rafmacalaba/datause-encoder-data huggingface dataset:
**Table A.8: Impact of Housing Finance on Inequality (Sensitivity and Robustness Check)**

||||**FGLS**|||**GMM**|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(8)**|
|Ineq L1|**---**|**---**|---|---|**---**|**1.013*****<br>**(0.007)**|
|Housing finance depth (X)|0.825<br>(0.907)|---|---|-1.52<br>(3.75)|12.46<br>(15.66)|-0.012<br>(0.01)|
|X<sup>2</sup>|**---**|**-0.056***<br>**(0.137)**|**---**|**-0.015****<br>**(2.039)**|**-0.031****<br>**(1.019)**|**-0.015***<br>**(0.017)**|
|e<sup>x</sup>|---|---|0.124|6.035|1.086|-0.006|
|Housing finance policy|**-0.006*****<br>**(0.0009)**|**-0.01*****<br>**(0.0008)**|(0.017)<br>**-0.005*****<br>**(0.0009)**|(5.06)<br>**-0.006*****<br>**(0.0009)**|(21.35)<br>**-0.005*****<br>**(0.0009)**|(0.009)<br>**-0.003****<br>**(0.001)**|
|Inflation|-6e-06<br>(0.00003)|-1.056<br>(0.002)|-5.6e-06<br>(0.00003)|-5.7e-06<br>(0.00003)|-5.6e-06<br>(0.00003)|-1e-07<br>(1e-07)|
|GDP per capita|-0.007<br>(0.008)|-0.058<br>(1.005)|-0.004<br>(0.008)|-0.004<br>(0.008)|-0.004<br>(0.007)|**0.004*****<br>**(0.00003)**|
|Human capital|**-0.01*****<br>**(0.001)**|0.0001<br>(1.281)|**-0.01*****<br>**(0.002)**|**-0.01*****<br>**(0.002)**|**-0.01*****<br>**(0.002)**|-0.00006<br>(0.00005)|
|Gender|---|0.022|0.0007|0.001|0.0005|-0.00008|
|||(1.007)|(0.004)|(0.003)|(0.004)|(0.00009)|
|Conflict dummy t-1|---|0.801<br>(2.729)|**0.269****<br>**(0.091)**|**0.274****<br>**(0.091)**|**0.255****<br>**(0.095)**|**---**|
|Political stability|---|-0.0005<br>(1.418)|**- 0.012****<br>**(0.004)**|**0.015****<br>**(0.003)**|**0.012****<br>**(0.002)**|**0.007****<br>**(0.0003)**|
|Hfinwealth|**---**|**---**|-6.09<br>(7.84)|---|---|---|
|Hfinlegal|**---**|**---**|---|-33.6<br>(36.56)|---|---|
|Hfinsahel|**---**|**---**|---|---|-43.86<br>(36.45)|---|
|Constant|5.5***|4.99***|5.13***|5.1***|5.16***|---|
||(0.082)|(0.053)|(0.296)|(0.292)|(0.302)||
|Auto|na|na|Na|Na|Na|**-1.089**<br>**(0.276)**|
|Sargan OIR|na|na|Na|Na|Na|**1.682**<br>**(0.794)**|
|Wald|na|na|Na|Na|Na|**70.374*****<br>**(0.000)**|
|Countries|45|45|45|45|45|45|
|Observations|457|502|446|446|446|367|

_Source_ : Estimation result observation by the authors. _Note_ : Hfinwealth (Housing finance depth*Wealth- _dummy_ : wealth-based decomposition); Hfinlegal (Housing finance depth*Legal- _dummy_ : legalbased decomposition); Hfinsahel (Housing finance depth*Sahel-dummy: Sahel-based decomposition). *,**,**: significance levels of 10%, 5% and 1% respectively. Values in bracket are standard errors. Auto: Autocorrelation test. OIR: Overidentifying Restrictions test. The significance of bold values is twofold. 1) The significance of estimated coefficients and the Wald statistics. 2) The failure to reject the null hypotheses of: a) no autocorrelation in the Auto tests and; b) the validity of the instruments in the Sargan OIR test. P-values in brackets. AR(1): First Order Autoregression. AR(2): Second Order Autoregression. Autoregression is on the Return variables.

