rafmacalaba/datause-encoder-data huggingface dataset:
Challenge 2 is related to challenge 1 in that relaxing the infrastructure constraint will raise the return to private capital. In addition to macroeconomic stabilization, a crucial factor for keeping interest rates and the cost of capital low is political stability, which will also lengthen private investment horizons and thus the threshold rate of return for investment projects. We conclude by discussing this _political stability_ challenge, which has superseded the economic policy challenge.

Notwithstanding significant reform spurred by a reduction in aid by donors on governance grounds and as an attempt to bolster credibility after the costly and disruptive Goldenberg scandal broke in 1992, tangible results took a decade to emerge in the form of sharply improved government debt dynamics, faster growth and lower interest rates. We argue that the peaceful presidential elections and smooth transfer of power in 2003 was a threshold event enabling positive economic outcomes. While the latter coincided with falling global interest rates and a pick-up in growth throughout the developing world, we base our evidence on the vastly improved debt dynamics of the government and surveys of private investors, who pointed to rising political stability as a major factor lengthening their business planning horizons. Besides, interest rates fell in spite of a shrinking primary surplus. Apart from lowered country risk, this has been facilitated by a rise in the revenue-to-GDP ratio after 2003 even though marginal tax rates were cut, attesting to better governance and tax administration.

Against this background, the new round of political instability unleashed by the flawed December 2007 presidential elections presents a formidable challenge. Not only is there need to achieve a speedy resolution, there is a fundamental need to establish political stability for an extended period of time—considerably longer than that achieved over the 5 years 2003-2007. This challenge promises to be at least as difficult as that of policy and institutional reform.

29
# Figure m.6 Malaysia-Policy responses and incentives to borrow

Monetary policy: components of reserve money growth Fiscal policy: fiscal impulse and demand pressures <!-- Start of picture text --> hisalaysia<br>percent Malaysia percent<br>1t0 -<br>150<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1999 1990 1991 1992 1993 1994 1 999 1999 1997<br>_ contribution ot domestic credit to reserve money growth - Excess demand<br>1 - contribution ot net reserve money growth toreign assets to reserve money growth -Fiscal (percent of GOP)impulse<br><!-- End of picture text --> Differentials between domestic deposit rates and Nominal exchange rate and variability of US **LIBOR** unpredictable component of nominal exchange rate movements.

<!-- Start of picture text --> P42aIysiUt<br>percent pe.d<br>percent 10 - nom.l xchnger - 112<br>10 Malaysia 9 (nsht axis) -10<br>6 0-~~~~~~~~~~~~~~~7< / 6 ~~~~~- - *oe<br>9- -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~104<br>9 - _ ' | 4- 2-~~~~~~~~~~~~~7 -go7 -192 102<br>3- ~~~~~~~~~~~~~~~-99<br>4 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~2- 9<br>4 ~~~~~~~~~~~~~~~~~-92<br>10- -9<br>2 I~~~~~~~~~~~~~~~~~-1<br>e vibity ofhen<br>EE -~~~~~~~~~~~~5- -s0<br>1 999 1 990 1 992 1 994 1996 1790 1999<br>- domestic deposit rates<br>MEM ditferential between domestic deposit rates and US LIBOR<br>ladjusted for en post exchange rate movement.)<br><!-- End of picture text -->

The variability of the unpredictable component is given by the estimation of an ARCH model of the nominal exchange rate on a constant and a time trend. Nominal exchange rate index 1990=100.

29
distributional effect of higher poverty (Proposition 2). Which effect dominates is an empirical question.

# **4. Data and descriptive statistics**

In keeping with the bulk of the literature, the country is the unit of observation.<sup>15</sup> However, unlike past data sets in the literature on growth empirics, this one is firmly anchored to the household surveys, in keeping with the focus on the role played by poverty and inequality, which is measured from surveys. By calculating the poverty and inequality statistics directly from the primary data, at least some of the comparability problems found in existing data compilations from secondary sources can be eliminated. However, there is no choice but to use household consumption or income, rather than the theoretically preferable concept of wealth.

