rafmacalaba/datause-encoder-data huggingface dataset:
# _Implications of the Divergence and Concentration of Export Structures_

It appears from the above that there are several inconsistencies in the region’s trade patterns that need further examination. First, there appears to be a significant divergence<sup>15</sup> between products that are exported to the region and to the rest of the world. While primary products dominate intra-regional trade, manufactured products dominate exports to the rest of the world. India is the only exception, having achieved some symmetry in exports with the region and the rest of the world. This is mainly a consequence of the restrictive trade regimes particularly in India, Bangladesh and Pakistan despite measures to reduce them in recent years.<sup>16</sup> Another explanation appears to be India’s growing efficiency in a range of agriculture as well as manufactured products (discussed further, below). The ability to increase regional exports is contingent on the degree to which dynamic exports<sup>17</sup> are incorporated in the regional export mix.

Secondly, the exports of most member countries (with the exception of India) seem to be confined to a few products, that is, a small number of products seem to accumulate a significant share of exports revenues. The likely success or failure of any proposed regional trade arrangement is contingent on the range of products prospective members have the capacity to export or import. If the members export a wide range of diversified goods, this is a positive factor. If their exports are concentrated, it will limit the prospects of increasing regional trade.<sup>18</sup> Table 7 presents a summary of the divergence and concentration estimates to further examine the two propositions, above. The summary is derived from raw data in annex Table A3. The data are for products that

> 15 Divergence is defined as the departure of intra-regional exports from extra-regional exports in major product categories. 16 See Pigato et. al and Pitigala et al (2001) for more details on trade reforms in South Asia in the 1990’s.

> 17 Dynamic exports are defined as those growing higher than the average.

> 18 “The underlying assumption is that the higher the level of export diversification, the better the prospects for a successful regional initiative. The more diversified a country’s exports the greater the range of potential products that can be traded with regional partners. If only a limited number of such goods exist, members of an RTA may have to rely heavily on third countries for a higher share of its key imports (and as a destination for their major exports), and this would be likely to reduce their commitment to and perceived benefits from the arrangement” (Yeats, 1998).

24
Figure 3.6 illustrates the importance of investment quality for sub-investment grade countries and the lack of such an effect for investment grade countries. A comparisons of panels 2B of Figure 3.6 with panel 3 of Figure 3.5 shows that the estimates for the non-investment grade subsample are more precisely estimated and have a steeper slope, implying a stronger effect of investment quality in the sub-investment grade subsample than in the full sample of Table 3.3.

# _Robustness checks._

We check whether our results are robust to using alternative estimation frameworks and measures of investment quality.

As a first step, we re-estimate our baseline model using the Mundlak-Chamberlain correlated random effects. This model yields estimates consistent with the fixed effects model while allowing for time-invariant covariates, which, inter alia, enables us to examine the direct impact of the PIQF index. Columns 1 and 2 of Annex Table A3 and associated marginal effects in Figure A3 show that PIQ-F has a negative and statistically significant impact on sovereign risk, although the nonlinear effect is not as strong in the case of sovereign ratings.

Next, we show that our baseline results are robust to dropping outliers (columns 3-4 of Table A3 and associated panels of Figure A3). Outliers here are defined as observations with values exceeding two standard deviations from the mean for public investment-to-GDP ratio, the PIQ index, and the sovereign risk measure used in the model.

We also show that the results are robust to using the Emerging Markets Bond Index (EMBI) spread as an alternative measure of sovereign risk (columns 5-6 of Table A3), as well as to using ordered logit and ordered probit with year fixed effects as an alternative estimator for the sovereign rating variable (columns 7-8 of Table A3).

Finally, we re-estimate our baseline models using the index computed over a 5-year estimation window (PIQ-Q). One advantage of the PIQ-Q index is that it allows us to use a panel with nonoverlapping periods (we can only do this for sovereign rating because the spreads data do not have sufficient time-variation). While PIQ-Q is based on a smaller number of projects, it produces results that are fully in line with our baseline (see Columns 1-4 of Table A4 and panels 1-4 of Figure A4). Columns 5 and 6 of Table A4 and the corresponding panels of Figure A4 show that our results are robust (in fact, they become stronger) when we use non-overlapping periods.

