rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 7**

# **Determinants of High School Enrollment Conditional on Completing Middle School, 1986-2003**

|||Dependent|Variable: E|nroll in Hig|h School N|ext Year =|1|
|---|---|---|---|---|---|---|---|
|Model|(0)|(1)|(2)|(3)|(4)|(5)|(6)|
||OLS|IV-GMM|IV-GMM|IV-GMM|IV-GMM|IV-GMM|IV-GMM|
|Share of Migrants in Village|-0.097|-2.434**|-2.217**|-1.981**|-1.868**|-1.801**|-2.237**|
|Labor Force|(0.132)|(1.040)|(0.914)|(0.877)|(0.843)|(0.836)|(1.060)|
|Gender (1=male)||||0.045**|0.051**|0.050**|0.050**|
|||||(0.019)|(0.018)|(0.018)|(0.019)|
|First Born (1=yes)||||0.043**|0.023|-0.030|-0.029|
|||||(0.019)|(0.019)|(0.027)|(0.027)|
|First Born in Household||||-0.072**|-0.082**|-0.069**|-0.072**|
|was Male (1=yes)||||(0.019)|(0.019)|(0.020)|(0.021)|
|Father's Years of Schooling|||||0.022**<br>(0.004)|0.022**<br>(0.004)|0.022**<br>(0.004)|
|Mother's Years of Schooling|||||0.026**<br>(0.004)|0.026**<br>(0.004)|0.026**<br>(0.004)|
|Number of Potential Migrants,||||||-0.053**|-0.053**|
|Household, Male||||||(0.020)|(0.020)|
|Number of Potential Migrants,||||||-0.028|-0.030|
|Household, Female||||||(0.018)|(0.018)|
|ln(Village Mean Income Per Capita)|||0.045|0.039|0.040|0.042|<0.001|
||||(0.066)|(0.064)|(0.062)|(0.062)|(0.071)|
|Village Total Land|||0.001|0.001|0.001|0.001|<0.001|
||||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|
|Village Cropped Land Per Capita Gini|||-0.056|-0.068|-0.111|-0.132|-0.135|
||||(0.314)|(0.299)|(0.285)|(0.282)|(0.308)|
|(Village Labor Force)/10|||-0.001|-0.001|-0.001|-0.001|-0.001|
||||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Cultivable Share of Village Land|||0.012|0.018|-0.039|-0.033|-0.042|
||||(0.139)|(0.133)|(0.127)|(0.126)|(0.137)|
|Additional Time-Varying Village<br>Variables?|no|no|no|no|no|no|yes|
|Chi-Square Test, Time-Varying Village||||||||
|Variables<br>p-value, Chi-Square Test|||||||4.57<br>0.600|
|Over-ID Test: Hansen J-Statistic||1.912|2.584|2.880|2.468|2.484|3.567|
|P-value, J-statistic||0.591|0.460|0.410|0.481|0.478|0.312|
|Cluster corrected F Statistic,<br>||3663|4400|4462|4462|4461|3141|
|instruments||.|.|.|.|.|.|
|CLR Test Statistic||10.98|11.02|9.47|8.82|8.35|10.71|
|CLR Test, p-value||0.002|0.002|0.004|0.006|0.007|0.003|
|Number of Obs.|3160|3160|3157|3157|3157|3157|3157|

Notes: In parentheses, we show standard errors clustered at the village-year level. All regressions control for factors related to village location with village fixed effects, and macroeconomic shocks using province*year fixed effects. Additional timevarying village level variables in model 6 include the average proportion of households with non-agricultural self-employment, the share of grain sold at quota, the logarithm of average household wealth, the share of land allocated to aquaculture, the share of land allocated to forestry, and , the share of land allocated to orchards. Models 1-6 are estimated using an instrumental variables-generalized method of moments estimator that is efficient in the presence of presence of heteroskedasticity and arbitrary within village-year cluster correlation (see Wooldridge 2002, page 193).

