rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5: Regression Analysis, Deposit Insurance Decision**

||Dep|endent Var|iable: Du|mmy =1. if|Explicit D|eposit Insu|rance Pro|gram|
|---|---|---|---|---|---|---|---|---|
|Explanatory<br>Variables||||Ado<br>(Probit E|pted<br>stimates)||||
||Probit<br>(z-<br>score)<br>(1)|dF/dX<br>%<br>change<br>(la)|Probit<br>(z-<br>score)<br>(2)|dF/dX<br>%<br>change<br>(2a)|Probit<br>(z-<br>score)<br>(3)|dF/dX<br>%<br>change<br>(3a)|Probit<br>(z-<br>score)<br>(4)|dF/dX<br>%<br>change<br>(4a)|
|Constant|.128||.117||-.332||-.500||
||(0.19)||(0.20)||(0.43)||(0.52)||
|Prior Instability|-.077<br>(2.29)|-3.04%|-.106<br>(2.52)|-4.07%|-.061<br>(1.76)|-2.40%|-.079<br>(1.68)|-2.93%|
|Recent Financial|.737|27.9%|.741|28.8%|.821|30.6%|.735|28.2%|
|Crisis|(1.49)||(1.37)||(1.64)||(1.17)||
|Legal Tradition|.203<br>(1.38)|8.06%|||.153<br>(0.96)|6.06%|.009<br>(0.04)|0.3%|
|Real GDP Per Capita|||.154<br>(1.96)|5.90%|||||
|% Neighbors Adopted|||||1.30|0.51%|||
|EDI (since 1980)|||||(1.04)||||
|% Neighbors Adopted|||||||3.07|1.14%|
|EDI (since 1985)|||||||(1.86)||
|Obs.|43||40||42||33||
|Chi-Sqr(3)|13.38||19.48||13.96||12.89||
|Prb > ChiSq|.0039||.0002||.0074||.0118||
|Pseudo R2|.225||.357||.240||.291||

Notes: z-scores in parentheses in Columns (1). (2), (3), and (4). % changes in columns la, 2a, 3a. 4a computed at the means of the independent variables. Prior Instability is the coefficient of variation (standard deviation divided by mean) for M2/GDP over the three years prior to the adoption of explicit deposit insurance. For those countries that retained implicit insurance, the coefficient of variation is calculated over all three-year periods from 1980-95 for which data were available. Recent Financial Crisis is a dumnnv variable = 1, if a systemic financial crisis occurred within the five years prior to the deposit insurance decision. For those with implicit deposit insurance, the five year period was 1989-93. Legal Tradition is a six-point index created by the Intemational Country Risk Guide. Higher scores indicate "sound political institutions, a strong court system, and provisions for an orderly succession of power." Legal tradition data averaged over 1985-1991. "% Neighbors adopted EDI" is the percentage of countries within a region that adopted explicit deposit insurance after the date specified (above). Regional pressure figures exclude the country in question from the calculation. The five regions for developing countries were Africa, Asia, Latin America and the Caribbean, the Middle East, and Transitional Socialist Economiies. Developed countries were all grouped into one category (region).

16
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3.88 Relatively little has been done in these components to address training opportunities for women, although a general thread of concern for better access for disadvantaged groups in general runs through the investments. Improving the income of disadvantaged groups, like most employment issues, will require more than training. Attention to employment codes and practices, and management attitudes and capacity, is also needed. There is no evidence that these issues have been addressed in the components sampled.

3.89 Sector analysis has been important in middle stages of investment programs, generally leading to significant investments in institutional development in middle income countries. Increasing attention in these countries is being given to science and technology development, representing a latter stage in the evolution of VET systems. Sector work in Africa is beginning to contribute to policy and institutional development, although the frequency and policy relevance of sector analysis in the region have lagged behind that in others.

3.90 Project objectives for most training investments in the African region (the majority of which were secondary vocational school components) tended to be less complex than in other regions. Nonformal projects were guided by fairly complex objectives. Manpower expansion and improvement in the quality of training were the dominant aims of all investments; over time, the objectives of investments gave greater emphasis to upgrading worker skills and to increased access to vocational training for disadvantaged groups, especially in Asia and EMENA. Time studies also indicated a shift toward policy, planning and analysis, and to management (see next chapter on systems development), with a corresponding decrease in attention given to linkage development as these became increasingly institutionalized.