# **3.4. Implications for public debt sustainability**

As a final empirical exercise, we test whether public investment quality affects the relationship between public investment and the evolution of the public debt-to-GDP ratio. Specifically, we compute impulse responses through a local projections model estimated separately for high- and low-PIQ countries. Formally, we estimate the following model: 20
# **Drivers and Obstacles to Banking SMEs:**

# **The Role of Competition and the Institutional Framework**

Augusto de la Torre

## María Soledad Martínez Pería

## Sergio L. Schmukler<sup>*</sup>

## **Abstract**

This paper studies the factors banks perceive as drivers and obstacles to financing small and medium enterprises (SMEs), focusing on the role of competition and the institutional framework. Using a survey of banks in Argentina and Chile, the paper shows that, despite alleged differences in the countries’ environments regarding rules, regulations, and ease of doing business, SMEs have become a strategic segment for most banks in both countries. In particular, banks have begun to target SMEs due to the significant competition in the corporate and retail sectors. They perceive the SMEs market as highly profitable, large, and with good prospects. Moreover, banks are developing coping mechanisms to overcome the particular institutional obstacles present in each country and to compete for SMEs. Banks’ interest in SMEs is not based on government programs, yet policy action might help reduce the cost of providing financing, especially long-term lending.

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

**Keywords:** small and medium enterprises, bank finance, financial constraints, banking market structure, institutional factors, regulation, competition

* This paper is part of a broader World Bank Group project to understand bank financing to SMEs. The information used for this paper was collected by a team that traveled to Argentina and Chile during December 2006 to interview banks and is described in detail in World Bank (2007a). We received very helpful comments and suggestions from many colleagues, including Martin Cave, Stijn Claessens, Vivek Ghosal, Rogelio Marchetti, Margaret Miller, Ary Naim, Sophie Sirtane, and Greg Udell. We also received many useful comments at presentations held at the Asobancaria (Bogota, Colombia), CASINIFC Forum on SMEs (Geneva, Switzerland), the Central Bank of Argentina (Buenos Aires, Argentina), the Central Bank of Chile (Santiago, Chile), the CESifo Summer Institute (Venice, Italy), the Finance Forum at the World Bank Group (Washington, DC, USA), the Latin American Congress on Banking and SMEs (Cartagena, Colombia), and the World Bank (Washington, DC, USA). Maria Bernarda Dall’Aglio, Noemí Soledad López, Mercedes Politi, Mira Olson, 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: <u>adelatorre@worldbank.org, mmartinezperia@worldbank.org, sschmukler@worldbank.org</u>
# **Table A30:  U.S. Department of State Trafficking in People Report (TPR)**

|**Data Provider**|United States Department of State|
|---|---|
|**Description**<br>|Foreign affairs department of the United States Government.|
|**Website**|www.state.gov|
|**Data Source**|Trafficking in People Report|
|**Type**|Expert assessments<br>|
|**Respondents**|United States embassy staff worldwide subject to centralized review|
|**Frequency**|Annual since 2001|
|**Coverage**|Global country sample|
|**Public Access**|Yes|
|**Description**|This report scores countries on a four-point scale based on the extent of government<br>efforts to combat "severe trafficking in persons" defined as (a) sex trafficking in<br>which a commercial sex act is induced by force, fraud, or coercion, or in which the<br>person induced to perform such act has not attained 18 years of age; or (b) the<br>recruitment, harboring, transportation, provision, or obtaining of a person for labor<br>or services, through the use of force, fraud or coercion for the purpose of subjection<br>to involuntary servitude, peonage, debt bondage, or slavery. Note that the reports<br>refer to data from the previous year: we therefore lag the data from this source by<br>one year.|

|**Voice and Accountability**|2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|
|NA|..|..|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Regulatory Quality**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Rule of Law**||||||||||
|Trafficking in People|X|X|X|X|X|X|X|..|..|
|**Control of Corruption**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Country coverage**|**153**|**151**|**149**|**142**|**131**|**116**|**82**|**..**|**..**|
|**Year of Publication**|**2008**|**2007**|**2006**|**2005**|**2004**|**2003**|**2001**|**..**|**..**|

68
average twice as restrictive as actual policies (Figure 3) (if countries made no Doha offer, existing commitments are used).

