effectively represent long-tail business. Because of the operation of the Unified Bureau, expense ratios are similar for large and small companies. Some of the smaller and/or weaker companies reportedly suffer from a greater number of disputes with claimants and from longer delays in settling claims.

There appears to be general dissatisfaction with the impersonal allocation mechanism of the Unified Bureau among both companies and policyholders. Drivers complain about the lack of individual choice and control in selecting an insurance company. Insurers, both large and small, also profess a preference for greater freedom, arguing for the right of insurers to reject bad risks and for encouraging the use of a market-based mechanism with greater competition. However, the survival of the Unified Bureau for nearly two decades and the fact that it favors smaller companies suggest that the majority of insurance companies support the current system.

|**Table 11: Performance**|**of Motor**|**Insuran**|**ce, 199**|**7-2002**|||
|---|---|---|---|---|---|---|
||1997|1998|1999|2000|2001|2002|
|Gross Premium Income (GPI) (mn JD)|38.55|44.68|45.15|44.48|49.24|64.56|
|GPI Growth (%)||15.9|1.0|-1.5|10.7|31.1|
|Reinsurance Ratio %|8.8|11.0|11.5|13.0|10.8|8.0|
|NIC/NEP|83.3|80.2|80.6|83.7|81.7|77.4|
|Net Commission (% of GPI)|6.0|4.9|4.6|5.0|4.4|3.7|
|Total Expenses (% of GPI)|17.9|17.6|17.9|19.5|17.6|14.3|
|Traditional Combined Ratio|107.1|102.6|103.1|108.2|103.6|95.3|
|Net Combined Ratio (% of NEP)|109.6|106.6|106.0|111.6|107.9|99.3|
|Underwriting Result (% of NEP)|-9.6|-6.6|-6.0|-11.6|-7.9|0.7|
|Unearned Premium Reserves (% of NEP)|41.9|40.8|40.3|41.6|40.5|40.4|
|Outstanding Claims Reserves (% of NEP)|34.9|34.6|34.9|39.5|40.1|38.0|
|Total Reserves (% of NEP)|76.8|75.5|75.1|81.0|80.6|78.4|
|Outstanding Claims Reserves (% of NIC)|41.9|43.2|43.3|47.2|49.1|49.2|

Source: Insurance Commission Development of an insurance information bureau to collect data on loss experience, driving records and fraudulent claims among all licensed companies would facilitate the sharing of data and the use of a malus-bonus system, rewarding good drivers with premium discounts and penalizing bad ones with heftier premiums. The development of an insurance information bureau could make easier the lifting of administered tariffs on TPML and the elimination of the unified bureau. As companies would retain the right to reject bad risks, the creation of a national pool for bad risks would also need to be considered.

14
29 number of approaches could be considered including: lifeline rates, vouchers, increasing social assistance payments, and general adjustments to wages and pensions."<sup>8</sup>

# _"Lifeline" rates_

Lifeline rates which set prices for small volumes of consumption at low levels, and "senior citizen rates", which provide pensioners on fixed incomes with low-cost energy, are often used to alleviate the social cost of raising energy prices to the low-income population. They may also help make energy price reforms more politically palatable -- depending on what income groups represent the constituency for low prices. Lifeline pricing is administered by the utilities which provide discounted prices to consumers for a single "needed" block of energy while charging full price for any consumption of excess energy over the limited lifeline block. Single (as distinct from multiple) block pricing has the advantage of simplicity: everyone gets to consume a certain amount of energy at a low price. But block rates involve "leakages", that is, the consumption of the non-poor is still subsidized."9 Alternative solutions that try to target lifeline price subsidies based on means-testing (or other targeting mechanisms) would clearly be administratively more complex. For example, the block rate could be given only to the "certified poor". But this may be administratively too complicated (it would require identifying the eligible poor and integrating this information into the utilities' billing systems). And, although it appears leakage-free, a growing gray economy and inadequate means-testing suggests that it is not (we have seen that household budget surveys significantly under report income). Thus, targeted bloc rates are imperfect in a transition economy and probably not cost effective. Finally, providing lifeline rates (unless the utility receives compensation from the budget implies a cost to the utility that will affect the utility's cost recovery. Ideally, lifeline subsidies would be compensated from the budget, and not financed internally by the utility itself through cross-subsidies that are inefficient, unfairly tax the higher priced consumer (usually industry) and thereby distort industrial growth and employment.

18/ Bahl and Linn 1992.

