rafmacalaba/datause-encoder-data huggingface dataset:
two countries. Whereas standard estimates place Turkey as the least corrupt of our seven countries, we place the levels of corruption of Sri Lanka and Peru below those of Turkey. Given that aid allocations are partially based on such rankings, it would seem prudent to use rankings based on models using structural parameters when the data are from sensitive questions asked on surveys.

Finally, we emphasize the applicability of our methodology to other survey settings where responses to sensitive questions may be influenced by reticent behavior. As noted earlier, two-stage questions regarding sensitive acts are common in many other settings, and our methodology provides a framework for handling reticence in either stage of the question. When considering applications of this methodology in other contexts, we can offer at least some hints of the wider external validity of this approach. For three of the countries included here, Bangladesh, India, and Sri Lanka, we have also applied our methodology in household surveys conducted by the Gallup World Poll. Despite the very different context of household (as opposed to firm) surveys, we find a rank ordering of countries by effective reticence that is remarkably consistent with this paper, with Sri Lanka the lowest, Bangladesh next, and India having much higher rates than the other two.

20
Table 4b: Shares of plants with financial loans by share of land and building values rented for ASI plants

|||||<=5|0% sha|re||||>50|% sha|re||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||1989 <br>|1994 <br>|2000 <br>|2005 <br>|2010 <br>|Average<br>|1989 <br>|1994 <br>|2000<br>|2005 <br>|2010 <br>|Average<br>|
|15|Food products and beverages|0.91|0.89|0.90|0.92|0.93|0.91|0.91|0.90|0.94|0.93|0.94|0.92|
|16|Tobacco products|0.32|0.35|0.50|0.45|0.47|0.42|0.28|0.62|0.59|0.73|0.52|0.55|
|17|Textiles|0.95|0.95|0.96|0.95|0.95|0.95|0.97|0.95|0.95|0.97|0.96|0.96|
|18|Wearing apparel; dressing and dyeing of fur|0.98|0.94|0.93|0.95|0.95|0.95|1.00|0.97|0.94|0.96|0.96|0.96|
|19|Leather tanning; luggage, handbags, footwear|0.93|0.92|0.92|0.89|0.92|0.92|0.95|0.90|0.93|0.99|0.97|0.95|
|20|Wood and wood products; straw and plating articles|0.80|0.80|0.76|0.87|0.89|0.82|0.84|0.84|0.81|0.87|0.79|0.83|
|21|Paper and paper products|0.97|0.96|0.95|0.97|0.96|0.96|0.95|0.96|0.98|0.97|0.95|0.96|
|22|Publishing, printing and media reproduction|0.83|0.85|0.92|0.92|0.94|0.89|0.90|0.94|0.90|0.94|0.94|0.92|
|23|Coke, refined petroleum and nuclear fuel|0.76|0.84|0.88|0.82|0.96|0.85|0.94|0.94|0.99|0.87|1.00|0.95|
|24|Chemicals and chemical products|0.93|0.93|0.94|0.91|0.89|0.92|0.94|0.94|0.95|0.92|0.93|0.94|
|25|Rubber and plastic products|0.99|0.95|0.97|0.95|0.94|0.96|0.97|0.99|0.98|0.98|0.95|0.97|
|26|Other non-metallic mineral products|0.88|0.87|0.82|0.82|0.80|0.84|0.69|0.68|0.70|0.66|0.64|0.68|
|27|Basic metals|0.98|0.98|0.98|0.97|0.97|0.97|0.97|0.98|0.91|0.98|0.94|0.96|
|28|Fabricated metal products, except machinery|0.96|0.96|0.95|0.95|0.95|0.95|0.97|0.96|0.94|0.96|0.95|0.96|
|29|Machinery and equipment, n.e.c.|0.96|0.95|0.94|0.95|0.94|0.95|0.94|0.94|0.93|0.95|0.95|0.94|
|30|Office, accounting and computing machinery|0.97|0.92|0.83|0.79|0.89|0.88|0.97|1.00|0.95|0.89|1.00|0.96|
|31|Electrical machinery and apparatus, n.e.c.|0.98|0.97|0.98|0.95|0.94|0.96|0.98|0.97|0.97|0.95|0.92|0.96|
|32|Radio, television, and comm. equipment|0.95|0.92|0.96|0.90|0.88|0.92|0.99|0.95|0.98|0.85|0.95|0.94|
|33|Medical, precision and optical instruments, watches|0.96|0.97|0.93|0.92|0.92|0.94|0.97|0.96|0.93|0.94|0.93|0.95|
|34|Motor vehicles, trailers and semi-trailers|0.96|0.95|0.97|0.95|0.95|0.96|0.95|0.97|0.99|0.95|0.96|0.97|
|35|Other transport equipment|0.96|0.95|0.96|0.95|0.95|0.95|0.95|0.98|0.95|0.96|0.90|0.95|
|36|Furniture, manufacturing n.e.c.|0.88|0.87|0.88|0.93|0.90|0.89|0.93|0.88|0.93|0.96|0.94|0.93|
||Nationwide|0.91|0.91|0.92|0.92|0.92|0.91|0.78|0.85|0.90|0.89|0.86|0.86|