**Figure 3: Existing commitments, Doha offers and actual policy by GDP per capita, 2010** (62 WTO Members that submitted Doha offers)

<!-- Start of picture text --> KEN NGAPAKNICNICINDIND PHL IDNIDN HND MAR BOL LKALKA PRY GTMGTM EGY TUNTUNTHATHACOLCOL CRIURY MUSPAN MYS CHLCHL TTOTTO BHRBHR<br>KENNNGAPAKPGANICIND AP IDN KH HLMARMARBOLLKANDPRYGTJORJORJOREGY CHN CHN M DOMDOMDOMALB ALB TUNPE RPE R PE R THA CO BRABBRAZAFZAF ZA L RA CF BGR URYBGRMUSMUSPANPANRI TURTURTUR ARG ARGMYSCHLMEXMEXMEX POLPOLPOLTULLTUL TU HUNHUNHUN PRTPRTPRTOMN OMN CZECZE C KORKORKORZE N NZL ZL GRCGG RC RCBHRESPESPESPITAITAITA FRAFRAFRAJPNJJP PN NAUS A ADEUDEUDEFINFINFINUS U BELBELBGBR GG SELSWESWESWE UB BRRDNKDNKDNKAUTAUT A UTCANCANCANNLDNLDNLD IRLIRLIRL USA USA<br>TTO<br>0 10000 20000 30000 40000<br>GDP per capita,PPP<br> Restrictive ness of GATS co mmitment Fitted values<br> Restrictive ness of DOHA Offers Fitted values<br> Restrictive ness of actual policy Fitted values<br>STRI for 61 countries, excluding Qatar and 31 countries that did not submit offers<br>Source: Borchert, Gootiiz and Mattoo (2010).<br>Figure 4: Existing commitments, Doha offers and actual policy by region<br>Binding gap, offer gap and applied policy for 93 countries<br>SAR GCC LAC AFR MENA EAP OECD ECA<br> Actual Policy<br>Offer gap (Doha Offer-Actual policy)<br>Offer Improvement (Uruguay Round commitment-Doha Offer)<br>100<br>80<br>60<br>40<br>20<br>Services trade restrictiveness index 0<br>80<br>60<br>40<br>20<br>Services trade restrictiveness index<br>0<br><!-- End of picture text --> Source: Borchert, Gootiiz and Mattoo (2010).

**Figure 4: Existing commitments, Doha offers and actual policy by region**

Source: Borchert, Gootiiz and Mattoo (2010).

As they stand today, Doha offers on services do not offer any liberalization of actual policy. Furthermore, two of the currently most protected areas in both industrial and developing countries, transport and professional services, are either not being negotiated at all or not with any degree of seriousness. In 2008 the chair of the Trade Negotiations Committee held a “signaling exercise” among a group of ministers, at the time that “modalities” in agriculture and non‐agricultural market 7
on how crime correlates with schooling outcomes. Students are assigned the annual homicide rate for their municipality (per 100,000 population), taken from the Online Police Statistical System ( _Sistema Estadístico Policial en Línea_ - SEPOL).<sup>9</sup> We also include the following municipal-level indicators from the 2001 Population Census: unsatisfied basic needs (UBNs) poverty, the share of ethnic minority population, average years of education, average pre-school enrollment rates, average rates of child labor, the adolescent birth rate, and the share of households with migrants outside Honduras.

