rafmacalaba/datause-encoder-data huggingface dataset:
**Table 9**

# **Reasons Given in Caskey Survey for Why Households Do Not Have Deposit Accounts**

|**Reason/reasons given for why households do not**<br>**have deposit accounts**|**Percentage**<br>**Giving this**<br>**Reason **|
|---|---|
|Don't need account because I have no savings|53.3|
|Bank account fees are too high or banks require too<br>much money to open or maintain an account|45.2|
|I want to keep my financial records private|21.6|
|Not comfortable dealing with banks|17.6|
|Banks won't let us open an account|9.5|
|No bank has convenient hours or location|8.5|

Source: Caskey (1997) As discussed earlier, large percentages of lower-income urban residents see themselves as having severely limited access to credit or turn to non-bank lenders, such as finance companies or payday loan shops. Although the OCC survey did not examine why people lack access to bank credit, other data support the conclusion that the main reason that people are excluded from bank credit is that they have impaired credit histories or, in some cases, no credit histories. In addition, many households have reached the limit of the credit that banks are willing to extend them.

A comparatively large percentage of lower-income and minority households report a history of failing to fulfill payment obligations on time, heavy debt-payment burdens, bankruptcy, or liens placed on their property. For example, Fair Isaac and Company Inc., one of the largest U.S. credit scoring bureaus, reported that it examined loan application data from "tens of thousands" of individuals applying for installment loans between July 1992 and December 1994 (Martell et al). Using its proprietary data base, Fair Isaac assigned scores for each applicant ranging from 0 to 240, with a higher score indicating a lower credit risk. The report (p. 14) notes that, “Many lenders set their cutoff score – the score...below which they decline applicants – around 200.” The study found that that 54 percent of low- and moderate-income individuals (defined in the study as individuals with annual incomes under $21,000 a year) had scores below 200, but only 33 percent of individuals with incomes over this amount had scores below this level. Similarly, the Freddie Mac Corporation (1999), a large government-sponsored housing enterprise, conducted a survey of 20,000 households with incomes under $75,000. The survey focused on the households' credit histories and financial behaviors. It classified a household as having a "bad" credit record if the household reported that:

- it had been at least 90 days late on a payment in the previous two years,

- it had been 30 days late on a payment more than once in the previous two years, or if

24
prices arising from transaction costs.<sup>This can result in householdschoosing not totrade</sup> goods which, were such transaction<sup>costsreducedthey would trade.21</sup> It may not be realistic to construct<sup>a full household level SAMfor every household in the</sup> dataset (although this is not impossible<sup>-indeed Cogneau and Robilliard(2000) and</sup> Robilliard, Bourguignon et al. (2001)<sup>have embeddedall the householdsin thesurvey</sup> into a full country level CGE). However,<sup>it is relativelystraightforwardto constructaset</sup> of SAMs for "representative households"<sup>andestimatetheimpacton each of these</sup> representative households separately.

The household modeling approach<sup>has the attractionthat it can allowfor all the above</sup> substitution effects within a consistent<sup>theoreticalframework,whilstonly requiring</sup> information typically available<sup>from a householdsurvey.However,it has theimportant</sup> disadvantage of remaining a fixed<sup>price model. Indeed it is perfectly possibleforeachof</sup> the representative households simulated<sup>tosimultaneouslysupply more labour to the</sup> market as a result of a structural<sup>reform without thishaving any effectupon the wage.</sup> Similarly household production<sup>responseschange the supply and demandfor</sup> commodities in the economy, but,<sup>in thesehousehold modelsthis has noeffect upon the</sup> prices of these commodities. In<sup>some circumstancesthis does not matter -if pricesare</sup> likely to remain fixed or if the nature<sup>of the changes can be assumed exogenously then it</sup> is not necessary to complicate the<sup>model by incorporating assumptionsabout the ways in</sup> which markets clear and prices adjust.<sup>However,in other circumstances accountingfor</sup> the likely changes in the prices of<sup>somekey commodities(notably staple foods)and some</sup> key factors (notably the unskilled<sup>wage)is important.Oneway in which these can be</sup> accommodated without moving<sup>to a country level CGE model is toemploy a multi-</sup> market model.