19/ To avoid this problem, the energy tariff on consumption after the first block, could be set _above_ the long run marginal cost so that the average price to consumers of large quantities is roughly the efficient price.
# **<u>D: Summary statistics of variables (yearly, 2020 only)</u>**

|Variable|N|Mean|S.D.|Min|Median|Max|
|---|---|---|---|---|---|---|
|Log on-chain crypto-assets volume/GDP (all transaction<br>sizes)|147|-4.37|1.15|-8.91|-4.31|-1.69|
|Log on-chain crypto-assets volume/GDP (transaction<br>size < US$10K)|147|-6.43|1.06|-10.24|-6.43|-3.75|
|Log GDP per capita|194|8.63|1.40|5.54|8.53|11.67|
|Credit to private sector (% of GDP)|194|66.10|49.94|1.93|52.97|266.61|
|Account ownership (% of adult population)|165|61.63|25.73|8.57|58.84|99.92|
|Personal remittances, received (% of GDP)|229|4.68|6.61|0.00|2.33|38.98|
|Monetary Freedom Index|184|74.65|10.88|0|76.9|87|
|Financial Freedom Index|181|48.95|19.28|0|50|90|
|Mobile cellular subscriptions (per 100 people)|188|111.68|30.17|43.93|112.06|291.65|
|Fixed broadband subscriptions(per 100people)|184|17.61|14.53|0.00|15.70|53.20|

43
following 2011 - contradicting the headline estimates of the leaked 2017 NSS survey. These apparently contradictory results, combined with restrictions on the release of the NSS-2017 consumption survey, has given rise to a new Great Indian Poverty Debate, a sequel to the debate from the 1990s (Deaton and Kozel, 2005; Kijima and Lanjouw, 2005).

The private sector has recently stepped in by fielding its own household consumption survey called the Consumer Pyramid Household Survey (CPHS). The CPHS may be preferred to alternative data sources used to date for several reasons (but remains second-best to the NSS household consumption expenditure survey for poverty measurement). First, it collects detailed expenditure information on about 115 items, offering household consumption data for the first time since the NSS-2011. Second, the CPHS contains a panel of approximately 174,000 households that covers 28 states representing over 95% of India’s population. Third, it is conducted continuously at four-month intervals since its launch in January 2014. This opens the possibility of tracking poverty and inequality at a frequency higher than what has been traditionally feasible based on NSO’s quinquennial consumption expenditure surveys. The CPHS is already being used in empirical research. Chanda and Cook (2020) and Chodorow-Reich et al. (2020) use it to estimate the impacts of the demonetization policy, Deshpande (2020) and Gupta et al. (2021a, 2021b) have used the survey to quantify the impact of Covid induced lockdowns on labor market indicators, and Ghatak et al. (2020) employ the CPHS to study rates of consumption and savings in low-income households in India.

Despite these advantages, the CPHS also has its limitations. The CPHS adopts a measure of consumption that is not readily comparable to that of the NSS, stemming from differences in survey instruments. Furthermore, scholars have questioned the representativeness of the survey compared to NSS surveys, due to differences in sample design and geographical coverage (for instance Somanchi and Dreze 2021 and Somanchi, 2021). Both of these differences will have important impacts on poverty estimates for India (e.g., Deaton, 2003).

The objective of this paper is two-fold. First, we conduct a comprehensive examination of potential biases in the CPHS survey and propose adjustments to the survey weights that transform the CPHS into a nationally representative dataset. The outcome of this work will hopefully serve as a public good for anyone looking to use the CPHS for their empirical research. Second, we use the reweighted CPHS to construct NSS-compatible measures of poverty and inequality for the years 2015 to 2019. The challenge in this second objective is similar to that of Tarozzi (2007) which seeks to establish comparability in welfare aggregates across rounds of NSS’ consumption ex3
properties (reported by 75% of respondents in the LFS but only 4% in Prindex) may be one factor contributing to this result, it highlights the need for careful assessment of Prindex’s conceptual foundations, sample selection, implementation protocol and actual data to help appreciate the type of issues it can address. Limitations imposed by the fact that in more than 50% of cases, data on documentation and perceived tenure security were reported by family members rather than owners suggests that methodological adjustments can help this initiative achieve its potential and make a meaningful input to the land policy debate while preventing contradictory policy recommendations from results that reflect differences in data collection protocols rather than ground reality.

The rest of the paper is structured as follows: Section 2 discusses the conceptual framework and relates it to the evolution of Zambia’s land tenure system. Section 3 presents data and descriptive evidence on genderdifferentiated access to formal documents, transferability, perceived tenure security, and demand for documentation at the household and parcel levels. Section 4 presents regression evidence on factors (including access to different types of documentation) associated with higher levels of tenure security, transferability, and demand for formal documents. It also compares our findings from LFS data to those emerging from the Prindex poll that are comparable and explores reasons that might lead to differences. Section 5 concludes by drawing out implications for policy, research, and future efforts at data collection on land rights as a part of the SDGs.