Notes: See Table 4a. Thresholds refer to share of total land and building values attributable to hired assets.
poverty by less than the customs duties because it is a more progressive tax<sup>70</sup> (Figure 6-4). The total PIT contribution in the combined West Bank and Gaza only increases the national poverty headcount by 0.4 percentage points. This is because the PIT is a smaller and more targeted tax than indirect taxes, and because labor informality is high in the West Bank and Gaza and reducing with income (the share of formal workers ranges from 4.3 percent of workers in decile 1 to 11.8 percent in decile 10). Fuel excises are poverty-increasing, and their indirect effects are more poverty-increasing with respect to their direct effects (2.3 vs. 0.5 percentage points, respectively). The indirect effects of fuel excises have a larger marginal contribution to the poverty increase relative to the direct effects because they are larger in size (75 percent of total fuel excises simulated); and for households in deciles 1-8 the indirect effects from fuel excises stem from their use of public transport (taxi fares). **We do not show the marginal contributions of the in-kind benefits to poverty-reduction (Figure 6.2).** A typical CEQ Assessment does not measure effects from in-kind benefits on household purchasing power, as outlined in the section on methodology.

# **Figure 6-2 In the West Bank and Gaza, direct transfers and the direct effects of electricity subsidies are the only poverty-reducing instruments**

Marginal contribution to poverty reduction of each fiscal instrument, in percentage points, measured at Consumable income <!-- Start of picture text --> Poverty reduction<br>Customs (direct effect) -5.3<br>VAT (direct + indirect effect) -4.8<br>VAT (indirect effect) -3.0<br>Fuel excise (total) -2.7<br>VAT (direct effect) -2.5<br>Fuel excise (indirect effect) -2.3<br>Pension contributions -1.6<br>Fuel excise (direct effect) -0.5<br>PIT Poverty increasing -0.4<br>Property -0.0<br>Fuel subs. (direct effect) 0.0<br>Near-cash transfers 0.2<br>Fuel subs. (total) 0.3<br>Fuel subs. (indirect effect) 0.3<br>Elec. subs. (direct effect) 0.6<br>Pension income 2.0<br>Cash Transfer Program (CTP) 2.2<br>-6 -4 -2 0 2<br>Marginal contribution at Consumable income (percentage points)<br><!-- End of picture text --> Source: Authors' estimates based on PECS 2016/2017; LFS 2017.

Notes:

- [1] Marginal contributions are calculated as the change in a poverty or inequality indicator that results with the inclusion of a tax or transfer of interest. [2] A positive bar represents a reduction in poverty or inequality, while a negative bar represents an increase. [4] We don't calculate contributions to poverty for in-kind subsidies.