> 21 More precisely, production and consumption<sup>decisions become non-separableso that the householdis in</sup> fact responding to an endogenously<sup>determined shadow price rather than to the market prices.</sup>

21
Carrillo, Paul, Mercedes Onofa, and Juan Ponce. 2011. “Information Technology and Student Achievement: Evidence from a Randomized Experiment in Ecuador.” IDB Working Paper No. 78. Washington, DC: Inter-American Development Bank. https://dx.doi.org/10.2139/ssrn.1818756 Conn, Katherine. 2014. “Identifying Effective Education Interventions in Sub-Saharan Africa: A MetaAnalysis of Rigorous Impact Evaluations.” PhD diss., Columbia University.

Cristia, Julián, Pablo Ibarrarán, Santiago Cueto, Ana Santiago, and Eugenio Severín. 2017. “Technology and Child Development: Evidence from the One Laptop Per Child Program.” _American Economic Journal: Applied Economics_ 9(3): 295–320. <u>https://doi.org/10.1257/app.20150385</u> Das, Jishnu, Stefan Dercon, James Habyarimana, Pramila Krishnan, Karthik Muralidharan, and Venkatesh Sundararaman. 2013. “School Inputs, Household Substitution, and Test Scores.” _American Economic Journal: Applied Economics_ 5(2): 29-57. <u>http://dx.doi.org/10.1257/app.5.2.29</u> de Melo, Gioia, Alina Machado, and Alfonso Miranda. 2014. “The Impact of a One Laptop per Child Program on Learning: Evidence from Uruguay.” IZA Discussion Paper No. 8489. Bonn, Germany: Institute for the Study of Labor.

de Ree, Joppe, Karthik Muralidharan, Menno Pradhan, and Halsey Rogers. 2017. “Double for Nothing? Experimental Evidence on an Unconditional Teacher Salary Increase in Indonesia.” _Quarterly Journal of Economics 133_ (2): 993-1039. https://doi.org/10.1093/qje/qjx040 Duflo, Esther, Pascaline Dupas, and Michael Kremer. 2015. “School Governance, Teacher Incentives, and Pupil-teacher Ratios: Experimental Evidence from Kenyan Primary Schools.” _Journal of Public Economics_ 123: 92-110. https://doi.org/10.1016/j.jpubeco.2014.11.008 Glewwe, Paul and Michael Kremer. 2006. “Schools, Teachers, and Education Outcomes in Developing Countries.” _Handbook of the Economics of Education_ 2: 945-1017. <u>https://doi.org/10.1016/S15740692(06)02016-2</u> <mark>Fairlie, Robert W. 2005. “The Effects of Home Computers on School Enrollment.”</mark> _<mark>Economics of Education Review 24</mark>_ <mark>(5): 533-547. https://doi.org/10.1016/j.econedurev.2004.08.008</mark> <mark>Fairlie, Robert W., Daniel O. Beltran, and Kuntal K. Das. 2010. “Home Computers and Educational Outcomes: Evidence from the NLSY97 and CPS.”</mark> _<mark>Economic Inquiry</mark>_ <mark>,</mark> _<mark>48</mark>_ <mark>(3), 771-792.</mark> <u><mark>https://doi.org/10.1111/j.1465-7295.2009.00218.x</mark></u> <mark>Fairlie, Robert W., and Rebecca A. London. 2012. “The Effects of Home Computers on Educational Outcomes: Evidence from a Field Experiment with Community College Students”.</mark> _<mark>The Economic Journal</mark>_ <mark>,</mark> _<mark>122</mark>_ <mark>(561), 727-753. https://doi.org/10.1111/j.1468-0297.2011.02484.x</mark> <mark>Fehr, Ernst, Erich Kirchler, Andreas Weichbold, and Simon Gächter. 1998. "When Social Norms Overpower Competition: Gift Exchange in Experimental Labor Markets,"</mark> _<mark>Journal of Labor Economics</mark>_ <mark>16, no. 2: 324-351</mark> 14
15 clientelistic mobilization strategies in both countries – the policy preferences of supporters of different parties in Ghana are nearly identical, in contrast to those of their counterparts in the US. The Afrobarometer 2002 survey of Ghana probed 1,200 respondents about their party preferences and also about their opinions about a market-based economy. About 10 percent of respondents (126 out of 1,200) agreed with the statement, “For someone like me, it does not matter what kind of economic system we have.” Thirty percent (372) agreed with the statement, “A government run economy is preferable to a free market economy” and half (612) agreed with the reverse statement, “A free market economy is preferable to a government run economy.” As Figure 1 indicates, however, and in sharp contrast to the supporters of the Republican and Democratic parties in the US, these responses were nearly the same across supporters of the two main Ghanaian parties, contrary to what one would expect if the parties made credible programmatic promises related to the role of government in the economy.<sup>8</sup>

