rafmacalaba/datause-encoder-data huggingface dataset:
|Table 12: Collateral and Down-payment Rules in MENA ......................................................20|
|---|
|Table 13: Operational Mechanisms adopted in the Benchmark Countries ..............................21|
|Table 14: Operational Mechanisms in MENA ........................................................................22|
|Table 15: Outreach of Guarantee Schemes in Benchmark Countries ......................................25|
|Table 16: Outreach of MENA PCGs .......................................................................................26|
|Table 17: Alternative Methods to Measure Additionality .......................................................29|
|Table 18: Findings of Selected Country Studies......................................................................30|
|Table 19: Building blocks for a comprehensive review ..........................................................34|

|Figures:|
|---|
|Figure 1: Maximum Size of Eligible loans: MENA and Benchmark countries ......................12|
|Figure 2: Standardized Fees .....................................................................................................17|
|Figure 3: Outstanding guarantees ............................................................................................27|
|Figure 4: Outstanding guarantees ............................................................................................27|
|Figure 6: Average size of guarantees .......................................................................................28|
|Figure 7: Equity multiplier.......................................................................................................32|
|Figure 8: Net loss ratios ...........................................................................................................32|

3
**Table 1.2: Lack of Structural Transformation in Africa** **<u>Value Added by Sector, 1960-2009</u>**

|**Ghana**|Agriculture|**1960**|**1970**<br>54|**1980**<br>60|**1990**<br>45|**2000**<br>39|**2005**<br>41|**2009**<br>32|
|---|---|---|---|---|---|---|---|---|
||Manufacturing||13|8|10|10|9|7|
||Industry||8|4|7|18|18|12|
||Services||25|28|38|32|32|49|
|**Kenya**|Agriculture|38|33|33|30|32|27|23|
||Manufacturing|9|12|13|12|12|12|9|
||Industry|9|8|8|7|5|7|7|
||Services|44|47|47|51|51|54|62|
|**South**|||||||||
|**Africa**|Agriculture|11|7|6|5|3|3|3|
||Manufacturing|20|23|22|24|19|18|15|
||Industry|18|15|27|16|13|13|16|
||Services|51|55|45|55|65|66|66|
|**Tanzania**|Agriculture<br>||||46|33|32|29|
||Manufacturing||||9|9|9|10|
||Industry||||8|10|14|15|
||Services||||36|47|46|47|
|Note: Industry<br>_Source:Worl_|is the sum of mining,<br>_d Development Indicato_|construction,<br>_rs, 2011_|electricity,|water, and|gas.||||

**Figure 1.4: Structural transformation in the developed countries: a reallocation of labor from agriculture to manufacturing industries**

