rafmacalaba/datause-encoder-data huggingface dataset:
In Appendix Section I, we present results on the effects of SNAP-benefit changes on firm dynamics and market structure in the grocery industry. The estimated effect on entry and exit is positive, but almost all of the estimates are statistically insignificant and economically small, suggesting SNAP-benefit changes had little impact on entry and exit margins and market structure.

# **4.2 Households**

To understand how consumption and shopping behavior responds across SNAP-eligible and -ineligible households, as well as how each type of household responds in SNAP-eligible and -ineligible product groups, we estimate equation (6) and present the results in Table 7. For expenditures, these results show a statistically significant MPC only for the both-eligible group, and the point estimate is positive and large despite somewhat large standard errors. The implied MPC is 0.44, which is close to previous estimates of MPC out of food stamps of 0.5-0.6 from HS. Note that our estimate of MPC also captures price responses whereas the estimate in HS does not, because their variation comes from individuals transitioning into and out of SNAP. Netting out the price response, our estimates of MPC would be slightly smaller. The MPC estimated from the both-eligible group is statistically larger than those from other groups. This finding suggests the state variation we are using is differentially impacting SNAP-eligible goods and households consistent with our assumptions. The point estimates from the product- or household-eligible-only groups are imprecisely estimated. This finding is suggestive of both increased prices raising food expenditures of the ineligible households and increased consumption of ineligible products among eligible households.<sup>34</sup> Furthermore, following Goldin et al. (2022), we estimate the response of consumption by week of the month. Whereas Goldin et al. (2022) show that the average consumption patterns follow state-specific SNAP-benefit-issuance schedules, we show the MPC is also higher in the weeks in which more SNAP benefits are issued to households, by interacting each eligibility indicator with the fraction of SNAP benefits issued in a given week by state. As shown in Appendix Table N12, the interaction coefficient for the MPC is positive and strongly significant for eligible households in both product groups, and significantly higher for eligible products. As suggested by Goldin et al. (2022) and Baker et al. (2017), consumption is complementary for both eligible and ineligible product groups due to fixed shopping-trip costs. The percentage increase in consumption during weeks with higher SNAP issuance is

> 34The noise in these point estimates could also be attributed to the fact that we are unable to identify household eligibility with complete accuracy given limited data on households selected by the Nielsen Consumer Panel. Note also that the point estimates include the price response; they would be smaller without the price response.

22
differently between the two modalities. A small difference between the modalities in the non-sensitive questions would further be consistent with the sample selection between the two modalities being similar.

# **Section 5. Baseline Empirical Strategy**

As discussed above, we assess the ability of novel and anonymous internet-based surveys to elicit more sensitive responses relative to mobile phone surveys by comparing the difference in the modalities in sensitive questions to non-sensitive questions. First, we create a series of indicator variables from both the sensitive and non-sensitive questions, and then stack the responses from the internet survey and from the mobile phone survey for each individual question. For sensitive questions, we make the answer to each indicator function to be the more sensitive option as identified in Table 2 (i.e., an indicator equaling one if the individual named a party to the conflict as being responsible for violence witnessed and not choosing the other option).

We then estimate the following specification for each individual question: where _Ind_<sup>_j_</sup> _i_<sup>denotestheresponsetoindicatorfunction</sup><sup>_j_forindividual</sup><sup>_i_;and</sup><sup>_Interneti_</sup> denotes an indicator equal to one if the response was from the internet survey. In this specification, _β_ 0 denotes the share of the respondents to the mobile phone survey that answered affirmatively to question _j_ , and _β_ 1<sup>_j_denoteshowmuchlargerthesharewasfor</sup> the respondents to the internet survey. If the internet surveys were more able to elicit sensitive information, we would expect for _β_ 1<sup>_j>_0forsensitivequestionsandforthe</sup> magnitude of _β_ 1<sup>_j_tobelargerinmagnitudeforsensitivequestionsthanfornon-sensitive</sup> questions (i.e., _β_ 1<sup>_sens_</sup> _> |β_ 1<sup>_non−sens_</sup> _|_ ).<sup>20</sup>

Importantly, as discussed in the Introduction, an estimate of _β_ 1<sup>_j>_0 for sensitive ques-</sup>

> 20Although we estimate the difference separately in the main text, in the appendix we jointly estimate the difference across questions and formally compare the difference for sensitive and non-sensitive questions. See Appendix 1.

