rafmacalaba/datause-encoder-data huggingface dataset:
- 145 -

trading; in cooperation with the private sector, it could develop a strategy for selling to countries who prefer to buy through monopoly state trading companies); and review of import prohibitions affecting agroindustrial products.

5.48 Competitiveness of a country's-agricultural sector can be heavily influenced by government investments in research and infrastructure, willingness to protect individual crops long enough for farmers and the marketing sector to move up a learning curve, "infant industry" protection, institutional arrangements that convey implicit or explicit subsidies, and foreign or domestic investors who transfer improved technology or specialized access to markets. Implicit or explicit promotion of certain crops by government policy is unavoidable and in some cases desirable.

5.49 A crucial aspect of the success of a promotional program is its continued financial and economic viability after the initial period of years so that public assistance can be withdrawn or substantially reduced. This viability requires that farmers, traders, and others involved with program activities not only start off with the right practices but also have the incentive to revise their decisions as circumstances change. For long-term viability, the special incentives initially introduced need to be gradually withddrawn. If a government agency remains involved for a long period of time, it must emphasize flexibility in decisionmaking and take account of the broader ramifications of the various measures taken to assist program participants. These broader ramifications mean that shortrun measures must be consistent with long-run goals. In particular, shortterm policies should point consistently toward long-run efficiency goals and the process of structural transformation.

# Structural Change. Diversification and Rural Poverty

5.50 The incidence of poverty remains substantial in most countries of Southeast and South Asia. In the 1980, World Development Report, it was estimated that of the total number of poor people in developing countries (excluding China), 520 million were in Asia, mainly in rural areas. By the mid-1980s, the number of poor Asians outside of China increased to
Emergencia (FISE). The nature of this Social Fund informs the structure of our model of project choice, which is presented in Section 3. Section 4 describes the data sets used in the analysis, with emphasis on the administrative FISE project monitoring and information data, and on the community-level estimates of poverty and inequality. Section 5 presents our empirical results, and section 6 concludes.

# **2. Social Funds and Ecuador’s FISE**

Social funds are (usually administratively autonomous) agencies that finance small sub-projects in several sectors - such as education, health, water, and sanitation - in response to demands articulated by local groups and screened against a set of eligibility criteria. They were set up in many countries in the 1980s and 1990s, initially as temporary measures to mitigate the social costs of structural adjustment programs but, with time, many have acquired a more permanent character. They operate as second-tier agencies that appraise, finance, and supervise implementation of social investments identified and executed by a wide range of actors, including local governments, NGOs, local offices of line ministries and community groups (White, 2002). Funds channeled through these agencies are not insubstantial.<sup>4</sup> In addition to accounting for increasingly large development flows, a number of studies suggest that most Social Fund projects are (mildly) pro-poor, and that they deliver projects to rural (and nowadays urban) areas which lack basic infrastructure.<sup>5</sup> Beneficiary assessments often indicate that the projects are a top priority for the community.<sup>6</sup> Social Funds have also drawn praise for their lean and efficient operation, which usually stands in stark contrast to the performance of line ministries in many developing and middleincome countries.

> 4 Over the last decade, the World Bank alone has financed close to $5 billion through Social Funds, in over 120 projects in almost 60 countries (van Domelen, 2002).

> 5 The literature evaluating Social Funds has grown rapidly in recent years.  Important contributions include Chase (2002) on Armenia; Chase and Sherbourne Benz (2001) on Zambia; Newman et al. (2002) on Bolivia; Paxson and Schady (2002) and Schady (2000) on Peru;  Pradhan and Rawlings (2002) on Nicaragua;  and Rao and Ibáñez (2005) on Jamaica. World Bank (2003) provides a multi-country analysis. 6 Although, Platteau and Gaspart (2003) argue that villagers may not express their reservations about the financed project or the role of the elite, for fear of losing the funds.  For the poor (or the non-elite), any public investment in the community might be better than nothing, even if it is not what they would have ideally wanted.

