rafmacalaba/datause-encoder-data huggingface dataset:
improved financing opportunities<sup>for SMEs and throughincreased overallflowsof bankcredit.We allow for</sup> different potential definitions of<sup>community banks,including the p6ssibilitythatstate-ownedbanks and foreign-</sup> owned banks may function<sup>as community banksin developing nationsas well assmall, private,domestically-</sup> owned banks. Our empirical<sup>analysis employsdata over the period 1993-2000on the economic performanceand</sup> financial systems of 49 nations,<sup>as well as detailedfinancial informationon theindividual banksthat operatein</sup> these nations.

We try to contribute to two<sup>important fields in finance -the finance-growthliteratureand the community</sup> banking literature. We try<sup>to add to thefinance-growth literatureby1) focusing on a dimension of the financial</sup> system that is typically not examined<sup>inthat line of research-the relativehealth of community banks -and2)</sup> investigating potential mechanisms<sup>for how its effects may be transmitted into economicgrowth.We try to extend</sup> the community banking<sup>researchby1)examiningtheeffectsofcommunitybanks on overalleconomic</sup> performance, 2) performing<sup>the analysis on an international basis using data from both developed and developing</sup> nations, 3) allowing for different<sup>potential definitionsof community banks,and4)investigatingthe effectsof</sup> efficiency rank as well as<sup>marketshareand their interactions,i.e.,investigating the quality as wellas quantity of</sup> community banking.

The literature on developed nations<sup>generally defines community banksas small, private,domestically-</sup> owned institutions (SMALL<sup>banks)because these banks are found to have comparativeadvantagesin some types</sup> of lending to SMEs.<sup>Our testresultsfrombothdevelopedanddeveloping nationsareconsistentwiththe</sup> hypothesis that relatively healthy<sup>SMALL banks are associated with faster GDP growth.Both the market shares</sup> and the weighted-average<sup>efficiency ranks of these institutions have positive, statistically significant coefficientsin</sup> the GDP growth regressions.<sup>The estimated effects of increasing the market sharesand efficiency ranks of these</sup> banks are also economically<sup>significant. Thecoefficientson theinteraction terms betweenmarketsharesand</sup> efficiency ranks are also positive<sup>and statistically significantfor both developedand developingnations,consistent</sup> with the hypothesis that the marginal<sup>benefits of higher shares for community banks are greater when these banks</sup> are more efficient.

The data provide only mixed support<sup>for the two hypothesized transmission mechanismsfrom the relative</sup> health of community banks to<sup>economicgrowth throughimprovedfinancing opportunitiesfor SMEs or through</sup> greater overall flows of bank<sup>credit.To scrutinize these mziechanisms,we test whether1) community bank health</sup> has positive effects on the SME<sup>employmentshareandtheratioof oVerallbanklendingto GDP,2) these</sup> 25
new entrants. Focusing on the productivity measures (both labour and TFP), exiting firms appear to be less productive than surviving firms, but are still more productive than new entrants.<sup>8</sup> As noted by Bartelsman and Doms (2000), this is a fairly common finding amongst productivity studies and provides contradictory evidence to the vintage capital model’s prediction (e.g. Caballero and Hammour, 1994) that new plants will enter towards the upper echelons of the productivity distribution.

# _2.5 Firm turnover trends_

Figure 1 shows entry and exit rates in Moroccan manufacturing from 1986-2001, along with the real growth rate of the manufacturing sector.<sup>9</sup> The entry rate is defined as the number of plants observed for the first time in year t but not in year t-1, divided by the total number of plants in year t-1. The exit rate is similarly defined, representing the number of plants observed for the last time in year t-1 but not in year t, divided by the total number of plants in year t-1.

There is an annual average exit rate of 5.2% and an entry rate of 9.1% during the sample period. The exit rate is somewhat less than those found in many other developing countries. Harding, Soderbom and Teal (2006) report annual exit rates of approximately

> 8 The TFP estimates cover fewer observations than the other variables in Table 4 due to the use of lagged variable information in the capital stock predictive regression in Table 2 which means that TFP estimates for 1985 are unavailable.

> 9 Less than 1% of firms in the sample experienced multiple entries during the sample period (i.e. exited and re-entered). In many census datasets, multiple entries can often be driven by sampling error whereby the survey only captures firms over a certain size threshold and firms who dip below this threshold in a given year can be excluded from the survey and falsely believed to have exited. This does not appear to be the case with the Moroccan data however as the census data does not have a size threshold. Further analysis of these multiple-spell firms also showed that these firms do not appear to be particularly small in size (measured both in terms of workers and revenues) prior to exit, which suggests that they do actually experience multiple spells during the sample period and are not falsely omitted from the census.  For the analysis here, we restrict our definition of ‘exit’ to the ‘final’ exit of a firm in the period and to not include multiple measures of exit for the same firm.