16
Table A2: Instrumental variables estimation of effects of SHG Membership on laboratory measures of pro-social norms

|IV Models|(1)<br>Risk taking|(2)<br>Impatience|(3)<br>Dictator|(4)<br>Public<br>good|(5)<br>Trust|(6)<br>Trustworthiness|
|---|---|---|---|---|---|---|
|_Full sample_|||||||
|SHG|||||||
|member|0.38|0.03|191.18|0.20|176.20|0.03|
||(0.41)|(1.31)|(220.95)|(0.20)|(365.76)|(0.06)|
|constant|2.08**|3.41**|687.64**|0.59**|566.44**|0.14**|
||(0.18)|(0.57)|(90.61)|(0.09)|(132.01)|(0.03)|
|N|524|510|524|523|251|271|
|_IDPoor_<br>_1&2_|||||||
|SHG|||||||
|member|0.28|0.23|253.31|0.17|321.17|0.01|
||(0.45)|(1.23)|(235.26)|(0.23)|(405.34)|(0.07)|
|constant|2.09**|3.35**|631.80**|0.59**|423.49**|0.15**|
||(0.21)|(0.57)|(104.82)|(0.10)|(167.40)|(0.03)|
|N|349|341|349|349|165|182|
|_Non-poor_|||||||
|SHG|||||||
|member|0.64|-0.54|-4.73|0.28|-285.26|0.10|
||(0.87)|(2.44)|(457.09)|(0.34)|(856.68)|(0.13)|
|constant|2.04**|3.56**|818.93**|0.58**|891.03**|0.12**|
||(0.34)|(0.93)|(172.19)|(0.14)|(310.80)|(0.05)|
|N|175|169|175|174|86|89|

Note: IV estimates with village-clustered standard errors. * = significant at the five percent level, **=significant at the one-percent level 44
Our results for Nigeria are very similar (16.5%) to their estimates. Moreover, with the exception of Kenya (19.2%), the recovered trade costs in the rest of the countries are close to those in the U.S.

When we consider the results based on the locally-produced goods samples, however, our results are more sensible and in line with Atkin and Donaldson (2015) for most countries. Coefficients are between 2.6 (Tanzania) and 14 (Kenya) times larger than in the U.S.

To try understand the sources of the differences in our estimates with those in Atkin and Donaldson (2015), we make a more precise comparison focusing on Nigeria, for which we have a similar sample covering the same time period, and re-estimate pass-through rates and trade costs restricting the sample to the goods that are included in Atkin and Donaldson (2015) (“AD-sample” from now on). The sample that we have access to includes 15 products out of the original 18 products in their paper and the total number of observations is lower, at 25,189, compared to 26,025 in Atkin and Donaldson (2015). Our results produce a mean pass-through rate of 0.484 (s.e. 3.2) compared to 0.390 (s.e. 0.66) in Atkin and Donaldson (2015).<sup>12</sup> Moreover, while Atkin and Donaldson (2015) estimate a significant negative relationship between the pass-through rate and distance (-0.101), we get positive relationship (0.01) which is not estimated with precision. These differences notwithstanding, the estimation of trade costs under the adjusted price gap specification produces identical results to Atkin and Donaldson (2015). That is, we get ˆ ˆ _β_ = 0 _._ 056 (0 _._ 007) compared to _β_ = 0 _._ 056 (0 _._ 008).

We can also compare our results to previous attempts to recover transport costs in Rwanda using this methodology, albeit with different data, by Bougna et al. (2020). Instead of using CPI data, Bougna et al. (2020) use market price data from November 2016 to July 2018 collected by the Government of Rwanda in collaboration with the World Bank in the context of an impact evaluation of a large feeder road rehabilitation program. They estimate a pass-through rate equal to 0.34, close to our estimate of 0.39, and _β_<sup>ˆ</sup> = 0 _._ 106, which is higher than our estimate using the full sample, but similar to our estimate using the sample of locally-produced goods only (0.120).

# **7 Conclusion**

We apply the methodology developed by Atkin and Donaldson (2015) to estimate intranational transport costs, accounting for spatial variation in markups across a sample of Eastern European and African countries. Our results suggest significant heterogeneity in the magnitude of intra-national transport costs across the countries in our sample. Moreover, we find that our estimates are sensitive to the choice of products, with most countries showing higher transport costs when we restrict the sample to locally produced goods, ex12The percentage of pass-through rates below one is slightly higher, 93.1%, and we obtain a lower fraction of estimates below zero, 12.6%, compared to 17%.