# **2. Conceptual framework and empirical relevance**

To link the conceptual framework to measurement, we use the literature on land tenure and socio-economic outcomes as well as sources of demand for documented tenure to identify aspects of gender-differentiated tenure security in terms of (i) the ability to transfer via bequest or sale; (ii) access to documented land rights, and (iii) perceived security of land rights. This is complemented with a discussion of demand for secure land rights as well as a description of Zambia’s land tenure system to highlight how data could make the policy dialogue more evidence-based and allow benchmarking of progress over time.

# **2.1 Evidence on benefits from land titling**

Development economists have long highlighted the central role of institutions, i.e., social constraints on human interaction enforced by the state or communities to structure incentives in any exchange, in shaping growth and the distribution of its gains among the population (Greif 1993; North 1971). Property rights are a key institution that allows individuals or groups to lay ‘a claim to a benefit or income stream that the state will agree to protect through the assignment of duty to others who may covet, or somehow interfere with, the benefit stream’ (Sjaastad and Bromley 2000). By defining who is entitled to the benefits flowing from a resource, how land rights are defined and can be accessed is a key element of societies’ social fabric.

4
employment losses. Although they were less likely to grow conditional on survival, larger firms were more likely to survive, such that, in net terms, larger firms shed less labor. Part of the sorting by gender also occurred along occupational lines.<sup>3435</sup> That is, men also suffered more because they were more likely to be working in white-collar jobs, which were more vulnerable.

Differential treatment effects varied by occupation as well as firm size, and are obscured in aggregate statistics, which reflect the combined effect of sorting and differential treatment. When firms contracted, the share of blue-collar workers that were female decreased somewhat, while the share of white-collar workers that were female increased. For blue-collar workers, these relationships were slightly exacerbated during the crisis, such that women were more likely to suffer job losses than their male colleagues within the same firms. By contrast, differential treatment protected white collar women relative to their male colleagues. The crisis mildly (but not consistently statistically significantly) reinforced the pattern that women in white collar jobs are more likely to be spared when firms contract. Moreover, they benefitted significantly more from firm expansion than during non-crisis times. However, these differential treatment effects were very small. That is, even with evidence of differential treatment, the claims about crises inducing a vast surge in employer discrimination discussed in the introduction appear grossly exaggerated; if discrimination was really driving women‘s vulnerability, one would have expected to see much more dramatic differences.

This paper has documented heterogeneity in the relative importance of these channels across different types of firms and workers in different occupations. It has, moreover, demonstrated how aggregate statistics may mask countervailing transmission channels. Examining to what extent this is the case using matched employer-employee data, and assessing to what extent our findings generalize to other sectors and other crises, are promising areas for further research. Overall, this paper underscores the importance of analyzing and documenting heterogeneity in firms‘ adjustment to major macroeconomic shocks in understanding the distributional impacts of crises.

> 34 However, white-collar workers comprise only a small share of all manufacturing employment, such that aggregate gender differences in crisis impact associated with sorting into different occupations are small.

> 35 We also examined whether or not the fact that white-collar workers were hit harder by the crisis was due to withinfirm adjustment in the occupational structure of the workforce (for example, one might speculate that firms have a proclivity to fire disproportionately more white-collar workers when they are confronted with a shock) but did not find any evidence that this was the case. Results are omitted to conserve space but available from the authors upon request.

