rafmacalaba/datause-encoder-data huggingface dataset:
variables included in the analysis and Tables A.3 and A.4 present descriptive statistics and correlations for all explanatory variables.

To assess the relationship between barriers and bank- and country-level characteristics, we utilize the following regression model Fi,k = β 0 + β 1 Bi + β 2 Ck + ε i,k , (1) where F is one of the barriers indicators for bank i in country k, B is a matrix of bank-level variables (the log of total assets in U.S. dollars, dummy variables for government and foreign ownership and the loan to asset ratio), and C is a country-level variable. While we include all bank variables in our regressions, we include only one country-level variable at a time given the limited number of countries in our sample and the high correlation between our variables (Appendix Table A.4). Critically, we do not control for GDP per capita because many of our explanatory country-level variables are highly correlated with economic development. Also, we are interested primarily in which components of economic development can explain crosscountry variations in barriers, as captured by individual country characteristics.

We utilize different estimation techniques depending on the nature of the dependent variable. Specifically, for all affordability indicators – constructed as minimum amounts and fees relative to GDP per capita-, we conduct OLS regressions of the log of one plus the variable – to account for the skewed distribution of these variables. Similarly, for the days to process loans and documentation requirements to open an account, we use OLS regressions. For the location variables (both for loans and deposits) capturing physical access, we utilize ordered probit estimations to take account of the polychotomous nature of these variables with natural order. In all cases, we drop the top 1% of the distribution of the dependent variables to control for outliers. The first four rows of Table 8 report the results of a regression on just the bank-level 20
Table 2: HTC Impacts on Objective and Self-Reported Outcomes

||||O|bjective|Panel|A: HTC Impac|Subjective<br>ts on the En|<br>tire Sample|1 if S<br>Self-R|Nb<br>eported|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||=1 if HIV<br>Positive|=1 if HIV<br>Positive|=1 if HSV-2<br>Positive|<br>=1 if HSV-2<br>Positive|<br>Achievement<br>Test Score|<br>Achievement<br>Test Score|Likelihood<br>of HIV<br>Infection|<br>Probability<br>Live to 50|= ex<br>Active Past 12<br>Months|<br>umer<br>Partners Past<br>12 Months|<br>=1 if Engage<br>in Risky Sex|=1 if in<br>School|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|HTC|-0.006|-0.003|0.003|0.010|0.137|0.073|0.001|-0.159|0.042|0.059*|0.024|-0.007|
||(0.011)|(0.010)|(0.014)|(0.014)|(0.118)|(0.099)|(0.008)|(0.222)|(0.027)|(0.034)|(0.028)|(0.022)|
|Number of observations|1,681|1,681|1,680|1,680|1,660|1,660|1,673|1,673|1,671|1,671|1,669|1,673|
|Mean in the controlgroup|0.050|0.050|0.074|0.074|0.000|0.000|0.041|5.370|0.353|0.360|0.332|0.525|
||||||Panel B:|HTC Impacts o|n the HIV-Ne|gative Sampl|e||||
|HTC|||-0.004|0.000|0.146|0.088|-0.010*|-0.097|0.040|0.057*|0.030|-0.003|
||||(0.012)|(0.013)|(0.122)|(0.104)|(0.006)|(0.227)|(0.028)|(0.033)|(0.029)|(0.022)|
|Number of observations|||1,607|1,607|1,591|1,591|1,602|1,602|1,600|1,600|1,600|1,602|
|Mean in the control group|||0.065|0.065|0.004|0.004|0.033|5.331|0.338|0.346|0.319|0.534|
||||||Panel C:|HTC Impacts o|n the HIV-P|ositive Sample|||||
|HTC|||0.231*|0.246***|-0.125|-0.233|0.291*|-0.423|0.109|0.133|-0.030|-0.060|
||||(0.137)|(0.069)|(0.230)|(0.172)|(0.162)|(0.949)|(0.098)|(0.092)|(0.079)|(0.135)|
|Number of observations|||73|73|69|69|71|71|71|71|69|71|
|Mean in the controlgroup|||0.255|0.255|-0.069|-0.069|0.192|6.140|0.629|0.629|0.579|0.356|
|Includes Controls|No|Yes|No|Yes|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. The achievement test score is the average percent across math, English and cognitive tests (standardized). The subjective likelihood of HIV infection takes on a value from 0 to 1 where zero is no self-reported chance of HIV infection. Risky sex takes on a value of 1 if the respondent does not always use condoms with all partners, and is zero for those who always use condoms or have not engaged in sexual activity in the past 12 months. Regressions that include controls include the following: age, an indicator for never having had sex, and school enrollment status at baseline, all of which are demeaned and fully interacted with the treatment indicator. Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.

