rafmacalaba/datause-encoder-data huggingface dataset:
decline. Average monthly maintenance fees, for example, dropped by around one‐third over these two years. The repeal of the cap, however, did not change the trend of slowing credit growth, which continued throughout 2016 as Zambia continued to struggle with the economic fallout from lower copper prices, El‐Nino related effects on agriculture, fiscal challenges and a sharp depreciation of the domestic currency.

**Figure 12: MFIs ‐ Larger loans and fewer branches after the implementation of the cap**

|A|verage loa<br>2011|n size ‐MFI<br>2012|s<br>2013|400<br>450<br>500|Number of non‐bank financial<br>instituion branches|
|---|---|---|---|---|---|
|Personal|4,006|6,378|8,898|350||
|Micro|895|1,263|1,584|250<br>300||
|Small|11,212|13,241|28,419|200||
|Medium|2,785|7,709|4,810|100<br>150||
|Large<br>_in kwacha_|53,789|101,585|191,484|0<br>50<br>|2012<br>2013<br>2014<br>2015|

_Source:_ World Bank (2014), Bank of Zambia

# Cambodia: Interest rate caps exclusively on microfinance institutions

In March 2017 the National Bank of Cambodia (NBC) imposed an interest rate ceiling of 18% per annum for loans of any maturity extended by microfinance institutions. The limit is set well below the average interest rate of 34.5% for loans in KHR and charged by MFIs in 2016<sup>25</sup> and even 2.5 percentage points below the average rate charged by commercial banks in 2016. The stated objective of the cap is to “... protect consumers from excessive interest charged by the institution [MFIs] and to effectively promote the use of affordable loans.”<sup>26</sup> The regulation came despite a report published by the NBC in November 2016 warning about the potential negative effects of interest rate caps on credit extension to small borrowers.<sup>27</sup>

> 25 https://www.nbc.org.kh/download_files/supervision/sup_an_rep_eng/Annual‐Report‐2016‐ENG‐Final.pdf

> 26 https://www.nbc.org.kh/download_files/legislation/prakas_eng/Prakas‐on‐Interest‐Rate‐Cap‐Eng.pdf

> 27 https://www.nbc.org.kh/download_files/research_papers/english/8074InterestRateUnderstandingand‐ PossibleDistortionsofInterestRateCapinCambodia.pdf

27
receiving transfers. We describe in the Appendix how this problem is solved given that we are working with a database of incomes for every household in the population census.

# **_7.5. Results of spatial targeting simulation_**

Table 2 presents the basic results from our simulations. There are a number of conclusions to be drawn. First, the availability of disaggregated data on poverty can help to improve on a uniform lump-sum transfer across the entire population. Targeting transfers to poor localities, in accordance with the optimization scheme outlined above, yields lower values of the national FGT2 than when the budget is transferred as a uniform lump-sum transfer to the entire population. Second, the more disaggregated the poverty map, the greater the improvement over the uniform lump-sum transfer. The simulations here suggest that with estimates of poverty at the province, district and commune levels, further improvements in terms of impact on the FGT2 with a given budget are attainable, and are non-negligible. Third, while the general patterns we observe are similar across our two poverty maps for 1999 and 2009 respectively, they are not identical. Notably, we see in Table 2 that while communelevel targeting in 1999 would reduce the FGT2 from a level of 0.0110, following a uniform transfer, to 0.0058 with commune level targeting (a 43 percentage point reduction), the improvement from commune level targeting in 2009 would be 66 percentage points – the FGT2 declining from 0.0166 to 0.0057 (Table 3). With district level targeting rather than commune-level targeting, the gains are slightly less marked but nonetheless a striking 58 percentage point reduction compared to uniform transfers.

Table 3 repeats the simulations presented in Table 2 but focuses now on the headcount, or FGT0, measure of poverty. As mentioned above the optimization procedure outlined in Kanbur (1987) applies to the squared poverty gap or FGT2 measure. There is no analogous optimization algorithm for the FGT0 measure. We report in Table 3, however, the resulting FGT0 estimates from having applied the procedure to allocate our budget in such a way as to minimize the resulting FGT2 measure. Table 3 reveals that the gains in terms of the FGT0 of geographic targeting are far less marked than was observed when the FGT2 measure was our reference measure.