21
result, we are able to demonstrate that depositors in Latin America do face some uncertainty and frictions when making their portfolio decisions.

We also explore some of the macroeconomic consequences of a dollarized economy. In particular, we find that past banking crises are good predictors of future crises. Our findings suggest that an increase of dollarization today is associated with a contemporaneous decrease in the probability of a banking crisis. In other words, having a highly dollarized economy increases intermediation, thus increasing the response of the banking system in the case of a bad shock which halts the outflow of capitals. On the other hand, once the crisis has occurred, having more dollar-denominated deposits in the banking system increases the probability of a longer crisis in the future, because it increases exchange rate exposure in an already weak banking system.

Finally, we show that the volatility of macroeconomic variables linked to the financial system increases whenever the economy becomes more dollarized, which in turn makes the choice of monetary targets more difficult.

# References

- [11 Agenor, P.R. and Khan, M.S. 1992. "Foreign Currency Deposits and the Demand for Money in Developing Countries." _IMF Working Paper._ WP/92/1, Washington, DC.

- [2] Amemiya, T. 1997. _Advanced Econometrics._ Ed. Harvard University Press, Cambridge Massachusetts.

- [3] Azariadis, C. 1981. "Self-Fulfilling Prophecies" _Journal of Economic Theory._ 25, pp. 380391.

- [4] Balifio, T., Bennett, A. and Borensztein, E. 1997. "Monetary Policy in Dollarized E- conomies." _Occasional Paper. IMF._

- [5] Bencivenga, V., Huybens, E. and Smith, B. 1998. "What to Stabilize in the Open Economy?: Some Notes on a Problem of Keynes." _Manuscript._ University of Texas.

- [6] Bordo, M. and Choudri E. 1982. "Currency Substitution and Demand for Money: Some Empirical Evidence for Canada." _Journal of Money, Credit and Banking._ 14, pp. 48-57.

- [7] Calvo, G.A. and Vegh, C.A. 1992. "Currency Substitution in Developing Countries: an Introduction." _Revista de Ana6lisis Economico._ 7 (1), pp. 3-28.

16
> <sup>- 14 -</sup>

The relative shares of different pension institutions have been remarkably stable. LD, corporate pension funds and banks suffered some decline in their market shares, while ATP, life insurance companies and multi-employer pension funds experienced increases.

**<u>Table 6  Pension Assets</u>**

||**1995**|**2000**|**2004**|
|---|---|---|---|
|Total Pension Assets (DKKbillion)|847.2|1507.2|1822.1|
|Total Pension Assets/GDP(%)|83.1%|116.5%|124.8%|
|ATP(% oftotal)|15.2%|16.4%|16.9%|
|SP(% oftotal)||1.4%|2.5%|
|LD(% oftotal)|4.3%|4.1%|3.2%|
|LifeInsurance Companies (% oftotal)|41.8%|43.1%|44.5%|
|Multi-Employer Pension Funds (% of<br>total)|17.2%|17.9%|18.6%|
|CorporatePension Funds (% oftotal)|4.0%|2.9%|2.2%|
|Banks (% oftotal)|17.5%|14.3%|12.2%|

Source: Statistics Denmark and the Danish Financial Supervisory Authority Pension institutions used to invest heavily in Danish bonds, especially Danish mortgage bonds that enjoyed a higher yield over government securities while being considered as highly safe. However, during the course of the 1990s, a shift toward Danish and foreign equities had taken place. As an example, in 1998, ATP invested 55 percent of its assets in Danish bonds, 26 percent in Danish equities and 10 percent in foreign equities (Table 7). Life insurance companies had more conservative portfolios because of the guaranteed elements of the products they offer, with Danish and foreign equities absorbing 25 percent of total assets in 1998. Pension funds had asset allocations that fell in between those of ATP and insurance companies.

However, the global decline in equity prices and a stronger emphasis on risk-based supervision induced a reconsideration of these investment policies. By 2004, ATP direct investments in equities, both domestic and foreign, fell to 18 percent of total assets and those of life insurance companies to 12 percent. For life insurance companies and pension funds, direct equity investments decreased to 15 percent of total assets. They increased their use of investment funds, which invest in diversified portfolios and may also incorporate various risk hedging elements. All types of institutions increased substantially their investments in foreign bonds, seeking higher yields and benefiting from the stability of the exchange rate between the Danish crown and the euro. They also increased their use of derivatives, especially long-term swap contracts in the euro market, to hedge their long-term liabilities.