<!-- Start of picture text --> 40<br>60<br>35<br>50<br>30<br>40<br>25<br>30<br>20<br>20<br>15<br>10<br>10<br>0 1871 1910 1930 1950 1990<br>1871 1910 1930 1950 1990<br>US UK Germany US UK Germany<br>Source: S.Kuznets: Modern Economic Growth - Rate, Structure and Spread, Yale University, 1966. New Haven and London, table 3.2<br>(%)<br>in total employment (%) Share of manufacturing<br>Share of agricultural employment employment in total employment<br><!-- End of picture text --> Countries that caught up with the Western countries in the post-World War II period also experienced a reallocation of labor from agriculture to industry. By the end of the 1980s, in 8
|**Table 6. Trade, diver**|**sification, a**|**nd growth: I**|**nter- and intr**|**a-regional tr**|**ade**||
|---|---|---|---|---|---|---|
|_Dependent variable, Grow_<br>_Estimation Method: GMM_|_th of real GDP_<br>_-IV System Esti_|_per worker (5-y_<br>_mator (Arellano_|_ear non-overlap_<br>_and Bover, 1995_|_ping averages)_<br>_; Blundell and_|_Bond,1998)_||
||[1]|[2]|[3]|[4]|[5]|[6]|
|Initial GDP per worker|-0.0245***|-0.0225***|-0.0214***|-0.0198<sup>***</sup>|-0.019<sup>***</sup>|-0.018***|
|_(in logs)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Exports, total|0.0610<sup>***</sup>||||||
|_(inter + intra)_|(0.000)||||||
|Exports, inter-regional||0.0869***<br>(0.000)|0.101***<br>(0.000)||||
|Exports, intra-regional||0.0137<br>(0.059)|0.00914<br>(0.291)||||
|Trade, total<br>_(inter + intra)_||||0.0350<sup>***</sup><br>(0.000)|||
|Trade, inter-regional|||||0.048<sup>***</sup>|0.054***|
||||||(0.000)|(0.000)|
|Trade, intra-regional|||||0.019<sup>**</sup><br>(0.003)|0.006<br>(0.174)|
|Natural Resource exports|||-0.00628***|||-0.020***|
|<br>_(% of GDP)_|||(0.000)|||(0.000)|
|Product Concentration|-0.0197<sup>***</sup>|-0.0214***|-0.0181***|-0.0215<sup>***</sup>|-0.023<sup>***</sup>|-0.005***|
|_(Herfindahl Index)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Secondary enrollment rate|0.00868<sup>***</sup>|0.00976***|0.00911***|0.00704<sup>***</sup>|0.007<sup>***</sup>|0.008***|
|<br>_(initial level, logs)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|ICRG Political Risk Index|0.00620<sup>***</sup>|0.00621***|0.00655***|0.00681<sup>***</sup>|0.007<sup>***</sup>|0.007***|
|_(principal components)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Credit to private sector|-0.0115<sup>***</sup>|-0.0107***|-0.0107***|-0.0135<sup>***</sup>|-0.013<sup>***</sup>|-0.013***|
|<br>_(% of GDP)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Government consumption|0.00806<sup>***</sup>|0.00738***|0.00613***|0.00495<sup>**</sup>|0.005<sup>**</sup>|0.005***|
|_(% of GDP)_|(0.000)|(0.000)|(0.000)|(0.003)|(0.003)|(0.000)|
|Growth volatility|0.181<sup>***</sup>|0.194***|0.217***|0.201<sup>***</sup>|0.215<sup>***</sup>|0.232***|
|<br>_(S.D. growth per worker)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Constant|0.151<sup>***</sup>|0.151***|0.163***|0.142<sup>***</sup>|0.130<sup>***</sup>|0.137***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Observations|<br>606|<br>606|<br>597|<br>602|<br>602|<br>594|
|_AR(1)_|0.000|0.000|0.001|0.000|0.000|0.001|
|<br>_AR(2)_|0.481|0.478|0.456|0.504|0.493|0.495|
|<br>_Hansen_|0.515|0.435|0.498|0.426|0.356|0.463|
|_p_-values in parentheses|||||||
|* _p_ <0.05,** _p_ <0.01,*** _p_|<0.001||||||

Our regression estimates from Table 6 yield the following results: first, the coefficient of interregional exports is positive and significant while that of intra-regional exports has a negligible impact on long-term growth (see columns [2] and [3] of Table 6). Second, inter-regional and intra-regional trade have a positive and significant impact on growth per worker; especially, when we do not account for 21
Table 10. Indonesian Exports, ICT use, and Log of Skills Measures. Male workers. 2005-2010

||||||N|on-routine<br>|skill measure<br>|s|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||Ana|lytical|||Interp|ersonal|||Ma|nual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.0000||-0.0001|-0.0005|0.0000||0.0000|-0.0005|-0.0001||0.0000|0.000466|
||(0.000253)||(0.000259)|(0.000421)|(0.000271)||(0.000278)|(0.000439)|(0.000276)||(0.000284)|(0.000451)|
|Share of hhlds with computer||0.0469|0.0728|0.0858||0.0313|0.0583|0.0714||-0.0173|-0.0473|-0.0621|
|||(0.0768)|(0.0662)|(0.0685)||(0.0718)|(0.0591)|(0.0615)||(0.0717)|(0.0571)|(0.0610)|
|IDN exports per worker *||||0.00242||||0.00277||||-0.00296|
|Share of hhlds with computer||||(0.00149)||||(0.00147)*||||(0.00148)**|
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|
|R-squared|0.763|0.763|0.764|0.763|0.311|0.309|0.311|0.310|0.819|0.819|0.819|0.819|
|||Ma|nual|Routine ski|ll measures|Cog|nitive||||||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|||||
|IDN exports per worker|0.000457||0.000489|0.000595|-0.0000346||-0.0000112|0.000592|||||
||(0.000212)**||(0.000214)**|(0.000341)*|(0.000302)||(0.000308)|(0.000496)|||||
|Share of hhlds with computer||-0.0249|-0.0578|-0.0804||-0.00569|-0.0283|-0.0347|||||
|||(0.0908)|(0.0851)|(0.0918)||(0.0686)|(0.0558)|(0.0543)|||||
|IDN exports per worker *||||-0.000384||||-0.00361|||||
|Share of hhlds with computer||||(0.00155)||||(0.00156)**|||||
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|||||
|R-squared|0.969|0.969|0.969|0.969|0.815|0.815|0.815|0.814|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and province linear time trends. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