44
income shares of the bottom (top) deciles. Moreover, this effect is accentuated in countries that are relatively abundant in capital (K/L) or in countries abundant in high skill labor (SK-ED)/(BS-ED). Increases in inequality continue to be correlated with reductions in tariffs in countries relatively well endowed in no educated people (SK+BS)/(NO-ED). On the other hand, this income-inequality increasing effect of trade liberalization is mitigated in countries abundant in arable land (AT/L). Finally, the control variables have the same effects as those discussed in section 3.<sup>20</sup> These results were submitted to several robustness checks (see Tables in the appendix; others available upon request). First, we ran the same regression without taking the logarithm of the variables, obtaining similar results. Regarding reverse causality, we ran the same regression using future trade rather than past values and the results become mostly insignificant, suggesting that reverse causality should not be a problem here.<sup>21</sup> As to control variables, in other specifications, we added government expenditure and/or an index of democracy, resulting in a large reduction in sample size. With few exceptions, estimated values of variables of interest remained quite close to the estimates discussed above (see Table A12). In sum, the results based on decile data are robust and coherent with the results obtained earlier in Table 6.

Since the correlation between tariff reductions and inequality after controlling for endowments is still significant in this shorter time span, we used the coefficient estimates in Table 6 to simulate the average impact of a 5

> 20 We also compared the results obtained by using the Theil and Gini coefficients. In all cases except for (K/L) and the GDP per capita (measured here by the mean income), (whereas these two coefficients were not significant in table 4), the sign of Gini and Theil coefficients are the same. Moreover, in all cases (except for the ratio (SK/(BS-ED)) and the two interaction terms (K/L) * (Tariffs) and (RM/L) * (Tariffs), the coefficients are also significant.

> 21 We thank Marcelo Olarreaga for this suggestion.

21
the degree of competitiveness. Importantly, the price has to be properly adjusted for lending risk.<sup>4</sup> Third, indicators of the regulatory framework can provide indications of the contestability of the banking system. Such measures include entry requirements, formal and informal barriers to entry for domestic and foreign banks, activity restrictions and other regulatory requirements, which might prevent new entrants from challenging incumbents. However, one can include even the wider institutional framework among these indicators, such as the contractual and informational framework, a topic to which we will return to in section 5.

An additional challenge in measuring competition is to properly define the relevant market. Cross-country studies typically define an economy as the relevant market, not necessarily a correct assumption. Studies for the U.S. have typically focused on the Metropolitan Statistical Areas (MSAs) as the relevant market. Further, market structure and competition indicators are typically measured on the institutional level, rather than the product level; i.e. competition is assumed to be the same across different product lines, such as deposit, lending and payment services.

# **3. Bank Competition and Stability: What Does Theory Predict?**

Theoretical models have made contrasting predictions on the relationship between bank concentration, competition and stability.<sup>5</sup> These predictions might differ in static and dynamic models and have important interactions with elements of the regulatory framework, such as deposit insurance. Most theoretical models do not make a distinction between market structure, such as concentration, and competition, but rather assume a one-to-one mapping from market structure to competitive behavior of banks. In the following, we will summarize the theoretical literature under two headings, depending whether the model predicts a positive or negative relationship between competition and stability.

> 4 Other performance measures such as interest rate spreads and margins are not necessarily good indicators of the competitiveness of a banking system as they are driven by other bank- and country-specific factors, such as bank size and business, contractual framework, taxation and macro performance.  See Beck (2007) for a discussion.

> 5 See Carletti and Hartmann (2003) for an in-depth literature survey and Allen and Gale (2004) for an excellent exposition on the different theoretical mechanisms that can lead to contrasting relationships between competition and stability.
where, _Ai_ 0 is the SNAP participation ratio in each county in the pre-period of 2006, which allows us to exploit county-level variation in the interaction variables.