25
**Figures 18(a)-(b):  Net Lending by World Bank Since 1970**

<!-- Start of picture text --> (a) Disbursements of IBRD+IDA, % of GDP of Low and (a) Net Disbursements of IDA, % of GDP of<br>Middle Income Countries Low Income Countries<br>1.8<br>0.6<br>Net Disbursements Gross Disbursements 1.6<br>0.5<br>1.4<br>0.4 1.2<br>1<br>0.3<br>0.8<br>0.2 0.6<br>0.4<br>0.1<br>0.2<br>0 0<br>-0.1<br>970 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009<br>1 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text --> Source: Authors‘ calculations based on data from the World Development Indicators.

Already, many loans to middle-income countries are sought after for an embodied knowledge function: why else would China continue to seek project finance from IBRD? It is highly likely that the main function of the WBG will increasingly shift to knowledge—whether that knowledge is produced as a stand-alone service, or bundled within the project preparation and supervision activities of a loan. This is also occurring to some extent in the regional development banks.

The Bank and other multilaterals have a long history of knowledge services bundled into lending activities. The concept of a project design involves Bank staff assessment of the technical and economic viability of the project. For example, Bank staff engineers examine the technical specifications and project economists assess the economic impact – in conjunction with government staff. The first developing country loan proposed to the World Bank underwent substantial revisions following the staff‘s evaluation.<sup>65</sup> The impact of project evaluation and project supervision is inherently difficult to measure. A recent study uses a database of World Bank financed projects to disentangle the impact of the country policy and institutional environment from project specific factors affecting the success of these projects.<sup>66</sup> The authors find that country-level "macro" measures of the quality of policies and institutions are very strongly correlated with project outcomes, confirming a common result from past research on aid effectiveness. At the project level, they find that the success of individual development projects varies much more within countries than it does between countries. Data were collated to identify the project-level "micro" correlates of project outcomes, and the authors conclude that project size, as well as supervision efforts significantly

> 65 See Kapur, Lewis and Webb (1997), page 82.  There were also some controversial issues surrounding arrears clearance to lenders prior to project approval.

> 66 See Denizer et al (2011).

42
to a report prepared by the Regional Environment Center (REC), urban water demand accounts for about 15 percent of the total amount of water used in 2003, and is expected to reach an estimated 23 percent by 2030 (REC-Turkey, 2007). Additionally, as large urban centers continue to grow, the industrial sector demand for water is also expected to increase, requiring the metropolitan municipalities to find new water supply sources. In time, although the total water consumption is expected to increase, the sectoral water use allocated to irrigation is expected to decrease in the medium to long run, shifting the resources to domestic and industrial use. In this sense, the share of water used for purposes of irrigation in total water consumption, which is about 74 percent in 2003, is expected to drop to 65 percent by 2030 (ibid.).

Increased urbanization is expected to lead to increased rural-urban competition for water within each region. To simulate increased urbanization and water use in Turkey, we increased the water supply in urban areas in the West region and reduced the irrigation water available for agriculture by the same magnitude, i.e. we reallocated the limited amount of water from agriculture to urban and industrial use in the West region. The main reason for only considering the West region is because the West region in TACOGEM-W covers the rapidly urbanizing metropolitan areas such as Istanbul, Izmir and Antalya. Simulations involve increasing urban water supply by 30 percent (Scenario 2.a) and 50 percent (Scenario 2.b) with a corresponding reduction in water used for irrigation in this region.