8
As expected, the average gains from these protectionist trade reforms are now negative in the vast majority of countries. While there are 45 winners under liberalization, we find between 43 and 48 losers with increased protection. The estimated average income gains ( _G_ (0)) across countries in the three scenarios are, respectively –0.2%, –1.3% and –5.7% (Panel A of Table 10). More importantly, the prevalence of trade-offs is widespread (Panel B). There are only 8-10 countries without trade-offs and, of these, between 6 and 9 prefer the status quo to more protection. There are between 44 and 46 countries with trade-offs. The resolution of these trade-offs is fairly stable in favor of the status quo for plausible levels of _ε_ (1, 1.5 or 2). That is, in all three protectionist scenarios the vast majority of countries attain higher levels of welfare with the pre-existing structure of trade protection rather than with more protection. Most countries would be hurt by protectionist trade reforms, even after inequality impacts are taken into consideration.

# **6 Conclusion**

Using household survey data for 54 low and middle income countries harmonized with trade and tariff data, this paper offers a quantitative assessment of the income gains and inequality costs of trade liberalization and the potential trade-off between them.

A stylized yet comprehensive model that allows for a rich range of first-order effects on household consumption and income is used to quantify welfare gains or losses for households in different parts of the expenditure distribution. These welfare impacts are subsequently explored by deploying the Atkinson social welfare function that allows us to decompose inequality adjusted gains into aggregate gains and equality (distributional) gains.

Liberalization is estimated to lead to income gains in 45 countries in our study, and to income losses in 9 countries. The developing world as a whole would enjoy gains of about 1.9 percent of real household expenditures, on average. These income gains are negatively correlated with equality gains, such that liberalization typically entails a trade-off between average incomes and income inequality. In fact, such trade-offs arise in 45 out of 54 different impacts on prices and because the household heterogeneity noted above implies that the inequality implications of a positive welfare effect may be quite different from those of negative effects.

30
We cannot rule out that the difference in caregiver-reported diarrhea prevalence between groups could be an artifact of biased outcome reporting. However, we do not observe differences between groups in falsification symptoms that could be subject to similar bias. Moreover, the results from the fecal samples (arguably more objective data than caregiver-reported diarrhea prevalence) show benefits accruing even more narrowly to non-poor households that had no sanitation at baseline, which are the group most likely to have built toilets as a result of the program. The weight and height improvements are also restricted to this group.

Positive externalities are another potential explanation for the large estimated impact on diarrhea prevalence. Perhaps only small improvements in sanitation are needed to give rise to large health effects given the benefits that flow across the community as a result of a less contaminated environment. The benefits of a household’s new toilet extend beyond that household’s. This is an area we have not fully explored in this report but is an area for future research.

# **10. CONCLUSION**

In conclusion, knowledge of the link between poor sanitation and health seems to be high in East Java even in control communities. Nevertheless, open defecation is still tolerated and many households believe that other people defecating in the open has no bearing on their own household’s health. Defecating in rivers is still a relatively attractive option for many households. A qualitative study conducted by WSP (2012) found that communities located near rivers are less likely to achieve ODF status. Households in these areas considered defecating in the river superior to using a pit latrine. This suggests that specific strategies focused directly at communities that are in close proximity to water bodies might be needed.

Nevertheless, we find that TSSM is associated with sanitation improvements. More households built toilets in treatment communities (3 percentage points higher) than control communities. This is a 31 percent increase in the rate of toilet construction in treatment communities relative to control communities. Further, fewer households reported that household members ever defecate in the open in treatment villages (4.4 percentage points lower than in control communities). The sanitation improvements were largely driven by toilet construction by non-poor households. Sanitation improvements were more limited for “particularly poor” households. Poor households are more likely credit-constrained and are thus less able to build toilets and so improve their access to sanitation.

In terms of health outcomes, we detect significant declines in the prevalence of diarrhea among young children (between two and five years old). Diarrhea prevalence decreased by 1.5 percentage points more (a decline of 30 – 45 percent depending on whether we examine sevenor two-day prevalence) in treatment communities than in control communities. This is a large impact. We also find decreases in the intensity of parasitic infestation, and increases in height and weight among non-poor households that had no sanitation at baseline.

31
production and export means that reforms cannot be envisioned without contemplating their short to medium term effect on output. This probably explains why, surprisingly, there is very little evidence of reform impacts on productivity, with the great majority of studies focusing on production (Akiyama _et al._ , 2003).

