rafmacalaba/datause-encoder-data huggingface dataset:
Another empirical illustration of our theoretical results involves the computation, based on equation (3.11), of the correction factors associated with the _PEGR_ . The results are presented in table 4.4. The adjustment factor is less than one for all observed growth curves, confirming that growth was not pro-poor in Indonesia in the 1990s. The counterfactual for 1996-1999 would have been pro-poor since the correction factor for this pattern is greater than one for all poverty measures considered.

Table 4.4. Estimates of the Correction Factor for the _PEGR_

||Watts|Poverty Gap|Squared Poverty Gap|
|---|---|---|---|
|1993-1996|0.64|0.65|0.64|
|1996-1999|0.60|0.61|0.58|
|1999-2002|0.74|0.74|0.73|
|1993-2002|0.79|0.77|0.80|
|1996-1999(Counterfactual)|1.40|1.39|1.42|

Source: Authors’ calculations.

The interpretation of the results presented in tables 4.3 and 4.4 follows the same logic as for the numerical example discussed earlier (see pages 11-12). For the period 1993-1996, the pro-poorness value associated with the Watts index is equal to -0.22. The underlying elasticities are φ _w(q0)_ = -0.62 and φ _w(q)_ = -0.40. In other words, for every 1% increase in per capita expenditure, a distributionally-neutral process would have reduced poverty by 0.22 % more than the observed pattern. In terms of Kakwani et al’s adjustment factor, we may say that distributionally neutral growth would have reduced poverty 1.55 times faster than the observed outcome. This is exactly the opposite of the first case discussed in the numerical example. The rest of the results are interpreted similarly.

The basic idea underlying these measures of pro-poorness is, in line with Osmani’s (2005) conceptual framework, to compare observed poverty outcomes to what they would have been under distributional neutrality. If observed outcomes are better than the hypothetical ones, economic growth is considered pro-poor, otherwise it is not. We can confirm our basic conclusion by considering the results of a Shapley 22
_25_ _Ways Out of Povertv - Diffising Best Practices and Creating Capabilities_ <!-- Start of picture text --> Figure 14: Decomposition of multifactor productivity growth<br>In manufacturing<br>(contribution of each component to total annual productivity growth)<br>1400<br>120.0<br>1000<br>*~80.0-<br>60 0-<br>0.. 40 0-<br>0.0<br>-20 0<br>Finland France Italy Netherlands United United<br>Kngsdom States<br>g Within-firm productivity growth * Output reallocation aniongst existing firms<br>o[]ntry of firms * Exit of firms<br>Source Scarpetta et al. (2002).<br><!-- End of picture text --> In this ecology of firms, markets leave ample room for new organizational<sup>species to</sup> emerge, spanning atomistic small firms, large hierarchical finns,<sup>internalfirm marketsfor</sup> corporate control, and all sorts of co-operative arrangements among firms.<sup>Customer</sup> demand provides the main feedback mechanism, as the firms survive<sup>that meet customer</sup> demand most effectively. The diffusion of best practice is accomplished<sup>this well-</sup> functioning ecology, not by any single measure or any single<sup>type of firm.Governments</sup> shape the ecology and the niches allowed by the way they set<sup>property rights, regulations,</sup> taxes and other relevant parameters.
<u>Table 2: Summary statistics of the main variables disaggregated based on urban status</u>