As a result of a reallocation of irrigation water to urban use (Scenario 2.a) by 30 percent in the West region, we see a decline in overall production (the sum of both irrigated and rainfed land) of all crops by 0.5 to 3 percent (Table 4.10). We notice that an overall decline in this region plays a very significant role because the West region produces 44 percent of all maize, 73 percent of all tobacco, 59 percent of all oil seeds, 60 percent of all fruits, 64 percent of all vegetables, 34 percent of all nuts, and similarly, 34 percent of all wheat in the country. In particular, we observe the highest decline in maize production, as maize is the most waterintensive crop among all crops considered. As expected, when the water supply in irrigated activities is reduced, a clear drop in production can be seen, and since the factors for production released by irrigated production activities are reallocated into rainfed activities, we would expect an increase in production in rainfed activities.

Most notably, maize and fruit production in irrigated land dropped by 7 percent, production of oil seeds dropped by 6 percent, vegetable production by 6.7 percent, and wheat 45
net harvests elsewhere. Therefore, the price increase associated with higher harvest during periods of no export bans could be due to factors outside Malawi.

**Table 4: Interaction between maize harvest and export bans**

|_(logarithm of price in kwacha_|_/kg)_||
|---|---|---|
||**(1)**|**(2)**|
|Difference in the association of harvest per capita and<br>prices in a period with and without an export ban<br>_(Malawi's harvest × Malawi’s export ban)_|−0.006<br>***<br>(0.00)||
|Harvest’s relation with prices in periods with and without<br>an export ban|||
|No export ban||0.003<br>***<br>(0.00)|
|Export ban||−0.005<br>***<br>(0.00)|
|Malawi's export ban|1.409<br>***|1.998<br>***|
||(0.09)|(0.10)|
|Malawi's harvest|0.003<br>***<br>(0.00)||
|Tanzania's export ban|−0.216<br>***|−0.230<br>***|
||(0.02)|(0.02)|
|Zambia's export ban|0.005<br>(0.004)|0.018<br>***<br>(0.004)|
|Observations|10,087|10,087|

_Note_ : Column (1) presents the regression results from estimating equation (4) and column (2) from equation (5). Market-year fixed effects are included in all regressions. Standard errors are clustered by region pairs. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01.

Subsequently, we examine whether the relationship between prices and export ban interaction with harvest levels varies across the agricultural seasons in Malawi using the regression specification in equation 6. We present the coefficients of interest from this regression in Figure 6. Overall, we find that export bans are also associated with reduced seasonal differences in prices for a given increase in harvest. During periods with no export ban, an increase in harvest initially decreases prices immediately after harvest, but prices start to increase after the season and more sharply during the lean period (January–March). For example, in the absence of an export ban, a 10 kg per capita increase in harvest is associated with a 16 percent decrease in price during the harvest period, but the price increases slowly and reaches an increase of 8 percent during the lean season. Whereas, when export bans are in place, an increase in harvest is generally associated with lower prices throughout the year, even if the magnitude of price decrease is lower (Figure 6).

19
Source: Flowminder / WorldPop. Note: The poverty incidence of each region does not include IDPs in settlements.

**Figure 8: Poverty gap**

**Figure 9: Poverty severity**

<!-- Start of picture text --> 50 30<br>Overall average Overall average<br>25<br>40<br>20<br>30<br>15<br>20<br>10<br>10<br>5<br>0 0<br>Mogadishu Other Rural IDPs in Nomads Mogadishu Other Rural IDPs in Nomads<br>urban settlements urban settlements<br>Source: Authors' calculations. Source: Authors' calculations.<br>% of poverty line<br>Poverty severity index<br><!-- End of picture text --> The average poverty gap in Somalia was estimated at 29 percent (Figure 8), implying that the average consumption level of a poor Somali is about 71 percent of the international poverty line. Poverty was deeper in rural areas and IDP settlements (34 percent for both), compared to Mogadishu (27 percent, p<0.1) and other urban areas (24 percent, p<0.05). A large share of Somalis living in poverty, together with a considerable shortfall in their consumption expenditure relative to the poverty line means that a substantial boost in consumption would be necessary to overcome poverty. A transfer of around US$ 1.64 billion per year would lift the poor population out of poverty, assuming a perfect targeting scheme and ignoring administrative and logistical costs.<sup>37</sup> In line with these results, the average poverty severity index is 15 percent pointing to inequalities among the poor. These inequalities were concentrated in rural areas and IDP settlements (Figure 9).