5
# **Table 5** : **Regressions of Social Welfare Growth on Average Income Growth:**

# **Alternative Measures of Real Income Growth**

|||||**Different Welfa**|**re Measures**||||
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
||**Bottom 10%**|**Bottom 20%**|**Bottom 40%**|**Bottom 90%**|**Atkinson Index**<br>**(1)**|**Atkinson Index**<br>**(2)**|**Atkinson Index**<br>**(3)**|**Sen Index**|
||||**Pane**|**l A: Survey data (in**|**come or consump**|**tion)**|||
|Avg. growth-Min 5 year spells|1.075***|1.006***|0.965***|0.963***|0.983***|1.005***|1.038***|0.967***|
||(0.100)|(0.0695)|(0.0501)|(0.0238)|(0.0275)|(0.0572)|(0.0798)|(0.0283)|
|Number of Observations|274|274|274|274|274|274|274|274|
|R-squared|0.383|0.565|0.722|0.927|0.902|0.645|0.485|0.899|
|Share of variance due to growth|0.356|0.561|0.749|0.963|0.917|0.642|0.467|0.929|
|P-value of wald test, slope=1|0.454|0.930|0.481|0.123|0.545|0.934|0.638|0.254|
||||**Panel B: Real pr**|**ivate consumption p**|**er capita (Nation**|**al accounts data)**|||
|Avg. growth-Min 5 year spells|1.146***|1.051***|1.011***|0.997***|1.011***|1.060***|1.105***|1.002***|
||(0.0938)|(0.0648)|(0.0463)|(0.0212)|(0.0241)|(0.0513)|(0.0723)|(0.0259)|
|Number of Observations|274|274|274|274|274|274|274|274|
|R-squared|0.301|0.463|0.634|0.891|0.855|0.552|0.394|0.851|
|Share of variance due to growth|0.262|0.440|0.626|0.894|0.846|0.521|0.357|0.849|
|P-value of wald test, slope=1|0.122|0.433|0.805|0.882|0.660|0.241|0.148|0.925|
||||**Panel C:**|**Mixed measure (arit**|**hmetic average f**|**rom A&B)**|||
|Avg. growth-Min 5 year spells|1.133***|1.030***|0.977***|0.969***|0.992***|1.034***|1.082***|0.975***|
||(0.109)|(0.0768)|(0.0551)|(0.0262)|(0.0299)|(0.0607)|(0.0846)|(0.0312)|
|Number of Observations|274|274|274|274|274|274|274|274|
|R-squared|0.299|0.457|0.621|0.888|0.852|0.543|0.388|0.847|
|Share of variance due to growth|0.264|0.443|0.636|0.916|0.859|0.526|0.358|0.869|
|P-value of wald test, slope=1|0.226|0.696|0.677|0.232|0.782|0.581|0.334|0.423|

Notes: This table reports the results from a regression of growth in the indicated social welfare function (in each column) on growth in average incomes, in the sample of non-overlapping spells lasting at least five years. The three panels correspond to the three different measures of mean income. Heteroskedasticityconsistent standard errors reported in parentheses. * (**) (***) indicate significant differences from zero at the 10 (5) (1) percent level. The last row in each <u>panel reports the p-value associated with the test of the null hypothesis that the estimated slope coefficient is equal to one.</u> 45
tion in which half the savings from debt reduction are applied to investment in education whereas the other half is allocated to investment in infrastructure. This mixed policy yields better results (in the sense of skilled unemployment being much lower) than the case in which public savings associated with debt relief are fully allocated to investment in education.

As urban unskilled workers enter the skilled labor force, and informal workers are absorbed into the formal sector, the expected average wage for unskilled workers in the urban labor market decreases (despite the fact that excess demand for labor in the informal sector tends to put upward pressure on wages there), but a larger initial decline in agricultural wages creates incentives to migrate from rural areas. This pushes workers out of the rural sector and into the urban unskilled labor market. However, migration flows are reversed in the long run as (expected) urban unskilled wages starts to decline.

Consumption-based poverty headcount indices show no decrease in poverty in the short run, and only workers in the agricultural nontraded sector benefit from poverty reduction in the long run. The aggregate income-based poverty headcount index shows little change in either the short or the long run. The economy-wide, consumption-based poverty gap index shows a decrease in both the short and the long run. In the short run, decreases in poverty gaps take place for rural non-traded and urban informal groups; in the long run, decreases are only observed for urban unskilled household categories, including formal and informal unskilled workers. Income-based poverty gap indices indicate a decrease in the poverty gap in the rural non-traded and urban informal sectors in the short run; this translates into a decrease in the aggregate poverty gap, despite an increase in the value of that indicator for rural traded and urban formal unskilled households. However, in the long run poverty increases in the rural and urban unskilled groups are greater than the poverty reduction in the urban informal group. This leads to an increase in the aggregate poverty gap in the long run. Finally, indices of income distribution again convey a mixed picture; the Gini coefficient shows a more egalitarian distribution whereas the Theil index indicates the opposite.