Fourth, we implement a quadruple-difference model at the household level. In analyzing the consumer panel data, we are able to exploit the richness of the data to further relax the identifying assumptions. Specifically, we expect a rise in SNAP benefits to have the largest effect on SNAP-eligible goods consumed by SNAP-eligible households. Neoclassical theory predicts that if households are inframarginal in food spending, they will treat benefit increases as cash and non-SNAP spending could rise. Nevertheless, HS find that most households are inframarginal and have a low MPC for SNAP-ineligible goods relative to SNAP-eligible goods, and also demonstrate that mental accounting is a channel that could raise the MPCF out of SNAP.

We test these predictions by the following specification in equation (6). Each observation is a household-product group-period and classified into one of four groups based on whether a household is SNAP-eligible as well as whether the expenditures are on products categorized as SNAP-eligible or not. We regress (log) expenditures of household _i_ in group _g_ in period _t_ on the (log) benefits per participant in state _s_ that household _i_ resides in period _t_ , and interact this variable with four group indicators _Tg_ . Household-month-of-the-year fixed effects are included to control for household-specific characteristics that may vary seasonally, along with group fixed effects and period fixed effects: This specification allows us to provide supporting evidence that the SNAP-benefit shocks are exogenous to unobservable recessionary shocks and other contextual factors, because unobservable shocks that affect spending are unlikely to differentially impact only SNAP-eligible households and SNAP-eligible product groups. The level-level specification, as opposed to the log-log specification, provides an estimate of the MPC of a SNAP household.

Fifth, we estimate IV regressions for each of the specifications discussed above. We construct the instrument, denoted as synthetic benefits per population, as illustrated in Section 2.2. For each of the specifications discussed above, we use the synthetic benefits per population as an IV for the actual benefits per population.

# **4 Main Results**

We first focus on results using the retail scanner data and then discuss our findings using the consumer panel data. All results use data from 2007-2010 only to focus on the period 17
- Mattoo, Aaditya, Randeep Rathindran and Arvind Subramanian (2006). “Measuring Services Trade Liberalization and its Impact on Economic Growth: an Illustration.” _Journal of Economic Integration_ 21: 64-98.

- Ministry of Commerce and Industry(2008). Indiastat.

Ministry of Shipping, Road Transport, and Highways (2008), Indiastat Ministry of Statistics and Programme Implementation (2008). Indiastat.

OECD (2011). _Economic Survey of India_ , OECD Publishing, Paris, France

- Olley, Steven and Ariel Pakes (1996). “The Dynamics of Productivity in the Telecommunications Equipment Industry.” _Econometrica_ 64:1263-1295

- Panagariya, Arvind (2008). _India: The Emerging Giant_ . Oxford: Oxford University Press.

- Pavcnik, Nina (2002) “Trade Liberalization, Exit, and Productivity Improvement: Evidence from Chilean Plants,” _Review of Economic Studies_ 69(1), 245-76.

- Rajan, Raghuram G. and Luigi Zingales (1998). “Financial Dependence and Growth.” _American Economic Review_ 88: 559-586.

- Reddy, Y.V. (2002). “Monetary and financial sector reforms in India: a practitioner‟s perspective.” Bank for International Settlements, http://www.bis.org/review/r020425d.pdf.

- Reddy, Y.V. (2005). “Banking Sector Reforms in India-an Overview” Bank for International Settlements, http://www.bis.org/review/r050519b.pdf.

- Reserve Bank of India. (2008) _Index Numbers of Industrial Production_ . Database.

- http://www.rbi.org.in/scripts/statistics.aspx.

- Sivadasan, Jagadeesh (2009). “Barriers to Competition and Productivity: Evidence from India.” _The B.E. Journal of Economic Analysis & Policy_ 9(1) (Advances): Article 42.

- World Bank (2004). “Sustaining India‟s Services Revolution: Access to Foreign Markets, Domestic Reform and International Negotiations.” World Bank Policy Research Working Paper 31795.

33
long-run. Overall, the authors conclude that, while increasing fuel prices improves air quality, the effect is small and other measures—such as more rigorous regulation on vehicle pollution intensity—might prove more effective.