I found almost 100 developing and transition countries with at least two suitable household surveys since about 1980. Virtually all of the surveys are nationally representative.<sup>16</sup> For the bulk of the analysis I restrict the sample to the 90 countries in which the earliest available survey finds that at least some households lived below the average poverty line for developing countries (described below).<sup>17</sup> This happens mechanically given that log transformations are used. However, it also has the defensible effect of dropping a number of the countries of Eastern Europe and Central Asia (EECA) (including the former Soviet Union); indeed, all of the countries with an initial poverty rate (by developing country standards) of zero are in EECA. As is well known, these countries started their transitions from socialist command economies to market economies with very low poverty rates, but poverty measures then rose sharply in the transition.<sup>18</sup> The earliest available surveys pick up these low poverty rates, with a number of countries having no sampled household living below the poverty lines typical of developing countries. With the subsequent rise in poverty incidence, this looks like ―convergence,‖ but it has little or nothing to do with neoclassical growth processes—rather it is a ―policy convergence‖

> 15 It is known that aggregation can hide the true relationships between the initial distribution and growth, given the nonlinearities involved at the micro level (Ravallion, 1998); identifying the deeper structural relationships would require micro data, and even then the identification problems can be formidable.

> 16 The only exception was that urban surveys were also used (for both the first and last survey) for Uruguay where over 90% of the population lives in urban areas. Results were robust to dropping these urban surveys.

> 17 The data set was constructed from _<u>PovcalNet</u>_ in December 2008.

> 18 Prior to the global financial crisis there were signs that poverty measures were finally falling in the region, since the later 1990s; see Chen and Ravallion (2010).

9
||||**X, * (workers)**|||
|---|---|---|---|---|---|
|Pakistan|||**X, X**||Hamid,Imaizumi and Blom(2010)|
|India|||**X, X**||Blom and Saeki(2011)|
|GAP||||||
|Western Europe||||**X**|Manpower(2012)|
|UK||*||**X**|Learningand Skills Council(2008)|
|UK||*,*||**X**|UKCES(2012)|
|UK||*|**X**||CBI(2012)|
|LAC||*|**X**||Bassi et al.(2012)|
|LAC||**X, ***|||IFC(2010)|
|MENA|||**X**||IFC(2010)|
|Philippines|||**X, ***||diGropello(2010)|
|Poland|||*|**X**|Arnhold et al.(2011)|
|Economysize||||||
|Level||||||
|Tonga|||**X, X**||Tonga Chamber of Commerce and<br>Industry (2010)|
|Solomon Islands||*|**X**||Close(2012)|
|St. Kitts|||**X, X**||cited in Blom and Hobbs(2008)|
|Pakistan|||**X, X**||Hamid,Imaizumi and Blom(2010)|
|Indonesia|**X (workers)**|**X (managers),**<br>*(workers)|*||diGropello (2011)|
|Russia||**X, X (managers)**|**X (managers)**<br>**X, X, X(non-managers)**|**X (non-**<br>**managers)**|Vasiliev et al. (2013)|
|US.||**X**|**X**||Zemsky (1997)|
|Gaps||||||
|Tonga|||**X,***||Tonga Chamber of Commerce and<br>Industry (2010)|
|Russia||**X, X (managers)**|**X (managers)**<br>**X, X, X(non-managers)**|**X (non-**<br>**managers)**|Vasiliev et al. (2013)|
|Solomon Islands||**X**,*|*||Close(2012)|
|Indonesia||**X**||*|diGropello(2011)|
|Manufacturingv. serv|ice industry|||||

37
<!-- Start of picture text -->
1.3<br>= 1241.18<br>i >.<br>So<br>a 11<br>= 1.10<br>Z 1.0 ------------- Qi KES one<br>Ss 60.9<br>3 ‘<br>245°.S.-\N 0.69<br>x—— 6-10 YoE w= 11-20 YoE x<br>=a06 ><br>— 21-30 YoE = +» =31+YoE 0.61<br>1993 1997 2001 2005 2009 2013<br><!-- End of picture text -->
evidence of consistently positive (or negative) coecients at other times.<sup>27</sup> However, there are some marginally signicant lags at t = 4 (a year before the transfer was paid) and t = 1 (the quarter after the transfer was paid) using the labor income specication and t = 1 (one quarter lead, GDP specication), and some other marginally insignicant leads and lags at the 10% level. The negative rst lead in the GDP specication seems to be due to the overlap of the placebo withdrawal of stimulus in 2008Q2 and the eect of the actual stimulus. If I run the same specication and omit 2008Q2 from the sample (when most of the transfers were actually paid) but keep 2008Q1 and the other quarters, the coecient on the lead halves, and the p-value increases to 0.4.