Dropout rates in Honduras vary across the school cycle. While only 8.8% of children drop out in primary school, this fraction increases substantially thereafter. Like Guatemala, dropout is particularly high in the transition from primary to lower secondary education ( _Ciclo Básico_ ), during lower secondary education, and in the transition from lower to upper secondary education ( _Ciclo Diversificado_ ). Figure 3 shows that 37.4% abandon school in the transition from primary to lower secondary, 18.4% drop out within lower secondary, and about 33.2% do not continue into upper secondary. Taken altogether, these figures imply a survival rate from sixth to tenth grade of 34%, meaning that the majority of sixth grade students drop out during this four-year period.

These rates are somewhat higher than household survey estimates but follow the same pattern. For the last available household survey in 2014, which collects data on children’s grade progression, the dropout rate for the transition into lower secondary is 27%, 13% within lower secondary, and 21% during the transition from lower to upper secondary. Adelman and Székely (2016) also report similar trends when using age groups.

As with the Guatemalan data, this method of data collection has only been recently implemented and there remains room for improvement. Teacher reporting of student outcomes is potentially prone to input errors, which may result in measurement bias, although we are unable to determine their extent or direction. As noted in the previous paragraph, one potential outcome is that we may overestimate dropout rates. However, the administrative data present many advantages over household surveys, mainly sample size and nationwide coverage. Data collection is also constantly improving, due to efforts from USINIEH to ensure correct data input and quality control at

> 9 Municipal-level homicide rates are publicly available at the SEPOL website: www.sepol.hn.

12
We use two different approaches to introducing uncertainty and risk in the analysis. To deal with vulnerability to specific shocks, the template provides a variety of single factor stress tests, as used for example in IMF(2002,2003). However to get a broader view on the riskiness of the basic projections, we also introduce the possibility of a full set of stochastic simulations using the stochastic properties of key variables in the debt dynamics process. While stress tests with respect to individual exogenous variables provide valuable insights to the robustness of the projections with respect to specific shocks, the stochastic simulations approach has the advantage of deriving the distribution of future debt stocks, based on stochastic realizations of key debt determinants, accounting for their variances and covariance structure. This allows a full assessment of the likelihood that the economy lands in dangerous waters. We also use graphical tools to represent the results, i.e. fan charts, like Celasun e.a. (2005). The fiscal sustainability tool goes beyond Celasun e.a. (2005) in presenting not just the distribution of debt output ratios at various moments into the future, but also the (distribution of the) fiscal adjustment necessary to restore consistency and stability to the process. This measure is more useful to policy makers.

Stochastic simulations maybe misleading if they use an assumption of unchanged primary deficits after stochastic shocks while the Government has a record of responding to debt shocks by tightening its belt. We therefore also incorporate **fiscal policy reaction functions** , endogenous debt feedback rules for the primary surplus, as an additional option for stochastic simulations and other stress tests, as suggested in Bohn (1998) and Celasun, Debrun and Ostry (2005). Bohn (1998) shows that the nature of fiscal reaction functions are a key requirement for stability of fiscal programs.

In what follows, section two outlines the analytical framework, section 3 demonstrates the application of the fiscal sustainability tool for the case of Turkey, and the last section presents concluding remarks.

# **2 Analytical Framework**

At the outset, it is important to stress that the fiscal sustainability analysis presented here is not based on a fully-specified macro model. Instead an accounting framework is at the basis of FSA, with the single exception of the fiscal policy reaction function mentioned before. Therefore, the FS template does not provide the government with a tool to optimally set policy variables such 5
discrepancies between household survey and national accounts based income estimates<sup>3</sup> we cannot deny that it is also possible that household surveys are not fully representative of the population of remittances recipients and hence that they may underestimate the relevance of remittances flows. This is an important caveat that should be kept in mind when analyzing the results presented in this paper, because in that case we could be under-estimating the poverty reducing effects of remittances (although in a similar way across countries). With these elements in mind, the first question which we would like to respond on the basis of household survey data has to do with the characteristics of those receiving remittances in the sample of Latin American countries under consideration.

