rafmacalaba/data-use-mentions-extended huggingface dataset:
market share of Suharto family firms and the profit elasticity in 1995, but this interaction is only significant at the 10% level.<sup>21</sup> Turning to the impact of Suharto’s fall, results for these individual indicators are mostly insignificant but consistent with a procompetitive impact of Suharto’s fall. Industries in which Suharto family firms had more market power during his tenure have significantly higher entry rates (at the 10% level) post regime change, with the exception of 2005, 2006 and 2007. The positive impact on entry is partially due to choosing 1996 as the base year given that entry rates were particularly low in Suharto dominated industries in 1995 and 1996. Price-costs margins also decrease more in industries where Suharto family firms had been important relative to industries in which they were not, with the difference with the latter group being significant at the 10% level from 2006 onwards. Regime change also appears to reduce, though not significantly so, the profit elasticity, the market share of the four largest firms and prices in industries where Suharto family firms had been important relative to industries in which they were either not present or not important. However, the Herfindahl index of concentration is rising more rapidly in industries in which Suharto family firms had been important whereas the number of firms seems to fall more rapidly in such industries, but these effects are not statistically significant either.

The evolution of the aggregate competition indicator is presented in Figure 2, clearly points to significant pro-competitive impacts of Suharto’s fall; industries in which Suharto family firms had been important experienced significantly faster improvements in competition after his fall. A 10 percentage points increase in the market share of Suharto family firms is associated with a 0.40 increase in the competition index in 2000 and a 0.35 increase in 2001. The standard deviation of the competition index is 3.92 so these increases amount to roughly a 0.1 standard deviation increase in the competition index. These positive pro-competitive effects fade somewhat over time
- Heckman, James J., Lance J. Lochner, and Petra E. Todd. 2003. _Fifty Years of Mincer Earnings Regressions_ . National Bureau of Economic Research Working Paper No. w9732.

- Hoogerheide, Lennart, Joern H. Block, and Roy Thurik. 2012. “Family Background Variables as Instruments for Education in Income Regressions: A Bayesian Analysis.” _Economics of Education Review_ 31(5):515–523.

- Ichino, Andrea and Rudolf Winter-Ebmer. 1999. "Lower and upper bounds of returns to schooling: An exercise in IV estimation with different instruments." _European Economic Review_ 43(4-6): 889-901.

- Kim, Jun Sung, Bin Jiang, Chuhui Li, and Hee-Seung Yang. 2019. “Returns to Women’s Education Using Optimal IV Selection.” _Applied Economics_ 51(8):815–830.

- Kozyreva, Polina, Mikhail Kosolapov, and Barry M. Popkin. 2016. “Data Resource Profile: The Russia Longitudinal Monitoring Survey—Higher School of Economics (RLMS-HSE) Phase II: Monitoring the Economic and Health Situation in Russia, 1994–2013.” _International Journal of Epidemiology_ 45(2):395–401.

- Kyui, Natalia. 2010. _Returns to Education and Education-Occupation Mismatch within a Transition Economy. Empirical Analysis for the Russian Federation_ . Université PanthéonSorbonne (Paris 1), Centre d'Economie de la Sorbonne.

- Kyui, Natalia. 2016. “Expansion of Higher Education, Employment and Wages: Evidence from the Russian Transition.” _Labour Economics_ 39:68–87.

- Luk’yanova, Anna L’vovna. 2010. “Returns to Education: What Meta-Analysis Reveals (Otdacha Ot Obrazovaniya: CHto Pokazyvaet Meta-Analiz).” _Higher School of Economics Journal_ 14(3).

- Mincer, Jacob A. 1974. _Schooling, Experience, and Earnings_ . New York: NBER Books.