Data for production (000 Tons), area (000 Ha) and yields (Kg/Ha) is available from the Food and Agriculture Organization of the United Nations (FAO) as well as from the International Cotton Advisory Committee (ICAC) for the years 1961-2008. The FAO data are reported in seed cotton terms whereas the ICAC data is reported in cotton lint terms. As the impact of weather conditions is likely to be more directly perceivable in seed cotton terms, we primarily use the FAO data.<sup>12</sup> The ICAC data is however used to perform data quality robustness checks. Our panel thus comprises the 16 SSA countries for which both the FAO and the ICAC provide data.<sup>13</sup> These countries correspond to the 13 biggest producers of rain-fed cotton in SSA between 1998 and 2008 (Benin, Burkina Faso, Cameroon, Chad, Côte d‟Ivoire, Mali, Mozambique, Nigeria, Tanzania, Togo, Uganda, Zambia and Zimbabwe), plus Malawi, Kenya, and Senegal. Cameroon, Chad, Mali and Senegal retained monopolistic cotton markets until 2008 and therefore constitute the control group in the most recent years when all other countries introduced reforms.

# _3.2.2 Market structure_

The institutional variables are taken from a database constructed in a companion paper (Delpeuch and Leblois, 2011), which provides precise and consistent market organization indices for 25 African cotton markets from 1961 to 2008. The first variable used, _Post Reform_ , is simply a dummy variable that takes on the value one when the traditional monopolistic market organization is abandoned for any other market organization system. The default category, pre-reform, is thus always a monopolistic market in which a single firm (at least partly public) enjoys both a monopoly on exports and a monopsony on buying raw cotton from farmers at a fixed price. The most interesting

> 12 Cotton lint is obtained through the ginning process which separates the cotton lint from the cotton seed. The ratio of lint to seed cotton is known as the ginning outturn ratio. Measures of performance in lint terms being affected by the evolution of the ginning outturn ratio, the effect of weather conditions is likely to be less clear. 13 Following Schlenker and Lobell‟s (2010) presumption that missing data are replaced by the observation for the previous year, we excluded observations from the estimation if a country had several consecutive years with identical yields (less than 1 percent of observations). Our regressions also exclude pre-independence observations in countries where independence was gained post-1961 as we lacked sufficient information to adequately characterize market structure in the pre-independence period. Our panel thus has 694 observations. Regressions have also been run including the excluded observations, with negligible effects on results.

11
features. For instance, _de facto_ female-headed households and couples with children are highly correlated as well as _de jure_ female-headed households and multigeneration families with children.

**Table 5.  Regression results for correlations between headship and demographic composition and income composition of households.**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|Variables|FHH|_De facto_<br>FHH|_De jure_<br>FHH|Single<br>Caregiver<br>s|Couples<br>with<br>children|Multigeneration<br>with children|Families<br>without<br>children|
|**Income composition**||||||||
|Remittance recipients|-0.079**|-0.161***|0.161***|0.253***|0.071*|-0.178***|-0.146***|
||(0.040)|(0.035)|(0.035)|(0.033)|(0.041)|(0.043)|(0.035)|
|Female single earner|0.122***|-0.206***|0.206***|0.387***|0.058|-0.219***|-0.225***|
||(0.036)|(0.032)|(0.032)|(0.029)|(0.036)|(0.038)|(0.031)|
|Male single earner|-0.229***|0.033|-0.033|0.054**|0.245***|-0.097***|-0.203***|
||(0.031)|(0.029)|(0.029)|(0.027)|(0.033)|(0.035)|(0.028)|
|Equal contribution|-0.120***|0.016|-0.016|0.043|0.245***|-0.076**|-0.212***|
||(0.032)|(0.029)|(0.029)|(0.026)|(0.033)|(0.035)|(0.028)|
|Majority female earners|0.024|-0.167***|0.167***|-0.005|0.012|0.060|-0.067*|
||(0.041)|(0.035)|(0.035)|(0.033)|(0.041)|(0.043)|(0.035)|
|Majority male earners|-0.196***|||||||
||(0.039)|||||||
|No earners||-0.143***|0.143***|0.243***|0.097**|-0.213***|-0.127***|
|||(0.035)|(0.035)|(0.032)|(0.040)|(0.042)|(0.034)|
|**Demographic composition**||||||||
|Single Caregivers|0.834***|0.271***|0.729***|||||
||(0.035)|(0.033)|(0.033)|||||
|Couples with children|0.583***|0.991***|0.009|||||
||(0.030)|(0.027)|(0.027)|||||
|Multigeneration with<br>children|0.686***|0.474***|0.526***|||||

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support for this hypothesis include: Persson and Tabellini (1991), Alesina and Rodrik (1994), and Perotti (1996).<sup>6</sup> It is argued that high inequality leads a relatively poor median voter to vote for high tax rates, which in turn reduce incentives for investment and cause low growth.<sup>7</sup> Although one could argue that a poor median voter might also vote for redistributive policies that are not necessarily bad for growth; think of investments in public education.<sup>8</sup> Li, Squire and Zou (1998) and Oded Galor in a number of papers, some of which were co-authored by Omer Moav (see Galor, 2009; Galor and Moav 2004) put the emphasis on credit market imperfections, namely the inability of the poor to get loans to finance their education. Therefore, absent a deep financial market, inequality would lead to lower growth. This view was, in the case of Galor and Moav, integrated with their overall argument that, in modern societies, the key to fast growth is not capital accumulation but improvements in human capital. This explained, in their view, why the relationship between inequality and growth historically switched from being positive to being negative.