**Figure 2. Share of households receiving remittances**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>Percent<br>Haiti Dom.Rep. El Salv. Nicaragua Honduras Guatemala Mexico Ecuador Paraguay Bolivia Peru<br><!-- End of picture text --> Source: Household surveys To start with, figure 2 reports the share of households receiving remittances in each of the eleven countries in our sample, suggesting that this share varies significantly. For example, in Haiti more than 25 percent of the households reported having received remittances in 2001. At the other extreme, only 3 percent of the Peruvian households would benefit from these flows. In between, remittances reach between 10 and 25 percent of the households in the Dominican Republic, El Salvador, Nicaragua, and Honduras; between 5 and 10 percent in Mexico and Guatemala; and between 3 and 5 percent in Bolivia, Ecuador and Paraguay. Thus, other things being equal one should expect that a given amount of remittances could have quite different effects on the welfare of the population of countries such as for instance Peru and Haiti.

Cross country differences in the welfare effects of remittances become even more likely once one considers that not all the households receiving remittances are poor, and that the share of remittance recipients living in poverty varies considerably across countries. This is apparent from Figure 3 which plots the percentage of households receiving remittances by quintile of the (non-remittances) income distribution. The figure indicates that while in some countries the recipients of remittances are predominantly poor (61 percent of Mexican

> 3 For example, Deaton (2005) estimates that on average survey income is less than 60 percent of GDP and discusses the reasons that may be behind survey and national accounts based estimates discrepancies.

9
(ETU, 32 beds on average), Ebola Holding Centers (EHC, 18 beds on average), and Community Care Centers (CCC, 10 beds on average). Only one ETU falls within our sample, and it is located in a control section; Table E.18 shows no significant difference in the counts — either combined or separate — of EHCs or CCCs.<sup>47</sup> Our results in Table 3 are robust to dropping the small number of sections that contain one or more of these specialized facilities: when analyzing total reported cases, the coefficient on our pooled treatment indicator increases from 0.173 to 0.177 (se = 0.091) when we drop these sections; for confirmed cases, it changes from 0.59 to 0.55 (se = 0.025).<sup>48</sup>

# **5. Conclusion**

This study shows that accountability interventions, such as community monitoring and non-financial awards, can boost the perceived quality of health care and improve health outcomes in a developing country setting — not only during “normal” times, but also during crises. We use a randomized experiment completed less than one year before the Ebola outbreak in Sierra Leone to test the effectiveness of two interventions: one focused on community monitoring of government-run health clinics and the other on status awards for clinic staff. In the medium term, we find that both interventions improve utilization of clinics, patient satisfaction, and beliefs about the relative efficacy of western medicine. Similar to previous work, we find large decreases in under-5 mortality following the community monitoring intervention (Björkman and Svensson 2009). During the Ebola crisis, we find that both interventions substantially increased reporting — by both patients who test positive and negative for the virus — and reduced mortality among patients.

Thus, we find evidence that accountability interventions which leverage social incentives to increase the perceived quality of health care can lead to greater utilization of health systems in a low-resource setting, in ways that help build their resiliency to confront crises.

We explore two alternative explanations for why the interventions increased reporting during the Ebola crisis: by unintentionally increasing exposure to Ebola; or by enabling more top-down surveillance efforts. We do not find support for these mechanisms. Specifically, we find no evidence to indicate that the interventions contributed to transmission at treated clinics, or that they raised the infection rate among patients

> 47 The absolute numbers here are instructive: there is 1 EHC in control sections, 1 in NFA sections, and 2 in CM sections. In Figures E.4 and E.5 we drop all triplets and pairs of triplets as a robustness check to address concerns that a small number of sections could drive our results.

> 48 The locating of specialized facilities in nearby sections could depress reported cases, as patients might report directly to those facilities and, thus, not be counted within their home section. In Table E.19 we find that treated sections are not significantly further from ETUs, EHCs or CCCs in the NERC data; the distance from NFA sections to the nearest CCCs is shorter when we use the UNMEER data.

36