**Figure 1:  Partisan divides on the growth agenda, Ghana and the United States**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>NPP NDC Republican Democratic<br>percent favoring markets percent favoring permanent tax cuts<br><!-- End of picture text --> Note: The figure depicts the percent of Ghanaian and US respondents to the 2002 Afrobarometer and Pew surveys, respectively, who expressed support for the indicated party and who agreed either that the market was a preferable form of economic organization (Ghana, NPP and NDC), or that tax cuts should be made permanent (the US, Republicans and Democrats).

8 Responses might have differed if the question had emphasized a potentially more salient issue, such as “The government should redistribute more from the rich to the poor”. The 2005 Afrobarometer survey asked whether respondents believed that people should be responsible for their own success, or whether the government should bear the main responsibility. Here, NPP supporters were more different than NDC supporters: 61 percent of NPP supporters, compared to 44 percent of NDC supporters, favored the selfreliance response. Even here, though, the differences are far smaller than those found between Republicans and Democrats.
owns them or not), and is the logarithm of material inputs of firm The value of the capital stock is only available for manufacturing firms.ii<sup>7</sup> is the measure of management practices and nn ii. is the measure of skills. For these variables normalized scores (z-indices) are used. ii is a vector MM of all other controls that are predicted to affect firm performance, such as firm age, manager age, ii ii SS ZZ and a set of industry dummies and location variables. In addition, we estimate models with labor productivity as the dependent variable: To assess the importance of firm capabilities, we test the hypothesis that and are equally effective (or ineffective): 4 5 = 0. We use an F test to check whether ii and iivariables are SS MM jointly statistically significant. We see F as a measure of the relative increase in the sum of squared ii ii αα = αα SS MM residuals (SSR) when moving from an unrestricted to a restricted model (Wooldridge, 2008). Nevertheless, this test does not provide information of partial effects on . Thus, we cannot use it to test differences in the effectiveness of the different capabilities’ drivers. YY The overall analysis does not allow to causally relate firm capabilities to performance, but it is an important step before promoting and testing individual policies. It allows to understand how high or low are Mozambique’s levels compared to other countries, which types of firm capabilities seem more relevant for firms that were able to grow and perform in Mozambique, and for which types of enterprises firm capabilities helps to explain further differences in performance.

# _Estimating heterogeneous effects_

To assess the heterogeneity of the findings by firm type (small versus large; young versus old; male versus female-owned; located in Maputo versus in other places; exporters versus nonexporters; foreign vs non-foreigner), we estimate the following model for formal firms with at least five employees:

> 7 In the Enterprise Survey questionnaire, capital stock is only available for manufacturing firms and not for services firms. As an alternative, some regressions use last year investment as a proxy for capital when estimating the results for all sectors.

14
**Table 8: Percentage distribution of Estimates of the Impact of Cash Transfers on Temptation Goods by Region**

||**Negative &**<br>**significant**|**Negative (or 0)**<br>**& insignificant**|**Positive &**<br>**insignificant**|**Positive &**<br>**significant**|**Total**|
|---|---|---|---|---|---|
|All estimates, Latin America|32%|46%|18%|4%|100%|
|All estimates, other regions|19%|69%|6%|6%|100%|
|All estimates, Latin America - RCTs only|10%|60%|30%|0%|100%|
|All estimates, other regions-RCTs only|0%|100%|0%|0%|100%|