- Montenegro, Claudio E. and Harry Anthony Patrinos. 2014. “Comparable Estimates of Returns to Schooling
in that area from the 2005/06 and 2009/10 LSMS‐ISA waves. The AEP is based on attainable crop yields across all agricultural zones using data from the International Institute for Systems Analysis and the Food and Agriculture Organization for medium input levels (Tóth et al., 2012). Uganda has the _highest AEP_ (and the 4<sup>th</sup> highest AEP per capita) compared to all other countries at $1,878 per hectare, twice as high as the second runner up, Malawi, at $999 per hectare. However, these calculations assume the use of improved varieties, adequate fallows, and some mechanization, fertilizer application, chemical pest, disease and weed control. Smallholder farmers, who may be stuck in poverty traps, comprise more than 50% of Uganda’s farmers and are precisely the individuals who may not be purchasing inputs, and who face variable input quality ‐ a key element in explaining the lack of returns to input use. Similarly, McCarthur and McCord (2017) also predict high potential returns to input use, but also under the assumption that inputs are of high quality.

As in Suri (2011), the differences in input use are large across consumption quintiles and regions, depending on the predominant crops grown in the region.<sup>5</sup> Overall, poorer households have lower input

> 5 The consumption aggregate used here is the aggregate constructed by the Uganda Bureau of Statistics, which uses the same method to generate the consumption aggregate used in their official poverty measures. The majority of the income aggregates come from the Rural Income Generating Activities (RIGA) database, which uses standardized

7
.005)|(0.013)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Prov-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Obs.|3399|3642|3642|3642|3642|3642|3639|3642|3640|3637|3636|
|# of Cities|262|281|281|281|281|281|281|281|281|281|281|
|Adj_R_<sup>2</sup>|0.61|0.50|0.58|0.57|0.60|0.53|0.53|0.55|0.56|0.49|0.40|

_Notes_ : This table presents several robustness checks for the political cycle of GDP manipulation. The dependent variable is the difference between reported real GDP growth and predicted real GDP growth imputed based on the nighttime lights data of prefecture-level cities. City controls include city-level time-varying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights) and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

52
Page 9 Albanian (with the expected negative sign) was lack of cohesion within members of the community. Students‟ excellence in math, however, was correlated with a larger set of factors. The proportion of students excelling in math was lower in schools with poor facilities. While teachers‟ characteristics did not appear significant, teacher appraisal and transparency with respect to teachers‟ absence from school were; indeed, students‟ excellence in math was more likely in schools where teachers believed they would be penalized if they underperformed, and where a teacher absence register existed and was regularly used. Lack of parental involvement in the school also correlated negatively, with students‟ performance in the final math exam. A father‟s education was strongly and positively associated with children‟s performance in math. And the extent of divide (lack of perceived cohesion) in the community appears to matter by reducing the proportion of students excelling in both math and language.

# **_1.c.4. Lab-type experiments and parents’ demand for accountability_**

31. The low levels of parent participation in school governance structures could reflect lack of information or parents‟ reluctance to hold teachers accountable. As discussed earlier **,** Albania has made concerted efforts to improve governance across the public sector. Its efforts in the education sector have been received positively by households. Seeking to complement governance improvements in education compact and the supply of education services, the Government has been investing in shortening the route to accountability, by strengthening participatory accountability structures (see again Figure 1). However, the data collected in the Albania School Stakeholder Survey indicate that parental participation in these structures is still very low. Is this lack of participation owing to poor institutions, a lack of knowledge and understanding, or a preference not to participate?

32. Parents appear seriously under-informed about accountability structures in their children‟s schools, and in many places these structures are not operating as they should. The findings summarized previously, highlight both a departure in _de
# **1 Introduction**

The eradication of poverty was the first Millennium Development Goals (MDGs) established by the United Nations in 2000 and continues as Sustainable Development Goals (SDG) 1.1.1, but governments can only properly target poverty if they know where it is. Traditionally, for a given country, the best source for information on the living standards of its population are household surveys. These surveys are a powerful tool towards defining and addressing the needs of people. These surveys, however, usually offer reliable information only at highly aggregated levels of the population. In other words, _direct_ survey estimates tend to be adequate for very large populations but inadequate for smaller populations.

