rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4788

# **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.

This paper—a product of the Development Economics Research Group and the Office of the Chief Economist, Latin America and the Caribbean Region—is part of a larger effort in the departments to understand financial development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.The authors may be contacted at adelatorre@worldbank.org, mmartinezperia@worldbank.org, and sschmukler@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **4. The Data**

The data used in this analysis are from the baseline (March 2007) and final round (March 2008) of the impact evaluation of the Malawi Cash Transfer Scheme. The latter, includes 767 households of which 374 had already started to receive the cash transfer at the time of the interview and 393 had not yet received it. We refer to the first as “treatment” group and the second as “control” group in the remainder of the paper. The sample is restricted to those households headed by an adult, thus excluding so-called child-headed household, which might be significantly different from adultheaded households and not directly comparable. The analytical sample used for the econometric analysis is therefore composed by 749 households (387 control and 362 treatment).

Table 1 provides descriptive statistics. The statistics are grouped according to the following categories: asset ownership, shocks, social safety nets, annual consumption expenditures, private transfers, household characteristics and head of household’s characteristics. The latter two categories show that the intervention and comparison households were not demographically identical at baseline, as the community social protection committees appear to have prioritized elderly households in the comparison village groups and households with more children in the intervention village groups. Still, as highlighted in Miller, Tsoka, Reichert (2010b), households were statistically the same in terms of consumption expenditures, food insecurity and asset ownership at baseline, thus confirming the robustness of the randomization. Table 1 indicates that, at baseline, the average household size was 4 members per household. More than half of the households in the sample had orphans; 20% had members with some forms of disabilities; over 30% included chronically ill members; 64% were female headed and 56% were headed by over 65 years old heads (Miller, Tsoka, Reichert 2010c). Descriptive statistics in the final round show several statistically significant differences between treatment and control groups. For instance, asset ownership (measured as the number of furniture, animals and agricultural tools) increased significantly for households in the treatment group. Moreover households that received cash transfers are more likely to face a shock related to livestock stolen or died, and are less exposed to income shocks due to house damages. They had also significantly reduced access to social safety nets based on free food distribution or agricultural inputs compared to control households. A relevant impact is observed on health-related variables: the self-reported index of poor health status for the head of the households decreases from 74% to 3% in the treatment group while it remains almost stable for the control. Similarly, the percentage of household heads who reported being sick 10
Pe? 7 10. Local telecommunications in Jamaica, as in most developing countries, difer from other public services, like water or mass transport. in that their main customers are middle and upper class households and businesses.' As we will see, though, these are precisely the marginal (or floating) voters. Thus, the availability and pricing of telecommunicatlons becomes a relatively Important political issue. In other words, In a contested political environment as the one in Jamaica, polifical pardes will be sensitive to telephone users' demands. On the othcar hand, the composition ot the demand for intemational communicationi **s** slightly different. In particular, loreign trade business and tourism are more importan **t** han local households. As a consequence, the pricing of intematonal calls should be slightly less politically sensitive. Furthermore, since until recently Jamaica's foreign trade regime discriminated against dynarmic export oniented sectors, the pricing of intematonal telecommunications was not of major concem for businesses.' Thus, policies attempting to keep domestic telephone prices low and expanding the network so as to provide access to the growing middle class, would be key features of both parties' policies. Thus, the Jamaican politics and political structure imply that we should expect relatve stability in telecommunications policy. Changes in government or ideology, then, should not be expected to be key determinants of telecommunications policy.

11. The mniddle class demands for network expansion at low prices require the development of institutional structures that provide incentives to the firms to inve-t in highly specific assets. Given Jamaica's political structure, though, legislation based constraints on administrative discretion may not be credible. Telecommunications politics in Jamaica, however, have traditionally been played in the shadow of the various licenses (and Telephone Acts) goveming the company/govemment relationship. Thus, it is not surprising that major tuming points in Jamaica's telecommunications regulation have 'For example, today more thanfree quanters oft a JTC'5 **CUStOIrfs** -e boated In the Klngston area, and the penetration Isless then 4 telephones per 100 residents. As mentioned above, the main changes in the distribution of the labor force rom the oaly 1940s to the oarly 980s are in the increae of the hghly pi professional tichnical and administrative pesonnel (from 2% to 11% of th labor force), whie farm and casual workers ell from 55% to 38%. Blum colar workers' pmrcipatlon also incresed from 13% to M9%. Stone (1985).