3.91 More attention was given to developing linkage relationships with employers in nonformal than in other types of training institutions. It was difficult to establish linkages for secondary vocational schools in low income countries. This is of concern due to the prevalence of stagnant labor markets and vocational secondary schools in many low income countries. Provision of training services to enterprises, a relatively new form of linkage associated with onformal centers, promises considerable potential.

3.92 Overall, few attempts were made to generate demand for training places; in cases where attempts were made to attract good students to the VET system, they were successful only in Mexico and Korea.

3.93 Investment in curriculum development was a nearly universal feature. In Africa, most curriculum development was associated with reliance on expatriate instructors and teacher trainers. In LAC, EMENA and Asia, the creation of institutional capacity for curriculum development was a common feature. In these three regions, curriculum
**Table 6. The Annual and Present Value of Projected Changes in African Countries' Exports due to a Liberalization of EEC, Japanese and US Most-Favored-Nation Tariffs.**

||Projected Cha<br>to EEC, Ja|nge in Exports<br>pan and US|Total (net)<br>ODA Aid|Long-Term<br>Outstanding|
|---|---|---|---|---|
|l|||~~~~~<br>~<br>|<br>~~~~~~~Flows<br>Debt<br>|
|Exporting Country|Annual Total<br>($000)|Present Value<br>($million)|($million)|<br>($million)|
|Angola|-3,497|-69.9|87.9|4,670|
|Botswana|399|8.0|120.0|544|
|Cameroon|-16,169|-323.4|300.6|4,186|
|Central African Republic|-1,387|-27.7|99.3|653|
|<br>Chad|-77|-1.5|128.1|377|
|Congo|-312|-6.2|79.1|3.218|
|Cote d'Ivoire|-57,405|-1,148.1|260.1|12,147|
|Ethiopia|-5,769|-115.4|377.8|6,860|
|Gabon|-2,071|-41.5|121.0|2,482|
|Ghana|-3,206|-64.1|350.4|3,346|
|Guinea|-1,227|-24.5|191.7|2,054|
|Kenya|-24,620|-492.4|620.6|5,652|
|<br>Liberia|-130|-2.6|38.5|1,425|
|Madagascar|-4,765|-95.3|175.4|3,415|
|Malawi|-2,776|-55.5|181.6|1,340|
|Mali|-380|-7.6|300.6|2,187|
|Mauritania|-10,249|-205.0|160.3|1,747|
|Mauritius|47,441|948.8|50.0|826|
|Niger|-629|-12.6|199.7|1,545|
|Nigeria|-2,377|-47.5|309.8|27,305|
|<br>Senegal|-17,015|-351.7|536.3|3,663|
|Sierra Leone|-599|-12.0|72.2|552|
|Sudan|-671|-13.4|434.3|8,665|
|Swaziland|-2,939|-58.8|11.6|255|
|Togo|-1,479|-29.6|108.0|1,036|
|<br>Uganda|-9,371|-187.4|158.9|1,619|
|United Republic Tanzania|-4,192|-83.8|687.6|4,029|
|<br>Zaire|<br>-8,171|-163.4|432.9|7,924|
|Zambia|-7,297|-145.9|314.2|4,922|
|Zimbabwe|-19,770|-395.4|227.5|2,731|
|All Above Countries|-160,710|-3,225.4|7,136.0|121,375|
|Excluding Mauritius|-208,151|-4,174.2|7,086.0|120,549|

Source: Long-tern debt and aid statistics are taken from UNCTAD, Handbook of International Trade and Development Statistics. 1991 (Tables 5.6 and 5.14). Aid disbursements include ODA flows, other official flows, and official and private export credits.
# **Table 4. Taiping Status and Idle Land**

|**Panel A. The Distribution of countie**<br>|**s in the control, Early, Late co**<br>Taiping Regi|**unties**<br>me|<br>|<br>|
|---|---|---|---|---|
||Early Taiping Regime<br>(AJH)|Late Taipijng<br>Regime (JZ)|Control|Total|
|Anhui|49||8|57|
|Jiangxi|64||14|78|
|Hubei|38||26|64|
|Jiangsu||27|31|58|
|Zhejiang||68|7|75|
|<br>Total (Provinces with Taiping|||||
|Regime)|151|95|86|332|
|**Panel B. Determinants of the Percen**|**tage of Wasteland in 1915**||||
||(1)|(2)||(3)|
|Taiping|2.710**||||
||[1.034]<br>||||
|Early||3.555***||3.855***|
|||[1.278]||[1.327]|
|Late||1.26||1.692|
|||[1.437]||[1.493]|
|Standardized ln(distance to Nanjing)||||-1.519|
|||||[1.252]|
|Initial Likin||||-1.025|
|||||[0.701]|
|Observations|332|332||332|
|R-squared|0.108|0.11||0.079|
|Control|Y|Y||Y|
|Province Fixed Effect|Y|Y||N|

