rafmacalaba/datause-encoder-data huggingface dataset:
control for perceptions of corruption in government, quality of infrastructure (satisfied with roads), and if the respondent has had money or property stolen in the past 12 months.<sup>7</sup> We estimate the model with a varying mix of controls to ensure robustness and avoid using closely related control variables. We have some priors on how these controls could affect trust. For example, education correlates with respondents’ expectations of government performance. If respondents’ expectations rise faster than the actual performance of governments, trust could decline (Dalton, 2005; OECD, 2013). Previous research has shown married people have higher levels of institutional trust than single people (Hudson, 2006). Household composition can dictate who can participate in civic activities that build interpersonal trust and determines the financial needs of the household. Studies have shown that working people trust national institutions more than the unemployed or those out of the workforce (Mishler and Rose, 2001; Hudson, 2006). In addition, household income is positively related with trust (Leblang et al., 2022).<sup>8</sup> We use GWP’s _Per Capita Income Quintile_ variable, where the lowest income quintile indicates the respondent’s per capita household income lies among the poorest 20% of the country’s population, and the highest income quintile indicates it lies among the richest 20% of the country’s population.

Citizens’ dissatisfaction with public infrastructure may be an important determinant of trust (Mischler and Rose, 2001; Corbacho et al., 2015). Thus, we include the dummy variable _Satisfied with roads_ , which equals one if the respondent is satisfied with the roads and highways in their area. Government corruption can determine perceptions of justice and fairness in encounters with national institutions (Corbacho et al., 2015). To control for this, we include a dummy variable for _Corruption in government_ which equals one if the respondent thinks that corruption is “widespread throughout the government in this country.” Similarly, since crime affects both vertical and horizontal dimensions of trust (Corbacho et al., 2015), we include the dummy variable _Money or property stolen_ , which equals one if the respondent has had money or property stolen in the last 12 months.

> 7 Our results are robust to excluding these three variables over concerns that they could also measure some amount of trust.

> 8 Respondents who have difficulty answering the income question are presented a set of ranges in local currency and are asked which group they fall into (Gallup, 2016). This measure also relies on multiple imputation methodology to replace missing values.

8
2011; Mihaljek, 2010).<sup>7</sup> However, foreign banks that participated in the Vienna Initiative – a public-private partnership between European governments, multinational banks, and international financial institutions in which foreign banks were eligible for financial support in return for commitments that they would maintain their exposures and keep subsidiaries adequately capitalized in affected host countries – were more stable lenders than those that did not, and had loan growth rates on par with domestic banks by the end of 2009 (de Haas, et al. 2011).<sup>8</sup> One important difference between this crisis and previous ones is that the recent episode started in foreign banks’ parent countries, while previous crises were endemic to developing countries.

In non-crisis periods, the shortcomings of government-owned banks have been welldocumented.<sup>9</sup> However, in times of crises, when private banks are likely to reduce their supply of credit, government-owned banks could potentially play a crucial stabilizing role. During financial crises in Asia and Latin America in the 1990s, government-owned banks did in fact expand credit faster (or cut it less) than domestic and foreign private banks (Hawkins and Mihaljek, 2001). And, in the recent crisis, government banks in some of the former Soviet Union economies were better able to maintain credit growth rates than private banks, especially foreignowned ones (de Haas et al., 2011).

> 7 Evidence is from bank-level regressions for 1,275 banks in Eastern Europe and Central Asia in de Haas et al. (2011) and from a survey of central bank governors in Mihaljek (2010).