'The T devlopmont of the data erty sedor during the 198Os creatd. however, a highly telcomnmunications senoitive sedr. Estimates of demand for intemabionnl service show some minor increaes in the easticity around 1978/1979.
20 followed by Brazil, Costa Rica and Mexico. Chile allots almost 0.5% of its GDP<sup>to nutrition, whilst</sup> the others allot 0.25% or more. Once external resources are added in, Bolivia,<sup>Haiti and Jamaica</sup> join the ranks of Latin countries with expenditures on feeding programs<sup>of over 0.25% of GDP.</sup> Three country studies were undertaken in order to try to supplement the rather<sup>meagre</sup> available data on financing of nutrition interventions. Data were generally available<sup>for these</sup> countries to estimate the contribution of central government and external funding,<sup>but not usually for</sup> that of local government.

For Chile (Appendix A), there are three major nutrition interventions (the National Supplementary Feeding Program, PNAC, for preschoolers, the School Lunch Program,<sup>PAE, and the</sup> National Association of Kindergartens, JUNJI, a daycare feeding program). Appendix table<sup>Cl</sup> presents information on expenditures on these programs from 1974 to 1988, and appendix tables C2 and C3 contain information on sources of financing. The preschool program (PNAC)<sup>obtains around</sup> a quarter of its funding from external donors (appendix table C2). The school feeding<sup>program</sup> (PAE) receives about 10% of its funding from municipal governments, and the rest from<sup>the central</sup> government, although the municipal share rose quite sharply in 1987 and 1988<sup>(Appendix table C3).</sup> Appendix table C4 presents some information on costs of different interventions, which<sup>show the</sup> relatively high costs of the daycare feeding program (JUNJI) compared to the other<sup>two. Further</sup> recent information on Chile's nutrition programs can be obtained from Muchnik and Vial (1990),<sup>and</sup> Castaneda (1990).

The Philippines (Appendix B) has a strong Institute of Food and Nutrition, domestic<sup>support</sup> for nutrition interventions, as well as long-established external assistance. It is<sup>quite surprising that</sup> nutrition expenditures are therefore not higher as a share of GDP (the World Bank figures<sup>cited in</sup> table 11 suggest that the Philippines allots a smaller percentage of GDP to nutrition<sup>than the other</sup> four countries with available data). Appendix table B1 lists in detail the different<sup>nutrition</sup>
|**Table 2: Ro**|**ots and D**|**erivatives f**|**rom Pseudo-**|**Panel An**<br>|**alysis, Quar**<br>Solution<sup>1</sup><br><br><br>**Linear Mo**|**terly Lag**<br>Derivative<sup>2</sup><br><br>**del**||||
|---|---|---|---|---|---|---|---|---|---|
|||||Min|3554|0.730||||
|||||25%|5312|0.730||||
|||||50%|6249|0.730||||
|||||75%|9705|0.730||||
||No|Measureme|nt Error|Max|18200<br>**Quadratic M**<br>|0.730<br>**odel**<br>Measurement E|rror Cor|rection||
||||||Vi R|<sup>3</sup>||Vi R|i|
||||||arance at|o=.8||arance a|to=.5|
|||Solution|Derivative||Solution|Derivative||Solution|Derivative|
||Min|-416471|1.226|Min|-416437|1.226|Min|-416386|1.226|
||25%|-406714|1.216|25%|-406678|1.216|25%|-406624|1.216|
|Solution 1|50%|-403459|1.213|50%|-403423|1.213|50%|-403368|1.213|
||75%|-402271|1.212|75%|-402234|1.212|75%|-402179|1.211|
||Max|-400502|1.210|Max|-400465|1.210|Max|-400410|1.210|
|||Solution|Derivative||Solution|Derivative||Solution|Derivative|
||Min|3636|0.790|Min|3599|0.790|Min|3544|0.790|
||25%|5405|0.788|25%|5368|0.788|25%|5313|0.789|
|Solution 2|50%|6593|0.787|50%|6557|0.787|50%|6502|0.787|
||75%|9848|0.784|75%|9812|0.784|75%|9758|0.784|
||Max|19605|0.774|Max|19571|0.774|Max|19520|0.774|
||||||<br>**Cubic Mo**|<br>**del**||||
||No|Measureme|nt Error||<br>|Measurement E<br>|rror Cor|rection<br>||
||||||Variance Ra|tio=.8||Variance Ra|tio=.5|
|||Solution|Derivative||Solution<br>|Derivative<br>||Solution<br>|Derivative<br>|
||Min|1831966|6.228|Min|1831958|6.228|Min|1831947|6.228|
||25%|1832363|6.233|25%|1832355|6.233|25%|1832343|6.232|
|Solution 1|50%|1832461|6.234|50%|1832453|6.234|50%|1832441|6.234|
||75%|1832491|6.234|75%|1832484|6.234|75%|1832472|6.234|
||Max|1832534|6.235|Max|1832526|6.234|Max|1832514|6.234|
|||Solution|Derivative||Solution|Derivative||Solution|Derivative|
||Min|-49921|1.213|Min|-50114|1.214|Min|-50402|1.215|
||25%|-38242|1.143|25%|-38528|1.145|25%|-38951|1.147|
|Solution 2|50%|-34352|1.120|50%|-34692|1.122|50%|-35192|1.125|
||75%|-32960|1.112|75%|-33324|1.114|75%|-33859|1.117|
||Max|-30870|1.100|Max|-31278|1.102|Max|-31873|1.106|
|||Solution|Derivative||Solution|Derivative||Solution|Derivative|
||Min|3965|0.902|Min|4381|0.900|Min|4988|0.897|
||25%|6098|0.890|25%|6470|0.888|25%|7017|0.885|
|Solution 3|50%|7521|0.883|50%|7869|0.881|50%|8381|0.878|
||75%|11508|0.861|75%|11802|0.859|75%|12238|0.857|
||Max|23584|0.796|Max|23785|0.795|Max|24085|0.793|