Note: The sample contains the cross-sectional county level data from 1915, coving the 5 provinces (Anhui, Jiangxi, Hubei, Jiangsu, and Zhejiang) within the Taiping regime. Only the estimation results of the key variables are presented. The control variables include the distance to Yangzi River, to the coastline; the dummy for being prefecture capital seat in 1820, dummy for being the most important county in 1820, and dummy for being trade center in 1915.

Standard errors are robust, clustered at the prefecture level.

*** p<0.01, ** p<0.05, * p<0.

49
# **What Have We Learned from the Enterprise Surveys Regarding Access to Credit by SMEs?**

Veselin Kuntchev, Rita Ramalho, Jorge Rodríguez-Meza, Judy S. Yang Keywords and JEL classification codes: D22 firm behavior, empirical analysis O16 financial markets, corporate finance, O570 comparative studies of countries, Sector Board: Financial and Private Sector Development (FPD)
considered poor in the world as a whole, a person needs to have attained both a minimum level of consumption—around $1.25 a day at 2005 purchasing power parity—and have enough to cover certain socially-determined needs, which are always positive but rise by $1 for every extra $3 of mean consumption. This fits very well with how poverty lines vary across the developing world. About half the population of the developing world in 2005 was poor by this definition and about half of these lived under $1.25 a day.

Acceptance of the global poverty measures derived from this approach depends on whether one believes that these weakly-relative lines are in fact welfare consistent (albeit for a concept of welfare that embodies social effects). A hard-line “absolutist” might respond that it is really the underlying welfare norms that vary across countries, making their poverty lines an unreliable basis for setting global (weakly) relative poverty lines. That argument cannot be accepted or refuted based solely on the “macro” data on average poverty lines across countries. However, the paper has also noted that there is a mounting body of micro evidence, including from subjective welfare data, suggesting that relative deprivation does have welfare significance, at least amongst all but the poorest. And the implied relativist gradients from this micro evidence seem to be in rough accord with that implied by comparing poverty lines across the world. Existing data may not prove the case, but they are at least consistent with this global definition of poverty.

26
22 In sum, households that escaped poverty during the period not only benefited from better producer prices, they also enjoyed a more favorable location, and were endowed with good access to infrastructure and better land. Those who remained poor or who fell into poverty, did so in part because they were badly placed in terms of location and land. They were also at the receiving end of particularly bad luck-they suffered most from poor rainfall and from ill health.

**Table 10: Ethiopia, decomposition of consumption growth per adult and poverty gap ratio (percentage points)**

|_Realcroppricechange_|_Ac_<br>_Growth_<br>_15_|_tual_<br>_Poverty_<br>-18|_Counter_<br>_Noreform_<br>_Growth_|_factual:_<br> _&peace_<br>_Poverty_|_Counte_<br>_No_<br>_Growth_<br>15|_rfactual:_<br> _risk_<br>_Poverty_<br>-16|
|---|---|---|---|---|---|---|
|_Changereturnstoroad_|19|-23|||19|-21|
|_infrastructure/location_|||||||
|_Privateendowments_|||||||
|Increaseinland|7|-10|1|-2|7|-8|
|Changeinreturnstoland|3|0|||3|-I|
|Increasesinadult labor|3|-4|3|-4|3|-4|
|Changesinretumstoeducatedadults|0|0|||0|0|
|Changeinadultequivalentunits|-5|7|-5|7|-5|7|
|_Shocks_|||||||
|Relativerainfallshock|-8|13|-8|14|||
|Illnessshocks|4|_5_|_-4_|_5_|||
|Residual|0|0|0|3|0|0|
|Percentagegrowthandpercentagepoint<br>povertychange(sumofabove)|32|-29|-13|23|42|-44|

Source: Dercon (2001) Deininger and Okidi (2001) analyze changes in consumption and income observed for a panel of about 1,200 Uganda households during the period 1992-2000. They regress household level changes in consumption and income against variables representing the change in relative producer prices of coffee, their access to infrastructure, their initial endowments of physical and human capital, the initial health status of households, and their social capital. They found these variables to be significant in explaining growth in Ugandan household incomes during the 1990s. As in Ethiopia, the effect of changes in relative prices (in this case an increase in farm-gate coffee prices largely brought about by market liberalization, but also by the devaluation and favorable world prices) on consumption growth was substantial.