> 8 Moreover, cross-border bank-to-bank flows to Eastern European countries with a higher foreign bank asset share were more stable during the crisis than flows to other emerging markets (Vogel and Winkler, 2011). And based on the sample of all syndicated loans to private borrowers from 2005 to 2009, de Haas and Van Horen (2011) find that geographic proximity, establishment as a subsidiary (rather than a branch), integration into a network of domestic co-lenders, and a history of syndicated lending to a particular country all enhanced the stability of foreign lending. 9 A large number of cross-country studies show that greater government participation in bank ownership tends to be associated with lower levels of financial development (Barth, Caprio, and Levine, 2001, 2004, La Porta et al., 2002), more politically motivated lending (Dinc, 2005, Micco, Panizza and Yañez, 2007), lower banking sector outreach (Beck, Demirguc-Kunt, and Martinez Peria, 2008), wider intermediation spreads and slower economic growth (La Porta et al., 2002),  and greater financial instability (La Porta et al., 2002; Caprio and Martínez Pería, 2002). Detailed within-country studies that are less susceptible to endogeneity concerns and are better able to identify the impact of government ownership than cross-country studies provide evidence consistent with the bulk of the crosscountry literature (see Khwaja and Mian, 2005; Cole 2009a,b; Carvalho 2010).

5
Annex 4: **Introduction to** ECOWAT2 The following section shows a printed version of analysis of water supply projects in Indonesia. If the ECOWATI--should be used, since it requires substantially less input from the user. ECOWAT2 is a Lotus 123 ros--that calculate conveniently and prints the results in a standard format.

intake, storage, and treatment; and to extend the distribuproject, would have the highest-cost water supply (includECOWAT2 requires data that describe the water efter project completion. Data input is divided into two parts: the first part refers to the "WITH" project, the second to the "WITHOUT" project alternatives. CalcuSensitivity analysis of different design altematives will tive?, and Is the present value of net benefits at least as the extension of an already existing system, the supplied quantity of water as a result of the implemented project consumers who no longer need to use other sources of program. water supply. Benefit from the incremental water use is equal to the consumers' willingness to pay, which can be determined by estimating the area under the consumers' water demand curve. When both parts of the benefits are quantified, the net benefits of the project can be calcualted by adding themr together and subtracting the cost of new water supply. This answers the first question. An affirmative answer to the second question should result components: to select a least-cost solution for raw water The following pages show what appears on the To **run** the **program.**

-Load LOTUS 123

*Type _"/"_

*Select FILE, RETRIEVE, ECOWAT2.WK1.

Comments are welcome. Please contact Laszlo 28
# **Table 2. Determinants of per capita expenditure for rural and urban areas**

|R|uralmodel|||Urbanmode|l|
|---|---|---|---|---|---|
|N|4269|||1730||
|R-sauared|0.536|||_0.550_||
|Variable<br>C|oefficient|t|Variable|Coefficient|t|
|hhsize|-0.0772|-19.5***|hhsize|-0.0785|-8.1|
|pelderly|-0.0831|-2.4**|pelderly|-0.1026|-1.6|
|pchild|-0.3353|-9.4*|pchild|-0.2368|-3.6|
|pfemale|-0.1177|-3.5*|pfemale|0.0386|0.5|
|ethnic|-0.0765|-1.9*|ethnic|0.0142|0.2|
|Iedchd_2|0.0585|3.4***|ledchd_2|0.0616|1.7|
|Iedchd_3|0.0883|4.5*|Iedchd_3|0.0338|1.3|
|ledchd_4|0.0884|3.3|**l** edchd_4|0.1368|3.2|
|ledchd_5|0.1355|4.2|Iedchd_5|0.1603|3.5|
|ledchd_6|0.2552|4.9|lIedchd_6|0.1843|3.7|
|lcdcsp_0|0.0173|1.0|Iedcsp_0|0.0344|0.8|
|ledcsp_2|0.0049|0.3|Iedcsp_2|0.0642|1.9|
|ledcsp_3|0.0132|0.6|ledcsp_3|0.0987|2.6|
|ledcsp_4|0.0107|0.3|ledcsp 4|0.1912|2.7|
|ledcsp_5|0.0921|2.3**|ledcsp_5|0.1285|3.2|
|ledcsp_6|0.1571|2.7***|ledcsp_6|0.1752|3.1|
|loccup_1|0.1414|3.5*|loccup_1|0.2312|3.0|
|loccup_2|0.1350|3.3*|loccup_2|0.0576|1.2|
|loccup_3|0.1362|3.4*|loccup_3|0.0357|0.9|
|loccup_4|-0.0163|-0.6|loccup_4|-0.0093|-0.2|
|loccup_5|0.0701|1.9*|loccup_5|0.0071|0.2|
|Ioccup_6|-0.0586|-1.7*|loccup_6|-0.1599|-2.9|
|Ihouse_1|-0.9228|-4.3*|Ihouse_1|-0.5194|-3.4|
|Ihouse_2|-0.3120|-3.6***|Ihouse_2|-0.4001|-3.8|
|htyplal|0.2958|5.7*|htyplal|0.2001|_5.4_|
|htypla2|0.1180|5.2***|htypla2|0.1403|4.6|
|electric|0.0765|2.7***|electric|-0.0026|0.0|
|Inwate_1|0.0828|1.4|Inwate_1|0.2289|5.3|
|Inwate_2|0.1157|4.4*|Inwate_2|0.0340|0.6|
|Itoile_l|0.2700|5.5*|Itoile_l|0.1311|2.2|
|Itoile_2|0.0556|2.6**|Itoile_2|0.0049|0.1|
|tv|0.2124|15.1**|tv|0.2167|5.5|
|radio|0.1009|7.0***|radio|0.1599|6.2|
|Ireg7_2|0.0314|0.6|Ireg7_2|0.0693|0.7|
|Ireg7_3|0.0485|0.8|Ireg7_3|0.0445|0.6|
|Ireg7_4|0.1373|2.2**|Ireg74|0.1460|1.9|
|Ireg7_5|0.1708|2.1**|Ireg7_5|variableomi|tted|
|Ireg7_6|0.5424|9.4*|Ireg7_6|0.4151|5.5|
|Ireg7_7|0.3011|_5.1_*|Ireg7_7|0.1895|2.1|
|cons|7.5327|108.7***|cons|7.7538|64.7|