70 VAT Progressivity in this fiscal incidence study is driven by the fact that the model accounts for households’ consumption informality (which is higher among poorer households), which improves VAT progressivity (Bacha et al. 2020). In contrast, for the case of customs duties, the model assumes the Law of One Price.

44
in the _Monga_ season, suggesting that borrowing is highly depressed in the _Monga_ season when credit demand is the highest. Credit demand is also found to respond positively to the availability of credit through credit agencies in the village. Supply of microcredit is higher in villages with higher number of MFIs working. Villages with a commercial bank network seem to have higher incidence of borrowing from informal sources, suggesting that developed villages have higher demand for credit that sometimes come from informal lenders rather than formal lenders.

The equations for borrowing (that is, microcredit, formal, informal and aggregate borrowing), the results of which are presented in Table 3a, are estimated independently, assuming that their error terms are not correlated. However, if the error terms are correlated (or ‘contemporaneously correlated’), the equations should be estimated jointly to get more efficient estimates. Such equations are called ‘seemingly unrelated regression equations’ or SURE, and the estimator for this problem is called SUR estimator, proposed by Zellner (see Zellner 1962; 1963).<sup>10</sup> Table 3b reports findings based on SUR estimator.<sup>11</sup> The effects of average seasonal income and share of actual seasonal income in average income are stronger based on SUR estimation. For example, a 10-percent increase in per capita average income raises the borrowing from microcredit lenders by almost 2 percent, and a one percentage point increase in the share of seasonal income is associated with 0.06 percent increase in microcredit borrowing. As for the independent effects of the seasons, except for microcredit borrowing, the borrowing from other sources seems to be affected by all three non-Monga seasons.

> 10 SUR estimator uses the asymptotically efficient, feasible, generalized least-squares algorithm to jointly estimate the equations. A detailed treatment of the SUR estimator can be found in Zellner (1962, and 1963).

> 11 These findings are based on random-effects (RE) estimates using panel data of 1998/99 and 2010/11. We also attempted fixed-effects (FE) implementation of the SUR estimator. The findings of FE model does not vary much from that of the RE model except for that the magnitude of the FE estimates is higher. We decided to report the findings of RE model to avoid upward biases, if any, in the findings of FE.

16
# **5 Conclusions**

This paper studies the impact of the financial crisis on the credit availability for micro, small and medium enterprises in Azerbaijan using a unique dataset of 168,483 loans made between January 2006 and August 2009 by AccessBank Azerbaijan. The timeline of events in Azerbaijan allows us to study a pure supply side shock to the bank’s refinancing in the second and third quarters of 2008, while firms had not yet been affected by the economic crisis. Then, we can contrast its impact on credit availability for firms of different sizes and industries from the impact of the economic crisis shock that reached Azerbaijan in the fourth quarter of 2008. On top of this, the structure of the dataset allows us to analyze credit availability on an aggregate level as well as on an individual loan level, thereby identifying two major channels of credit constraints.

By analyzing aggregate numbers of loan applications and approvals, we derive the first channel of credit constraints. We observe that its tight refinancing in the second and third quarters of 2008 seems to lead the bank to discourage (potential) borrowers from applying for new or additional loans. The bank’s temporary liquidity squeeze therefore resulted in a slowdown of its credit portfolio so that not all the demand could be met during this limited period of time. At the individual loan level, for the funding shock we find that approval rates for those (potential) borrowers who actually apply for a loan are not negatively affected in any borrower group. For the real economy shock, by contrast, our findings suggest that credit availability for agro loan borrowers is hardly affected by this event while micro loans face a moderate and SME loans a considerable reduction in approval rates. Apart from that, our results show that bank relationships help increase credit availability in good as in bad times.

To gain deeper insights into the differential effects of the two crisis shocks on loan approval, we study an exogenous geographic variation in the severity of the crisis shock by distinguishing between locations in which the oil and gas industry is most important and the rest of the country. These results confirm that the two shocks differently impact on credit availability. The real economy shock affects credit availability negatively especially in those locations that can be expected to be hit hardest, i.e. the petroleum locations. Our results for the funding shock, on the contrary are in line with our previous finding that the refinancing delays are a pure supply shock impacting on credit availability mainly through the discouragement channel.