**Table 9: Studies with estimated survey or focus group levels of transfer on alcohol or tobacco expenditure**

|**Country**|**Program name**|**Temptation**<br>**good**|**Impact**|**Reference**|
|---|---|---|---|---|
|Democratic Republic||Doughnuts and|<1% of households|Aker 2013|
|of Congo||beer|||
|Jordan|UNHCR cash grants|Alcohol,<br>tobacco, and<br>medicines|1.3% of households|Biron 2012|
|Kenya|Kerio Valley Cash Transfer<br>Pilot|General|No reports of use on<br>temptation goods|Brewin 2008|
|Lesotho|World Vision Cash and Food<br>Transfers Pilot Project|Alcohol &<br>tobacco|No significant<br>increase|Slater & Mphale<br>2008|
|Lesotho|The Cash and Food<br>Transfers Pilot Project|Alcohol and<br>cigarettes|6.4% of recipients|Devereux &<br>Mhlanga 2008|
|Malawi|Mchinji Social Cash Transfer<br>Pilot Scheme|Alcohol|1.1% of recipients|Miller, Tsoka &<br>Reichert 2008|
|Malawi|Food & Cash Transfers|Alcohol,<br>cigarettes,<br>entertainment|0.1% of transfer|Devereux, Mvula<br>& Solomon 2006|
|Malawi & Zambia|Oxfam's cash transfers|Alcohol|No reports of use on<br>temptation goods.|Harvey & Savage<br>2006|
|Vietnam|Non-emergency cash grants<br>in An Loc commune|Alcohol and<br>gambling|No reports of use on<br>temptation goods.|Humphreys 2008|
|Zimbabwe|Government of Zimbabwe<br>Harmonised Social Cash<br>Transfer|Alcohol|Marginal increase in<br>consumption|Phiri 2012|
|Zimbabwe|Zimbabwe Emergency Cash<br>Transfer (ZECT) Pilot<br>Program|Alcohol &<br>tobacco|<0.5% of transfer<br>used on temptation<br>goods|Román 2010|

26
(see Table 2).

Unfortunately – and surprisingly, given that randomization was conducted by computer – there is imbalance in pre-program food security at both the village and household levels. This is apparent in the estimates of equation (1) for nine food security measures from the IHS3, reported in Table 3.<sup>11</sup> Untreated households in PWP village had worse food security than households in control villages according to six of eight measures (columns 1-6, but not 7 and 8) and the PCA index; four of the differences are statistically significant at the 95 percent confidence level. Treated households fared better than their untreated neighbors by all measures, and significantly better in terms of the number of food groups consumed and the PCA index. However, the treated households still have worse food security than households in control villages according to six of eight measures and the PCA index. Four of the differences are statistically significant at the 90 percent confidence level; p-values for the test that treated households have the same food security as households in control villages and 95 percent confidence intervals are displayed below the point estimates.

The nature of the imbalance is markedly different in the North, where treated and untreated households have better baseline food security than households in control villages, than in the Center or South. We report balancing tests for each of the three regions in Appendix Table A1.

As expected, the IHS sample is well-balanced for a range of non food-security outcomes. Appendix Table A2 reports these tests. Out of 24 coefficients, only one is statistically different from zero. This makes the pre-program differences in food security all the more difficult to understand, but suggests that treated households are not systematically better off than untreated households along multiple dimensions of wellbeing.

Because of the imbalance, we present three sets of results for our analysis of food security: estimates without baseline (IHS3) covariates from the full sample of 182 villages, estimates without covariates from the subsample of 159 IHS3 villages, and estimates with covariates from the IHS3 villages. This strategy makes clear the extent to which changes in point estimates are due to limiting the sample versus controlling for pre-treatment outcomes.

> 11IHS3 surveys were conducted from March 2010 until March 2011. Balancing tests control for month and year of survey. To control for pre-treatment levels of outcome variables in subsequent regressions given the strong seasonality in these measures, we use, we use the residual of each measure regressed on month and year of survey indicators.

12
likelihood of IPV. Specifically, individuals choose a consumption bundle ( _Ci_<sup>1)basedonprices</sup><sup>_p_</sup> and wealth _wi_ in the initial period; and then a disaster occurs in the second period that reduces _<u>i</u>_ consumption by<sup>_δC_</sup> _δS_<sup>1</sup><sup>_<_0.Ineachperiod,ahusbandbecomessufficientlystressedanditbecomes</sup> possible that he might engage in IPV if the consumption bundle is below a minimum threshold: where _I_ is an indicator function equaling one if household consumption is above their minimum bundle _C_<sup> ̄</sup> _i_ .