The other marginally signicant coecients are to be expected. Under a random allocation with no true eect, the _≈_ 40 placebo regressions in Figure 2 would produce two false positives on average when testing at the 5% level. Moreover, the chance of no false positives is also very low at around 12% ( = 0 _._ 95<sup>42</sup> under random allocation).

Having now established the robustness of the main results, I now consider two extensions involving heterogeneity and dynamics.

3.5. Extension 1: Heterogeneity by Quarter and Transfer Policy. So far, I have presented regression results that restrict the temporary transfer multiplier to be identical across payment and withdrawal quarters and across policies. In this subsection I relax those assumptions, with results presented in Table 2 (using the benchmark specication).<sup>28</sup> I rst allow for heterogeneity by payment or withdrawal quarter, and then by transfer component, and discuss the results in the context of the large literature on the MPC of one-o transfer payments.

Table 2 Column A relaxes the symmetry restriction in Equation 2, by allowing multipliers to vary across payment and withdrawal quarters. Despite substantial heterogeneity in estimated coecients, I fail to reject the restriction

> 27Both stimulus payments were legislated a quarter in advance, but there is little robust evidence of signicant negative anticipation eects. In any case, Ricardian households who respond to news also save temporary transfers.

> 28Results using the parsimonious specication are shown in Appendix Table 4 and are generally similar.

25
There is every reason to suppose that per capita (in the present framework, per household) benefits will grow as a result of changes in the rest of the economic environment, and good ones to suppose that an all-weather road will enable those living in its catchment area to exploit these opportunities more fully. As agricultural productivity increases, so will the volumes of goods traded within and across the catchment’s boundaries, as will the numbers of trips taken by individuals, for the purposes of business, work, education, health and pleasure. As their incomes rise, individuals will, moreover, place higher values on education and health. In the light of these considerations, it is plausible that providing a village with an all-weather road has a continuing effect on the growth rate of incomes rather than just on their levels, where the latter correspond to _B_ 1<sup>+(</sup><sup>_ρ,_0).Howbigisthisgrowth-effectlikelytobe?Inacarefulstudyofapanelof</sup> thousands of households drawn from 240 villages spread over 15 of the biggest states, Foster and Rosenzweig (2004) found that per capita incomes rose by 70 percent over the period 1982-1999, implying an average annual growth rate of 3.1 percent. Much of this growth stemmed from increased earnings from non-agricultural activities, for which transport connections are important. In the coming decades, this overall pace is likely to accelerate somewhat, as the economy becomes better integrated. This does not imply, of course, that the growth-effect that concerns us here is at least 3.1 percent a year; for what we are seeking is the size of an inter-action effect between changes in the rest of the economy and the provision of a road to the villages in a particular catchment area. Given the prospect of faster growth in the future than during the period 1982-1999 and the enabling effect of all-weather roads, I hazard the guess that _gb_ will average 2 percent a year over the next 30 years.

With this preliminary, it is instructive to return to the actual examples in Section 4.1. The estimated value of _B_ 1<sup>+(</sup><sup>_ρ_= 0</sup><sup>_._10</sup><sup>_,_0)fortheaffluentvillageinHimachalPradeshis</sup> Rs 3.28 million a year. A conservative estimate of the aggregate commercial benefits is Rs 2.05 million, leaving Rs.1.23 million to be found in other forms. As noted earlier, the villagers had rather good access to education and health facilities even before the road was completed, so help from those quarters may well be limited. Data of the kind provided by the Orissa Survey are lacking, moreover, which adds to the uncertainties in the calculations that follow. There are about 2,000 persons in some 350 households, with a school-age population of, say, 500. In the light of the findings for Orissa, the annual savings in travel-time and reductions in days lost due to bad weather are unlikely to exceed 5 days each per child. Suppose the daily wage rate for juveniles in counting. Since the discount rate also affects the present value of costs, it is essential to modify _ρ_ ; but the per capita benefits may not be scaled up. In what follows, we do neither.