Poverty reduction under this scenario is clearly not as substantial as in the case where savings are allocated to infrastructure. This is due in part to the fact that investment in education does not necessarily translate into a shift in the private sector production possibilities frontier (PPF) as long as newlyskilled workers remain unemployed, whereas investment in infrastructure (as indicated earlier) does so unambiguously. Comparison between investment in 73
<!-- Start of picture text --> NeSE: Robusiness checks (Coefficient on IBRD country)<br>(1) (2) (3) (4) (9) (6)<br>Log of total loans Log of HD loans Log of HD sector loans<br>Base specification -0.508* -0.651*** -0.774** — -0.868*** -0.587*** — -0.691***<br>(all countries, 1988-2016) (0.275) (0.249) (0.239) (0.245) (0.212) (0.219)<br>Longer time frame -0.549%* -0.395** -0.346"* -0.485** -0.267** -0.376**<br>(all countries, 1961-2016) (0.172) (0.196) (0.121) (0.160) (0.126) (0.161)<br>Exclude reverse graduates -0.517 -0.629** -0.748%* -0.797** -0.562** -0.621**<br>(1988-2016, 5 excluded) (0.319) (0.293) (0.276) (0.283) (0.245) (0.248)<br>Exclude small and large countries -0.502 -0.636** -0.668%* -0.775*** -0.482** -0.593**<br>(1988-2016, 11 excluded) (0.329) (0.285) (0.208) (0.217) (0.194) (0.199)<br>Additional control variables -0.281 -0.489* -0.816*"** -0.925*** -0.552** -0.710*"**<br>(all countries, 1988-2016) (0.277) (0.281) (0.283) (0.292) (0.241) (0.246)<br><!-- End of picture text -->

|N|SE:Robusine|sschecks(Coe|fficientonIBR|Dcountry)|||
|---|---|---|---|---|---|---|
||<br>(1)<br>Logoft|<br>(2)<br>otal loans|<br>(3)<br>LogofH|<br>(4)<br>D loans|(9)<br>LogofHD|(6)<br> sector loans|
|Base specification<br>|-0.508*<br>|-0.651***<br>|-0.774** <br>|—-0.868***<br>|-0.587*** <br>|—-0.691***<br>|
|(all countries, 1988-2016)|(0.275)|(0.249)|(0.239)|(0.245)|(0.212)|(0.219)|
|Longertime frame|-0.549%*|-0.395**|-0.346"*|-0.485**|-0.267**|-0.376**|
|(all countries, 1961-2016)|(0.172)|(0.196)|(0.121)|(0.160)|(0.126)|(0.161)|
|Exclude reverse graduates|-0.517|-0.629**|-0.748%*|-0.797**|-0.562**|-0.621**|
|(1988-2016, 5 excluded)|(0.319)|(0.293)|(0.276)|(0.283)|(0.245)|(0.248)|
|Exclude small and large countries<br>|-0.502<br>|-0.636**<br>|-0.668%*<br>|-0.775***<br>|-0.482**<br>|-0.593**<br>|
|(1988-2016, 11 excluded)|(0.329)|(0.285)|(0.208)|(0.217)|(0.194)|(0.199)|
|Additional control variables<br>|-0.281<br>|-0.489*<br>|-0.816*"**<br>|-0.925***<br>|-0.552**<br>|-0.710*"**<br>|
|(all countries, 1988-2016)|(0.277)|(0.281)|(0.283)|(0.292)|(0.241)|(0.246)|
|Controls|no|yes|no|yes|no|yes|
|Country fixed effects|yes|yes|yes|yes|yes|yes|
|Fiscalyearfixedeffects|yes|yes|yes|yes|yes|yes|

Notes: Each observation is a country year, thus the coefficients can be interpreted as the average differences between IDA/Blend and IBRD country. Control variables in the even numbered columns are (a) log of GNI per capita, (6) log of population, and (c) Net aid flow. The rows with additional countrol variables include (a) Polity Index (-10 to 10), (6) Number of years left in current term of the executive, (3) If executive has finite terms in office, and (4) Indicators for left and nght leaning executive. Standard errors are clustered at the country level. * Significant at 10%, ** Significant at 5%, Significant at 1%.
Tanzania, Madagascar, South Africa, and Zambia; nevertheless, the contributions of transport services to average log productivity are relatively lower than the impact of the _provision of electricity_ or the _customs clearance_ subgroups. On the other hand, the use of own-transport infrastructure stimulates productivity growth; in all the cases in which any variable belonging to this subgroup was significant in the productivity regressions, it appeared with a positive sign. But the positive effects on productivity of these factors were concentrated in only seven countries: Malawi, Benin, Senegal, Eritrea, Tanzania, Kenya, and Egypt.