In contrast to anecdotal evidence from some more advanced and competitive markets in Eastern Europe (e.g. ProCredit Holding (2009)) which suggests that banks worried especially 24
As a result of demographic decline the average school size dropped by one-third from 326.8 students per school back in 1990/1991 to 211.8 in 2010/2011 school year. Interestingly, the slump was most dramatic in urban areas where school size almost halved over the last two decades compared to a 27 percent decline in rural schools. While in 1990/1991 urban schools were about 5 times larger than rural schools, this ratio has decreased to about 4.

Class-size has also been steadily decreasing and reached 18.1 students per class (23.2 – in urban areas and 12.5 – in rural areas)<sup>19</sup> .

In this paper, we investigate school-size and class-size effects on school performance in Ukraine using schools‟ graduates in Ukraine‟s External Independent Test (EIT)<sup>20</sup> . It is important to note that this test only covers part of the graduates of secondary schools.

- The EIT exams are only taken by upper secondary education graduates (grades 11-12), so we miss around 40 percent of lower secondary school graduates in urban areas and almost 50 percent in rural areas (see table 1). Most of them leave for vocational schools as an ultimate goal or as the transit route to the higher education institutions and thus omit the EIT exams. The share of students leaving after the 9<sup>th</sup> grade significantly increased over the last three years which is likely to be the combination of EIT effect and expected enforcement of the three year study in the upper secondary school that was envisaged until mid-2010. Thus, parents and students tried to avoid an extra year in the secondary school and to profit from the opportunity to get into the HEIs through the vocational schools (in some cases – with direct admission to the second or third year of study).

**_Table 1: Number of students that continue study in upper secondary daily schools_**

|**Beginning of**<br>**the school**<br>**year**|9<sup>th</sup>grade<br>graduates|**Total **<br>of them<br>- 10-<br>graders|percent of<br>students<br>continuing<br>study in<br>USS|9<sup>th</sup>grade<br>graduates|**Urban area**<br>of them<br>- 10-<br>graders|**s**<br>percent of<br>students<br>continuing<br>study in<br>USS|9<sup>th</sup>grade<br>graduates|**Rural area**<br>of them<br>- 10-<br>graders|**s**<br>percent of<br>students<br>continuing<br>study in<br>USS|
|---|---|---|---|---|---|---|---|---|---|
|2008/2009|538,689|352,473|65.4|358,120|246,176|68.7|180,569|105,297|58.9|
|2009/2010|311,247|195,319|62.8|167,379|115,096|68.8|143,868|80,223|55.8|

19 The Law of Ukraine on General Secondary Education (Article 14) limits the class size to a maximum of 30 students and when there are less than 5 students in the schools in rural areas the individual training should apply to ensure quality. Classes may be split into groups for teaching particular subjects. Number of students in special schools (boarding schools) is defined by the Ministry of education and science, youth and sports of Ukraine by agreement with the Ministry of Health of Ukraine and the Ministry of Finance.

20 This test became the key basis for entrance to higher education institutions in the country since 2008.

12
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16 programs and institutions in different stages of the impact generation and transmission process. Among the income variables, a distinction is made between AGLINCOM and FAMINCOM with the former covering only the income from agriculture and the latter covering both agricultural and livestock incomes). But, LABINCOM, the income of laborers, covers wage and livestock incomes. These three income variables are needed to capture the differential income potentials between those with and without land and livestock. Given the set of variables listed in Table 1, the institution-impact framework in Figure 5 can be formally represented in a mathematical form with a set of following 21 equations that comprise the system model of institution-impact interaction.

