rafmacalaba/datause-encoder-data huggingface dataset:
When we measure the impact of various types of decentralization on preference costs (table 11), we find decentralization of administration has the strongest effect. The second-order effect for decentralized administrative costs is positive and significant at an 85 percent level of confidence, while the third order effect is negative but significant only at a 70 percent level of confidence. These results indicate a mildly convex relationship between preference costs and decentralization of administration, and that the optimal level is less than 100 percent but more than 50 percent decentralization (see figure 13). The theoretical assumption on decreasing returns to decentralization gain empirical proof from case of Korea. These results provide further evidence from the perspective of road users, that there is a need to keep some control in the hands of the central government, with respect to administering road provision, as well as setting standards for road conditions in maintenance and construction.

**Table 11: Impact of Decentralization on Preference Costs--Case of Korea**

|Variable|Construction|Maintenance|Administration|Total Effect|
|---|---|---|---|---|
|year|4**.**22<br>.6 ...||||
|per capita GNP|-0.15<br>(-1.18)|-0.20<br>(-1.44)**|-0.23<br>(-1.32)**|-0.13<br>(-1.03)|
|fiscal|-0.02|0.10||_-0.05_|
|decentralization'|(-0.21)|(0.48)|_|(-0.46)|
|fiscal|0.0006|-0.002|0.004|0.002|
|decentralization<sup>2</sup>|(0.22)|(-0.30)|(1.48)*|(0.48)|
|fiscal|-8.76e-06|8.23e-06|-0.00002|-0.00002|
|decentralization<sup>3</sup>|(-0.37)|(0.17)|(-1.08)|(-0.62)|
|Constant|(59_)|S6<br>~<br>(&90)|8453.7<br>(5.31)|_6 f_|
|rho|9|(L0.92<br>(22.15)|<br>~(63.18)||
|R-square|0.75|0.79|0.75|0.76|
|Observations|24|24|24|24|

Fiscal decentralization measured separately as the percentage local govemment contribution to construction, maintenance, administration, and road provision in general in the first, second and third order respectively.

Shaded portions significant at **90%** level of confidence.

* significant at 85% level of confidence.

** significant at the 80% level of confidence.

We also looked at the simultaneous impact (model 3) of decentralization on both resource and preference costs (the results are displayed in table 12). All types of decentralization had no impact on preference costs, with the exception of decentralized administration. Road conditions improve with increasing degrees of decentralization; note that the first order coefficient for decentralized administration is negative and 29
# **7. REFERENCES**

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Babatunde, R. O. and Qaim, M (2010). “Impact of off-farm income on food security and nutrition in Nigeria”. _Food Policy, Vol. 35_ (4), 303-311.

Bardasi, E. and Abay ,G. (2007). “Gender and Entrepreneurship in Ethiopia.” Background paper to the Ethiopia Investment Climate Assessment. Washington, DC: The World Bank.

Bardasi, E. and Sabarwal, S. (2009). “Gender, Access to Finance, and Entrepreneurial Performance in Africa.” World Bank Mimeo.

Blanchflower, D.G., Levine, P. and Zimmerman, D. (2003). “Discrimination in the small business credit market.” _Review of Economics and Statistics, Vol. 85_ (4), 930-943.

Bruhm, M. (2009). “Female-Owned Firms in Latin America: Characteristics, Performance, and Obstacles to Growth.” World Bank Policy Research Working Paper No. 5122 Brush, C. G. (1992). “Research on Women Business Owners: Past Trends, a New Perspective and Future Directions.” _Entrepreneurship: Theory and Practice 16._ Buvinić, M. and Gupta, G.R. (1997). “Female-Headed Households and Female-Maintained Families: Are They Worth Targeting to Reduce Poverty in Developing Countries?” _Economic Development and Cultural Change, Vol. 45_ (2), 259-280.