Small area estimation (SAE) is a branch of statistics focused on improving the reliability of estimates and the associated measures of uncertainty for populations where samples cannot produce sufficiently reliable estimates (Rao and Molina, 2015). Small areas can be any population subgroup and are not necessarily tied to geographical areas. According to Ghosh and Rao (1994), the use of small area statistics increased towards the end of the 20th century due to improved computing power and the advent of theoretically sound statistical methods. The main principle behind small area statistical methods is to use modeling to “borrow strength” from auxiliary data sources (e.g. census or administrative data) to produce more efficient estimators than direct survey data alone. Model-based techniques for small area estimation commonly fall within two groups: i) area based models; for examples see Fay and Herriot (1979) and Torabi and Rao (2014); ii) unit-level models; for examples see Molina and Rao (2010) and Elbers, Lanjouw and Lanjouw (2003). The focus of this paper is to conduct model and design-based evaluation of different unit-level SAE methods. Model-based methods assume a data generating process for the population that follows the model’s assumptions, and the target parameters are assumed to be random (Tzavidis et al., 2018). In this
**_Figure 6_** _: Change in total number of trips per hour in the two metropolitan areas in 2020 compared to 2019 (%)_

<!-- Start of picture text --> a. Bogota b. AMBA<br>AM peak PM peak AM PM peak<br>50% hour hour 50% peak hour<br>hour<br>30% 30%<br>10% 10%<br>-10% -10%<br>-17.2%<br>-21.0%<br>-30% -30%<br>-28.3%<br>-32.0% -31.6%<br>-38.0%<br>-50% Hour of day -50% Hour of day<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223<br>Source: Authors based on CDR data<br><!-- End of picture text --> The initial reduction of public transport was much more drastic in AMBA than in Bogotá, declining from a modal share of 24.8 percent in October 2019 to 9 percent a year later, compared to a decline from 34.7 percent to 28.2 percent in Bogota. The drop in public transport demand occurred mainly during peak hours and late in the day in Bogota while it was quite uniform throughout the day in AMBA. While the drop was less steep, also in Bogota the isolation measures imposed by the national government and the users' perception of a high probability of contagion in the public transport system drove a sharp reduction in trips (by more than 30 percent on weekdays, with the sharpest declines in Bogotá D.C.) and fare revenues. In the case of Transmilenio, while transport supply was reduced during
parameters for our model. This database measures trade in value added following the procedure described by Koopman et al. (2010). From the same database, we also use data on the compensation of employees for each industry. Using these data we compute the share of value added in gross output, _{γi_<sup>_j}n,j_,andtheinput-output</sup> coefficients _{γjk}j,k_ .<sup>31</sup> Table 13 reports the corresponding parameters for _{γjk}j,k_ . 73% of the material inputs in the production of manufacturing goods come from manufacturing sector itself; 18% are produced by the service sectors; 7% by the ICT sector and only 1% by the retail and wholesale trade sector. The ICT sector buys 66% of its material inputs from the service sector, 18% from manufacturing, 13% from retail and wholesale trade, and 2% from ICT.

> 30The first two questions are also asked for year 2016.

> 31We restrict the analysis to the industries included in our survey. The four industry groupings in our survey are matched to the following industries from the EORA database. Coefficients for manufacturing are estimated using data from the following industries: Food & Beverages; Textiles and Wearing Apparel; Wood and Paper; Petroleum, Chemical and Non-Metallic Mineral Products; Metal Products; Electrical and Machinery; Transport Equipment; Other Manufacturing. Coefficients for the wholesale and retail trade are estimated using data from the following industries: Maintenance and Repair; Wholesale Trade; Retail Trade. Coefficients for IT are estimated using data from Post and Telecommunications. Coefficients for the other industries are estimated using data from: Construction; Hotels and Restaurants; Transport; Financial Intermediation and Business Activities; Education, Health and Other Services.

29
Estimates of rural electrification’s benefits are highly subject to the reliability of electricity supply. If supply is constrained due to a host of factors, the effects will be sub-optimal, meaning that beneficiaries would not reap the maximum benefits possible from household electrification. The 2005 IHDS data show that lack of reliable electricity service is pervasive in rural areas of India, which may have kept electricity access and consumption low. The effects of service quality on electricity access and consumption, discussed previously (Table 3), clearly demonstrate the substantial consumption gains that can accrue from small improvements in reliability. Similarly, villages without power outages have an electrification rate of 81 percent, while those with more than 20 hours of power outages per day have an access rate of only about 38 percent, which affects their electricity consumption (Table 9).