The investment performance of different types of pension institutions was broadly similar. In a competitive market, where each company decides its own investment policy, some will perform above and some below market average, but for practical purposes the performance of the different groups of institutions should be considered as very similar.
# **REFERENCES:**

- Adams, R. (2003), _Economic Growth, Inequality and Poverty: Findings from a New Data Set_ , World Bank Policy Research Working Paper 2972, Washington, D.C.

- Bourguignon, F. (2002), “The Growth Elasticity to Poverty Reduction: Explaining Heterogeneity across Countries and Time Periods,” in T. Eicher and S. Turnovsky (eds.), _Growth and Inequality_ , Cambridge, Mass: MIT Press.

- Bruno, M., M. Ravallion and L. Squire (1998), “Equity and Growth in Developing Countries: Old and New Perspectives on the Policy Issues,” in Vito Tani and Ke-Young Chu (eds.), _Income Distribution and High Growth_ , Cambridge, Mass:  MIT Press.

- Kakwani, N.C. (1977), Measurement of Tax Progressivity: An International Comparison. Economic Journal, 87, 71-80.

- Pen, J. (1971), _Income Distribution_ , New York:  Praeger Publishers.

Pinto, B. (2002), Poland – Macro-Micro Policy Linkages, Unpublished World Bank Mimeo.

- Ravallion, M. (1995), “Growth and Poverty: Evidence for Developing Countries in the 1990s,” _Economics Letters,_ 48.

- Ravaillon M. (2001), _Measuring Aggregate Welfare in Developing Countries: How Well Do National Accounts and Surveys Agree?_ , World Bank Policy Research Working Paper 2665, Washington, D.C.

- Ravaillon, M., and S. Chen (1997), “What Can New Survey Data Tell Us about Recent Changes in Distribution and Poverty?”, World Bank Economic Review, 11(2).

- Ravaillon M., and S. Chen (2003), “Measuring Pro-poor Growth,” _Economic Letters_ , 78.

Shorrocks, A. (1982), “Inequality Decomposition by Factor Components,” _Econometrica,_ 50 (1).

Theil, H. (1967), _Economics and Information Theory,_ North Holland, Amsterdam.

- World Bank (2000), _Making Transition Work for Everyone: Poverty and Inequality in Europe and Central Asia_ , World Bank, Washington D.C.

- World Bank (2001), _Poland's Labor Market: The Challenge of Job Creation_ , World Bank, Washington D.C.

World Bank (2002), _Transition: The First Ten Years_ , World Bank, Washington D.C.

- World Bank (2003), _Poland: Towards a Fiscal Framework for Growth: A Public Expenditure and Institutional Review_ , World Bank, Washington D.C.

- World Bank (2004), Poland Living Standard Assessment: _How far is Warsaw from Lisbon_ , World Bank, Washington D.C.

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**Table 5a. Banks' Buffering Role: Informational and Contractual Complexity**

||Log of # of<br>Exporters|Log of Export Value<br>per Exporter|Exporter Entry<br>Rate|Exporter Exit<br>Rate|Exporter Turnover<br>Rate|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|||_Panel A: Country-L_<br>|_evel Credit Informa_|_tion Coverage_<br>||
|1. Foreign Bank*ExtFin|-0.114|-0.612***|0.028|0.043**|0.080**|
||(0.105)|(0.179)|(0.018)|(0.019)|(0.031)|
|2. Foreign Bank*ExtFin*Credit bureau cover.|0.001|-0.008|-0.000|-0.001|-0.001|
||(0.003)|(0.006)|(0.001)|(0.001)|(0.001)|
|Observations|18,114|17,689|16,327|16,316|16,171|
|R-squared|0.873|0.540|0.469|0.464|0.506|
||_Pane_<br>|_l B: Product-Level Inform_|_ational Complexity*_<br>|_Country-Level Rul_<br>|_e of Law_|
|3. Foreign Bank*Rauch|1.068***|-0.067|-0.042*|-0.052*|-0.080|
||(0.166)|(0.253)|(0.026)|(0.027)|(0.050)|
|4. Foreign Bank*Rauch*Rule of law|-0.707***|-0.349|-0.028|-0.030|-0.082|
||(0.225)|(0.390)|(0.047)|(0.049)|(0.094)|
|Observations|18,114|17,689|16,327|16,316|16,171|
|R-squared|0.880|0.542|0.471|0.465|0.508|
||_Panel C: Pr_<br>|_oduct-Level Informationa_|_l Complexity*Count_|_ry-Level Strength_|_of Legal Rights_|
|5. Foreign Bank*Rauch|1.067***|0.064|-0.007|-0.022|-0.014|
||(0.176)|(0.301)|(0.025)|(0.026)|(0.049)|
|6. Foreign Bank*Rauch*Strength of legal rights|-0.110*|0.288**|-0.005|-0.013|-0.022|
||(0.067)|(0.142)|(0.011)|(0.011)|(0.020)|
|Observations|18,114|17,689|16,327|16,316|16,171|
|R-squared|0.880|0.543|0.472|0.466|0.509|