- Similar effects have been documented in Brisbane with respect to the impact of fuel price changes on air quality (Barnett and Knibbs 2014). Their findings indicate that, although higher fuel prices do not lead to a significant change in PM2.5 and PM10 concentrations, increasing diesel prices leads to substantial decreases in nitrogen oxide concentrations. They estimate that increasing the cost of diesel by AUS$0.15 per liter would have tangible health benefits, decreasing daily emergency hospital admissions by 2 percent or approximately 215 people per year.

- A study of 11 regions in China between 2006 and 2015 finds that vehicle volumes, energy consumption, and gross domestic product (GDP) per capita are all significantly correlated with increased PM2.5, PM10, and ozone concentrations (Xu et al. 2019), with increased vehicle volumes in particular consistently increasing pollution levels. However, another study of 21 cities in northern China shows that more than half of the major air pollutants (that is, PM2.5, PM10, and sulfur dioxide) originate from coal-fired heating in autumn and winter (Lin and Ling 2021). But changes in coal prices do not affect air pollution levels, as the government controls heating prices.

# 2.3. Entry points for mitigating air pollution

There are several reasons why the relationship between fuel prices and air pollution may not be as pronounced as that between prices and consumption. One explanation is the effect of available technology on the impact of price changes. For example, vehicle fleets with better fuel efficiency and exhaust filters would register a smaller change in PM2.5 concentration for the same reduction in distance traveled when compared to a “dirtier” fleet. Therefore, depending on fleet composition, PM2.5 concentration in some countries might respond more or less strongly to a price change. Additionally, as indicated by the higher-magnitude elasticities in the long run, responses to a price change might take time to manifest. For example, if switching from coal to liquefied petroleum gas (LPG) as a cooking fuel necessitates a new stove purchase, it might be difficult for households, and therefore take time. The price elasticity of gasoline demand also varies significantly between countries and cities. For example, it is easier for consumers to substitute away from driving in a city with a welldeveloped public transit system. Finally, fuels can have multiple uses, contributing to inelastic demand. For example, in countries where gasoline is a domestic power source as well as a transport fuel, demand is highly inelastic. Due to these factors, we expect there to be ample heterogeneity in the impact of prices on PM2.5 concentrations.

6
19 coal consumption dropped significantly, there was evidence that people consumed coal stored from the previous year and also switched to wood consumption.

_Consumption Patterns by Employment Status._ There are no significant differences between the long-term unemployed and the not long-term unemployed in terms of energy consumption (Table 6). Their consumption is broadly the same as the population at large taken- together -- 9.5% of expenditures. Consumption of subsidized energy is slightly higher among the long term unemployed (7.6% vs 7.4% of spending).

Table 6: **Poland** - **Energy as a percentage of total expenditure by employment class**

|Employment|Heat|Electricity|Gas|Major energy|All|
|---|---|---|---|---|---|
|category||||sources|energy|
|Long term|2.0|3.7|1.9|7.6|9.2|
|Unemployed'||||||
|Not-long term|2.2|3.3|1.9|7.4|9.5|
|Unemployed||||||

a/ Defined as households with one or more members who have been unemployed for more than one year. Source: Poland Household Budget Survey.

_Special problems in raising household energy prices in transition economies_ _Income decline and fixed incomes._ There are unique problems in the former socialist economies in adjusting household energy prices. First, incomes have fallen over the transition<sup>-- in</sup> - some countries by 30 percent or more since 1989 and poverty has increased (Milanovic, 1996 forthcoming). Where the poverty incidence used to be 2-4 percent before the transition it is now upwards of 15 percent. Unemployment levels are at historically high levels and in some countries five times higher than in the pre-transition period, and a larger fraction of the population<sup>than in the</sup> market economies are on fixed incomes (and hence vulnerable to inflation), judging by the higher system dependency rates of their social security pension systems. Indeed, in Poland, some 22 percent of the Polish population, and fully 49 percent of the labor force receive (fixed)<sup>pensions,</sup>
# **_Financial Condition_** _of AFPs_

