rafmacalaba/datause-encoder-data huggingface dataset:
**Table 38. Governance drivers of political risk: Bureaucratic discretion**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0142|0.00633|||0.0348|0.0254|||
||(0.0564)|(0.0565)|||(0.110)|(0.112)|||
|Export % of sales|-0.00146**|-0.000577|-0.00136*|-0.000498|-0.00284**|-0.000957|-0.00260*|-0.000792|
||(0.000736)|(0.000775)|(0.000756)|(0.000786)|(0.00144)|(0.00153)|(0.00151)|(0.00160)|
|Share of sourcing via imports|0.00227***|0.00181**|0.00251***|0.00204**|0.00459***|0.00392**|0.00513***|0.00446**|
||(0.000797)|(0.000817)|(0.000828)|(0.000847)|(0.00155)|(0.00163)|(0.00165)|(0.00173)|
|High-income source country|0.0329|0.0855|0.0399|0.0959|0.0750|0.203|0.0899|0.219|
||(0.0818)|(0.0822)|(0.0827)|(0.0830)|(0.164)|(0.169)|(0.169)|(0.174)|
|>250 employees|0.0600|0.0494|0.0552|0.0431|0.118|0.0932|0.102|0.0779|
||(0.0693)|(0.0703)|(0.0688)|(0.0700)|(0.133)|(0.137)|(0.136)|(0.140)|
|Cumulative inv. >US$10M|-0.0318|-0.0865|-0.0472|-0.103|-0.0502|-0.170|-0.0937|-0.212|
||(0.0687)|(0.0695)|(0.0684)|(0.0695)|(0.132)|(0.136)|(0.135)|(0.140)|
|Years in country|0.000243|6.01e-05|0.000604|0.000401|-0.000377|-0.000800|0.000600|-4.40e-06|
||(0.00197)|(0.00195)|(0.00201)|(0.00200)|(0.00411)|(0.00417)|(0.00426)|(0.00438)|
|Level of foreign ownership (%)|-0.00186|-0.000690|-0.00187*|-0.000542|-0.00413*|-0.00153|-0.00430*|-0.00150|
||(0.00114)|(0.00121)|(0.00110)|(0.00116)|(0.00233)|(0.00249)|(0.00229)|(0.00246)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.011|0.049|0.033|0.071|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Discretion of the bureaucracy]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

55
13 economic price of wood and biomass fuels c.an approach zero. In addition, small scale heat gasifiers are amongst the lowest cost systems shown in Figure 4 because they are often manually fed. For these reasons, the economic feasibility of large scale heat gasifiers will be affected first by decreasing petroleum prices. Small scale installations in remote areas may continue to be competitive because of access to and reliability of low cost biomass fuel supply and high transport costs of petroleum fuels.

# Power Gasifiers

38. Biomass power gasifiers from 5 kW to IMW are commercially available. A power gasifier systcm consists of a manual or automatic feed system, a reactor chamber, a gas clean up system and either a diesel or spark ignition engine. If a diesel engine is used, some diesel fuel still must be used to induce ignition. Most commercially available power gasifiers are designed to operate with a specific biomass fuel: wood, charcoal, rice husks or coconut husks. The analysis below focuses on wood and charcoal power gasifiers as these are the most common.

39. The economics of power gasifiers hinges on the savings that can be realized by switching from high cost liquid fuels (i.e., diesel) to low cost biomass fuels. These fuel cost savings must be weighed against the additional capital costs of the gasifier, the increase in operation and maintenance costs, and the reduced reliability of the system. One way to evaluate the tradeoff between capital costs and O&M costs is to compare the levelized costs of electricity generated by each system. This has been done in an ongoing IENHE study which compares generic wood and charcoal gasifiers to diesel stand-alone systems from 5kW to 1MW.'<sup>5</sup> ' 40. The IENHE study has evaluated commercially available power gasifiers in the following ranges:

- a. Manually fed charcoal gasifiers from 5 kW to 200 kW;

- b. Manually fed wood gasifiers from _5_ kW to 200 kW, and;

- c. Automatic feed wood gasifiers from 100 kW to 1 MW.

Figure 5 presents some of the principle findings of this study. The analysis indicates that power gasifier economics is most strongly affected by system size and by the relative cost of petroleum and biomass fuels.