||||Obs|ervations|from all, rural<br>|, and urban ar|eas|||Mean<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|||All|||Rural|||Urban||Difference|
||N|Mean|SD|N|Mean|SD|N|Mean|SD|Rural-Urban|
|Total consumption expenditure|13391|6255.255|6210.662|9472|5236.817|4885.779|3919|8716.763|8096.422|(-)***|
|Food consumption expenditure|13391|4645.919|5019.261|9472|4241.888|4212.748|3919|5622.441|6469.122|(-)***|
|Education expenditure|13391|143.623|745.448|9472|50.866|246.679|3919|367.809|1296.514|(-)***|
|Other non-food expenditure|13391|1465.713|2391.347|9472|944.063|1861.122|3919|2726.512|2987.065|(-)***|
|Consumption poverty|13391|0.347|0.476|9472|0.424|0.494|3919|0.160|0.367|(+)***|
|Wealth|13925|0.008|2.881|9868|-1.352|1.248|4057|3.317|3.042|(-)***|
|Wealth poverty|13925|0.393|0.489|9868|0.544|0.498|4057|0.027|0.162|(+)***|
|Employed|33691|0.194|0.395|23974|0.147|0.354|9717|0.308|0.462|(-)***|
|Permanently employed|33858|0.105|0.306|24095|0.044|0.204|9763|0.255|0.436|(-)***|
|Hours worked, permanent|33853|171.321|597.256|24090|55.684|344.298|9763|456.652|911.073|(-)***|
|Temporarily Employed|33738|0.096|0.294|23991|0.109|0.311|9747|0.063|0.243|(+)***|
|Days worked, temporary|33725|5.052|24.946|23984|4.799|21.657|9741|5.676|31.612|(-)***|
|Owns an NFE|13889|0.337|0.473|9835|0.280|0.449|4054|0.476|0.499|(-)***|
|# of NFEs owned|13889|0.406|0.699|9835|0.377|0.676|4054|0.476|0.747|(-)***|
|Plans to open a new NFE|13811|0.199|0.399|9769|0.164|0.370|4042|0.282|0.450|(-)***|
|Worked for other HHs for free|33741|0.232|0.422|24004|0.286|0.452|9737|0.099|0.299|(+)***|
|# of other HHs worked for|33740|1.015|2.865|24003|1.268|3.041|9737|0.390|2.257|(+)***|
|A fall in the prices of food items|13924|0.029|0.168|9868|0.030|0.171|4056|0.026|0.159|(+)|
|A rise in the prices of food items|13923|0.214|0.410|9867|0.199|0.399|4056|0.250|0.433|(-)***|
|A rise in the prices of agri. inputs|13924|0.094|0.292|9868|0.117|0.322|4056|0.038|0.192|(+)***|
|Refugee intensity|1198|954.556|1639.084|869|833.355|905.654|329|1274.691|2737.182|(-)***|

_Notes_ : Consumption expenditure measures are in per capita terms. N, SD, and NFE stand for number of observations, standard deviation, and non-farm enterprise, respectively. The symbols (+) and (-) indicate whether the corresponding mean difference is positive and negative, respectively. Asterisks: *, **, and *** indicate statistical significance of the mean difference at 10%, 5%, and 1% levels, respectively, based on the standard two-sided t-test.

40
## **Table A0: Literature review (continuation)**

|**Paper**|**Fiscal Adjustment**|**Succesful fiscal adjustment**|**Multivariate analysis?**|**Sample**|
|---|---|---|---|---|
|Kaplanoglou|Attempts are defined as an|At least one of the two following criteria holds: 1) in the|Logit model.|29 (OECD),|
|et al. (2015)|improvement in the CAPB of at<br>least 1.5% of GDP taking place in<br>one year or taking place over<br>three years, if in each and every<br>year the CAPB does not<br>deteriorate by more than 0.5% of<br>GDP.|three years after the attempt, the ratio of the cyclically<br>adjusted primary deficit to GDP is on average at least<br>2% of GDP below the attempt year, 2) three years after<br>the attempt, the ratio of the debt-to-GDP is at least 5%<br>of GDP below the levelk of the attempt year. (following<br>Alesina Perotti 1997)_||1971-09|
|Gupta et al.<br>(2017)|Updates data of Devries et al.<br>(2011).|Analyses difference between the size of planned fiscal<br>adjustment and the size of the realized fiscal<br>adjustment as measured by changes in the primary<br>budget balance (all expressed in percent of GDP)|Analyses difference<br>between planned and<br>actual fiscal<br>consolidations.|17 (OECD),<br>1978-15|
|Wiese et al.<br>(2018)|BP test on cyclically adjusted fiscal<br>balance to identify the beginning<br>of an adjustment and then it<br>continues as long as the change is<br>positive.|BP test to the growth rate of the debt-to-GDP ratio. If<br>fiscal adjustments are identified prior to, or<br>simultaneously with the beginning of regimes with<br>negative growth rates, and the periods are not more<br>than 4 years apart, we code it as succesful.|Probit model with<br>random effects<br>pretesting using Mudlak<br>approach.|<br>20 (OECD),<br>1967-13|