24
Table 3: Regression of municipal cohort birthrate on municipal secondary school density

|_Outcome: Number of births conc_|_eived between_|_age 15–19, per_|_100 females_|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Secondary school density (_t-4_)|-0.269***|-0.250**|-0.252**|-0.563***|-0.499***|
||(0.115)|(0.112)|(0.112)|(0.196)|(0.200)|
|_Control variables_||||||
|Primary enrolment (_t-5_)†|-0.014***|-0.015***|-0.015***|-0.013**|-0.011*|
||(0.003)|(0.004)|(0.004)|(0.005)|(0.006)|
|Pre-school rooms‡|0.001*|0.001**|0.007***|0.001|-0.001|
||(0.000)|(0.000)|(0.002)|-(0.004)|-(0.004)|
|Male/Female ratio|19.264***|18.753***|18.698***|19.406***|19.769***|
||(1.025)|(1.000)|(1.002)|(1.161)|(1.234)|
|Bolsa Família (log)||-0.985***|-1.093***|-0.618***|-0.803***|
|||(0.114)|(0.123)|(0.176)|(0.192)|
|Bolsa Família (pre recipient)||0.018|0.021|0.041**|0.036**|
|||(0.015)|(0.015)|(0.016)|(0.017)|
|Year (linear trend)|X|X|X|X|X|
|Municipal fixed effects|X|X|X|X|X|
|Municipal spending and GDP||X|X|X|X|
|State by year effects||X|X|X|X<br>|
||||||Pop 10k–100k|
|Municipalities in sample|All|All|Pop <500k|Pop 10k–100k|+ new school<br>1997–2005|
|R2 (within)|0.130|0.195|0.195|0.296|0.316|
|R2 (between)|0.078|0.052|0.050|0.050|0.054|
|Observations|43,941|43,941|43,635|22,576|17,963|
|Municipalities<br>|4,884<br>|4,884<br>|4,850<br>|2,550<br>|2,037<br>|
|Notes: This table reports the resul<br>the number of secondary schools p<br>5 condition log-municipality expen<br>omits municipalities with populati<br>populations greater than 10,000 a<br>received at least one new schoo<br>standard errors are reported in par<br>_†Primary enrolment_is the lagged<br>municipality.<br>‡_Pre-school room_reflects the con|ts of regressing<br>er 100 in perio<br>ditures and the<br>ons greater than<br>nd less than 1<br>l in the period<br>enthesis. ***, *<br>number of stud<br>emoraneous|cohort births,<br>dt−4. In add<br>change in log<br>500,000 from<br>00,000. Colum<br>s 1997–2009<br>*, and * denot<br>ents enrolled i<br>umber of mu|from age 15 t<br>ition to the rep<br>-municipality e<br>the sample. Co<br>ns 5 and 6 ad<br>and 1997–200<br>e statistical sig<br>n year 8 of pri<br>icial re-sch|o age 19, per 100 f<br>orted variables, esti<br>xpenditures. The th<br>lumns 4–6 restrict t<br>ditionally restrict t<br>5, respectively. M<br>nificance at 1%, 5%<br>mary school in peri<br>ol classrooms a r|emales in periodton<br>mates in Columns 2–<br>ird column of results<br>o municipalities with<br>o municipalities that<br>unicipality-clustered<br>and 10%.<br>odt−5in each<br>ox for re-school|

‡ _Pre-school room_ reflects the contemporaneous number of municipal pre-school classrooms, a proxy for pre-school availability.

Source: Authors’ analysis based on school data from the 2002 wave of the Brazilian School Census; official population estimates from the Brazilian Census Bureau; births by age from Brazilian Vital Statistics.

33
Table 6: Prediction 5: Effects on Education Performance and Female Winners

|**Panel A**|**Edu index in 2004-05**<br>*** AM edu in 2004-05**|**Edu index in 2004-05**<br>*** BM edu in 2004-05**|**Woman Wins**<br>*** AM edu in 2004-05**|**Woman Wins**<br>*** BM edu in 2004-05**|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Treat_|0.796|2.736|0.055|0.142|
||(0.568)|(1.352)**|(0.049)|(0.064)**|
||[0.121]|[0.095]*|[0.154]|[0.095]*|
|**Panel B**||**First **|**Stage**||
|_Cut-of_|-0.274**|-0.264**|-0.213**|-0.231***|
||(0.129)|(0.117)|(0.089)|(0.086)|
|Untreated _y_ Mean|0.430|-0.450|0.023|0.030|
|Observations|158|215|1,449|1,527|
|State FE|x|x|x|x|
|Year FE|x|x|x|x|
|State*Year FE|-|-|x|x|
|Clusters|154|208|200|211|
|Bandwidth|8.519|11.210|11.897|12.447|

_Notes: y_ refers to the dependent variable. Estimates in columns (3) and (4) are for unreserved seats only. BM and AM denote below median and above median respectively. Columns (1) and (2) include the following district-level covariates from the 2001 Census: the child sex ratio, decadal population growth, population share of scheduled castes/tribes, and share of villages with safe drinking water, electricity, a paved road approach, a primary health centre, and a telephone/telegraph/post office. Year fixed effects in columns (1) and (2) are for the first year of DPEP implementation in the state. Robust standard errors clustered by the running variable in parentheses. Anderson (2008) q-values in square brackets. Bandwidth is reported in female literacy rate percentage points around treatment cut-off of 39.3 percent. *** _p <_ 0 _._ 01 ; ** _p <_ 0 _._ 05 ; * _p <_ 0 _._ 10.
**Table 2: Environmental Benefit per TCE and the Total Losses from Lost Wind-Based Generation, Three-North Region, 2010**

|Pollutants|Units|SEPA<br>study|Pollution index<br>per TCE(t)|Environmental benefit<br>losses per TCE<br>(US$/TCE)|Losses from<br>lost<br>generation<br>(million<br>US$)|
|---|---|---|---|---|---|
|TSP|US$/ton|5,801|0.017|98.617|183|
|SO2|US$/ton|379|0.022|8.338|16|
|NOx|US$/ton|269|0.01|2.69|5|
|CO2|US$/ton|10|2.7|27|50|
|Total|||2.749|137|254|