Consumption was relatively homogenous due to the high levels of monetary deprivation shared by most households. Hence, inequality was relatively low with a Gini index of 34 percent in 2017. Other low‐ income countries in Sub‐Saharan Africa with similar levels of poverty tend to have higher levels of inequality. For example, Malawi and South Sudan which have a poverty incidence of 69 and 82 percent respectively, have around a 12 percentage points higher Gini index than Somalia (Figure 10). The Gini index is 41 percent in rural areas, 34 percent in other urban areas and 26 percent in Mogadishu (Figure 11). Donor support concentrated in urban areas due to insecurity and accessibility constraints may help in leveling the consumption of the urban population, leading to lower levels of inequality.

Overall inequality stems largely from differences within regions and population groups, rather than from differences between them. The Theil index indicates that between 98 and 99 percent of total inequality is the result of inequality within groups (Table 11). Differences between households from within the same

> 37 Corresponds to an annual value for all the regions, including areas not covered in wave 2 of the SHFS. For these, the same poverty incidence and gap was assumed as in regions covered by the survey.

34
-b

<!-- Start of picture text --> FRANC ZONE: (log) CPI<br>*A<br>96 Central African<br>, Renublic<br>a cf *0 am*e<br>40~~~~~~~~~~~~~~~~~~~~~~~~0<br>FRANC ZONE: (1*9) CPI<br>4, ~ ~ ~~~~~4P~<br>e w .<*oev.e*<br>:__. C_t T) jvir<br>o Fa . Se . e<br><!-- End of picture text --> <!-- Start of picture text --> FRA ZONE: (log) WI1<br>Burk~ina Faso<br>.<br>we . Pov<br><!-- End of picture text --> <!-- Start of picture text --> FRANC ZONE: (log) CPI<br>I- Ntrer<br>e e9 . SC . ,><br><!-- End of picture text --> <!-- Start of picture text --> FRANC ZONE: (log) CPI<br><!-- End of picture text --> <!-- Start of picture text --> FRANC ZONE: (log) CPI<br><!-- End of picture text --> <!-- Start of picture text --> , pww ae * so *<br>France w *,..e + l<br>9.0<br>04,<br>.0<br>*07<br>.40 .O06W-W40<br>.0<br>FRANC~~~~llR ZO2 45~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0(log) Actua and Prinio: 196-8 (lmonen)<br>-4*~~~~~~~~~~~~~~~~~~~~~~~~~~~~0<br>45 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~0<br>4<br>Go * PoPa OM<br><!-- End of picture text --> FIrLURE 2 CPI: Actual and Princinal Comoonents 1964-87
# **Table 2.2. Availability of infrastructure poverty related indicators in ECA**

|**Indicator**|**% of**<br>**Surveys**|**Indicator**|**% of**<br>**Surveys**|
|---|---|---|---|
|**DELIVERY BASED INDICATORS**||**DEMAND BASED INDICATORS**||
|Availability||Potential demand|100%|
|Water connection|100%|||
|District heating connection|100%|**WELFARE BASED INDICATORS**||
|Natural gas connection|75%|Environmental||
|Electricity connection|50%|Lacking waste water treatment|75%|
|Telephone connection|100%|Lacking waste disposal|35%|
|Time/distance to nearest bus stop|35%|Using dirty fuels|85%|
|Car ownership|100%|Health||
|Reliability||Activities interrupted by health problems|70%|
|Potable water 24 hours per day|45%|Education||
|Potable water≤4 hours/day|35%|Head of HH with less than secondary<br>education|100%|
|District heating for 3 or more months per|25%|||
|year||||
|Electricity 24 hours per day|45%|Living Conditions||
|Electricity≤6 hours/day|30%|Crowding|95%|
|Affordability||Economic Opportunities||
|Reporting any payment for central water|85%|Unemployment|100%|
|Reporting any payment for district heat|85%|Security/Disruption||
|Reporting any payment for electricity|80%|Owning principal dwelling|100%|
|Reporting any paymentfor naturalgas|70%|Moved withinthelastfive years|35%|

Source: see Table 2.1.