31
Table 8: Spillover effects of electricity provision on tax compliance

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||||Conditioned on||
||No HH A|ccess to Elec|No HH Access t|o Pipe-borne Water|
||||IV Regression||
|Electricity Access in Town|0.901*<br>(0.539)||0.372***<br>(0.139)||
|Piped Water in Town|-0.209|-0.454|-0.098***|-0.076**|
||(0.128)|(0.587)|(0.037)|(0.033)|
|F Stat|2.893|0.581|14.721|7.825|
|Observations|16626|16626|19736|19734|

Notes: Robust standard errors in parentheses are clustered at PSU level. F-stat is the IV first-stage F test of instrument strength. All regressions in the table control for individual/household-, community-, and country-level variables. Individual/household-level variables include the respondent’s gender, age, education, quality of housing, employment status, size of the household, and ownership of a TV or mobile phone. Community-level variables include the presence of piped-water systems, paved roads, schools, and hospitals. Country-level variables include total population, GDP PPP, share of the urban population, population density, value added of agriculture and service as a share of GDP, total natural-resources rents (% of GDP), IIAG score, and ethno-linguistic fragmentation score.

> _∗_ Significant at 10 percent level

> _∗∗_ Significant at 5 percent level

> _∗∗∗_ Significant at 1 percent level

30
43 individualized diet strategies. The median cost per household-market in every month from January 2013 to July 2017 is $1.79/person/day (2011 US$ PPP) for the individualized diets but $2.26 for the shared diet.<sup>7</sup> Individualized diets are feasible 90% of the time compared to 60% of the time for shared diets. Further, when considering infeasible diets to have very high cost instead of being infeasible altogether (infinite cost), we estimate that the extent of seasonal fluctuation for the shared diet is at least 116%, constituting a lower bound on the extent of seasonality when optimizing for shared meals.

We have shown that the seasonal gap in the cost of the lower bound diet is similar to that of food groups when the diet is feasible, and lower than that of individual prices, suggesting that substituting items within food groups to meet nutrient requirements can stabilize diet cost throughout the year. We find that seasonality is a factor in the feasibility of a nutrient-adequate diet under both scenarios, driven in the lower bound case by certain nutritionally vulnerable household members for whom the diet is not always feasible (Supplementary Table D), specifically breastfeeding women, children three years and under, and elderly adults. The observed seasonal gap in the cost of the lower bound diet – which we estimate to be approximately 13% when the diet is feasible and at least 30% when considering infeasible diets to have infinite cost – can be considered the amount of seasonality in the cost of nutritious diets that would be unavoidable under current conditions or the best possible smoothing under current conditions. Clearly, seasonality in diet costs in Malawi’s current food system remains substantial and cannot be ignored.

> 7 These compare with an unweighted population average individual cost of a nutrient-adequate diet in Malawi calculated by (Herforth et al. 2020) of $1.29/person/day (2011 US$ PPP), which was calculated using different food price and nutrient composition data for global comparability. That paper used the food list and prices from the World Bank’s International Comparison Project and used USDA food composition data (Herforth et al. 2020).
# **3. Description of the Program**

Colombia Mayor was launched in 2003 as the Programa de Protección Social para el Adulto Mayor (PPSAM). The aim was to provide basic subsistence through noncontributory pension benefits to elderly people who had no pension income and were living in extreme poverty. In 2010, program administration was outsourced to the privately run Consorcio Colombia Mayor, which reports to the Ministry of Labor. The purpose of the change was to accelerate rollout among all elderly who were living in conditions of extreme poverty. The amount of the transfer is adjusted annually based on budgetary considerations, but it can never exceed half the value of the minimum wage. The program also provides a number of indirect nonmonetary benefits that are supplied through specially established centers catering to the needs of the elderly population and administered and cofinanced by the municipalities.