41. Under baseline price assumptions (diesel @ 40o/l, charcoal @ US$ 80/mt, and fuelwood @ US$ 20/mt), the full range of charcoal gasifiers, manually fed wood gasifiers above 30kW, and automatically fed wood gasifiers above 300kW are economically competitive with diesel systems. In this analysis, a 50% fall in diesel prices, to 20e/L would make electricity generation by IS/ Assumptions used in the **study** are presented in the Annex.
-17-

**Table 7. Public investments and the ERR of tradable projects**

|||Public inv|estment/G|DP||Public inv|estment/tot|al investme|nt|
|---|---|---|---|---|---|---|---|---|---|
|||All|Low|premia||All|Low|premia|High<br>premia|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Intercept|4.6|12.6|6.9|11.1|7.0|9.1|_5.5_|11.4|7.3|
|Public<br>investment a/|1.22<br>(2.7)***|0.65<br>(3.1)***|1.23<br>(2.4)**|1.12<br>(2.2)**|0.26<br>(2.2)**|0.28<br>(2.4)**|0.35<br>(2.8)***|0.31<br>(2.4)**|-0.08 c/<br>(0.9)|
|High public<br>investment b/|-1.61<br>(2.3)**|-0.76<br>(1.1)|-2.06<br>(2.4)**|-1.95<br>(2.3)**|-0.60<br>(3.5)***|-0.52<br>(3.1)***|-0.69<br>(3.5)***|-0.68<br>(3.5)***||
|Black Market<br>Premia|-|-0.059<br>(5.4)***|-<br>|-|-|-0.057<br>(5.2)***|-|-||
|Terms of trade<br>change|-|0.06<br>(0.5)|-|0.02<br>(0.2)|-|0.06<br>(0.6)|-|0.03<br>(0.2)|0.26<br>(I.1)|
|Project<br>complexity|-|-2.47<br>(1.7)*|-|-2.34<br>(1.4)|-|-2.54<br>(1.7)*|-|-2.73<br>(1.6)|-0.86<br>(0.3)|
|Years of<br>Education|-|-0.09<br>(0.2)|-|_-0.50_<br>(1.2)|-|-0.09<br>(0.2)|-|-0.66<br>_(1.5)_|1.43<br>(1.9)**|
|Log likelihood|-1607|-1588|-1255|-1253|-1601|-1584|-1252|-1249|-336|
|Number of<br>observations|422|422|321|321|422<br>I|422<br>I|321|321|101|

Notes: a/ For public investment/GDP, the segment up to 9.5% of GDP; for public investrnent/total investment, the segment up to 40% of total investment.

b/ For public investment/GDP, the segment exceeding 9.5% of GDP; for public investment/total investment, the segment exceeding 40% of total investment.

c/ This specification (in column 9) is linear, not kinked, since there were no significant breaks in the relationship between public investment and ERRs in regimes with high black market premia sample. Numbers in parentheses are t-statistics.

Significance levels: *** = 99 percent; ** = 95 percent; * = 90 percent.

Source: Authors' calculations.

These results suggest two powerful aspects of policy reform. The best public investment "balance" cannot compensate for poor macroeconomic, trade and pricing policies. In addition, undistorted policies are necessary for high productivity of projects in the tradeable sectors, but **in** themselves they may not always be sufficient: they need to be complemented by appropriate public investments. In a sense, the quality of the public investment program can be regarded as another policy variable--one that is subject to significant distortions. As such, a good policy environment requires more than correct macro-fundamentals and relative pricing: it also requires an appropriate public investment program. 26

> 26 Easterly and Rebelo (1993), using aggregate cross-country data, find that investment in transport and communication is consistently correlated with GDP growth.
Compared to non-poor workers, poor workers are more likely to engage with jobs that are in the private sector, informal, part-time, and self-employment. Working in the public sector is a limited option to poor workers (10 percent, as opposed to 19 percent among non-poor workers) (Table 6). More than 70 percent of poor workers have informal jobs (as opposed to 49 percent among nonpoor workers). Poor workers are also more likely to work with part-time jobs (37 percent) and selfemployment jobs (57 percent).