# Appendix 2: Episodes of fiscal need and fiscal adjustment

**Table 10: Adjustment need in advanced countries under current conditions**

|**Country**|**Begin**|**End**|**Country**|**Begin**|**End**|
|---|---|---|---|---|---|
|Australia|1992|1994|Japan|1994|1996|
|Australia|2010|2010|Japan|1999|2014|
|Austria|1994|1996|Latvia|2010|2010|
|Canada|1991|1994|Lithuania|2010|2010|
|Cyprus|2010|2013|Netherlands|2010|2012|
|Czech Republic|2001|2003|New Zealand|2010|2011|
|Czech Republic|2010|2010|Norway|1993|1994|
|Estonia|2009|2009|Portugal|1994|1994|
|Finland|1992|1996|Portugal|2005|2005|
|France|1992|1997|Portugal|2010|2014|
|France|2010|2010|Slovak Republic|2001|2002|
|Germany|1996|1997|Slovak Republic|2010|2010|
|Germany|2003|2003|Slovenia|2010|2013|
|Greece|1993|1993|Spain|1993|1995|
|Greece|2009|2015|Spain|2009|2014|
|Iceland|1993|1993|Sweden|1993|1995|
|Iceland|2009|2010|United Kingdom|1992|1995|
|Ireland|2009|2013|United Kingdom|2009|2013|
|Israel|2003|2003|United States|2009|2012|

**Table 11: Adjustment need in developing countries under current conditions**

|**Country**|**Begin**|**End**|**Country**|**Begin**|**End**|
|---|---|---|---|---|---|
|Angola|2015|2015|India|2012|2012|
|Argentina|1996|1996|Iraq|2014|2016|
|Argentina|1999|2004|Jordan|2012|2014|

- 37 -
70. **It is a reasonable assumption, nonetheless, that (in the absence of compensatory measures) many developing countries will be net losers from the impact on both their import and, perhaps less certainly, export prices** . One reason may be that, even collectively—and here excluding large emerging economies such as China—they are typically small in world markets, albeit with some exceptions is relation to particular commodities. Two other potential reasons are that (1) the import and export activity of developing countries tend to be heavily concentrated on ̳bulky‘ goods with high transport costs relative to their values (although this explanation is somewhat muted by the Stochniol (2011c) argument cited below); and (2) import and export volumes to and from poor countries (with small economies) are in many cases too small to enable fully efficient transport (and so are also subject to greater increases in transport rates when a fuel charge is imposed).

71. **The welfare impact on any country of increased freight costs depends on the elasticity of its demand for imports and the elasticity of export supply from the rest of the world** . Box 10 sets out a simple formula reflecting this consideration. Preliminary calculations, using country-specific estimates of import demand elasticities from Tokarick (2010), suggest two key points. First, the welfare impacts, relative to GDP, of increased transport costs even in the order of 1 percent of goods prices have only a relatively small welfare impact. Second, relative to GDP, these losses are greatest in higher income countries—essentially because they tend to be more open.

# **Box 10. Compensating for Increased Transport Costs on all Trade**

When increased transportation costs apply to the generality of imports and exports, cross-price effects cannot be ignored (as they were in Box 5). The compensation required (expressed in terms of the imported good) is shown in Appendix 2 to be: One important implication is that, even with full pass-though of fuel charges into transportation costs, full return of all charges on the transportation of all charges bearing on both imports and exports would be overcompensation for any country that does not face perfectly elastic supply from the rest of the world.

72. **Ideally, a charge would be levied by port of arrival that reflects associated fuel use and adjusted to reflect behavioral responses, but for reasons set out above this appears infeasible** . This would correspond to the precise compensation described in Appendix 2. But each step of such a procedure is problematic: the first on practicability grounds, the latter in view of ignorance of the relevant elasticities.

73. **One pragmatic proposal that has attracted attention is rebate mechanisms based on shares of global import value.** This scheme, proposed by the World Wildlife Fund<sup>49</sup> and set out in detail in Stochniol (2011c), is described in Box 11. Though by no means the only possibility, it merits consideration here for both possibilities it suggest and a flavor of the challenges to be addressed in this:

> 49 IMO (2010b).