The total capital of the AFP industry amounted to about 250 billion pesos (about US$ 236 million) at the end of 1996. Due to high operational losses of more than 170 billion pesos (about US$ 160 million), which corresponded to almost 70 percent of total capital, the net worth of the AFP system amounted to only 73 billion (about US$68 million). The operational losses were basically due to high advertising costs and salaries for the salesforce. In 1996, about 40 percent of operational expenditure was spent on the salesforce and 10 percent on advertising. The share of advertising expenditure is relatively low in the AFPs' financial statements, because the superintendency took account of the high start-up costs in advertising and allowed the AFPs to amortize the costs incurred in 1994, i.e. during the first 9 months of the system, over a two-year period. Compared to the treatment of start-up costs of the Argentine system, this regulation is still relatively strict. In Argentina, the AFJPs were first given a 3-year period to amortize the costs, but this was later extended to 10 years. According to AFP industry studies, the Colombian companies expect to break even after 4 years of operations.

According to the law, AFPs are allowed to charge 5 different kinds of commissions:

- a commission for the management of mandatory contributions;

- * a commission for the management of voluntary contributions;

- a commission for the management of dormant balances; * a commission for the administration of phased withdrawal of balances upon retirement; and

- a commission for the transfer of balances of affiliates switching to other AFPs.

The AFPs are free to choose the base for the calculation of commissions, i.e. they can charge fees as a percentage of the affiliates' base salary, of the contributions, or of the balance in the individual account. The only restriction imposed by the law is that total commissions, including the premiums charged for disability and survivors' insurance, may not exceed 3.5 percent of the contributor's base salary. Also, the Colombian law allows for fee differentiation for affiliates within the same AFP, for example to offer discounts to affiliates who stay for a certain minimum period in one AFP. **All of the commissions currently charged are variable commissions calculated as a percentage of the contributor's base salary; none of the AFPs is charging a fixed commission or any asset-based fees.** 29
An alternative explanation is that customers may have felt more comfortable doing larger transactions at branches because they perceived a higher level of privacy there. Agents are more physically and socially proximate to clients than bank tellers, but that could raise privacy concerns because an agent may talk to friends and relatives of the client about the transaction, making it harder for the client to fend off cash demands from relatives and friends. In contrast, at the branch, the transaction and its details are known only to the teller, who is highly unlikely to have personal dealings with the client or her social network. This could provide a rationale for why individuals would choose the branch for large transactions and the agent for smaller ones. Focus group interviews and evidence from lab-in-the-field experiments with BSN clients provide support for this hypothesis (Buri et al., 2018b) .

Another lens on the importance of privacy for clients comes from an analysis of a large database of transactions from one of the largest microfinance institutions in DRC (Chamboko et al., 2020). A key finding is that clients were significantly more likely to transact with agents of their own gender. Women, in particular, were significantly more likely to transact with female agents, a tendency which increased both with the value of transactions and the customer’s balance. These patterns held when controlling for customer characteristics, agent experience, and market-level covariates and when the analysis was restricted to markets with substantial representation of female agents. That pattern was also detected in data from the experiment with BSN clients in Senegal: the probability that female clients used female agents increased with the size of the transaction (Buri et al., 2018a; Chamboko et al., 2020).

Trust and the desire for privacy are factors that could help account for women’s transactional behavior with female agents. Since the agents could see not only the amount transacted but also the customer’s account balance, women may have preferred to go to a female agent, especially when they had large balances or wanted to make high-value transactions. The salience of trust in explaining the strong tendency of women to seek out female agents is also consistent with the literature showing that women’s trust is more context sensitive than men’s (Croson and Gneezy, 2009; Eckel and Wilson, 2004; Bohnet 2007; Greig and Bohnet, 2009).

# **_Building Experience and Financial Capability among Clients_**

agents. While these concerns may be valid in other contexts, liquidity, security, and technological constraints were not reported to be binding by MSN agents, however (Buri et al., 2018a).

10