**Table 6: Employment conditions in Kinshasa by age (percentage)**

|||**Age **||**S**|**ex**|**Po**|**verty**|
|---|---|---|---|---|---|---|---|
||**15–24**|**25–34**|**35–64**|**Male**|**Female**|**Poor**|**Non-poor**|
|Private|93.8|90.2|82.5|82.0|90.6|90.0|80.9|
|Public|6.2|9.3|17.3|18.0|9.4|10.0|19.1|
|Formal|30.0|39.1|42.8|52.1|28.8|28.9|51.2|
|Informal|70.0|60.9|57.2|47.9|71.2|71.1|48.8|
|Part-time (<25 hours/week)|44.6|30.4|26.6|27.4|32.4|36.9|23.2|
|Full-time (>25 hours/week)|55.4|69.6|73.4|72.6|67.6|63.1|76.8|
|_Types of occupation_||||||||
|Managers|2.3|9.6|12.4|14.5|6.0|6.0|15.9|
|Skilled employee|9.9|18.0|17.1|23.2|9.0|13.1|22.5|
|Unskilled employee|16.9|10.1|9.1|14.2|5.5|9.9|9.4|
|Family worker|26.8|6.9|9.1|12.1|8.3|14.1|4.9|
|Own-account worker|44.1|55.3|52.2|35.9|71.1|56.9|47.2|

_Source_ : Kinshasa Survey 2018.

Workers’ education levels are clearly associated with their employment conditions. More specifically, workers with higher education are more likely to have wage/salaried jobs (76 percent), and work in the formal (68 percent) and/or public sectors (36 percent) compared to those with lower levels of education (Figure 6). In contrast, workers who have only preschool/primary education usually end up in the informal sector (83 percent) and are mostly self-employed (64 percent) in the private sector.

13
Given the wide range of instruments to send money to developing countries, what types of transactions are recorded as remittances by central banks? According to this survey, in most countries (33 out of 40 countries, or 83% of countries) remittance transactions that occur via electronic wires transmitted by commercial banks are captured in official statistics. Remittances that occur via money orders and drafts – regardless of which type of financial or non-financial institution pays the beneficiary -- are documented as remittances in 63% and 45% of countries in this survey, respectively. Not surprisingly, remittances that are collected by beneficiaries through cash withdrawals at ATMs (using dual cards) are hardly reported, only 23% of central banks cover them in their remittances statistics.

<!-- Start of picture text --> Chart 2. Types of Transactions Recorded as Remittances.<br>Percent of Central Banks that Record the following<br>Transactions<br>Withdrawals at ATMs<br>Drafts<br>Money orders<br>Electronic transfers<br>0% 20% 40% 60% 80% 100%<br>Percent of central banks that collect data<br>Percent of central banks that do NOT collect data<br><!-- End of picture text --> With the increasing number of money transfer products available in the marketplace, it is becoming more difficult for central banks to cover remittance transactions, especially when the transactions occur through new products, such as dual debit cards or smart cards. Moreover, it is also becoming more difficult to distinguish which transactions are actually remittances from those that are not. For instance, a cash withdrawal at an ATM using a card issued by a bank in a foreign jurisdiction may not necessarily be a remittance transaction. It may just be a cash withdrawal by a foreign tourist.

It has also become difficult for central banks to identify the source country of remittances, as well as the number of transactions that occur through the different financial instruments used by migrants to send remittances. According to the survey, only 50% of central banks have information on the number of remittance transactions (drafts, electronics wires, etc) that they cover in their statistics. For other countries, remittance data is available only on an aggregated basis.

10
second semester of 2010.<sup>5</sup> Trends for the four CLS-WILL regions show a wide variation between and within regions. Three of the four regions faced at least one massacre between 1993 and 2009. While the Southern and Coffee Regions suffered massacres at the end of the 1990s, the Atlantic region faced massacres in 2003.

# [Graph 2 goes about here]

Escalating aggressions against the civilian population produced massive outflows of forcefully displaced persons. During the period ranging from 1999 to 2009, 3’303,979<sup>6</sup> persons were forcefully displaced after being the victims of an attack or in an effort to avoid being victimized. This figure, which is equivalent to 7.9 percent of the Colombian population, is the second highest magnitude worldwide after Sudan.<sup>7</sup> Incidences of violence in 2009 for the four CLS-WILL regions and Colombia are presented in Table 1. The figures reveal a wide variation across regions with respect to incidences of different types of violent events in Colombia. The homicide rates are highest in the Coffee region; the figure is well above the national rate. On the other hand, the forced displacement rate is indeed large in the Southern Region (almost double the national rate), which indicates that direct aggression against the civilian population is high in this region. The Central region reports the lowest incidence of violence.