Source: Regression analysis of 1998 Viet Nam Living Standards Survey.

Note: The dependent variable is log of per capita expenditure.

* coefficient is significant at the 10% level, ** at the 5% level, and *** at the 1% level.

14
spending in upturns has not been excessive (although may be not smoothing volatility enough either), but adjustments in down turns have been harsh as access to capital markets dried up at the very moment it was needed most, when oil prices slumped.

Section II analyzes Nigeria's macroeconomic record as an oil producer, its uses of the windfall gains, and the macroeconomic consequences of the fiscal and exchange rate policies followed in response to the vagaries of the oil market. In Section III we ask the question of why Nigeria did so poorly in terms of economic growth. Key conclusions are that fiscal and exchange rate policies added to, rather than smoothed out, the volatility of oil prices. Thus the government itself became an additional source of macroeconomic volatility. Section IV provides econometric evidence on the main drivers behind that perverse role of fiscal policy, pointing to debt overhang as a major factor explaining the strongly procyclical role of fiscal policy in Nigeria. Section V benchmarks Nigeria's macroeconomic record with managing the recent oil windfall with a peer group of 22 ORCs and Section VI concludes.

# **II The Macroeconomic Record**

Nigeria discovered oil in 1956 and began to export oil in 1958. Since the oil discoveries in the early 1970s, oil has become the dominant factor in Nigeria’s economy. Using 1970 as a benchmark, Nigeria gained an extra $390 billion in oil-related fiscal revenue over the period 1971-2005, or 4.5 times 2005 GDP, expressed in constant 2000 dollars. The sizeable oil windfall of course does present net wealth and thus additional spending room, but it has also complicated macroeconomic management and led to an extreme dependency on oil, a highly volatile source of income. The share of mining in total GDP increased substantially, representing about a half of GDP in 2005. Oil also accounts for about 90 percent of total exports and about four-fifths of total government revenues (see Figure 1).