27
In this formulation, the coefficients γt adjust for the difference in the ln odds of deforestation between two-year and one year observation periods. The coefficients δt measure the differential impact of LOWCOVER (or other variable of interest) in preprogram vs. post program years.

The sample consists only of points having forest cover at the beginning of the observation period t, where t is in the set (1996-97, 1998-99, 2000-01, 2002). To reduce spatial and temporal autocorrelation, we chose a different subsample of the 1 km gridded points for each time period, and merged them into a single multiperiod data set. The sample for each time period consists of one out of nine of the original sample points, chosen as points on a 3 km by 3 km grid.

Table 8 contains the summary statistics of the regressors used in the single-period regressions, where deforestation was estimated separately for each observation period; the regressions themselves are in Tables 9-12. Instead of coefficients, the tables show ∆F/∆X, the effect of each categorical variable X on the probability F of deforestation rate, holding other variables at sample means. For instance, in Table 9, location in high cover areas is associated with a 2.76 percentage point lower probability of deforestation, other things constant, as compared to low cover areas.

To facilitate a comparison of the single-period estimations, the effects and their statistical significance are summarized in Table 13. (All deforestation rates are true rates, e.g. expressed as proportions of the standing forest.) Specifically, the single-period estimations find:

- (i) Compared to areas outside the initial enforcement target zones, the probability of deforestation within the initial (2000-2001) enforcement target zones was strongly significantly higher by 1.04% in 1996-97, significantly higher by 0.40% in 1998-99, insignificantly higher by 0.25%, and insignificantly lower by 0.1% in 2002.  In other words, areas with high deforestation rates in 1996-99 were designated as priority enforcement zones for 2000-01.  After that designation, observed deforestation rates

31
which is induced by the instrument (the exposure to attacks combined with the rate of growth of US air/drone attacks against Al-Shabaab). Nevertheless, the IV estimates (Table 4) reinforce the DiD results: a negative immediate effect on consumption in urban areas with exposed and control households in Waves 1 and 2, mainly driven by a reduction of food consumption. The decline in consumption also increases the share of population with a consumption level below the poverty line, which has a similar magnitude between the IV and DiD estimates.

Table 4: IV estimates for the effect of terrorist attacks against civilians in urban areas with exposed and control households in Wave 1 and 2

||Log of core<br>consumption<br>(1)|Log of food<br>consumption<br>(2)|Poverty<br>status<br>(3)|Poverty<br>gap<br>(4)|Experienced<br>hunger<br>(5)|Police<br>competence<br>(6)|
|---|---|---|---|---|---|---|
|IV coefficient (δ)|-1. 715***<br>(0.540)|-1.924***<br>(0.692)|1.564***<br>(0.371)|0.248<br>(0.208)|2.688***<br>(0.667)|-0.710<br>(0.478)|
|Region fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Characteristics of<br>household & head|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling<br>characteristics|Yes|Yes|Yes|Yes|Yes|Yes|
|Drought affected<br>status|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|2,241|2,241|2,241|2,241|2,234|2,167|

Source: Authors’ calculations based on data from the SHFS and ACLED. Note: Estimated coefficients from an IV regression. Characteristics of household refer to size and receiving remittances, while those from household head refer to age, sex and literacy. Dwelling characteristics include tenure, roof and floor material, water source and sanitation type. Humanitarian assistance is not included due to collinearity with the region fixed effects. Standard errors considering sampling weights in parenthesis; *** p<0.01, ** p<0.05, * p<0.1.

The estimated coefficient for the effect on the poverty gap is positive and larger with the IV approach (Table 4), compared to the DiD result (Table 3). Despite this, the IV estimate is not statistically significant. Contrary to this result, the IV coefficient of hunger is positive and statistically significant, unlike the DiD estimate. For the perception of police competence, the IV result also suggests a negative effect, as our DiD estimate, but the coefficient is not significant. Even though the IV and DiD coefficients are estimated from different samples of households, the differences in results seem to be related to larger standard errors from the IV approach. For the effect of incidents on the poverty gap and police competence, the 95% confidence interval of the DiD coefficient lies within the confidence interval of the respective IV estimate. The imprecision of IV estimates, combined with the fact that the point estimates are consistent on the direction on the effect, could suggest the result might not be different between DiD and IV, ultimately 17
6 The Network Code processes are performed in four stages, according to their position relative to the gas day, the day when actual transportation of gas takes place (figure 1). The gas day runs 24 hours, from 6:00 a.m. on the current day to 6:00 a.m. on the next day.