Note: This table reports the effects of financial structures on the number, size and dynamics of exporters in 2-digit ISIC industries depending on the degree of informational and contractual complexity. The financial structure is captured by the share of foreign bank assets in total bank assets in the country. The dependent variables are the number of exporters; the average foreign sales of exporters; the entry rate of exporters; the exit rate of exporters; the turnover (entry+exit) rate of exporters. All regressions include FinDev*ExtFin, Bank Concentration*ExtFin, country-year fixed effects and industry-year fixed effects, where FinDev is measured as all credit over GDP to control for total financial development and Bank Concentration is used to control for the concentration of the banking sector. Regressions in panel A also include ExtFin*Credit bureau coverage. Credit bureau coverage is the percentage of adults covered by credit bureaus in the country. Regressions in panel B also include Foreign Bank*ExtFin and Rauch*Rule of Law. The Rauch indicator of contractual complexity is defined in Section 3.3. The rule of law index is from the Worldwide Governance Indicators. Regressions in panel C also include Foreign Bank*ExtFin and Rauch*Strength of legal rights. The strength of legal rights is from Doing Business. Credit bureau coverage, rule of law, and strength of legal rights are demeaned by the sample average. Robust standard errors, clustered at the country-year level, are in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% level, respectively.
Based on the pattern exhibited in Figure 2, we differentiate among three intervals: years 1-6 after the start of the crisis, years 7-13, and years 14 and above. Consistent with the quadratic results in equation 2, we find the strongest effects in the middle interval. Whereas the first six years of the crisis do not have any significant effects on disbursements, in years 7 to 13 disbursements are down by an average of some 30 percent (significant at the 10 percent level). But while the effect is very long-lasting, it is not permanent: the dummy for the last period, 14 years and beyond, is not significantly negative in any of the tests reported in Table 1.

_Banking crisis effects: Robustness to changes in crisis countries and years_ These estimates are based on a number of assumptions about financial crises – most notably, when they started and whether they were systemic or not. Table 2 shows the results of reversing some of these assumptions (one at a time, so as not to shrink the sample too much), and checks the robustness of results to dropping potentially influential cases. We use the model specification of Table 1, equation 1, but to conserve space report in Table 2 only the banking crisis coefficients for comparison.

In every case, the crisis coefficient estimates are consistent with those reported in Table 1. The first row shows that the results are not sensitive to dropping a very small donor (Iceland) rather than giving it equal weight. Row 2 instead drops Finland, shown by Roodman (2008) to have the deepest post-crisis trough in aid among the four cases he examined. Korea is the donor dropped in row 3. It became a donor only in 1990, and its aid exhibits substantial volatility around an overall upward trend, and with a banking crisis beginning in 1997. Dropping Korea strengthens the negative impact of crises somewhat.

In row 4, we change the onset date of Japan’s banking crisis from 1997 (Laeven and Valencia’s preferred year) to 1992, right after the Japanese asset bubbles burst (CKLN’s view). In row 5, we examine how results are affected by re-coding the U.S. as having no banking crisis until 2007. Although LV includes the U.S. savings and loan crisis beginning in 1988 as systemic, CKLN codes it as non-systemic. Results in rows 4 and 5 show that the estimated impacts of banking crises on aid are fairly insensitive to these judgments on crises in the U.S. and Japan.

11