The individual-specific threshold _C_<sup> ̄</sup> _i_ can be influenced by gender norms, by intrahousehold relationships and responsibilities, and all the other issues that have been illustrated to determine the prevalence of IPV (e.g., Council of Europe 2019). The framework incorporates the two limiting cases in which husbands are always potentially willing to commit IPV regardless of economic factors ( _C_<sup> ̄</sup> _i_ larger than any feasible consumption value) and in which there is no level of economic well-being such that a husband will commit an act of IPV ( _C_<sup> ̄</sup> _i_ = 0).

In this simple setting, if disasters only impact the economic well-being of a household, it is necessarily the case that disasters will increase the likelihood of IPV (<sup>_δIPV_</sup> _δS_ _<u>i</u>_<sup>_t_</sup> _>_ 0). Specifically, the share of husbands that would change from being unwilling to commit IPV to those that might would be the share in households that had pre-disaster consumption just above the consumption threshold.<sup>12</sup> Depending on the share of such individuals, which further depends on the size of the consumption shock, the actual change in IPV could either be small or large.

However, the above situation critically assumes that the threshold at which a husband might commit acts of IPV does not change (<sup>_<u>δ</u>_</sup> _δSC_<sup> ̄</sup> _<u>i</u>_<sup>=0).Butifthedisasterissufficientlyturbulentthatit</sup> changes social norms, responsibilities of individual family members, or any of the other factors that are incorporated in the individual-specific consumption threshold, it is possible that the economic threshold at which a husband might commit acts of IPV also changes (e.g.,<sup>_<u>δ</u>_</sup> _δSC_<sup> ̄</sup> _<u>i</u>_<sup>_<_0).13Andinthe</sup> case that the consumption threshold declines significantly, the share of husbands that might commit acts of IPV actually can decrease (<sup>_δIPV_</sup> _δS_ _<u>i</u>_<sup>_t_</sup> _<_ 0). The key comparison is the magnitude of the change

> 12Specifically, all husbands in households with consumption in the first period _C_  ̄ _i_ +<sup>_δC_</sup> _δSi_<sup>1</sup> _< Ci_<sup>1</sup><sup>_<C_ ̄</sup><sup>_i−δC_</sup> _δSi_<sup>1</sup> will change from being unwilling to commit an act of IPV to potentially being able to.

> 13For example, in the case of a disaster where women assume new roles and responsibilities in the household, or in a case where there is community trauma from many deaths and fear for safety, the threshold at which a husband might commit IPV might possibly change.

6
affects firms’ ability to attract financing— and, through that channel, growth— is to investigate if external financially dependent sectors grow faster in countries with greater competition. Building on the empirical methodology of Rajan and Zingales (1998), the cross-sectional study of Cetorelli and Gambera (2001) documents that banking sector concentration exerts a depressing effect on overall economic growth even as it promotes the growth of industries that depend heavily on external finance. Using the same data and similar methodology, Deidda and Fattouh (2002) find that banking concentration is negatively associated with per capita growth and industrial growth only in low-income countries, while there is no significant relationship between banking concentration and growth in high-income countries. Dell’Ariccia and Bonaccorsi di Patti (2004) also employ this approach and find that banking system concentration has a positive effect on firm creation. They also find, however, that the degree of information asymmetries in the country limits the overall positive effects of banking system concentration on firm credit, which is consistent with theories claiming that concentration may reduce credit to informationally opaque firms. Cetorelli (2001) also uses this methodology and finds that banking concentration enhances industry concentration, especially in sectors highly dependent on external finance, although these effects are stronger in countries with less-developed financial systems.

Thus, although existing empirical work has shown some relationships between market structure and growth, the link from market structure to degree of competition is not clear—as highlighted by the general contestability literature (Baumol et al. 1982). Specifically, measures such as banking system concentration do not necessarily capture the degree of effective competition, which actually depends on the contestability of the system. An industrial 8