Another important drawback is the fact that the infrastructure module is not tailored to reflect the specific conditions that differ in urban and rural areas. Most questions in infrastructure modules relate to central connections which only makes sense in urban areas, as discussed above. In rural areas, however, central connection is not necessarily desirable for all types of services (e.g., district heating) and the absence of connections does not necessarily mean poor access to basic services, as other types of measures are usually used.

In addition to those indicators which were reasonably expected to be available, and for which some were not available (the discussion of the 26 indicators above), is another issue. This concerns the indicators that were ideally desired but for which there was little possibility, at this time, that they would be available.

In the early stages of this research some 80 ideally desired indicators were developed. Initial screening of data availability reduced this set to the 26 indicators which were used for analysis. Annex 1 provides a table of these ideally desired indicators and the original framework used to develop the set of 80, as well as discussion of the problems encountered.

10
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 4.9. Summary of cost-benefit analysis of regional integration**

||**Broadband pric**|**e (US$/mo.)**|**Broadband su**<br>**(‘000**|**bscriptions**<br>**s)**|**Benefits (US$**<br>**millions per**|**Costs (US$**|**Rate of return**|
|---|---|---|---|---|---|---|---|
||**Baseline 2008**|**Induced**|**Baseline 2008**|**Induced**|**year)**|**millions)**|**(%)**|
|Burundi|||0.2|||2||
|Ethiopia|486.50|304.1|0.4|5.5|6|11|59|
|Kenya|39.82|24.9|3.3|120.9|11|24|46|
|Rwanda|88.02|55|4.2|6.5|2|5|42|
|Sudan|23.25|14.5|44.6|354.3|21|18|116|
|Tanzania|63.56|39.7|6.4|43.5|7|33|22|
|Uganda|194.37|121.5|4.8|9.1|6|2|304|

Source: AICD calculations.

56
of the short-term adjustment costs. Natural land protection costs per hectare are also assumed a quadratic function of protected natural land: In equation (B.2), the parameter _ξ_ 0<sup>_R_determinesthelong-runcostsofpro-</sup> tecting land. The parameter _ξ_ 1<sup>_R_capturestheshort-termadjustmentcosts.The</sup> parameter values dening natural land access cost function (B.1), and natural land protection cost function (B.2) are calibrated based on FAO (2010) data to match deforestation rates in 2004 and ensure stable rates of natural land access and protection.

# Managed forests

We set the number of forest tree vintages to 50 and assume that average densities of managed forest land corresponding to dierent tree ages are uniformly distributed.

# B.1.1.2 Fossil Fuels

The primary fossil fuels linked to the economic analysis of land use are petroleum products and natural gas. Biofuels substitute for petroleum products and, to lesser extent, natural gas, in energy demand for transportation services. The natural gas is also the key input in nitrogen fertilizers' production. To determine the amount of newly discovered oil and gas reserves, ∆<sup>_F,D_</sup> _t_ we estimate dierent econometric specications based on historical data from the BP Statistical Review of World Energy database (2013)<sup>1</sup> globally over the period of 1980 - 2010. We nd that newly discovered oil and gas reserves exhibit a linear trend, adding a constant amount across time, i.e., ∆<sup>_F,D_</sup> _t_ = ∆<sup>_F,D_</sup> .