# **_Box_** _1:_ **_Participants in th0e U.K NaturalGa try_**

|**Delivery**<br>**y operators**<br>**dathi**<br>**rators**of the gas<br>si<br>ts at entry terminals.<br>**Public gas transporter is,an operator**oftaission<br>p<br>e<br>r<br>a license granted by|
|---|
|Ofgas. At present BGT and several small transmission companies have public gas transporter<br>licenses.|
|**St:orage amaagers**are the rator<br>of s<br>g<br>ilities. At present. BGT is the onLy storage<br>manager linked to the Ipipeline network.|
|Shippers<br>**Arins**with ashppesicensethatb<br>sm<br>erfs, sell it to suppliers, andt<br>contract 4apblicgastanp4rfrtasottoofhestoosur.|
|**S=ppliers**al<br>ad<br>then sell it to<br>consumers. Spliers<br>d<br>'<br>c<br>gas transporters. Many<br>companies have both splesadsipr<br>iess|
|**The Office of Gas Supply**(Of<br>it<br>regulator of the ral<br>gas indstry in the'U.K..<br>**Traders**are firms that buy and sell natural gas in the spot makt<br>Traders do not deal directly<br>with pubicgas transporters.orcustomers.|
|**Allocation agents**are the ad<br>nistratois of the entry points in the pipeline system. These agents<br>act on behalf of shippers and calclate how much of the gs injected at a terminal belongs to<br>each shipper. Then they infor<br>a public gastraspor<br>w<br>ch calculates the transportation|
|~charges forf each siper|
|**Shippers 'agents**aruthorzdtary<br>eon<br>behalfof shippers.|
|**Top-up manager is a unit of BGT thate**<br>**sgs**<br>is'stoe tomeet demand in the<br>event of severe weather duing the following winter.|
|**Shrinkage provider**is a unit ofBG that<br>isresponsible fo**r** obtaiing<br>the gas needed to fuel<br>compressors and balance any leakage in the pipeline system.|

|_Source:_BGT 1996.|
|---|
# IX. References

Arunachalam, Raj, and Manisha Shah. 2008. “Prostitutes _and_ Brides?” _American Economic_ _Review (Papers & Proceedings)_ 98(2): 516-522.

Caldwell, John C., Pat Caldwell, and Pat Quiggin. 1989. “The Social Context of AIDS in SubSaharan Africa.” _Population and Development Review_ 15(2): 185-205.

Case, Anne and Alicia Menendez. 2009. “Requiescat in Pace? The Consequences of High Prices Funerals in South Africa.” NBER Working Paper #14998.

Central Bureau of Statistics. 2001. _1999 Housing and Population Census_ . Nairobi, Kenya: Central Bureau of Statistics.

Central Bureau of Statistics, Ministry of Health, and ORC Macro. 2004. _Kenya Demographic and Health Survey 2003_ . Calverton, Maryland: Central Bureau of Statistics, Ministry of Health, and ORC Macro.

Della Giusta, Marina, Maria Laura Di Tommaso and Steinar Strom. 2008. _Sex Markets: A Denied Industry_ . Routledge: New York, NY.

Dupas, Pascaline and Jonathan Robinson. 2009. “Savings Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya.” NBER Working Paper

#14693.

__________. 2010a. “Coping with Political Instability: Micro Evidence from Kenya’s 2007 Election Crisis.” _American Economic Review (Papers & Proceedings Issue)_ 100(2): 120124.

__________. 2010b. “The (Hidden) Costs of Political Instability: Evidence from Kenya's 2007 Election Crisis.” Working paper, UCLA and UCSC.

34
# **This assessment can be made systematic by considering each country’s rules against**

**established criteria** . Kopits and Symansky (1998) set out and explain eight such criteria:

- Definition: are the rules sufficiently well specified?

- Transparency: can compliance with the rules be monitored in a timely manner, according to high-quality accounting standards?