3
Table 7: Labor hours per rubber plot

||Total hrs per<br>week per rubber<br>plot hectare|<br>Planter hrs per<br>week per rubber<br>plot hectare|<br>Spouse hrs per<br>week per rubber<br>plot hectare<br>|HH hrs per<br>week per rubber<br>plot hectare|<br>Hired labor hrs<br>per week per<br>rubber plot<br>hectare|
|---|---|---|---|---|---|
|Seedling Subsidy|1.81|1.02|0.11|-0.33|0.85|
||(2.35)|(0.74)|(0.35)|(0.53)|(1.11)|
|Couples' Training|3.28|0.74|0.82**|0.19|1.32|
||(2.70)|(0.83)|(0.41)|(0.52)|(1.63)|
|Seedling Subsidy + Couples'<br>Training (beta)|5.09**|1.76***|0.93**|-0.14|2.18|
|H0: Seedling Subsidy +<br>Couples' Training = 0 (p-val)|0.04|0.01|0.04|0.78|0.14|
|Observations|1435|1435|1435|1435|1435|
|Mean for control group|16.63|4.64|2.07|2.59|6.72|
|Mean for individual training|18.90|5.79|2.28|2.21|7.90|

Robust and clustered standard errors in parentheses. All values are winsorized at 99%. * p<0.1 ** p<0.05 *** p<0.01 Note: Household (HH) hours exclude planter and spouse work hours. Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

Appendix Table 8 examines whether this increased investment in agriculture comes at a cost in terms of non-agricultural employment. Compared to the individual training group, both the planter and the spouse in the couples’ group are less likely to engage in off-farm self-employment, though this does not translate into a significant change in non-agricultural income. The second and third columns of Table 8 show no significant changes in income from processing, livestock or poultry rearing in either of the treatment groups. The last four columns of the table report how much income is earned in the past month from off-farm work. We find no significant difference in off-farm income (self-employment or wage income) between the individual training group and the couples’ group. Interestingly, we see a significant increase in monthly self-employment income for planters in the individual training group, but this is not significant for couples’ planters. Overall, the magnitude of any changes to this monthly income is very small compared to the impact on total harvest (Table 8, first column).

1
# Cost-of-Living Differences between Urban and Rural Areas of Indonesia

|by|
|---|

# Martin Ravallion and Dominique van de Walle

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|The Theory of Spatial Cost-of-Living Comparisons|4|
|III.|Methods of Demand Analysis|8|
|IV.|Estimated Demand Parameters|12|
|V.|Spatial Cost-of-LIving Indices|14|
|VI.|Conclusions|18|
|Footn|otes|20|
|Refer|ences|23|
**The investment decline is primarily due to lower domestic investment.** Of the overall investment decline, domestic investment fell by 80 percent, while foreign investment decreased by 15 percent (Figure 4).<sup>7</sup> Adding an element of rigidity to the downturn, 40 percent of the overall decline was due to lower construction (Figure 5).<sup>8</sup>

**Figure 4. Domestic investment falls ...**

<!-- Start of picture text --> 35<br>Aquino's assassination<br>Marcos leaves<br>30<br>Coup attempts<br>Economic reforms<br>25<br>Asian crisis<br>Total Domestic Investment<br>20<br>Private Domestic Investment<br>15<br>10<br>Public Domestic Investment<br>5<br>FDI<br>0<br>19801982198419861988199019921994199619982000200220042006<br>% GDP<br><!-- End of picture text --> _Source:_ National Statistical Coordination Board

**Figure 5. ... because of lower private outlays**

<!-- Start of picture text --> Others<br><!-- End of picture text --> <!-- Start of picture text --> Fixed capital formation<br>35<br>Others<br>Construction (public)<br>30 Construction (private)<br>Durable Equipment<br>25<br>20<br>15<br>10<br>5<br>0<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br>% GDP<br><!-- End of picture text --> _Source:_ National Statistical Coordination Board, 2007

**Private investment fell despite rising growth.** The private sector found serious impediments to investing in the poor transportation network, the declining quality of education and the high cost of inputs, particularly electricity.<sup>9</sup> Between 2000 and 2004, private investment - deterred by political instability and systemic uncertainties - did not respond to higher growth (Figure 4 and Figure 5), and private sector credit fell by 40 percent as a share of GDP.

> 7 In recent years, foreign investments have grown from a small base (from 2005 to 2006, net FDI inflows have reached $2 billion, growing to 14 percent of the overall private investment), but are still too small to matter, and many investment approvals have not translated into actual flows.