Initial private endowments of education and other assets (mainly land) were also crucial for consumption growth. For example, if households had had 6 years of completed schooling on
Table D.5 – _Continued from previous page_

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Observations|156|156|708|708|751|751|
|R2|0.42|0.41|0.47|0.47|0.43|0.42|

_Notes_ : The dependent variables are in log, measured in 2017 PPP US$by year. Robust standard errors are clustered at the enumeration area level. *** p _<_ 0.01, ** p _<_ 0.05, and * p _<_ 0.1.
McIntosh, and Wydick (2007) and de Janvry, McIntosh, and Sadoulet (2010) in Guatemala; Behr and Sonnekalb (2012) in Albania; and Cheng and Degryse (2010) in China – all document the positive effects of the introduction of a new credit registry in these countries.

Overall, the finding from this literature is that additional information about borrowers from introduction or expansion of these information sharing systems leads to an improvement in the efficiency of credit allocation decisions and loan performance. There is also some evidence that public credit registries have more limited effects on the allocation of credit compared to private credit bureaus (Love and Mylenko, 2003; Djankov, McLiesh, and Shleifer, 2007; Peria and Singh, 2014). Other studies point to some adverse effects associated with public credit registries. For instance, Hertzberg, Liberti, and Paravasini (2011) use the expansion of the Public Credit Registry in Argentina in 1998 as a natural experiment to show that public information exacerbates lender coordination and increases the incidence of firm financial distress. Using the same reform, Giannetti, Liberti, and Sturgess (2016) show that banks manipulate credit ratings before being compelled to share them by the expansion of the public credit registry, thus limiting the positive effects of information sharing. We need more research into the design of public credit registries to understand what forms are most effective in improving the allocation of credit.

# **_C. Creditor Rights and Collateral Laws_**

Secured loans are the most common type of loans in the formal financial sector throughout the world. The enterprise surveys show that 75% of firms 14
**–** **<u>Table 3: Changes across survey rounds Village Infrastructure</u>**

||**2007**|**2009**|**Difference**|**Stdev**|**Pval**|
|---|---|---|---|---|---|
|Private primary school exists|19%|23%|4%|0.016|0.006|
|Private middle school exists|24%|25%|1%|0.013|0.546|
|Private secondary school exists|10%|15%|4%|0.018|0.019|
|Any overhead tank exists|72%|80%|8%|0.030|0.009|
|No. of government school|1.8|1.8|0.01|0.044|0.910|
|No. of teachers in govt. schools|10.4|10.7|0.30|0.283|0.292|
|No. of anganwadis|1.8|2.0|0.11|0.042|0.011|
|No. of employees in anganwadi|3.7|3.9|0.18|0.119|0.134|
|No. of handpumps|4.1|4.3|0.24|0.311|0.434|
|No. of tubewells|2.9|1.3|-1.61|0.565|0.005|
|No. of water sources constructed- last 12 months|0.9|0.4|-0.58|0.351|0.099|
|No. of roads|24.7|27.7|3.07|0.788|0.000|
|No. of gravel roads|2.1|1.9|-0.24|0.365|0.522|
|No. of roads constructed- last 12 months|2.0|2.0|0.01|0.388|0.980|
|Number of roads free of garbage|10.3|11.5|1.16|1.086|0.289|
|Number of roads with a drain|8.0|6.1|-1.88|0.502|0.000|

***Note:** The standard errors for the difference in means are clustered at the level the Gram Panchayat.

**Table 4: Comparison of Means - Household Characteristics (2007)**

|||**B**|**aseline**|||
|---|---|---|---|---|---|
||**Comparison**|**Treatment**|<br>**Diff**|**Std. Dev.**|**P-value**|
|**Demographics Characteristics**||||||
|Respondent Age|37|38|0.6|0.388|0.130|
|Head of HH is Illiterate|51%|48%|-3.0%|0.040|0.446|
|Female head of HH|16%|17%|1.2%|0.018|0.529|
|Female Respondent<br>|50%|49%|-0.6%|0.006|0.394|
|SC/ST HH|42%|40%|-2.1%|0.042|0.626|
|Religious Minority|1%|1%|0.1%|0.005|0.825|
|HH size|6.0|5.9|-0.1|0.129|0.537|
|**Economic Characteristics**||||||
|Reside in Kachcha house|43%|51%|7.8%|0.051|0.130|
|TV Owners|25%|30%|5.0%|0.029|0.094|
|Radio Owners|13%|13%|0.1%|0.014|0.971|
|Land Owners|75%|74%|-0.6%|0.026|0.831|
|Irrigated Land Owners|25%|24%|-1.1%|0.039|0.776|
|Agricultural Laborer|52%|48%|-4.1%|0.030|0.173|
|Agriculturist|38%|40%|2.3%|0.030|0.451|