|BULKWATD|=**_f1_** (SYSREHAB,<br>LANTENUR<br>)....................................................[1]|
|---|---|
|CROPDIVR|=**_f2_**(BULKWATD,FAMINSTN<br>)...................................................[2]|
|CROPATEN|=**_f3_**(CROPDIVR,LANTENUR,<br>CUSINSTN<br>) ...................................[3]|
|WATINSTN|=**_f4_**(BULKWATD,LANTENUR,<br>CUSINSTN<br>) ..................................[4]|
|WATPRODY|=**_f5_**(CROPATEN, WATINSTN,FAMINSTN<br>)...................................[5]|
|LANHELTH|=**_f6_**(CROPATEN, WATPRODY,LANTENUR<br>) ..................................[6]|
|LANPRODY|=**_f7_**(CROPATEN, LANHELTH,FAMINSTN<br>) ...................................[7]|
|FEDSUPLY|=**_f8_**(CROPATEN,CUSINSTN<br>)....................................................[8]|
|LIVSTOCK|=**_f9_**(FEDSUPLY,TRDPOLCY<br>).....................................................[9]|
|NFAMENTS|=**_f10_**(CROPATEN,RDVPOLCY<br>) ................................................[10]|
|LABPRODY|=**_f11_**(LANPRODY, CROPATEN) ................................................[11]|
|WAGERATE|=**_f12_**(LABPRODY, NFAMENTS,WAGELAWS<br>) ..............................[12]|
|RURALEMP|=**_f13_**(LANPRODY, WAGERATE, NFAMENTS, LIVSTOCK)...............[13]|
|CULTCOST|=**_f14_**(CROPATEN, WAGERATE,FAMINSTN<br>, SUBPOLCY<br>) ..............[14]|
|AGLINCOM|=**_f15_**(LANPRODY, CULTCOST,MKTINSTN<br>)................................[15]|
|FAMINCOM|=**_f16_**(AGLINCOM, NFAMENTS, LIVSTOCK) ................................[16]|
|LABINCOM|=**_f17_**(RURALEMP, NFAMENTS, LIVSTOCK,SAMPOLCY<br>)...............[17]|
|FOODPROD|=**_f18_**(CROPATEN, LANPRODY, WATPRODY)...............................[18]|
|FOODAVAL|=**_f19_**(FOODPROD,TRDPOLCY<br>,MKTINSTN<br>) ...............................[19]|
|FOODPRIC|=**_f20_**(FOODPROD,PRICREGL<br>,MKTINSTN<br>)................................[20]|
|FOODSECT|=**_f21_ **(FOODAVAL, FOODPRIC, FAMINCOM, LABINCOM) ..............[21]|
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21
<u>Table 4: First stage instrumental variables regressions. Entry Cost Export Cost Own Tariff</u> Distance -0.551*** — -0.005 -0.004 [0.204] [0.081] [0.014] Tariff (EU) -1.734 -1.466* -0.145 [1.678] [0.795] [0.131] GDP 2000 -0.149*** — -0.084*** — 0.001 [0.050] [0.022] [0.003] GDPPC 2000 0.415*** — -0.100** -0.014*** [0.085] [0.038] [0.005] Col. GBR 0.306 -0.225* 0.012 [0.276] [0.134] [0.016] Col. FRA 0.746*** = -0.161 0.026 [0.262] [0.138] [0.021] Col. ESP 1.532*** = -0.227 -0.012 [0.481] [0.184] [0.021] Col. PRT 1.328** 0.039 -0.015 [0.668] [0.162] [0.019] Col. NLD 1.494*** = -0.315 -0.029 [0.544] [0.251] [0.019] Col. RUS -0.572** — 0.353** -0.050*** [0.284] [0.178] [0.016] Latitude -0.197* -0.047 -0.002 [0.101] [0.063] [0.005] Constant 10.191*** 9.134*** 0.302*** [2.131] [0.801] [0.112] Obs. 11328 11328 11328 R2 0.43 0.33 0.29

1. Estimation is by OLS with dependent variables as indicated. All variables are in logarithms. All models include fixed effects by 2-digit sector.

2. Robust standard errors, adjusted for clustering by exporter, are in square brackets. Statistical significance is indicated by * (10%), ** (5%), and *** (1%).

36