# [Table 1 goes about here]

The official figures for violent events are confirmed by the responses to the rural district questionnaire of the CLS-WILL. Table 2 reports the results for the questions on the rural district questionnaire related to conflict and aggressions against the civilian population. The results show that the presence of armed groups during the last 10 years is frequent for the four regions, and ranges from 15.8 percent of rural districts in the Atlantic Region to 44.1 percent of rural districts in the Southern Region. Direct violent attacks against the population are widespread, while aggressions that cause immediate economic consequences are less frequent. For example, in the Southern Region, threats were reported in 20.3 percent of rural districts, whereas the illegal seizure of livestock,

> 5 <u>www.eltiempo.com/justicia/ARTICULO-WEB-NEW_NOTA_INTERIOR-8371361.html, retrieved on</u> the 24<sup>th</sup> of November. During the week between the 8<sup>th</sup> and 14<sup>th</sup> of November, eight massacres were perpetrated in five regions of Colombia.

> 6 <u>www.accionsocial.gov.co, retrieved 1</u><sup>st</sup> of June 2010.

> 7 <u>www.internal-displacement.org, retrieved 19</u><sup>th</sup> of May 2010.

12
15 reduced,3 and a 20% tax was to be imposed on distributed dividends. The new simplified structure thus set a uniform rate for non-financial enterprises.> For commercial banks, insurance companies, and the State Savings Bank, there was also to be a uniform rate, of 50 percent.

These changes left unresolved the problem that the tax base for profits and turnover taxes continued to consist basically of the eroding surpluses of the state and municipal enterprise sector. The erosion of the state and municipal sector tax base was moreover greater than indicated by the budgetary statistics. Figure 1 shows the net contribution to the budget of state enterprises, when net flows from state enterprises to the budget are recalculated to include the difference between profits taxes and non-tax revenues, and subsidies and debt write-offs. As a result of reduced subsidies, the state enterprises' budgetary contribution increased from minus 2 percent of GDP in 1990 to 8.42 percent in 1991, but then fell to around 6 percent of GDP in 1992, as compared to 8% recorded in the budgetary statistics of Table 1. State enterprise cross-subsidization further reduced the revenue contribution of the state enterprise sector to the

> 23/ Exemptions and concessions were for sheltered workshops, approved investments to meet environmental objectives, charitable donations, and dividends received on which taxes have been paid.

There had been a previous proposal to favor private enterprises, with a 30 percent rate. This rate would have applied to domestic enterprises with more than 50 percent foreign participation, domestic trading enterprises with no state or municipal participation, and privatized enterprises with more than 50 percent private equity.
models, the simulated random component is no longer common to urban and rural areas in the same municipality. This underestimates uncertainty in the model by incorrectly assuming that the income of urban and rural households in the same municipality are independent. To address this, we estimate the covariance across municipalities of the estimated urban and rural poverty rates and account for it when estimating the variance of the municipal poverty estimates. This in turn overestimates the uncertainty associated with the estimates, which counteracts the underestimated uncertainty due to assuming the variance is known rather than estimated. However, the MSE and coverage rate is still slightly below the baseline estimates, which use spatially deflated welfare and a national model, because the latter allow for positive covariance between urban and rural areas of a municipality.

# _6.4 Using 2016 sample data_

The analysis up to this point has all used a single household survey, the 2014 MCS-ENIGH, to estimate municipal-level poverty. While this sample was drawn to generate official measures of poverty, it is also useful to check that the results are robust to the use of an alternative sample. We therefore repeat the analysis using the 2016 MCS-ENIGH instead of the 2014 round, which contains a different set of selected AGEBs. This also eliminates any possible mechanical correlation between the small area estimates and the benchmark CONEVAL estimates, which occurs because both use the 2014 MCS-ENIGH survey to estimate the empirical best prediction model. Table 7 reports the results when using the 2016 sample for the baseline specification. The main difference is that the estimates are moderately less accurate, due to the use of survey data that differs from that used to generate the benchmark. The correlation with the benchmark is now only 0.81, as opposed to 0.86 when using the 2014 survey. The same pattern of results holds, however, when ranking across methods. For in-sample areas, the household model gives moderately more accurate estimates than the sub-area model (correlation of 0.78) while the arealevel model and direct estimates give less accurate estimates (correlation of 0.75 each). Furthermore, the household-model is far more precisely estimated for in-sample municipalities (median CV of 21 vs. 31 for the area-level model). Uncertainty is significantly underestimated in 29
5 explicit deposit insurance scheme has implicit deposit insurance. **_Explicit_** takes a value of one if the country has explicit deposit insurance, and zero if implicit. Table 1 lists all countries with explicit deposit insurance.