31
unadjusted labor variable. This finding unambiguously suggests that the effect of the increase of education, now captured by the labor quality index, was the main driving force behind TFP growth during the past decades.

By contrast, quality-adjusted capital only explains about 28 percent of growth, compared to 32 percent explained by the unadjusted variable. Consistent with earlier findings, the decrease of capital accumulation in explaining GDP growth reflects the deterioration of the quality of the country’s physical capital base. This is most likely associated with the disastrous effect of the civil war and a negative investment climate, among other factors. The finding of a negative rate of TFP growth of about 6 percent for the period 1971-2002 is a somewhat odd result. Rather than ‘technological regress’ it should be interpreted as an indication of the declining efficiency of the economy, due to the conflicting political and social environment of the country. Notice that TFP growth is consistent with the earlier regression results. In most specifications the constant term was found to be significantly negative.

How stable are these findings? The TFP estimate was found sufficiently robust. A sensitivity analysis based on alternative assumptions on the factor shares yielded TFP growth estimates ranging from –4 percent (capital share 0.4 and labor share 0.6) to –1 percent (capital share 0.5 and labor share 0.5). The associated changes of the contribution of labor and capital was negligible. Applying alternative data sources to calculate the residual was not found to be helpful. The robustness tests of the regression analyses clearly indicate that both the labor (based on IGSS data) and capital variable (5 percent depreciation) provide a higher explanatory power than other sources.

# **7.3 Disaggregation by Education Level**

Table 13 shows the results of the extended growth accounting exercise for the period 1951-2002. The human capital variables now enter directly into the production function by level of education. They capture improvements in the country’s skill base, which were formerly measured by quality-adjusted labor. At first sight, the overall results are somewhat similar to the decomposition of GDP growth in the traditional framework. With about 32 percent explaining growth, the role of physical capital accumulation is moderate.

At second sight, the contrast to the traditional framework is apparent. Table 13 suggests that human resources are the main engine of growth. In fact, the human capital variables alone explain approximately 50 percent of output growth. Of these, the main contribution comes from secondary education with about 21 percent. This is closely followed by primary education, which explains about 19 percent of growth. The contribution of tertiary education was only 10 percent.

Insofar, _both_ primary and secondary schooling constitute major determinants of GDP growth. The fact that secondary education constitutes the dominant role reflects its rapid increase in the share of the economically active population. Approximately 20

-56-
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45
areas were combined with areas that already had the NGO P4P schemes. Because P4P could not be “removed” from health facilities that were already implementing the system, the GoR required that all facilities within those new districts be reassigned to treatment. This led the evaluation team to switch the assignment of treatment and comparison for two of the eight pairs. As a result, 166 of Rwanda’s 401 primary care facilities were included in the study.

The facilities in the treatment group started receiving P4P in 2006, while the facilities in the comparison group continued with input-based financing for an additional 24 months. P4P was implemented in treatment districts over a 5-month period, yielding a minimum 18-month exposure period. Since a primary objective of the evaluation was to isolate the P4P incentive effect separately from the effect of an increase in financial resources, it was necessary to hold the level of resources constant across treatment and comparison facilities. To accomplish this, comparison facilities’ input-based budgets were increased by the average amount of P4P payments to treatment facilities on a quarterly basis during the 24-month treatment window.

We conducted a baseline survey and a follow-up survey 24 months later of the 166 facilities, as well as a random sample of 13 households living in each facility’s catchment area. For the household sample, we first sampled 13 zones (each with approximately 15-20 households) from each facility’s official list of zones in their catchment area. We then physically listed all households in the sampled zones and randomly selected one household with at least one child under 6 years old from each zone. This sample size is large enough to estimate program effects of at least 30% in the number of prenatal care visits and the probability of an institutional delivery with 90% power and a significance level of 0.05 based on data and intra-cluster correlations from the 2005 Rwandan DHS data in rural areas.

5