14
investment grade instruments (i.e. securities with ratings above a certain threshold). Thus, changes in ratings, downgrading (upgrading) sovereign debt below (above) investment grade, may have a drastic impact on prices, because these rating changes affect the pool of investors.<sup>1</sup> Rating changes may also unveil new (private) information about a country and thus they may fuel rallies or downturns. This effect is likely to be stronger in emerging markets, where problems of asymmetric information and transparency are more severe. Finally, changes in ratings might act as a wake-up call, with rating changes for one country affecting other countries with similar economies.

Research on the effects of changes in sovereign ratings has flourished in the 1990s. This work has mostly focused on the effects of ratings on the instruments being rated. For example, Cantor and Packer (1996) and Reisen and Von Maltzan (1997) and (1999) examine the effects of rating changes of sovereign debt and find a significant effect on bond yield spreads. Similarly, Hand, Holthausen, and Leftwich (1992) show that rating announcements directly affect corporate securities. Richards and Deddouche (1999), using emerging market bank-level data, examine the impact of rating changes on bank stock prices, but do not find statistically significant effects.

Previous research has not examined, however, whether rating changes for one country trigger contagious fluctuations in asset markets in neighboring countries nor has it examined whether ratings for one type of security affect other asset markets. To our knowledge, the only exception is Kaminsky and Schmukler (1999), who examine spillover effects of rating changes, among different types of news, in neighboring

> 1 These effects are not just confined to the pool of investors acquiring sovereign debt.  When a credit rating agency downgrades sovereign debt of a country, all debt instruments from that country might have to be downgraded accordingly because of the sovereign ceiling doctrine.  As a result, commercial banks in the country that turn out to be rated as sub-investment grade can no longer issue internationally recognized

2
**Figure 9: Annuity Rate, Adjusted Annuity Rate, Central Bank Bonds and Corporate Bonds 1993-2009 (%)**

<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>1993 1995 1997 1999 2001 2003 2005 2007 2009<br>Average Annuity Rate (RV‐04) Adjusted Annuity Rate BCU‐10 Corporate Bonds<br><!-- End of picture text --> Source: Central Bank of Chile, SVS, SAFP and authors’ estimates Providers’ costs include the commissions paid to annuity brokers and all operating costs. As shown in Figure 10, commissions averaged 3 percent of the premium in the early 1990s, increased continuously to almost 6 percent at the end of the decade and then decreased sharply to levels of around 2 percent. The increase in the 1990s reflected the practice of charging higher commissions and providing an informal (and illegal) cash rebate to retirees. This cash rebate amounted _de facto_ to a partial lump sum and proved a popular marketing device, but it was also inconsistent with the intention of the law and prompted a reaction from policy-makers, which at the end of 2000 submitted a draft new pension law to Congress, that, among other things, capped broker’s commissions and proposed the new electronic quotation system. Although the law that imposed a 2.5 percent cap on fees was only passed in 2004, the threat of the law and political pressures induced a change n behavior, as indicated by the sharp decline in fees in 2001.<sup>36</sup>

**Figure 10: Commission Rates 1991-2008** (as a percentage of the premium)

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1991 1993 1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text --> Source: SVS 36Walker (2005) examines the relationship between the annuity rate and the risk-free rate and concludes that the threat of the new pension law did produce a change in behavior.

29
Fourth, there is scope to apply and adapt the proposed implementation of connectivity to other modes of transport or international networks. Maritime transport is a particular priority, because it still represents the dominant mode of transport for international trade transactions. This position is particularly true for developing countries. UNCTAD's _Liner Shipping Connectivity Index_ has already made critical contributions in this area. However, as explained in Section 2, there are essential differences in the way information is incorporated in the two methodologies. The two approaches should prove to be strongly complementary in future policy work.