39
WPS4009

# **Azerbaijan’s Household Survey Data: Explaining Why Inequality is So Low**

Lire Ersado<sup>1</sup> Human Development Network Europe and Central Asia Region World Bank

**Abstract**

While the Azerbaijan household income and expenditure survey (HIES) data satisfy most empirical regularities expected in a typical household survey data, the inequality measures based on the data are unusually low. For example, for the latest three years for which we have data (2002-2004), the consumption Gini coefficient—the commonly used summary measure of inequality—is in the range of 16—18 percent. This is among the lowest Gini coefficients ever observed in any country, and is extremely low even with the standard of countries generally considered as most equal in the world. Azerbaijan, a transitional economy with a significant natural resource base, is very unlikely to be the most equal country in the world. The objective of this paper is to investigate why inequality measures are unusually low in the Azerbaijan household survey data. The paper presents a methodology for diagnosing and identifying the potential sources of low inequality in the data, including cluster analysis at the primary sampling unit level. The main inference from the findings of the cluster analysis is that the observed low inequality indices are not due to poor supervision of the interviewers and the data collection process. We find that the main culprits for the observed low inequality in the HIES data are (1) the low participation rates of wealthy households in the household surveys, and (2) the widespread availability of well targeted public and private transfers.

World Bank Policy Research Working Paper 4009, September 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 Send all comments to <u>lersado@worldbank.org. This paper is part of the Azerbaijan</u> Programmatic Poverty Assessment work. I would like to thank Branko Milanovic for insightful comments and suggestions on inequality issues and Christian Petersen for review and useful comments on an earlier draft. Arup Banerji and Aleksandra Posarac have provided overall guidance for Azerbaijan Programmatic Poverty Assessment work.
While the univariate comparisons over time describe changes in regulation and supervision in broad terms, they cannot explain why countries chose the approaches that they did. We therefore turn to regressions to investigate the factors that were associated with changes in bank regulation and supervision, focusing specifically on capital regulation. We confirm that the countries that experienced a crisis were more likely to increase their capital requirements. In particular, these crisis countries were more likely to increase their capital holdings as reflected in their tier 1 capital ratios. But those increases hinge on what constitutes tier 1 capital in the numerator and on the way risk-weighted assets are calculated in the denominator of the tier 1 capital ratio. For other, simpler measures of capital adequacy such as the leverage ratio, banks in crisis countries showed no statistically significant improvement. We then examine the impact of risk-weights and tier 1 stringency on solvency risk using bank level data. Examining over 20,000 banks in 158 countries, we find that controlling for the overall level of capital, bank risk is more sensitive to regulatory capital if a higher share of capital is in the form of higher quality tier 1 capital. We also find that bank risk is more sensitive to regulatory capital ratios and risk-weights if the domestic regulators impose greater stringency on what constitutes higher quality capital. Finally, when we examine the motives for the adoption of the latest capital regulations, we find evidence of peer country effects in three new capital regulations that have been implemented in the aftermath of the global financial crisis. In particular, we show that countries were more likely to adopt Basel III and countercyclical and conservation capital buffers if countries in the same region also adopted these new capital regulations.

The rest of the paper is organized as follows. Section 2 summarizes the implementation of the survey and presents some evidence on a subset of questions focusing on capital regulation, private monitoring by market participants and supervisory monitoring. Section 3 analyzes the drivers of post-crisis changes in capital regulation and their impact on bank risk. Section 4 discusses policy implications and concludes.

3
but housework can account for the size of the point estimate. For 16 to 18 year-old females, the increase in housework as the main activity more than accounts for the decrease in schooling, with a decrease in non-school work also needed to account for the large increase in housework.

# **7. Conclusion**

This paper examined the overall impact of migration on educational attainment in rural Mexico. This impact is the sum of three main effects: the effect of remittances on the feasible amount of education investment, which is likely to be positive where liquidity constraints are binding; the effect of having parents absent from the household as a result of migration, which may translate into less parental inputs into education acquisition and maybe into more house and farm work by remaining household members, including children; and the effect of migration prospects on the desired amount of education, which is likely to be negative, as we argued, in the face of lower returns to schooling in the U.S. than in Mexico, especially in a context of illegal immigration.