Households without power outages consume up to 69 kWh per month, compared to only about 46 kWh per month for those that experience severe outages. Clearly, the amount of electricity consumed depends not only on whether service is available but also on how long service is available, and the extent of impacts depends on the level of consumption. With improved reliability, electricity may play a stronger role in improving income and productivity, as ensuring only access is not enough. In short, electricity access, in conjunction with service reliability, is what matters in improving household welfare in rural India.

# **9. Conclusion**

The analysis in this paper unpacks the causal chain from provision of electricity to the various benefits it is claimed to bring and quantifies these benefits. The estimation results indicate that electrification has significant positive effects on time allocation for fuel collection, as well as income, expenditure, and poverty incidence. It also has a positive impact on children’s schooling, which can increase future income; thus, electricity not only alleviates poverty in the near term but also holds 20
are homogeneous of degree one.<sup>40</sup> Most of the literature whose primarily goal is to measure the welfare gains from transit infrastructure within cities has focused on the first term and direct effects, by assuming that there are no wedges in the economy and that it operates under perfect competition. I contribute to this literature by analyzing the effect of transit improvements on the second and third margins.<sup>41</sup>

# **5 Empirical Strategy and Estimation**

In this section, I describe the main empirical strategy and estimation of the main parameters. This section is divided into four parts: parametrization of commuting and trade costs; estimation of trade and commuting elasticities; estimation of the labor supply elasticity across sectors - _κ_ -, and model inversion to recover the fundamentals of the economy such as technological and amenity parameters.

## **5.1 Trade and Commuting Costs**

For the counterfactual analysis, I parametrize commuting costs as in the urban economics literature (Ahlfeldt et al., 2015; Heblich et al., 2018; Tsivanidis, 2019). I assume that both iceberg commuting and trade costs are parametrized using the following expressions: where time _ni_ is the average travel time in minutes across different transportation modes of moving from location _n_ to location _i_ .<sup>42</sup> The main objects of interest are the parameters _δd_ , and _δτ_ that transform travel times to iceberg costs. I estimate these parameters from a nested logit specification using the 2017 Origin-Destination Survey. I use trips to from home to work and vice-versa to estimate _δd_ , and trips to restaurants, outlets, and retail shops to obtain the parameter _δτ_ .

The estimation is based on the following choice model. A worker _ω_ is choosing between different transportation modes to travel from _n_ to _i_ . These transportation modes are grouped into different nests, for example public or private nests denoted by _G_ . Denote the set of transportation modes in _g_ , by Υ _g_ . The
Table 6: Refugee resettlement and crime

|Dep. var:|Number|of criminal|offences in 2|015 (log)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Share of 1928 refugees in pop1928|0.516|-2.468**|-3.871***|-2.497**|
||(1.469)|(0.974)|(1.046)|(1.103)|
|_R_<sup>2</sup>|0.002|0.618|0.675|0.725|
|Observations|55|55|55|55|
|Average outcome|5.908|5.908|5.908|5.908|
|Population 2011||Y|Y|Y|
|Geography controls|||Y|Y|
|Socio-Economic Development||||Y|

**Notes** The table presents OLS regression estimates measuring the effect of the share of refugees in 1928 on the number of crimes at the department level. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on the 1928 population census and crime data from Vasilakis (2018).