As petroleum products' and natural gas prices are closely related in the long run (Hartley et al. 2008), we use the same cost function for both types of fossil fuels. Specically, we assume that the cost of fossil fuels is a nonlinear quadratic function with accelerating costs as the stock of fossil fuels depletes (Nordhaus and Boyer 2000):

> 1The database can be accessed online at http://www.bp.com/en/global/corporate/ about-bp/energy-economics/statistical-review-of-world-energy-2013.html

11
# **The Impact of the Business Environment on the Business Creation Process**

by

> **Leora Klapper, Anat Lewin, and Juan Manuel Quesada Delgado (World Bank)** *

_JEL Classification_ : G18, G38, L51, M13 _Key Words_ : Entrepreneurship, Business Statistics, Economic Development, Business Registries, electronic Business Registries.

> * Corresponding author:  Leora Klapper, ph: 1-202-473-8738, e-mail: lklapper@worldbank.org.  We thank the Ewing Marion Kauffman Foundation, the Development Research Group and Investment Climate Advisory Services Group at the World Bank,  and the International Finance Corporation  for financial support.  This paper was prepared with outstanding assistance from Anna Cusnir and Ana Ribeiro.  Thanks to Maxwell Aitken, Laurence Carter, Asli Demirguc-Kunt, Andrei Mikhnev, Raphael Amit and Mauro Guillen for valuable comments.  This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank.
||(4.24)**|(0.43)|(2.36)*|(1.98)|
|---|---|---|---|---|
|fivondroanana716|-0.202|0.868|-0.336||
||(1.53)|(1.77)|(1.35)||
|Constant|4.986|12.192|16.551|12.906|
||(0.61)|(1.73)|(2.38)*|(1.75)|
|Observations|91|222|174|222|
|R-squared|0.32|0.12|0.34|0.11|

Dependent variable: EPM-reported vanilla producer prices (unit values), uncorrected for CPI Absolute value of t statistics in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1% Number of strata: 12, Number of PSUs: 398

For each year, fivondroanana effects are relative to the omitted one, which is the one with the smallest number of vanilla farmers.

With this caveat in mind, can we find in the data any prima-facie evidence of the reform’s effects on producer prices and intermediation margins? The elimination of Madagascar’s marketing board implies the replacement of a state-owned monopoly by a market structure for intermediaries that can be expected to be fairly concentrated at the outset but to become more competitive over time as entry is attracted by high intermediation margins. Thus, except under very peculiar settings, one should expect intermediation margins to narrow down over time, especially given that, as explained in the previous section, CAVAGI’s margins were vastly in excess of even a rational monopoly’s optimal level. Table 5 shows the evolution of prices and intermediation margins over 1991-2001.

Table 5 Farmgate, FOB and CIF vanilla prices, 1991-2001

|Year|FAO<br>farmgate<br>price, FMG|FAO FOB<br>price, FMG|% of FOB<br>market price<br>received by<br>producers|COMTRADE<br>CIF unit<br>value, MFG|% of CIF<br>market price<br>received by<br>producers|EPM<br>average<br>producer<br>price|
|---|---|---|---|---|---|---|
|1991|2'000|132'743|1.51<br>|135'620|1.47||
|1992|5'000|136'363|3.67<br>|131'161|3.81||
|1993|5'000|136'182|3.67<br>|130'555|3.83<br>|4'062|
|1994|8'450|213'532|3.96<br>|186'325|4.54||
|1995|10'000|237'090|4.22<br>|210'557|4.75||
|1996|5'150|75'216|6.85<br>|124'044|4.15||
|1997|10'500|74'789|14.04<br>|101'230|10.37<br>|17'788|
|1998|10'500|99'031|10.60<br>|126'767|8.28||
|1999|25'000|106'650|23.44<br>|142'435|17.55<br>|21'950|
|2000|66'250|219'897|30.13<br>|254'324|26.05||
|2001|69'828|598'189|11.67<br>|608'232|11.48<br>|133'559|

Source: FAO, COMTRADE, EPM.

23