Figure 6.25 provides similar information, but, in this case, we have grouped the different infrastructure factors in only five groups: _customs clearance, electricity, water, telecommunications and ICT,_ and _transportation_ . From this figure the high influence of _electricity_ factors become even clearer. When we include the _provision of electricity_ and _use of generators_ or _power infrastructure_ into a single group, the resulting block of electricity factors can explain more than half of average log productivity in Uganda, Ethiopia, Zambia, Eritrea, Swaziland, and Botswana. The water group is relatively important in Tanzania, Kenya, and Egypt. The customs clearance group gains importance in those countries with a more patent export orientation, such as Mauritius, Botswana, and Namibia. Finally, as has been signaled, the transportation subgroup explains more than half of the whole infrastructure impact on average log productivity in Senegal and Madagascar.

**Figure 6.25 Infrastructure impact on average log productivity by key factors (II)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br><!-- End of picture text --> <!-- Start of picture text --> MWI UGA BEN SEN ETH ZMB ERI MLI CMR DZA MRT BFA NER TZA KEN MAR MDG ZAF EGY SWZ MUS BWA NAM<br>Customs clearance Electricity Water Telecoms and ICT Transportation Other<br><!-- End of picture text --> <!-- Start of picture text --> Customs clearance<br><!-- End of picture text --> <!-- Start of picture text --> Water<br><!-- End of picture text --> <!-- Start of picture text --> Telecoms and ICT<br><!-- End of picture text --> <!-- Start of picture text --> Transportation<br><!-- End of picture text --> <!-- Start of picture text --> Other<br><!-- End of picture text --> _Source_ : Author’s calculations with IC data.

_Note_ : For a description of the variables contained in each group see footnote in Figure 6.24 Figure 6.26 reports the absolute percentage contribution of infrastructure by key factors via simulations. The results are fully consistent with the ones provided by figure 6.24. The relative weights of the electricity factors dominate in more than half the countries, and the water and telecommunications and ICT subgroups tend to play a secondary role in explaining average productivity when compared to electricity. The main difference with respect to figure 6.24 is the lower relative weight of the customs clearance group. Once again, it should be pointed out that the 62
# **Concentration in the Banking Sector and Financial Stability: New Evidence**

Pietro Calice<sup>†</sup> and Leone Leonida<sup>‡</sup> JEL: C52; E58; G21; G28.

Keywords: Banking crises; Concentration; Logit model; Interaction effects

> † World Bank Group, Washington DC, USA. Email address: <u>pcalice@worldbank.org</u>

> ‡ King’s Business School, King's College London, London, UK. Email address: leone.leonida@kcl.ac.uk.

We are indebted to Thorsten Beck for his valuable comments. Any error or omission is only ours.
their managers. We interpret these results to suggest that managers' incentives to restructure decrease when they perceive their newly-acquired ownership as a windfall gain.

We choose to study the restructuring process in these two transition economies since they both had privatization programs which favored incumbent managers, making* it easier to construct controlled samples of firms. Georgia introduced voucher privatization with concessions to managers as the primary privatization method, and only a handful of firms were sold for cash to their managers or outside investors. In contrast, the majority of Moldovan enterpnrses was acquired by private investment funds in Czech-style auctions, while a large second group of enterprises were privatized through cash sales to managers (see EBRD, 1997, Table 5.7). Our surveys make it possible to distinguish between the two privatization methods in each country. Thus the data can be used to investigate not only the effects of privatization, but also the effects of the modalities of a particular type of privatization - privatization with concessions to insiders - on subsecquent enterprise performance.

An additional reason for focusing on Georgia and Moldova was the comparability of the _survey_ nrmethodology, as both surveys were cornducted by the author as inputs in World Bank private sector reviews. Finally, the two countries have much in common in terms of their external (to the firm) envrironment. The early part of the transition period was marked by either civil war (Georgia) or ethnic tensions (Moldova) which stopped the production process in many enterprises, and resulted in the cessation of large industnral regions (Abhasia in Georgia; Transnistria in Moldova) and the related loss of supplier and customer networks. Both countries also cut off economic relations with Russia during part of the transition period. These negative shocks made the restructuring process all the more difficult and necessary.