19
absence of the reform. Bruhn (2008 and 2011) performs a number of checks suggesting that this assumption holds. Both the levels and the pre-reforms changes in outcomes variables do not display systematic statistical differences across municipalities in the sample. In line with Bruhn (2008 and 2011), the sample used in this paper here only includes the 34 municipalities that adopted SARE by December 2004<sup>8</sup> . Municipalities that adopted the reform later or that have not yet adopted tend to be less comparable to the ones that adopted the reform early.

In order to study the differential effect of SARE on informal business owners in the wage worker and formal business owner species, I make use of the panel structure of the ENE. For the analysis, I only keep the individuals who report being informal business owners when I first observe them in the pre-reform period. The species classification exercise in Section 2 assigns each of these individuals to a species based on the data from their first quarter of observation. I then drop the first quarter of observation for each person and use the remaining data in the regression outlined above, running separate regressions for the wage worker and formal business owner species<sup>9</sup> . The outcome variables (occupation dummies) in these regressions can be interpreted as occupational transition probabilities. That this, they represent the average fraction of informal business owners that is employed in each occupation during the following quarters<sup>10</sup> Table 4 displays the probabilities of transitioning into different occupations in the prereform period, indicating a high degree of mobility in the informal sector<sup>11</sup> . Only 55 (44) percent of informal business owners in the formal business owner (wage worker) species remain informal business owners during the following quarters. About 12 percent of individuals in the formal business owner species switch to being formal business owners. This fraction is smaller

> 8 According to COFEMER’s website, by November 2011, 191 municipalities had adopted SARE (http://www.cofemer.gob.mx/contenido.aspx?contenido=122).

> 9 Bruhn (2008 and 2010) also controls for personal background characteristics in the regressions. I do not include these variables here since they were already used to separate the sample into different species.

> 10 Some industries were not eligible for SARE. In this paper, I only study the impact of SARE on informal business owners in eligible industries since individuals in non-eligible industries are not able to register their business through SARE. Informal business owners in eligible industries make up 96.5 percent of informal business owners in the ENE.

> 11 High mobility across occupational sectors has also been observed in earlier work with the Mexican Labor Market Survey (Bosch and Maloney, 2006, and Woodruff, 2007). The labor market is similarly dynamic in Brazil (Bosch and Maloney, 2010).

9
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE concession arrangements in East Africa, the first for TRC and the second for the for Kenya-Uganda railway. The concession arrangements have helped to boost operational efficiency for TRC in particular, and its performance is now substantially better than the region’s major publicly owned railways based on measures of labor and rolling stock productivity (table 2.4).

**Table 2.4 . Performance of East African railways, 2005**

||Labor<br>productivity|Carriage<br>productivity|Locomotive<br>productivity|Wagon<br>productivity|Freight yield|Passenger<br>yield|
|---|---|---|---|---|---|---|
|Djibouti, CDE|||||12.5|3.1|
|Ethiopia/Djibouti, CDE|71|3,037|11|156|||
|Kenya, KRC (RVRC)|203|1,159|24|218|3.8|0.6|
|Sudan, Sudan|77|329|9|225|||
|Tanzania, TRC|259|3,264|25|583|4|1.6|
|Tanzania, TAZARA|352|2,770|13|551|3|1.1|
|Uganda, URC (RVRC)|156||9|144|||
|Rail concessions|350|2,945|23|491|5|2|

Legend: Labor productivity = ̳000s traffic units per employee; Locomotive productivity = millions of traffic units per locomotive; carriage productivity = ̳000s passenger-kilometers per carriage; wagon productivity = ̳000s net tonne-kilometers per wagon. Source: AICD railways database.