> 8 There is evidence that busts in the real estate tend to be longer-lived than in other asset markets, and tend to be associated with more severe downturns in overall economic activity. For example, in industrial countries, the evidence for housing market busts suggests they have an average duration of 4-5 years compared to 2-3 years for equity market busts (World Bank, 2006a).

9 Based on the competitiveness ranking of the World Economic Forum on basic infrastructure, the Philippines placed 89th out of 102 countries surveyed.

10
**TABLE 7: Effect of Parenting and Classroom Quality on Child Outcomes (Instrumental Variables) - 18-Month Follow-Up**

|**PANEL A: Parenting Quality**||||**Dependen**|**t variable:**||||
|---|---|---|---|---|---|---|---|---|
|**Sample restricted to T2 (caregiver**<br>**training) and T4 (T2 + parenting training)**|**Malaw**|**i Developm**<br>**Tool**|**ental Asse**<br>**Score**|**ssment**|**St**|**rengths an**<br>**Questionn**|**d Difficult**<br>**aire Score**|**ies**<br>|
||**Langua**|**ge Skills**|**Fine M**<br>**Perce**<br>**Sk**|**otor /**<br>**ption**<br>**ills**|**T**<br>**Diffi**<br>**(Inv**|**otal**<br>**culties**<br>**erted)**|**Pro**|**social**|
|**Variables**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|**Second Stage**|||||||||
|Prntin Qlit Ind Midlin|0.717***|0.711***|0.488**|0.423**|0.479**|0.422**|0.617***|0.540**|
|aeg uay ex (e)|(0.226)|(0.205)|(0.194)|(0.198)|(0.187)|(0.178)|(0.222)|(0.237)|
|Ld Dd Vibl Bli||0.336***||0.344***||0.249***||0.064|
|agge epenent arae (asene)||(0.050)||(0.068)||(0.046)||(0.046)|
|Missing Lagged Dependent Variable (Baseline)||||||0.020<br>(0.171)||-0.265**<br>(0.113)|
|**First Stage**|||**Dependent**|**variable: Pa**|**renting Qu**|**ality Index**|||
||0.284***|0.282***|0.284***|0.278***|0.274***|0.279***|0.274***|0.268***|
|T4|(0.058)|(0.058)|(0.058)|(0.058)|(0.058)|(0.059)|(0.058)|(0.058)|
|Lagged Dependent Variable (Baseline)||0.170***<br>(0.039)||0.217***<br>(0.048)||0.149***<br>(0.037)||0.079*<br>(0.042)|
|Missing Lagged Dependent Variable (Baseline)||||||0.312<br>(0.261)||0.288<br>(0.266)|
|F-test for Significance of T4(F-stat)|23.75|23.61|23.75|22.67|22.10|22.03|22.10|20.99|
|District-bin Fixed Effects?|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Lagged Dependent Variable and Age<br>Dummies?|No|Yes|No|Yes|No|Yes|No|Yes|
|Number of observations|988|988|988|988|996|996|996|996|

49
7 _Table 1: Annual Average Number of inpatient and Outpatient Visits in Provinces by Quintiles Per Capita Income_

|_Quintiles of Per_<br>_Capita Income_|_1980_||_199_|_3_|
|---|---|---|---|---|
|_in Provinces_|_Inpatient_|_Outpati_|_ent Inpatie_|_nt Outpatient_|
|Poorest<br>I|2.9|3.7|1.8|2.8|
|II|2.6|3.5|1.7|2.7|
|III|2.7|3.4|1.9|2.4|
|IV|2.2|2.8|2.1|2.5|
|Richest<br>V|1.8|2.5|1.6|2.2|