- Adequacy: will compliance with the rules lead to the achievement of the rules’ declared objectives?

- Consistency: are the rules consistent with each other and with the monetary regime?

- Simplicity: is it easy for politicians and others to understand the rules?

- Flexibility: do the rules allow the government to run deficits during recessions and after other major shocks?

- Enforceability: are the rules, especially those applied to subnational governments, enforceable?

- Efficiency: does compliance with the rules allow for efficient allocation of resources?

Table 4 summarizes an assessment of the rules against these criteria.

**Table 4. Rules Assessed against the Kopits-Symansky Criteria**

||Albania|Bosnia and<br>Herzegovina|Kosovo|Montenegro|Serbia|
|---|---|---|---|---|---|
|Well defined|+|+|++|++|+|
|Transparent|++|+|++||++|
|Adequate|+|++|++||+|
|Consistent|+|+|+||+|
|Simple|++|+++|+|++|+|
|Flexible|++|++|++|++|++|
|Enforceable|++|+|+|+|++|
|Efficient|+||++||++|

Key +++ very good; ++ good; + fair. Source: Kopits (2018).

# **_What are the quantitative implications of the rules for fiscal outcomes?_**

**This qualitative assessment can be supplemented by quantitative analysis.** In a simple, static setting, it is possible to assess how quickly compliance with the overall-deficit rules would bring debt to safer levels, focusing on those countries where debt is currently high. Second, it is

20
where loan loss provisions over total assets _(LLP)_ for bank i at time t are a function of profits before tax and loan loss provisions over total assets _(EBP)_ for bank i at time t, total loans over total assets (L) for bank i at time t, loan growth in real terms (LG) for bank i at time t, the natural logarithm of total assets (A) for bank i at time t, and year dummies (T). The dependent variable of the regression in (5) is the level of loan loss provisions scaled by total assets. Our critical explanatory variable is given by bank income before taxes and provisions. We also control for bank asset portfolio composition, its risk profile and its size. We use the ratio of loan to total assets as a proxy of portfolio composition and we expect provisions to be positively related to the loans' share. As proxy of credit risk exposures we use the real growth rate of bank loans. This indicator is thought to be positively associated with bank risk, given that rapid growth of bank lending is generally associated with lower monitoring efforts and a deterioration of the quality of loan portfolios. A prudent bank should therefore show a positive association between the amount of loan loss provisions and the growth rate of its loan portfolio. We add the logarithm of total assets to control for a potential size effect: larger banks are expected to be better equipped to manage their risk exposure or simply may benefit from larger portfolio diversification opportunities. Year control dummies are intended to catch time-specific effects such as trends in the regulatory stance.

In order to capture both economic upswings and downturns we need to use bank data for a sufficiently long period. We collect bank balance sheet information from Bankscope for the period 1988-99. This period captures both the economic slowdown in the US of the early 1990s (Peek and Rosengren, 1995) and the following upswing in the mid and late 1990s. For other countries this period captures at least one business cycle, 10
**<u>Table 1: Perceptions of bribery in different institutions, Uganda by private and public sector (percentages)</u>**

||Sector of r|espondent|Overall|
|---|---|---|---|
||Private|Public||
|**Low level of corruption**||||
|Central Bank|3.26|3.72|3.46|
|Ministry of Foreign Affairs|3.77|4.84|4.24|
|Office of Ethics and Integrity|4.12|5.21|4.60|
|Ministry of Labor|5.49|7.64|6.43|
|National water and sewerage corporation|6.69|7.64|7.11|
|Prisons|8.58|8.75|8.65|
|Ministry of Finance|8.23|13.04|10.33|
|Ministry of Works|8.58|15.08|11.42|
|Ministry of Agriculture and Forestry|13.55|9.12|11.61|
|Administrator General's office|14.07|11.17|12.80|
|Ministry of Public Service|17.67|13.22|15.72|
|Ministry of Education|19.73|14.9|17.62|
|DPP|17.32|18.25|17.73|
|Parliament|15.78|23.46|19.14|
|Private companies|21.27|16.57|19.22|
|State House/President’s Office|21.78|17.69|19.99|
|**Medium level of corruption**||||
|IGG's office|25.56|13.59|20.33|
|LC 1|22.3|18.62|20.69|
|Sub-county Councils|20.58|25.14|22.57|
|NSSF|30.53|16.95|24.59|
|Local Administration|30.36|20.86|26.21|
|Ministry of Health|31.22|25.7|28.81|
|District councils|31.39|33.15|32.16|
|Privatization Unit|39.97|29.42|35.36|
|Immigration Department|45.10|30.78|38.75|
|Courts|35.47|43.46|39.02|
|**High level of corruption**||||
|Ministry of Defense|44.6|36.69|41.14|
|Police excluding traffic police|58.66|52.51|55.97|
|Electoral commission|59.69|52.7|56.63|
|Tender boards|59.52|55.12|57.60|
|Uganda Revenue Authority|72.38|54.93|64.75|
|Trafficpolice|73.76|64.99|69.93|
|No. of observations|618|480|1098|