1Solution refers to the solution to the equation Yi,t=β1Yi,t + β2Yi,t2 +β3Yi,t3 + αc, where the number of terms in the polynomial correspond to the linear, quadratic and cubic models. 2Derivative refers to the derivative of the polynomial evaluated at the solution 3Variance Ratio refers to Var(Yi,t*)/Var(Yi, t), where * indicates truth
results at face value. Consider for example VA. The GCS survey questions we use here focus on the extent to which firms are informed by government of regulations, which is a very narrow dimension of accountability, while the expert assessments capture much broader notions of democratic accountability. This may explain the relatively low correlation of the expert assessments with the survey. To see how important this is, we took one specific question from the GCS about firms' perceptions of press freedom, and compared it with two expert assessments of press freedom (RSF, and a specific press freedom rating from FRH). As shown in the row labeled "Press Freedom", we find that the correlation between FRH and RSF is much closer to the average correlation of both of these with the survey, implying a much lower estimate of λ and suggesting a much smaller role for shared prejudices among the expert assessments.<sup>18</sup> Overall this example suggests that the role of shared prejudices in expert assessments in at most minor.

As noted above, in this example finding positive estimates of λ as we have in some cases in Table 6 suggests that it would be appropriate to slightly decrease the weight placed on the expert assessments in our aggregate indicators. We do not make this adjustment to our benchmark governance indicators. This is because our estimate of the role of shared prejudices that we report here is very much in the spirit of an illustrative example, rather than as a definitive analysis. We have made several simplifying assumptions in order to generate these estimates as transparently as possible, and these may or may not be appropriate for our aggregate indicators that rely on our full dataset. However, we do note that a modest reduction in the weight of expert assessments in our aggregate indicators suggested by this example is unlikely to have major systematic effects on our estimates of governance. This is because, as we have mentioned earlier, the expert assessments on which we rely are in most cases quite strongly correlated with other data sources: recall that the average value of the 18 One possible objection to this exercise is that the GCS surveys firms, and it could be that such respondents share a common set of business-oriented prejudices with commercial risk rating agencies. To assess this we would need to have a large cross-country survey of individuals. Unfortunately we have only limited information to address this issue, with two of our regional surveys of individuals, Latinobarometro and Afrobarometer, covering only very small samples of countries. One possibility is to look at the Gallup survey we use that covers a somewhat larger set of 62 countries. In 2004 and 2005 we have survey questions on corruption from this source. These are typically quite highly correlated with expert assessments at about 0.7, while the expert assessments are correlated only slightly more about 0.8 in the same set of countries. This again suggests at most a modest importance of shared prejudices.

24
demand in the export sector (say, agro-manufactures) and _V pm_ , the labor demand in the _m_ sector. Because of the frictions mentioned above, the equilibrium wage _<u>w</u>_ is above the competitive wage so that the labor market does not clear completely and there is unemployment, _U_ = _L_ ( _pa, pm_ ) _− La − Lm_ .<sup>6</sup> In Figure 1, the impacts of trade can be assessed by considering an increase in the price of agro-manufactured exports, _pa_ . Although I do not model the price changes explicitly, export prices for Argentine products like dairy, oils and fats, mills products and beef—goods that form the bulk of manufactured exports of the country—may increase due to trade liberalization in developed countries. These countries intervene in world markets with an extensive set of tariffs, non-tariff barriers, and subsidies and it is feasible to think that the elimination of (some of) these barriers will bring about increases in export prices. The increase in _pa_ raises labor demand in the agro-industrial sector. This is captured by an outward shift in the demand curve to _V p_<sup>_′_</sup> _a_<sup>.In addition, there may be a change in the market</sup> wage, say to _<u>w</u>_<sup>_~~′~~_</sup> . Finally, there may a change in total labor supply, say to _L_<sup>_′_</sup> . The impacts of a higher _pa_ on unemployment is thus ambiguous. The initial shift in labor demand tends to reduce unemployment, but higher wages cause firms to move backwards along the labor demand curves, thus reducing employment. Finally, higher wages lead to more participation and higher unemployment (ceteris paribus).