Finally, the theoretical foundation of our approach, which bridges spatial modeling and trade theory, makes it suitable for a broad range of applications to spatial economic interactions between countries or other nodes. Applications to international trade immediately come to mind, in particular in measuring connectivity in relation to international production networks, for instance. In this respect, there are areas for further research and improvement, for example when it comes to the robustness of the specification for the impedances and the cost function; the dependence on distance used here may not be suitable for other problems, and will need to be addressed on a case by case basis.

In particular, the implementation of the model departs from the simplicity of the solutions promoted in the network literature (Appendix 3), such as clustering and centrality indicators. In the absence of economic interactions between nodes, generic network analysis does not need to refer to bilateral costs on the network and indicators do not depend on exogenous independent variables, in this case bilateral distance. Developing a fully endogenous model would mean that not only the potentials, as here, but also the impedances would be determined from the data, in a way which is consistent with the generalized gravity framework. It is an apparently open and seemingly formidable problem.

38
samples were distinguished in Malawi, as the crop calendar differs by latitude – see FAO (2004). Urban, rural areas with a single harvest and rural areas with two harvests were distinguished in Uganda and Tanzania.

# **3 The information base**

Monthly price series for a wide array of staple and non-staple crops and food products in wholesale and retail markets during 2000-2012 were obtained from national statistical offices (Malawi and Tanzania) and a private marketing agency (Uganda). Eight markets were covered in Uganda, 20 in Tanzania and 72 in Malawi. Monthly prices were collected on between five (Malawi) and 23 (Uganda) food products, including maize, rice, millet and sorghum, beans, cassava (the key staples), groundnuts, and some vegetables and fruits (e.g. tomatoes and bananas). Special attention will be given to maize (the main staple in Malawi and Tanzania and also an important one in Uganda – see Table 3 below), with the other crops acting as comparators, also to explore internal consistency in the findings. Vegetables and fruits are for example highly perishable and their prices are thus expected to be much more seasonal. For a number of products both wholesale and retail prices were collected (not in Malawi). Together this yielded a total of 890 crop-market price series (Table 1). Prices are all expressed in nominal terms and in local currency.

Less than 5% of the price data points used in the estimation was missing. To work with the longest possible time series, these data points were imputed following Gilbert (2010a). In particular, when there is a month for which prices are available for at least 50% of the marketplaces, the median price<sup>19</sup> across all non-missing values for available marketplaces for each commodity in the same country is calculated, as well as the margin between the actual marketplace values and the median for all non-missing values of that particular month. This margin is then estimated as a second order auto-regressive pooled OLS across months and marketplaces for each commodity. The

> 19 The median is used to avoid undue influence from extreme observations. It is also less affected than the mean by missing values.

14
Dix-Carneiro, R., and B. Kovak (2017). Trade Liberalization and Regional Dynamics, American Economic Review, vol. 107(10), pp. 2908-2946.

Dixit, A. K. and R. S. Pindyck (1994). Investment Under Uncertainty, Princeton, New Jersey: Princeton University Press.

Dunne, T., M. Roberts, and L. Samuelson (1988). Patterns of Firm Entry and Exit in U.S. Manufacturing Industries RAND Journal of Economics, 19(4), 495515.

Dunne, T., M. Roberts, and L. Samuelson (1989). The Growth and Failure of U.S. Manufacturing Plants, Quarterly Journal of Economics, 104(4) 671698.

Dvorkin, M. (2014). Sectoral Shocks, Reallocation and Unemployment in Competitive Labor Markets, mimeo.

Goldberg, P. and N. Pavcnik (2005). Trade, Wages, and the Political Economy of Trade Protection: Evidence from the Colombian Trade Reforms, Journal of International Economics, 66(1), pp. 75 105.

Hakobyan, S. and J. McLaren (2016). Looking for Local Labor Market Eects of NAFTA, Review of Economics and Statistics, vol. 98(4), pp. 728-741.