_Note:_ For coal, the price is US$100/TCE, and the coal consumption for power generation is 334 g/kWh. _Source:_ Beijing Energy Saving and Environment Protection Center and China’s New Renewable Energy Target: The Green Leap Forward (ESMAP and the World Bank Policy Note). _Source:_ The benefit lost resulting from wind generation loss is based on the data in Table 3.

25. The wind generation losses in 2009 and 2010 in selected regions are in Table 3.

**Table 3: National wind power generation loss in selected region, 2009 and 2010**<sup>**12**</sup>

||**On-gri**<br>**(**|**d generation**<br>**GWh)**|**Generat**|**ion loss (GWh)**|**Share**<br>**loss a**<br>**ge**|**of generation**<br>**s % of wind**<br>**neration**|
|---|---|---|---|---|---|---|
||2009|2010|2009|2010|2009|2010|
|**Inner**<br>**Mongolia**|8,470|14,366|1,986|4,202|19.0|22.6|
|**West**|5,971|10,506|1,468|3,054|19.7|22.5|
|**East**|2,499|3,860|518|1,148|17.2|22.9|
|**Jilin**|2,009|2,852|194|520|8.8|15.4|
|**Gansu**|1,153|1,876|181|248|13.6|11.7|
|**Heilongjiang**|1,497|2,816|113|332|7.0|10.5|
|**Liaoning**|2,537|4,568|23|128|0.9|2.7|
|**Hebei**|2,333|4,604|264|122|10.2|2.6|
|**Others**|7,611|13,426|0|0|0.0|0.0|
|**Total**|25,610|44,508|2,761|5,552|||

_Note:_ Data for 2010 are estimated by multiplying the data in the first half of 2010 by 2<sup>13</sup> . _Source: Inspection Report of Wind Power and Photovoltaic Generation_ , State Electricity Regulatory Commission (SERC), January 2011, and team’s calculation.

12 According to SERC’s report, the generation losses are 12.3 TWh and 20 TWh in 2011 and 2012 respectively.

13 According to statistics in SERC’s report, the total wind power generation was 49.4 TWh. The generation losses was about 10 percent of it.

9
# iii. Robustness checks

The previous results might be subject to endogeneity deriving from omitted variables bias and reverse causality. Omitted variables bias arises when there are unobserved country‐pair specific policy variables (peaceful relationship, common legal origin, etc.) that affect both exports and trading times. The rich set of fixed effects that are included in the regressions together with the policy controls that vary at the country‐pair level control for a significant degree of the omitted variables. Moreover, in a second exercise that is presented in sub‐section (iv), we estimate the impact of trading times on exports of time sensitive products using a difference in difference approach. In this case, the higher number of fixed effects that are included in the regression allows us to fully control for omitted variables (see section‐below).

Reverse causality may arise when the time to export variables are likely to be correlated with country exports. An improvement of rail or ports infrastructure and administrative time costs has positive effects on exports. However, trade partners that have better trade prospects or that wish to improve their current trade volume will also invest more on such trade facilitating infrastructure. Such incentives of infrastructure reform are clearly important in the case of the Belt and Road Initiative. In particular, the rising trade prospects of China and its increasing need for energy supplies are some of the major incentives to improve infrastructure in the context of the BRI. To control for reverse causality, we use three types of robustness checks. First, we estimate the regressions excluding the nodal countries in the BRI, mainly Europe and China, which are those that are trading more between them. Second, we re‐estimate the regressions excluding the extractive industries. Third, we follow an instrumental variable approach to estimate equation (1).

For the instrumental variables estimation, we follow the literature and instrument travel times between trading partners using the mean physical geography features of transit countries along any trade route.<sup>11</sup> The set of geographical variables that are used to compute the instruments are taken from Nunn and Puga (2009) and include the percentage of moderately to highly rugged

> 11 Geographical characteristics of a country have been used in the past by papers such as Limao and Venables (2001) to instrument for transport infrastructure.

12