There are three main program eligibility criteria. The first is that the beneficiary must not be within three years of reaching the official retirement age. During the period under study, this meant a maximum age of eligibility for the benefit of 52 years among women and 57 years among men. (These ages were subsequently raised by two years.) The second criterion establishes that the beneficiary’s household must not score above a certain threshold in Colombia’s system for the identification of potential social program beneficiaries (in the Spanish acronym, Sisben). The Sisben score represents an estimate of the living conditions of households registered in the system. It is based on information on households collected through a system survey, including the quality of the dwelling, the number of dependents, disability status, income, ownership of durable goods, and so on. The Sisben score is used to determine the eligibility for all the country’s social programs at the national level. It has gone through two mayor modifications, such that the current, third, version is usually referred to as Sisben III. Each program is associated with a unique maximum score to identify beneficiaries. The maximum scores usually differ depending on whether a household resides in one of the country’s 14 major metropolitan areas, in other urban areas, or in the countryside. Moreover, for many programs, eligibility is subdivided into up to three different levels, corresponding to different maximum scores. For prioritization purposes, Colombia Mayor distinguishes between two levels. The maximum scores for level 1 are 36.32 for the 14 major 7
financing as in Myers and Majluf (1984).<sup>9</sup> These standard firm‐level determinants of capital structure include firm size, asset tangibility, growth opportunities, and profitability.

Firm size is expected to have a positive impact on firm’s capital structures since larger firms tend to have higher survival rates than smaller firms, are generally less risky and more diversified, and hence less likely to default on their debt obligations.<sup>10</sup> Moreover, these firms are more transparent than smaller firms, because they are more likely to have adequate financial records to document their performance, and therefore should have easier access to credit. Last, various fixed costs of financial transactions or of contract enforcement often make lending to small firms more costly.

The existing empirical evidence supports the view that firms strive to match the maturity of their assets and of their liabilities (Rajan and Zingales, 1995; Booth et al, 2001). This evidence is supported by theory that suggests that the optimal payment structure for debt should match the timing of project returns (Hart and Moore 1995). Indeed, working capital and other short term investments are better financed with short‐term debt, while firms with a higher proportion of tangible fixed assets in their total assets (FA/TA) require a higher proportion of long‐term funding. Moreover, firms with a larger share of tangible assets that can be pledged as collateral to outside financiers to secure their claims can more easily raise longer‐term debt, because the cost of defaulting on debt obligations are higher. Overall, the use of long‐term debt allows firms

> 9 See Harris and Raviv (1991) for an early review of the literature.

> 10 For OECD cross‐country evidence on firm survival rates and the relationship with size, see Bartelsman, Scarpetta, Schivardi (2003).

12
# **6.2 A supplementary information experiment**

These impacts are higher than has been observed in similar studies in other contexts when formalization also has some tax implications. Importantly, this is the case not only for the groups providing additional incentives to formalization but also for the group only providing (in-person) information. One key question is then whether the relatively high impact measured in group 1 is due mainly to information (i.e. firms decided to formalize when they learned that registration is free of charge and easy to do) or to the fact that the information was delivered inperson by highly trained and qualified CGA advisors who tried to convince business owners of the benefits of formalizing, and provided assistance with forms and the process as needed.

To answer this question, we designed an additional experiment that was implemented during the two year follow-up survey. Fifty percent of the control group (600 firms) was randomly selected<sup>11</sup> to receive two program leaflets just after the completion of the survey (so we are sure that survey answers were not affected by the “leaflets intervention”). The two program leaflets were identical to the leaflets given to group 1 firms when the program started and were introduced by the surveyor with a short script mentioning that the _entreprenant_ status is now available for free and in one day to all businesses, and explaining the location of the one-stop shop for business registration. This small intervention tests whether surveyors only providing information on the new status but not in charge of convincing the business of the benefits of formalizing or assisting them with forms can have similar impact on formalization rate. Table A5 presents the results of this “leaflets intervention”.

It shows that the leaflets intervention had no significant impact on formalization decision. It means that simply providing (in-person) information on the new status was not sufficient to increase formalization and that the impact measured for group 1 is also due to the fact that the information was provided by trained and qualified staff who took time to convince business owners to formalize.

# **6.3 Heterogeneity of impact and usefulness of targeting**

Table 4 examines heterogeneity in the impact of our interventions by pre-specified business characteristics. We find that male business owners were significantly more likely to formalize

> 11 With stratification on the following variables: gender, operates in Dantokpa market and trader.

19
payment, while in 1998 out-of-pocket payments increase the normnalized gap by 0.2 percentage points.

## **_6.4. The impoverishing effects of hospital_** _vs._ **_other health costs in Vietnam_**

The impoverishment measurement methodology can be used to quantify the different poverty impacts of hospital and other health spending. In the 1998 Vietnam data, we separated hospital expenses (defined as costs associated with inpatient care over the previous 12 months) and all other health care costs over the previous 12 months. On average, the former account for around 20% of the total.