# _Source:_ Staff Estimates from MOPH data.

Indeed, according to the 1993 National Health Survey (NHS), utilization varies positively with household income per capita. Province-wide data indicate greater under-use of service in poorer provinces is associated with economic reasons, and vice versa. Of total households, 58.8 percent in rural areas and 39.8 percent in urban areas do not use public health facilities due to economic difficulties. Another factor is the deterioration in quality of public health services due to the reduced public spending since the mid-1980s. The emergence of private sector and liberalization of pharnaceutical sales permitted users, particularly the rich, to move away from public sector to seek costly and perhaps better quality private health care providers. This is partly substantiated by the 1993 NHS. Of total households 11 percent in urban areas and 13 percent in rural areas did not seek public health provider due to poor quality, and about 7 percent used private health clinics but mostly in big cities and urban areas. A third factor may be the declining agespecific morbidity rates, requiring fewer visits to health facilities. But, age and gender-specific mortality and morbidity rates have shifted from infectious to more NCD and injuries, and not declined dramatically to warrant such marked reduction in the utilization of health services. This overall decline in public inpatient and outpatient utilization in poorer provinces indicates that the redistributive impact of public health expenditures is increasingly becoming less favorable.

_Disparity in the Health Status:_ Table 2 displays the percentage change in two health outcomes and coverage of three public health services by provinces, ranked by per capita income from poor to rich. Overall, for the period of 1983-93, disparity in health outcomes--both infant and maternal mortality rates-- has widened, as mortality declined at a faster rate in the wealthiest provinces like Zhejiang, Liaoning, Guangdong (quintile V) than in poorest provinces like Anhui, Henan and Sichuwan (quintile I). Likewise, except richest provinces in quintile V, public health services like coverage of safewater and sanitation generally varied directly with income, that is, the richer the province, the wider the coverage. Thus, except for three shots of diphtheriapertusis-tetanus (DPT3) immunization, the progressivity in the access to two basic public health services has diminished over time.
**<u>Table A3. Capital: Selected Responses</u>**

||**ALL**|**ADV**|**EMDEs**|
|---|---|---|---|
|**A. Overview**|**% Yes**|**% Yes**|**% Yes**|
|Do you require banks to perform an internal assessment of their capital adequacy<br>against their economic capital?|60|89|49|
|Do you review internal assessments performed by banks?|95|100|91|
|Does your agency have the legal authority to require additional capital that is over-<br>and-above the minimum required capital for individual banks if deemed necessary?|88|100|83|
|Is Common equity allowed in regulatory capital?|84|92|81|
|Is Tier 1 allowed in regulatory capital?|94|100|92|
|Is Tier 2 allowed in regulatory capital?|88|100|83|
|Is Tier 3 allowed in regulatory capital?|36|69|23|
|**B.**<br>**Minimum Regulatory Capital Ratio Vs. Actual Capital Ratio in Banking**<br>**System **|**Median**|**Median**|**Median**|
|What was the minimum required risk-based regulatory capital ratio as of end of:<br>2010 (%)<br>2005(%)|8.3<br>(8.0)|8.0<br>(8.0)|10.0<br>(8.0)|
|What was the actual risk based capital ratio of the banking system as of end of:<br>2010 (%)<br>2005(%)|16.2<br>(14.3)|15.4<br>(12.3)|17.1<br>(14.7)|
|What was the actual Tier 1 capital ratio of the banking system as of end of:<br>2010 (%)<br>2005(%)|14.1<br>(12.5)|12.7<br>(9.6)|14.6<br>(13.4)|
|**C. Regulatory Capital Adequacy Regimes**|**% Yes**|**% Yes**|**% Yes**|
|Leverage Ratio|15<br>(19)|14<br>(9)|15<br>(23)|
|Basel I|47|14|59|
|Basel II|57|95|42|
|Simplified standardized approach (SSA)|15|16|14|
|Standardized approach (SA)|80<br>(96)|95<br>(100)|70<br>(94)|
|Foundation internal ratings-based approach (F-IRB)|55<br>(55)|92<br>(86)|30<br>(40)|
|Advanced internal ratings-based approach (A-IRB)|55<br>(49)|95<br>(83)|28<br>(33)|
|Basel II: Will Implement by 2014|17|5|21|
|Percent of Countries Implementing Basel II by 2014|73|100|62|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey, which reflects values as of 2010. Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey, which captures data as of 2005. ADV: advanced economies, EMDEs: emerging and developing economies (Table 1 provides the complete list of countries) 29