32
**Table 2: Revenues Estimates**

||MK0|MK10|MK20|MK50|MK100|MK300|MK500|
|---|---|---|---|---|---|---|---|
|Price|0|10|20|50|100|300|500|
|Demand vs.MK0<sup>17</sup>|0.00|-0.28|-0.42|-0.61|-0.75|-1.26|-1.34|
|Busrides tomarket town inonemonth|1.35|1.07|0.93|0.74|0.60|0.09|0.01|
|Busrides tomarket townperday|0.023|0.018|0.016|0.013|0.010|0.002|0.000|
|AdultPopulation|1200|1200|1200|1200|1200|1200|1200|
|Total adult market town bus rides per da|y 27.93|22.18|19.29|15.22|12.38|1.96|0.21|
|Total Revenue|0.00|221.76|385.79|761.22|1237.68|587.14|103.45|
|Marginal Per Passenger Revenue|0|10|8.50|24.66|38.50|-332.40|-2337.85|
|AveragePer Passenger Revenue|0|10|20|50|100|300|500|
|Projected Profit (Daily)|-13583|-13296|-13099|-12681|-10082|-10624|-11097|

Notes: Values in Malawi Kwacha. Demand estimates are based on coefficients from unconditional regression of the total phase 2 bus rides on a set of
# **II:  The Urban Unbanked in the United States**

This section uses a survey of households conducted in lower-income communities in New York City and Los Angeles to profile the unbanked and to explain why they are unbanked. The Office of the Comptroller of the Currency (OCC), the agency that charters and regulates national banks, conducted this high-quality survey of the use of financial services by residents in low- and moderate-income census tracts in both cities in 1998. The survey sampled 2,000 adults asking, among other things, numerous questions about how they receive and make payments. The surveys were conducted by telephone and in face-to-face interviews. The interviews were conducted in either English or Spanish depending on the respondent’s choice. Because of careful design and persistent efforts, the OCC obtained a remarkable 70 percent response rate. For people using the survey data, the OCC supplies a set of weights to convert the sample responses into responses representative of the 2.6 million adults living in the low- and moderate-income census tracts of the two cities. All of the tables in this paper based on the OCC survey use the weighted data.

For our purposes, there are two weaknesses to the survey. First, it focused on only the residents of two large cities who may not be representative of urbanized lower-income households generally. As a result the percentage of unbanked in this survey is higher than that given by the Federal Reserve’s 1997 percentage for the country at large<sup>5</sup> . Both of these cities, for example, have much larger Hispanic and immigrant populations than is typical of American cities. And New York has a lower percentage of homeowners than is typical of American cities. Second, the OCC with little discernable rational basis decided to omit a number of survey responses from the publicly released data base. Consider one example. The OCC survey asked people without bank accounts why they did not have an account and read a list of reasons that they could agree with,
experiment with all combinations of caste statuses in the principal and agent roles. We found no statistically significant caste difference in sharing. In fact, slightly more principals shared when both principal and agent were high caste than when both were low caste.

Another hypothesis that would explain the caste difference in behavior in the Stag Hunt experiment is a low sense of self-efficacy among low-caste men compared to high-caste men. That hypothesis could also explain a pattern that emerged in our vignette-based survey, which is that low-caste men were much less likely than high-caste men to want to respond violently to the transgressions described in the vignettes. We conducted a second survey to test for caste differences in self-efficacy and found no evidence that low-caste men have lower self-efficacy than high-caste men.

This paper contributes to the rapidly growing literature on the interaction between culture and economic behavior (see, for example, Greif, 2006; B ́enabou, 2008; Herrmann, Th ̈oni and G ̈achter, 2008; Hoff and Stiglitz, 2010, 2016; Hoff, Kshetramade and Fehr, 2011; Nunn, 2012; Jackson and Xing, 2014; World Bank, 2015). Our contribution is to show that culture can block the emergence of efficient conventions in a strategic setting in which prior experimental work with US subjects found that the outcome was almost always efficient. To the best of our knowledge, this is the first paper to link culture to the ability to form efficient conventions in coordination games of common interest.

Numerous studies, including those cited above, have shown that people from different cultures will behave differently in otherwise identical situations, and also that behavior can depend on seemingly irrelevant details of context (Kahneman and Tversky, 1984). An explanation that is broadly accepted in behavioral economics, psychology, sociology, and anthropology is that human decision making is carried out using a set of mental tools, which have been variously referred to and characterized as schemas and mental models (Swidler, 1986; D’Andrade, 1995; Nisbett and Cohen, 1996;