The paper is organized as follows. Section 2 suggests some indicators of enterprise restructuring and discusses the relevant literature on its determinants in transition economies. Section 3 describes the survey methodology and the data sample. Section 4 details the regression analysis and provides interpretation of the main findings. Section 5 concludes.

2
**Table 5: Georgia’s elasticity of labor market outcomes with respect to exporting, IV**

|**Destination market**|**Coefficient**|**Employment**|**Female employment**|**Wages**|**Female wages**|
|---|---|---|---|---|---|
|World|Export share|0.01213+|0.01492*|-0.01832***|-0.01516**|
|||[0.00750]|[0.00820]|[0.00697]|[0.00731]|
|EU|Export share|0.01203+|0.01453*|-0.01793***|-0.01495**|
|||[0.00746]|[0.00843]|[0.00695]|[0.00732]|
||EU market share|0.00053|0.00361+|-0.00216|-0.00219|
|||[0.00223]|[0.00265]|[0.00235]|[0.00263]|
|Russia|Export share|0.12181|0.26543|-0.02507|-0.09927|
|||[0.30288]|[1.39088]|[0.04939]|[1.52254]|
||Russia market share|0.07970|0.19164|-0.00572|-0.07098|
|||[0.21238]|[1.04916]|[0.04006]|[1.27907]|
|ECA|Export share|0.01338*|0.01648**|-0.01639**|-0.01430**|
|||[0.00733]|[0.00791]|[0.00637]|[0.00677]|
||ECA market share|-0.00251|-0.00214|-0.00387**|-0.00125|
|||[0.00217]|[0.00247]|[0.00197]|[0.00226]|
|HIC|Export share|0.01119+|0.01402*|-0.01702***|-0.01523**|
|||[0.00708]|[0.00823]|[0.00660]|[0.00715]|
||HI market share|0.00292|0.00496|-0.00434|0.00042|
|||[0.00431]|[0.00465]|[0.00459]|[0.00457]|
|UMIC|Export share|0.00711|0.00550|-0.01911**|-0.01838**|
|||[0.00834]|[0.00826]|[0.00839]|[0.00888]|
||UMI market share|-0.00291+|-0.00669**|-0.00055|-0.00244|
|||[0.00211]|[0.00262]|[0.00207]|[0.00242]|
|LMIC|Export share|0.00835|0.01259+|-0.01389**|-0.01417*|
|||[0.00695]|[0.00772]|[0.00650]|[0.00727]|

15
that cannot be recovered in case the outcome turns out to be unfavorable. If there is free entry _ex post_ , no entrepreneur may be willing to make the investment required to discover the profitability of activity _x_ : if someone does make the investment and the activity turns out to be profitable, other entrepreneurs will be attracted to the same activity thereby eliminating all rents. It is worth noting that Baldwin’s (1969) classic paper anticipates Rodrik’s argument almost exactly. He wrote “...suppose, for example, that a potential entrant into a new industry, if he could provide potential investors with a detailed market analysis of the industry, could borrow funds from investors at a rate that would make the project socially profitable. However, should this information become freely available to other investors and potential competitors, the initial firm might not be able to recoup the cost of making the market study....under these circumstances the firm will not finance the cost of the study, and a socially beneficial industry will not be established.” Similarly, in the context of adoption of high yielding varieties of crops by farmers in developing countries, Besley and Case (1993) note that late adopters may learn from early adopters when “a technology is of uncertain profitability, some potential adopters may wait until they observe whether others have fared well by using it” and that such “externalities are potentially important in agricultural technology adoption.” Given the importance of this argument for the debate on industrial policy, it is useful to consider the framework presented in Hausmann and Rodrik (2003) in some detail. They consider a small open economy comprised of two sectors: traditional and modern. Labor can move freely across the two sectors. The production technology in the traditional sector is constant returns to scale and the presence of a fixed factor generates diminishing returns to labor. In the modern sector (that consists of many goods), there are constant returns to scale in production but the cost of production of a good depends upon an unobserved productivity parameter ( θ i) that becomes known only when the production of a good is attempted (something that requires a time period in which resources must be utilized but no production takes place – this is what Baldwin called a ‘market study’). The basic idea here is that entrepreneurs lack information about the profitability of production of various goods in the modern sector and this information can be obtained only by undertaking a sunk investment.

After uncertainty regarding θ i is resolved, entrepreneurs compare their production 18