In the remainder of this section, I develop an empirical model of trade, unemployment and labor supply that captures these features. My goal is to estimate the model using Argentine household survey data. I do not attempt to identify any of the theoretical models of trade and unemployment. Rather, I am interested in exploring the impacts of higher 5
i

# TABLE OP CONTENTS

|||Page|
|---|---|---|
|11|INTRO|DUCTION<br>. . . . . . .<br>. .<br>. . . . . . . . . .<br>. . . . . . .1|
|2.|THE<br>AFRIC|DEMAND FOR ADJUSTMENT AND ECONOMIC PERFORMANCE IN SUB-SAHARAN<br>A:<br>APRELIMINARY<br>ANALYSIS<br>.................. .<br>9|
||2.1<br>2.2|The Determinants<br>of the Demand for Adjustment<br>. . . . .<br>. . . .<br>9<br>A Preliminary<br>Look at Economic<br>Performance<br>Before and<br>After Adjustment<br>0<br>.................<br>. 16|
|3.|ANALY|SIS OF COUNTRY PERFORMANCEs<br>MODIFIED CONTROL-GROUP<br>APPROACH<br>. . 27|
|4.|EMPIR|ICAL RESULTS<br>..........<br>. 35|
||4.1|The Econometric<br>Results . . . . . . . . .<br>. . . . .<br>. . . . . . 36|
|5.|CONCL|USION<br>. . . . .<br>. . . . . . . . . . . . . .<br>. . .<br>. . . . . . . 44|
|REFE|RENCES|8..48|
|APPE|NDIX A|..<br>50|
||A.1|Some Definitions<br>. . . .<br>. .<br>. . . . . . . . . . . . . . .<br>. . 50|
||A.2|Overview of the Data<br>.<br>.<br>.<br>.<br>.<br>. . . . . . . . . . . . .<br>. . . 51|
|APPE|NDIX<br>B|.54|
||B.1|Some Specification<br>Tests<br>.<br>. . . .<br>. .<br>. . . . . . . . . . . . 54|
30

## VI.4. "Other sectors" (1993-1994) and Industry (1995)

_Table 17. Determinants of Subsidies to Other Industries, 1993-95_

|**Pre-transfer regional**|**1993***<br>**(N=86)**<br>.118|**1994****<br>**(N=86)**<br>.122|**Pre-transfer**|**1995*****<br>**(N=85)**<br>.108|
|---|---|---|---|---|
|**revenue**|(10.3)|(14.0)|**regional**<br>**revenue**|(10.1)|
|**Federal transfers**||.037|**Federal**|.033|
|||(7.0)|**transfers**|(2.3)|
|**Household incomes**|-.014|-.017<br>|||
||(-3.1)|(-4.5)|||
|**School children, per**||.677|**School children,**|.674|
|**1000**||(2.6)|**per 1000**|(2.1)|
|**Share of loss-making**|1.85||||
|**enterprises, 1994**|(3.5)||||
|**data**|||||
|**Share of rural**|1.45||||
|**population, 1994**|(3.5)||||
|**data**|||||
|**Constant**|-70.65|-89.2|**Constant**|-145.1|
||(-3.2)|(-2.2)||(??)|
|**Adjusted****_12_**|.69|.84|**Adjusted R2**|.60|

*Excluding Ulyanovskaya oblast. **Excluding Gornyi Altay. ***Excluding Yamalo-Nenetsky AO.

Although the two categories 'Other sectors" and 'Industry" (representing industry, energy and construction) are not identical, the analysis of their determinants reveals<sup>some</sup> interesting similarities. Primarily this concerns the high degree of significance and the stability<sup>of</sup> the contribution of pre-transfer revenue in determining expenditures and the relatively small<sup>but</sup> significant role of transfers in the same process.