Barro (2000) found the relationship between inequality and growth to be inconclusive. When he split his sample into a low income- and a high-income sample, the results revealed a negative relationship for low income countries and a borderline positive, if any, relationship between inequality and growth for high income countries. (One might argue that inefficiencies such as credit market imperfections are more likely to play a role in developing countries than in high income countries.) Banerjee and Duflo (2003) also failed to obtain conclusive results from their linear specifications, which prompted them to explore non-linear alternatives. They concluded that all departures from the existing inequality towards either more or less inequality were associated with lower growth, and the greater the change in inequality, the greater the negative effect on growth. This result is not entirely intuitive as it seems to imply that each country had already attained its

> 6 Other authors (Keefer and Knack, 2002) looked at a link between inequality and political instability which would depress investments and economic growth.

> 7 These results too raised a number of questions. For example, the studies do not adequately distinguish between inequality of market income which determines the median voter’s position in the distribution and thus her desired tax rate, and inequality of disposable income which is the end product of the tax-and-transfer redistribution (for a critique see Milanovic 2000). It should be noted however that the micro household data, needed to get the distribution of market income, were seldom available.

> 8 Ostry et al. (2014), in a recent study on redistribution, inequality and growth, also note that equalityenhancing interventions need not necessarily be bad for growth.

5
few extremely narrow product segments. Once again, India’s success in software products is an example. Hausmann and Rodrik (2002) look in detail at the exports to the US of Bangladesh, Pakistan, Honduras, Dominican Republic, Korea, and Taiwan, using data at the 6-digit level (eg ‘hats and other headgear knitted or from textile material not in strips’). Even at this very fine level of disaggregation, for each of these countries, their top four product lines account for more than 30% of exports to the US. Furthermore, there is surprisingly little overlap in the top product lines of quite similar countries. For example, Bangladesh is successful in exporting shirts, trousers and hats (but not bed linen or footballs), while Pakistan does well in bed linen and footballs. Only six product lines are in the top 25 for both these countries. Hausmann and Rodrik conclude that ‘for all economies except possibly the most sophisticated, industrial success entails concentration in a relatively narrow range of high productivity activities’ (p26).

These findings are not inconsistent with the broad pattern of ‘development paths’ identified by earlier researchers. However, they indicate that these paths are only part of the story. Aggregate studies can conclude that a country has a comparative advantage in a laborintensive activity such as textiles, but provide no basis for predicting what particular product line will be produced. To understand the economic success stories we must also know why it is that this spatial and sectoral concentration occurs.

A corollary of spatial and sectoral concentration is that exports are essential; narrow sectoral specialisation can develop only if output is exported from the city, region or country. Increasing shares of exports in income are observed for many countries. Between 1980-82 and 1998-2000 the export to GDP ratio for the East Asia and Pacific region rose from 22% to 41%, and for South Asia from 8% to 14%, (compared to, from 20% to 23% for the world as a whole, and 28% to 29% for Sub-Saharan Africa).<sup>2</sup>

> 2  Averages for periods 1980-82 and 1998-2000, source World Development Indicators.

6
<!-- Start of picture text --> Chart 16. Comparison of Claims on Resources, 1978-94<br>(percent of OECD GDP)<br>'II<br>0<br>-2-<br><-<<br>-4<br>78 80 82 84 86 88 90 92 94<br>Soume: OECD and Wbnd Bank data<br><!-- End of picture text --> countries, it is not trends in capital markets at the global level that will mainly determine inflows but their own policies and prospects that affect foreign investors' perceptions of their creditwortiiness and viability. And, as emphasized above, this includes in particular conditions that promote high domestic investment and saving. Where domestic saving remains low, or worse still falls as foreign saving becomes a substitute for domestic saving, significant private flows will either not be attracted or, as happened in Mexico, not be sustainable.

For most low-income, low-growth developing countries, private capital inflows will remain limited, until the time they are able to achieve substantial improvement in their policies, performance and prospects. These countries in general will have to continue to rely mainly on official sources to meet their foreign capital requirements for some time to come, and could face financing constraints if the recent stagnation in official flows continues. The prospective 27