# **Coverage**

Explicit deposit insurance schemes typically insure deposits up to a statutory coverage limit. Particularly during banking crises, countries often issue guarantees on top of pre-announced, statutory limits. We provide information on both the statutory limits, and the limits taking into account additional government guarantees. **_Coverage_** is the coverage limit in local currency. It takes on a numerical value or “unlimited” if a full guarantee is in place. **_Coverage / GDP per Capita_** is the ratio of the coverage limit to per capita GDP, expressed as a percentage, and based on the statutory coverage limit. Table 2 reports these coverage limits both in reported (typically local) currency and translated in US dollars (using end-of-year exchange rates). Data on GDP per capita is taken from the April 2014 IMF WEO database, unless otherwise noted. Footnotes accompanying Table 2 specify the coverage limits for individual countries. For countries with coinsurance, coinsurance rules are also described.

# **Organization and administration**

The organizational and administrative structures of DIS vary markedly, and this can have an important bearing on its independence and efficacy. DIS can be organized as a separate legal entity, or may be placed within a country’s supervisory structure or under the jurisdiction of the national central bank, or other government ministry such as the Ministry of Finance or Department of Treasury. These categories are mutually exclusive – any DIS must be legally separate or located within the central bank, banking supervisor, or government ministry. Some DIS are organized as separate legal entities but are hosted within and supported by the central bank. We code such DIS as legally separate. . The variable **_Type_** is coded one if the DIS is legally separate, and two if it is contained within the central bank, banking supervisor, or government ministry.

Countries may choose an explicit DIS that is administered privately, publicly, or jointly through some combination of the two. For example, Germany’s two statutory guarantee schemes have a mixed private/public component where they are privately administered but established in law and with public elements such as delegated public policy functions and oversight by the supervisory agency. This choice is often based on country-specific experience with historical banking failures and on whether private actors exist to potentially administer an explicit DIS (such as, for example, bankers’ associations in Switzerland). **_Administration_** is coded one if the DIS is administered privately, two if it is administered publicly, and three if it is administered jointly. These categories are mutually exclusive.

# **Role**
_IMPACT OF WEDP LOANS_ _5_

# _B. Empirical Strategy_

# 1. Progression of firms from baseline to follow-up

For outcomes in which the same question was asked pre-program and postprogram, the regression specification will be the following analysis of covariance (ANCOVA) estimator: Where _Yit_ is the outcome variable measured in 3 time periods: pre-program (2012), in 2014 and in 2016. _Post_ 2016 is a time dummy for the 2016 follow-up round. _Loan_ 2014 _i_ and _Loan_ 2016 _i_ are the treatment dummy variables taking the value of one if the firm reported receiving a WEDP loan in either 2014 and/or 2016. _β_ 1 and _β_ 2 will measure the treatment effect as compared to the control group that did not receive a loan. _Y_ 2012 _i_ is the pre-program value of the outcome variable measured retrospectively for the year 2012. _X_ 2014<sup>_′_</sup> _i_<sup>isavectorofbaseline</sup> control variables such as age and marital status. In cases when a control variable is missing, its value is set to zero and a dummy is included for whether the variable is missing. _εit_ is the error term.

The second specification is the difference-in-difference (DID) estimator: 2014<sup>_′_</sup> _i_<sup>_β_4 +</sup><sup>_εit_</sup> ( _E_ 2) Where _Yit_ is the outcome variable measured pre-program (2012), in 2014 and in 2016. _Loan_ 2014 _i_ and _Loan_ 2016 _i_ are the treatment dummy variables taking the value of one if the firm reported receiving a WEDP loan in either 2014 and/or 2016. _Post_ 1 and _Post_ 2 are dummy variables taking the value of one in the two respective follow-up time periods. The interaction terms _β_ 3 and _β_ 4 will measure the treatment The regression specifications in E1 and E2 will allow us to analyze the impact of receiving a loan on performance outcomes versus a control group in both time periods (2014 and 2016) so that we can pinpoint when the impact occurs. In all the regressions we control for whether the firm received any WEDP business training and if the 2013 retrospective estimate was used for the pre-program outcome rather than the 2012 estimate. All variables denominated in Ethiopian Birr are winsorized at the 99th percentile to deal with the possibility of sensitivity of the results to outliers.

Since receiving a loan is a choice rather than randomly assigned, the propensity score matching (PSM) method is used to adjust for any potential selection bias. PSM balances the distributions of observed covariates between a treatment group and a control group based on similarity of their predicted probabilities of receiving a loan (i.e. on their propensity scores). We use the predicted values from a