24
8 Jaggers, 2003), more than 40% of the world’s countries with more than 50% of the world’s population can now be counted as democratic (Gleditsch, 2007; Gleditsch & Ward, 2006). Countries characterized by Freedom House (2007) as ‘free’ have nearly half (47%) of world population and 77% live in countries that are either free or partly free<sup>12</sup> . The share of countries with electoral democracy is 83%. While the Polity project and Freedom House differ in their evaluation of some countries, they agree that the global level of democratization has never been higher. The same conclusion can be drawn from the Vanhanen’s Polyarchy scale.<sup>13</sup> The movement towards greater democracy has not been linear or even monotonic. It can be debated whether or not Huntington (1991) is correct in describing three waves of democratization; some may discern four or even five waves. In any case, it seems reasonable to say that there are three waves in the twentieth century, the first peaking in the early 1920s and the second in the late 1950s.

The notion of waves implies that there have also been democratic reversals. The first was associated with the rise of the two totalitarian movements in Europe in the 1920s. The second was in part a product of the many failed democratizations in the Third World after independence, but also with the continued spread of Marxist, personalist, and military regimes in many areas of the world. Zakaria (1997) and others have pointed to the fragile nature of many of the new democracies in the third wave, labeling this ‘the rise of illiberal democracy’. Freedom House (2007) sees the expansion of freedom as stagnating. While the third wave of democratization may appear to have leveled out, warnings of the imminent coming of the third reversal (Diamond, 1996) have so far proved to be premature.

Liberals see democratization in a mutually reinforcing relationship with other liberal values, such as economic interdependence and international law and organization. All of these, independently and together, strengthen international and domestic peace, in the liberal view (Russett & Oneal, 2001; Schneider, Barbieri & Gleditsch, 2003).

> 12 For both these indicators, the percentage of countries is the same as the percentage of world population.

> 13 See Vanhanen (2000), data available at <u>www.prio.no/jpr/datasets.</u>
# **Figure 3: In some developing regions, per capita incomes will begin to converge with those in high-income countries**

Index: high-income countries = 100.

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2005 2010 2015 2020 2025 2030<br>CHN XEA SAS ECA MNA SSA ARG BRA CHL COL MEX PER CAC XLC<br><!-- End of picture text --> Source: World Bank simulations using the Linkage model. Notes: Ratio of PPP-adjusted per capita incomes relative to high-income average, PPP is fixed at 2004 level.

# **3.2 Structural change**

As average incomes of developing countries converge to OECD levels, richer consumers in developing countries will demand more services (income elasticity of demand for services is greater than one). At the same time, a slower productivity growth in services relative to manufacturing by rising costs and prices of services more rapidly than those of manufacturing, will contribute to the growing value share of services in total output. The increase in prices and demand for services in low and middle income countries, combined with the sustained demand for health and public services by the aging OECD populations, is likely to translate into a pronounced shift of production towards service activities at a global level (see Figure 4). The share of services will increase by 5 percentage points in China from 33% to 38%, while in LAC, it will increase between 3.5 percentage points and 9.6 percentage points in Central America and the Caribbean (CAC) over 2005-2030.

12
during this period while TFP growth explains about half of that performance. Additionally, demographics also contributed positively as the share of working age population increased.

**Figure 14. Growth decomposition of high performers during the Africa Rising period, 2000-14**

<!-- Start of picture text -->
-1<br>-2<br>Physical Capital  Human Capital  Demographics  TFP  Output per capita<br>SSA Tanzania Ghana Mauritius Zambia Angola Zimbabwe Liberia Nigeria Lesotho Mozambique Rwanda Mali Ethiopia Sierra Leone<br><!-- End of picture text -->

_Source:_ Penn World Tables 10.01 (Feenstra, Inklaar, and Timmer 2015). _Note:_ SSA=Sub-Saharan Africa.

The contribution of total factor productivity (TFP) growth is positive and large for all high-growth performers in Sub-Saharan Africa. TFP contributes over half of growth in nine of the 14 fastest growing economies in Figure 14 and is positive in the remaining five. It should be noted though that the high share of TFP contribution, the residual not explained by physical and human capital growth, in resource abundant countries might result from an intensive use of natural capital. Of the six resource abundant countries, two are oil abundant (Angola and Nigeria) while the remaining four are abundant in metals and minerals (Liberia, Mali, Sierra Leone, and Uganda). More specifically, accounting for the accumulation of natural capital reduces the contribution of TFP to growth per worker by almost 1 percentage point per year (Calderon 2021). On the other hand, factor accumulation accounts for more than half of growth per capita in five of the 14 fastest growing countries in the region, namely, Ethiopia, Mauritius, Mozambique, Rwanda, and Tanzania.