The positive relationship in 1994 and 1995 between the relative number of school children and subsidies to industry reflects the fact that those regions that have high birth rates (notably some of the Russian autonomous republics) tend to have lower household real incomes<sup>and to</sup> experience lower rates of economic reforms, and to allocate higher than average transfers to local industry.

# VI. 5. Transportation

Here, too, federal transfers are relatively less successful in explaining the allocation of subsidies to the transportation sector, and are significant and positive only in 1994.<sup>Not</sup> surprisingly, more urbanized regions tend to spend more on public transportation, as evidenced by the negative values of the 13 coefficients in all three years for the factor 'Rural population." In
||D|ropping di|stance ban|ds|UsingA|ddis Abab|a as the on|ly Aggl.||Sample per|iod 1999-|2004||Sample p|eriod 200|5-09|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||O|ECD|B|irch|OE|CD|B|irch|O|ECD||Birch|OE|CD||Birch|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|(16)|
|Initial TFP|0.201***|0.201***|0.266***|0.265***|0.209***|0.208***|0.271***|0.270***|0.176***|0.174***|0.300**|*<br>0.302***|0.250***|0.252**|*<br>0.289**|*<br>0.290***|
||(0.037)|(0.036)|(0.051)|= (0.051)|(0.037)|(0.036)|(0.050)|(0.050)|(0.060)|(0.061)|(0.088)|(0.089)|(0.050)|(0.050)|(0.058)|(0.058)|
|Initialage|0.956***|0.956***|0.427*|0.424*|0.940***|0.934***|0.416|0.415|1.474***|1.475***|0.826**|0.741*|0.622**|0.618**|0.369|0.368|
||(0.223)|(0.223)|(0.253)|(0.253)|(0.224)|(0.223)|(0.254)|(0.254)|(0.373)|(0.377)|(0.412)|(0.413)|(0.294)|(0.293)|(0.317)|(0.317)|
|Initial size|-0.489***|-0.489***|-0.094|-0.097|-0.504***|-0.502***|-0.099|-0.098|-0.330*|-0.342*|-0.063|-0.083|-0.523***|-0.524**|* 0.100|0.097|
||(0.130)|(0.130)|(0.114)|= (0.114)|(0.132)|(0.131)|(0.115)|(0.115)|(0.186)|(0.189)|(0.152)|(0.157)|(0.158)|(0.157)|(0.135)|(0.136)|
|Initialage<br>x Initial size|-1.071***|-1.072***|-0.510**|-0.506**|-1.058***|-1.053***|-0.503**|-0.500**|-1.541***|-1,539**|* -0.746*|— -0.670|-0.830***|-0.827**|* -0.611*|— -0.608*|
||(0.223)|(0.223)|(0.251)|(0.252)|(0.225)|(0.224)|(0.252)|(0.252)|(0.371)|(0.375)|(0.405)|(0.407)|(0.295)|(0.293)|(0.319)|(0.319)|
|Initial average wage|0.406***|0.406***|0.353***|0.354***|0.399***|0.399*#*|0.348***|0.347%#*|0.345***|0.360***|0.404**|*<br>0.412***|0.403***|0.401**|*<br>0.279**|0.276**|
||(0.077)|(0.077)|(0.089)|(0.089)|(0.077)|(0.077)|(0.089)|(0.089)|(0.120)|(0.121)|(0.145)|(0.144)|(0.104)|(0.104)|(0.113)|(0.112)|
|Agglomeration centerdummy|0.224**|0.220**|0.171|0.194|0.298**|0.331***|0.256*|0.295**|0.360|0.551*|0.232|0.781**|0.600**|0.610**|0.415|0.424|
||(0.101)|(0.103)|(0.124)|= (0.129)|(0.121)|(0.123)|(0.133)|(0.138)|(0.263)|(0.313)|(0.262)|(0.363)|(0.276)|(0.276)|(0.280)|(0.301)|
|InitialEGI|-0.048|-0.027|-0.153|-0.253|-0.051|-0.228|-0.151|-0.359|-0.005|-0.614*|0.030|-1.395***|-0.051|-0.173|-0.394*|=-0.524|
||(0.104)|(0.156)|(0.121)|= (0.165)|(0.104)|(0.163)|(0.120)|= (0.221)|(0.124)|(0.346)|(0.168)|(0.483)|(0.166)|(0.430)|(0.236)|(0.744)|
|Aggl. center<br>x Initial EGI||0 -0.029||0 0.141|0|0.176||0 0.245||0 0.615*||0 1.538***||0 0.113||0 0.139|
|||0(0.128)||0(0.142)|0|(0.157)||0(0.206)||0(0.339)||0(0.473)||0(0.432)||0(0.764)|

Notes: See Table 3.