Source: Authors’ own calculations based on the Institutional Module of the Uganda Second National Integrity Survey 2002.

17
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Figure 2.1   Infrastructure endowments plotted against national income, by world region, continued**

|g. Mobile telephone subscribers per 1,000 inhabitants<br>|h. Landline telephone|subscribers per 1,000 inhabitants|
|---|---|---|
|CENTRAL<br>EAC<br>ECOWAS<br>SADC<br>EAP<br>ECA<br>LCR<br>MNA<br>SAS<br>SSA<br>0<br>100<br>200<br>300<br>400<br>500<br>Mobile Subscribers Per 1000 Inhabitants|CEN<br>EAC<br>ECOW<br>SA<br><br>0<br>50<br>100<br>150<br>200<br>250<br>Mainline Subscribers Per 1000 Inhabitants|TRAL<br>AS<br>SADC<br>EAP<br>ECA<br>LCR<br>MNA<br>S<br>SSA|
|0<br>1000<br>2000<br>3000<br>4000<br>Income Per-Capita<br>_Source_: As for table 2.1.<br>Variations across types of countries<br>Relative to other developing regions, Sub-Saharan<br>development of infrastructure, and the region’s infrastr<br>environmental disadvantages (table 2.4). Building infra<br>countries characterized by low population density and l<br>governance, high incidence of conflict, and geographic<br>Africa from other developing regions. Africa has the lo<br>governance index, and the second-lowest urbanization<br>incidence of conflict in Africa is similar to that in other<br>absolute number of countries in conflict is higher in Af<br>**Table 2.4 Difficulty of infrastructure environment across regi**<br>n|0<br>1000<br>Africa faces a dif<br>ucture shortfall m<br>structure tends to<br>ow levels of urba<br>al isolation—all o<br>west population d<br>rate of all develop<br>regions in percen<br>rica than in any ot<br>**ons**<br>d|2000<br>3000<br>4000<br>Income Per-Capita<br>ficult environment for the<br>ay be traceable to those<br>be more difficult in<br>nization, weak<br>f which distinguish<br>ensity, the lowest<br>ing regions. The<br>tage terms, but the<br>her region.<br> <br> <br>a<br>an<br>|
|ahara<br>|Asia<br>sia an<br>c|e and<br>al Asia<br>Americ<br>aribbe<br>e East<br>orth<br>|
|b-S<br>ica|uth<br>st A<br>cifi|rop<br>ntr<br>in<br>d C<br>dl<br>d N<br>ica|
|Regional characteristic<br>Su<br>Afr|So<br>Ea<br>Pa|Eu<br>Ce<br>Lat<br>an<br>Mid<br>an<br>Afr|
|Population density (pop./km2)<br>67|436<br>121|71<br>133<br>150|
|Urbanization rate (%)<br>37|23<br>42|57<br>63<br>69|
|Governance index (0=most corrupt; 10=least corrupt)<br>3.7|4.3<br>4.2|3.9<br>3.8<br>4.5|
|Landlocked countries (%)<br>30|38<br>9|29<br>6<br>0|
|Countries in conflict (%)<br>23|25<br>14|25<br>12<br>25|
|Income per capita (US$)<br>999|842<br>1,801|3,628<br>3,649<br>4,193|

_Sources_ : As for table 2.1.

To explore the potential impact of environmental variables, we compare the infrastructure endowment of countries _within the region_ according to the difficulty of their environment. As 10
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