Table 7 shows the results of an analysis of the poverty impacts of these two categories of expense, using the extreme food-based poverty line in order to explore which of the two types is the main source of impoverishment. Looking at hospital costs, the increase in the headcount _(p1H_ ) is a mere 0.5 percentage points, while the value of **_PI1_** associated with non-hospital expenses is 2.9 percentage points. The values of the impact on the mean poverty gap( **_plG_** _)_ are 1.07 and 8.54 respectively. Clearly, and perhaps in contrast to prior expectations, non-hospital expenditure has a larger poverty impact in Vietnam than hospital expenditure. What is striking for hospital costs, however, is that although most of the rise in the poverty gap is still due to poor people getting poorer, this element is proportionally less than in the case of non-hospital expenses. In other words, the share of the rise in the poverty gap accounted for by deepening poverty among the pre-payment poor is smaller in the case of hospital costs than in the case of non-hospital costs.

# **7. Summary and conclusions**

As noted in the Introduction, much has been written recently about equity or fairness in payments for health care, "catastrophic" health care payments, and the impoverishing effects of health care outlays. The aim of this paper is to clarify the meaning of these termns, to show how each might be measured, and to compare the different measures. We illustrate each using household data on annual out-of-pocket expenditures on health care taken from the 1992-93 and 1997-98 Vietnam Living Standards Surveys (VLSS).

We distinguish between two strands in the literature-approaches inspired by egalitarian concepts of equity, and approaches focusing on "minimum standards". Underlying the egalitarian approach is the view that payments should be linked directly _and continuously_ to ability to pay (ATP) rather than to usage of health services. The _30_
# **Figure 3a: Attitudes towards courts and governance structures in relations with suppliers**

"the court system is fair, impartial and uncorrupted" <!-- Start of picture text -->
1.00<br>0.01 0.02 0.03 0.01<br>0.90 0.13 0.17<br>0.28 0.31<br>0.80 0.14<br>0.70 0.21<br>0.60 0.19 0.11<br>0.50<br>0.40<br>0.72<br>0.30 0.60 0.57<br>0.50<br>0.20<br>0.10<br>0.00<br>Strongly Tend to Tend to Strongly<br>disagree disagree agree agree<br>B BP BL SC<br><!-- End of picture text -->

degree to which courts are an obstacle to current operations <!-- Start of picture text -->
1.00 0.01 0.03 0.01 0.01 0.03<br>0.10<br>0.90 0.19 0.16<br>0.19 0.22<br>0.12<br>0.80<br>0.70 0.16 0.19 0.24 0.16<br>0.60<br>0.50<br>0.40 0.77<br>0.30 0.61 0.60 0.59 0.60<br>0.20<br>0.10<br>0.00<br>No Minor Moderate Major Very<br>severe<br>B BP BL SC<br><!-- End of picture text -->

B = bilateralism, BP = bilateralism with private support, BL = bilateralism with legal support, SC = strong comprehensive

# **Figure 3b: Attitudes towards courts and governance structures in relations with customers**

"the court system is fair, impartial and uncorrupted" <!-- Start of picture text -->
1.00<br>0.05 0.06 0.02 0.03<br>0.90<br>0.80 0.23 0.23 0.33 0.28<br>0.70<br>0.13 0.09<br>0.60 0.15<br>0.18<br>0.50<br>0.40<br>0.30 0.59 0.55 0.60<br>0.47<br>0.20<br>0.10<br>0.00<br>Strongly Tend to Tend to Strongly<br>disagree disagree agree agree<br>B BW BP WC<br><!-- End of picture text -->

degree to which courts are an obstacle to current operations <!-- Start of picture text -->
1.00<br>0.04 0.05 0.03 0.06<br>0.12<br>0.90 0.13<br>0.22<br>0.80 0.37 0.38 0.19<br>0.17<br>0.70<br>0.13<br>0.60 0.14<br>0.10<br>0.50 0.17<br>0.40<br>0.66<br>0.30 0.59<br>0.55<br>0.50<br>0.20 0.41<br>0.10<br>0.00<br>No Minor Moderate Major Very<br>severe<br>B BW BP WC<br><!-- End of picture text -->

B = bilateralism, BW = bilateralism with weak support, BP = bilateralism with private support, WC = weak comprehensive 39