# **4. Growth Miracles: What Could We Learn from Successful Growth Stories?**

This section identifies “growth miracles”— as defined by episodes of sustained growth for a prolonged period—around the globe from 1960 to 2019. Once identified, we compare the episodes in Sub-Saharan Africa with those of other world regions over time and by sector, before synthesizing lessons from the region’s best performers. We find that “growth miracles” in the Sub-Saharan African region performs as well as those in other regions; however, they are in short supply. This implies that the successful growth experiences in the region are not learnt by the rest of a continent that still struggles with low and volatile growth. While growth performance across these “miracle episodes” are similarly impressive, we will explore their differences and commonalities.
# **Data Appendix**

## **Appendix 1. Description of Outcomes and Other Variables**

### **Outcomes from individual-level administrative data**

_Learning outcomes._ We use individual-level standardized test scores on the reading and quantitative components of the SABER PRO exam taken by the cohort of students who that took the test in 20112 as our learning outcomes. As in Domingue et al. (2017), we focus on reading and quantitative reasoning scores because these general skills may be more directly related to SCP value added.

_Educational attainment._ Using data from SPADIES, we create an indicator for graduation as our measure of educational attainment. Every semester, we track cohorts of students who entered higher education between 2007-1 and 2009-2 and define graduation as an indicator that takes the value of 1 if the student graduated within six years after entering the program, and 0 if the student dropped out or has not graduated within six years.

_Labor market outcomes:_

- <u>Formal employment: Since we only observe the status of individuals who make social security</u> contributions (paid employees or self-employed entrepreneurs), we define formal employment as a binary variable that equals one if, having graduated between 2010 and 2012, the individual was employed or self-employed during any period between 2010 and 2013. Note that individuals who do not make social security contributions could either be unemployed or employed in the informal sector.

- <u>Annual wage: To construct this outcome, we use only the labor income reported during the</u> first year of the individual’s employment between 2010 and 2013 to avoid salary changes associated with experience. Moreover, we use only the sample of paid employees because the salaries of self-employed entrepreneurs are recorded as zero in OLE. In sum, the wage measure is the total annual labor income during the first year of formal employment after graduation. Annual wages are expressed in dollars; we conducted a purchasing power parity (PPP) adjustment of wages using the 2019 PPP conversion factor.

### **Individual characteristics**

_Academic readiness for higher education._ To proxy for a student’s ability before enrolling in an SCP, we use the individual’s standardized SABER 11 score. This is a mandatory that test evaluates 49
purposes, a measure of financial dollarization<sup>6</sup> from Levy-Yeyati (2006) is also used, namely the ratio of deposit dollarization.

Our proxy for fiscal discipline is the _central government balance as percentage of GDP_ and the data is obtained from WDI and the IMF’s World Economic Outlook (WEO). Savings is measured as the ratio of gross domestic savings to GDP in local currency units taken from WDI whereas _private consumption_ is the ratio of household final consumption expenditures to GDP in local currency units from WDI. Finally, _export growth_ is annual percentage growth rate of exports of goods and services, _gross domestic investment_ is calculated as the ratio of gross capital formation to GDP in local currency units, and _inflation_ is the percentage change in consumer price index. All the variables mentioned above are constructed using data from WDI.

# **3. Econometric Methodology**

This section describes the econometric techniques we use to examine whether policymakers are able to sustain real exchange rate misalignments –and, more specifically, undervaluations, through policy actions. As a result, we empirically model the likelihood of sustaining a RER undervaluation as well as the magnitude of this undervaluation using limited dependent variable and censored variable techniques. In particular, we examine the impact of active economic policies on the likelihood (or incidence) of real exchange rate undervaluations using the _Probit_ analysis while the _Tobit analysis_ is used to assess the effects of economic policy on the size or magnitude of RER undervaluations.