Our results are in line with these predictions. Using historical migration rates by state to instrument for current migration, we find evidence of a significant negative effect migration on schooling attendance and attainments of 12 to 18 year-old boys and of 16 to 18 year-old girls. IV-Censored Ordered Probit results show that living in a migrant household lowers the chances of boys completing junior high school (by 22 percent) and of boys and girls completing high school (by 13 to 15 percent). This is consistent with migration increasing the opportunity cost, and lowering the expected return to education. However, the negative effect of migration on schooling is somewhat mitigated for younger girls with low educated mothers, which is consistent 26
That food aid to particular countries is persistent has been well established in the literature (Barrett, 1998; Jayne et al, 2002; Barrett and Heisey, 2002). Figure A2 demonstrates this persistence for wheat aid in the particular years and countries of the NQ dataset by showing the distribution of aid spells by length, where a “spell” is defined as a continuous stretch of years in which a country receives aid in each year and spells are separated by those where there was a conflict in the country in the first year of the spell and those where the country was not in conflict when the spell began. Once aid starts, the country is likely to continue receiving aid for many subsequent years. This is especially true if there is a conflict in the country when the spell starts. This highlights the fact that aid allocations are highly endogenous; once aid starts flowing to a country, it is likely to continue, and this is especially true in conflict situations.

The degree of persistence has also changed over time as aid allocation priorities have changed. Figure A3 shows the percentage of aid spells that last at least five years by the year in which the spell started. Wheat aid was significantly more persistent in the 1970s when most food aid flowed as (Title I PL480) annual concessional exports to governments with established Title I programs. This persistence lessened in the 1980s and 1990s as Title II grants to NGOs and WFP began to replace Title I, catering to a different set of countries. The persistence grew stronger again in the 2000s when USAID began concentrating non-emergency Title II flows on just a few countries that routinely had emergency Title II flows for two reasons: First, an aspiration that non-emergency might preempt some of the need for emergency food aid flows, and, second, the administrative logistical advantages in rapid emergency response that come from having non-emergency food aid distribution pipelines in place and operational already when a disaster strikes or a conflict erupts (Barrett and Maxwell, 2005).

Figure A4 shows a lowess plot of conflict incidence over the period in Nunn and Qian’s study. On average, the incidence of conflict followed a similar trend to 41
# **Bank Involvement with SMEs: Beyond Relationship Lending**

Augusto de la Torre María Soledad Martínez Pería Sergio L. Schmukler<sup>*</sup>

## **JEL Classification Numbers:** G21, G28, L25, O12, O16

## **Keywords:** small and medium enterprises, bank finance, financial constraints, banking market structure

* This paper is part of a broader project to understand bank financing to SMEs, undertaken in the World Bank Group through the Latin American and the Caribbean Region, the Finance and Private Sector Development Network, the Development Economics Research Group, and the International Finance Corporation. We thank many colleagues at the IFC and the World Bank and are particularly grateful to: Sonja Brajovic Bratanovic and Constantinos Stephanou, who gave us data on Serbia and Colombia, respectively, and to Arantxa Veraza López, Manuel López Hernández, Melina Mirlsmulstein, Ary Naim, Camila Rodríguez, who interacted with us at different phases of this project. We are also indebted to the IFC SME team and FRS (Inmark Group) for sharing with us unique data. We received very helpful comments and suggestions from Luis Ahumada, Pablo Carbajo, Stijn Claessens, Kevin Cowan, Rogelio Marchetti, Hernan Lacunza, Margaret Miller, Ary Naim, Cristina Pailhé, Gastón Repetto, José Rutman, Sophie Sirtaine, and Greg Udell. We also received useful comments at presentations held at the Asobancaria Conference on SME Lending (Bogota, Colombia), the CASIN-IFC Forum on SMEs (Geneva, Switzerland), the Central Bank of Argentina (Buenos Aires, Argentina), the Central Bank of Chile (Santiago, Chile), the Latin American Congress on Banking and SMEs (Cartagena, Colombia), the World Bank Small Business Finance Conference (Washington, DC, USA), and the World Bank (Washington, DC, USA). Maria Bernarda Dall’Aglio, Noemí Soledad López, Mira Olson, Mercedes Politi, and Victoria Vanasco provided excellent research assistance at different stages of the project. We thank all the banks that participated in the study for their generosity in sharing information and for interacting with all the members of the team. The views expressed in this paper are entirely those of the authors and do not necessarily represent the opinions of the World Bank Group. Email addresses: adelatorre@worldbank.org, <u>mmartinezperia@worldbank.org, sschmukler@worldbank.org</u>