Handley, K. and N. Limao (2015). Trade and Investment under Policy Uncertainty: Theory and Firm Evidence, American Economic Journal Policy, vol. 7(4), pp. 189222.

Hopenhayn, H. (1992). Entry, Exit and Firm Dynamics in Long Run Equilibrium, Econometrica 60(5), 11271150.

Hotz, J., and R. Miller (1993). Conditional Choice Probabilities and the Estimation of Dynamic Models, Review of Economic Studies, 60, 497529.

Jovanovic, B. (1982). Selection and The Evolution of Industry, Econometrica 50(3), 649670.

Kambourov, G. (2009). Labour Market Regulations and the Sectoral Reallocation of Workers: The Case of Trade Reforms, Review of Economic Studies, 76(4), pp. 13211358.

Kovak, B. (2013). Regional Efects of Trade Reform: What Is the Correct Measure of Liberalization?, American Economic Review, 103(5),pp. 19601976.

31
<mark>HETFA Research Institute, Budapest. -</mark> <u><mark>http://hetfa.hu/wp content/uploads/2018/02/hetfa_wp_21_mike_kiss.pdf</mark></u>

- Mike, Károly and Kiss, Gábor. 2019. Combining formal and informal contract enforcement in a developed legal system. A latent class approach. _Journal of Institutional Economics_ , forthcoming.

- Murrell, Peter and Radu Paun. 2010. Measuring Transaction Costs: Using Survey Data on Sales Agreements. SSRN.

- North, Douglass C. (1990), _Institutions, Institutional Change, and Economic Development_ , Cambridge: Cambridge University Press.

- Nunn, Nathan. 2007. Relationship-specificity, Incomplete Contracts, and the Pattern of Trade. _The Quarterly Journal of Economics_ , 122(2): 569-600.

- Owen, Ann L., Julio Videras and Lewis Davis. 2009. Do All Countries Follow the Same Growth Process? _Journal of Economic Growth_ , Vol. 14, No. 4 (Dec., 2009), pp. 265-286

- Peng, Mike W. 2003. Institutional Transitions and Strategic Choices. _Academy of Management Review_ , Vol, 28, No, 2, 275-296,

- Poppo, L. and Zenger T. 2002. Do formal contracts and relational governance function as substitutes or complements?, _Strategic Management Journal_ , 23(8): 707-725.

- Rajan, Raghuram G. and Luigi Zingales. 1998. Financial Dependence and Growth. _The American Economic Review,_ Vol. 88, No. 3 (Jun., 1998), pp. 559-586

- Vermunt, Jeroen K. and Jay Magidson. 2002. Latent Class Cluster Analysis. In Applied Latent Class Analysis. Edited by Jacques A. Hagenaars and Allan L. McCutcheon. Cambridge University press, Cambridge, 2002: 89-106.

- Vermunt, Jeroen K. and Jay Magidson. _Technical Guide for Latent GOLD 5.1: Basic, Advanced, and Syntax_ . January 9, 2016. Statistical Innovations Inc. <u>http://www.statisticalinnovations.com</u>

- <mark>Vermunt,</mark> Jeroen K <mark>., 2010. Latent class modeling with covariates: Two improved three-step approaches.</mark> _<mark>Political Analysis</mark>_ <mark>, 18(4), pp.450-469.</mark>

- Williamson, Oliver E. 1979. Transaction-Cost Economics: The Governance of Contractual Relations _The Journal of Law & Economics_ , 22(2), Oct.: 233-261.

- Williamson, Oliver E. 1985. _The Economic Institutions of Capitalism_ . New York: Free Press.

- <mark>Williamson, O</mark> liver E. <mark>1991. Comparative economic organization: The analysis of discrete structural alternatives.</mark> _<mark>Administrative science quarterly</mark>_ <mark>, pp.269-296.</mark>

World Bank 2014. BEEPS At-A-Glance 2013. Kyrgyz Republic. Washington D.C.