## **3.1. The** **_Probit_ Model**

The _Probit_ model is a model of binary choice where the dependent variable takes the value of one whenever there is a sharp real undervaluation of the currency and zero otherwise. Suppose that _X_ is a binary variable that can only take two possible outcomes, zero ( _0_ ) and one ( _1_ ). We also have a vector _z_ of variables that is assumed to have an effect on the outcome _X_ . Hence, we assume that our probabilistic model ( _Probit_ ) takes the following form: Pr _ob_  _X_  1   _F_  _z_ , 

> 6 Dollarization data by Levy-Yeyati (2006) does not have enough coverage. Therefore, we use this data only for robustness purposes.

12
# Contents

|I.|Introduction ........................................................................................................................................... 3|
|---|---|
||Costs of Disruption ................................................................................................................................ 3|
||The Resilience Concept .......................................................................................................................... 5|
||Resilience in Infrastructure Decision-Making ........................................................................................ 6|
|II.|Background ............................................................................................................................................ 7|
||Malaita Road Network ........................................................................................................................... 8|
||Operationalizing the Resilience Concept ............................................................................................. 11|
||Selecting Loss of Functionality (LoF) Indicators for the East Road Case .............................................. 11|
|III.|Estimating Resilience Indicators for the East Road .............................................................................. 13|
||A. Road Upgradation Options .............................................................................................................. 14|
||B. Climate-events likely in Malaita ....................................................................................................... 16|
||C. Predicting Road Disruptions ............................................................................................................ 18|
||D. Estimating Functionalities ............................................................................................................... 19|
||E. Estimating Time for Recovery .......................................................................................................... 23|
|IV.|Estimating Accumulated Loss of Functionality (ALF) ........................................................................... 24|
|V.|Estimating Cost for different Road Designs ......................................................................................... 26|
|VI.|Conclusion and Discussion ................................................................................................................... 28|
|VII.|Technical Annexes ............................................................................................................................... 31|
||Annex 1. Rain Event Repair Costs ........................................................................................................ 31|
||Annex 2. Methodology for Calculating Affected Population ................................................................ 32|

2
a two-sided test of the null hypothesis of no impact of the program can be rejected at a 5 percent significance level if the mean percentile rank statistic, �̅, falls below 0.377 or above 0.623.

Dube and Zipperer (2015) suggest several advantages in conducting pooled inference in this manner. Of these, the most relevant are that 1) tests involving the percentile rank statistic are a natural generalization of the placebo tests conducted in SCM studies with a single treated unit, 2) the mean of the percentile ranks has a known distribution under the sharp null hypothesis (allowing for exact inference), and 3) the use of the percentile rank statistic �̅ diminishes the impact of outliers, relative to tests that involve the point estimates �<sup>�</sup> �. A limitation of the test is that it is an evaluation of the sharp null hypothesis that the effect of the IHC program is zero for _all_ treated firms, rather than a test that the mean effect of the IHC program across treated firms is zero. This limitation can be addressed by testing for heterogeneous treatment effects, as described in Section 3.4.

# **3.3 Calculating Hodges-Lehmann Statistics Using Inverted Rank Statistics**

Comparisons of estimated mean treatment effects (�̅) and median treatment effects of the IHC program on a given outcome in Section 5 will demonstrate that the estimated mean treatment effect is affected by outliers. While p-values associated with the mean of the percentile rank statistic can provide information about the statistical significance of the estimate, they do not offer much guidance about the range of plausible treatment effects. Dube and Zipperer (2015) propose the use of inverted percentile rank statistics as formulated by Hodges Jr and Lehmann (1963) for characterizing the 95% confidence intervals for the mean treatment effect. The confidence interval defines the set of critical values  for which the estimated mean treatment effect, �̅, cannot be rejected when those values of  are chosen as the null hypothesis.

Formally the 95% Hodges-Lehmann confidence interval is calculated by inverting a meanadjusted percentile rank statistic at the desired critical values using the empirical distribution of treatment effects for the placebo firms. Following Dube and Zipperer we define the mean-adjusted percentile rank statistic as: 19
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