World Development Indicators, The World Bank World Justice Project. 2018. Rule of Law Index <u><mark>(http://worldjusticeproject.org)</mark></u> Worldwide Governance Indicators (www.govindicators.org) 28
**Table 6: Naïve estimates of the farm size productivity relationship for all Ethiopian producers**

||**Maize**|**Sorghum **|**Teff**|**Wheat**|**H. Beans**|**Sesame**|**Coffee**|
|---|---|---|---|---|---|---|---|
||||**Panel**<br>|**A: All obser**<br>|**vations**<br>|||
|Cropped area (α1)|-0.337***|-0.294***|-0.270***|-0.134**|-0.602***|-0.434***|-0.279***|
||(-8.213)|(-8.129)|(-5.553)|(-2.032)|(-9.064)|(-6.512)|(-3.357)|
|Cropped area squared(α2)|-0.007|0.020**|0.015|0.023|-0.019**|-0.007|0.012|
||(-1.016)|(2.282)|(1.029)|(1.626)|(-2.020)|(-0.330)|(1.050)|
|Commercial farm dummy|1.236***|1.345***|0.333***|1.584***|3.319***|0.883***|1.167*|
||(9.116)|(10.084)|(2.620)|(4.938)|(10.259)|(5.349)|(1.842)|
|Cropped area * commercial (β1)|0.454***|0.146**|0.203*|-0.250*|0.507***|0.561***|-0.270|
||(5.654)|(2.061)|(1.883)|(-1.834)|(3.270)|(6.402)|(-0.998)|
|C. area squared * commercial (β2)|-0.021|-0.002|-0.016|0.002|-0.016|-0.014|0.062**|
||(-1.592)|(-0.169)|(-0.446)|(0.124)|(-0.673)|(-0.669)|(2.076)|
|Constant|1.892***|1.877***|1.407***|2.063***|0.246*|1.061***|1.047***|
||(32.994)|(35.378)|(34.047)|(28.930)|(1.845)|(9.348)|(7.203)|
|Number +of observations<br>|2,124|2,559|1,403|970|625|1,699|1,019|
|R<sup>2</sup>|0.148|0.135|0.114|0.083|0.379|0.082|0.163|

_Note:_ Woreda fixed effects included throughout. Cropped area is in logs, denominated in hectares.

20
3 accentuated where there is a third party picking up part of the cost, especially one that is simply reimbursing (a fraction of) the costs incurred by the provider. Self-regulation by the Chinese medical profession is limited, and while the Chinese government has identified the delivery of unnecessary and poor quality care as a matter for concern, there are no formal complaint procedures for patients who feel that they have been over-treated. In such a setting, it seems perfectly plausible that at least some patients may end up not only getting more care than would have been the case if they had been uninsured but also paying more out of pocket. Insurance in such a setting may, in other words, actually _increase_ the probability of large out-of-pocket payments and hence exposure to financial risk.

There have already been some studies that claim to explore the extent to which health insurance reduces financial risk or ‘financial protection’. Mostly, these do so by looking at the effects of health insurance on _expected_ health care payments.<sup>2</sup> However, this does not get at the question of risk. Indeed, in the classic textbook case of actuarially fair insurance, expected health care payments, gross of the premium, are the same with and without insurance. A better approach, but one adopted in only one paper to date to our knowledge, is to model the impact of insurance on the probability of individuals incurring ‘large’ out-of-pocket payments.<sup>3</sup> One could define ‘large’ either in relation to the household’s own per capita income (or consumption), or in relation to the sample average income (or consumption). We employ both approaches, using data from three separate surveys spanning the period 1991-2004 and covering half of China’s 32 provinces.

> 2  See, for example, the studies in Preker and Carrin (2003).

> 3  O’Donnell et al. (2005) explore the factors influencing the probability of a household having catastrophic health care payments, defined as payments that exceed 10% of per capita household consumption. In only three of the six territories studied, do O’Donnell et al. include health insurance: in Hong Kong, no significant effect is found; in Thailand, the only insurance category with a significant coefficient is the ‘other cover’, and the sign is positive; in Vietnam, insurance is modeled as the number of household members with insurance, and the significant coefficients are negative (they are for the number of children and adults aged less than 65). Unlike the regressions reported in this paper, their regressions are estimated at the household level and do not control for health status.