According to the 2010 LPI, the quality of East Africa’s rail infrastructure is remarkably low compared with that of the other parts of Africa. This highlights the need for facilitation services to further develop and promote the use of railways (World Bank 2010b).

# Ports

East African ports (figure 2.7) substantially increased their container and general cargo traffic between 1995 and 2005. The average annual rate of growth of container traffic and general cargo traffic through East African ports is 10.7 percent and 10.8 percent per year, respectively (table 2.5). Though not as high as the growth of container traffic in West Africa or of general cargo traffic in southern Africa, this expansion is nonetheless substantial. The overall growth in traffic was propelled by rapid economic growth in Sub-Saharan Africa, a rising tide of global trade, the privatization of ports, and the advent of modern container vessels.

Owing to the rapid expansion of traffic, a few of the region’s ports are beginning to experience capacity constraints (figure 2.8). This is most notable in the case of Mombasa (Kenya) and Dar es Salaam (Tanzania), where the volume of general cargo and container traffic significantly exceeds design capacity. Port Sudan (Sudan) is also experiencing capacity constraints with respect to container traffic. All three of these ports are also reaching their limits with respect to dry-bulk cargo. There is some scope for easing capacity constraints by improving the efficiency of port performance, although ultimately new investments will be required.

The ports of Mombasa and Tanzania have traditionally been the most important players in the container sector on the east coast of Africa. Double-digit growth in this sector has now pushed both ports to their limit, however, and new capacity is essential.

21
https://www.cgdev.org/publication/impact-covid-19-lockdowns-and-expanded-social-assistanceinequality-poverty-and-mobility Melo, P., Abdul-Salam, Y., Roberts, D., Gilbert, A., Matthews, R., Colen, L., Mary, S., Gomez Y Paloma, S.

(2015) ; Income Elasticities of Food Demand in Africa: A Meta-Analysis; EUR 27650 EN; doi:10.2791/661366 Meriggi, N., Humphreys, M., Kamara, A. B., Krupoff, M., Levine, M., Mcleod, H., Mobarak, M., Prichard, W., Shridhar, A., Van Der Windt, P., & Voors, M. (2020). _Tracking the economic consequences and responses to COVID-19 in Sierra Leone_ (Working Paper - IGC, Issue May). https://sldashboard.github.io/corona/ Ohnsorge, F., Yu, S. (2022). _The Long Shadow of Informality : Challenges and Policies_ . World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/35782 Sanford, J. 2023. Expanding safety nets in response to shocks: The Experience of the Government of Sierra Leone’s Ep Fet Po Programme’s COVID-19 Response. Forthcoming.

Stoeffler, Q., Mills, B., & Premand, P. (2020). Poor households’ productive investments of cash transfers: Quasi-experimental evidence from Niger. _Journal of African Economies_ , _29_ (1), 63–89. https://doi.org/10.1093/jae/ejz017 Varshney, D., Kumar, A., Mishra, A. K., Rashid, S., & Joshi, P. K. (2021). India’s COVID-19 social assistance package and its impact on the agriculture sector. _Agricultural Systems_ , _189_ (July 2020). https://doi.org/10.1016/j.agsy.2021.103049 World Bank (2014). Vitamin A at a glance. World Bank, Washington, DC.

http://web.worldbank.org/archive/website01213/WEB/IMAGES/AAGENGVI.PDF. Accessed in March 2023.

30
deeper cooperation arrangements are best pursued in the presence of a regional market for power; an organization or forum for cross-country coordination that can help sustain agreements on market access, among other elements; and an institutional mechanism for coordinating and maintaining transmission in an effective and nondiscriminatory manner. Deeper levels of electricity cooperation in turn will depend on a greater degree of harmonization of reforms in national electricity markets, a gradual process. The different stages of regional cooperation can co-exist and gradually converge as sector reforms in the region progress. It must be emphasized, however, that gains from regional cooperation will be limited without development of market institutions and coordination institutions/forum, in addition to the desired domestic policy reforms.

Strong political will and policy continuity remain crucial for achieving higher levels of electricity cooperation considering the lengthiness of the cooperation process. Dismantling political barriers in South Asia to regional electricity cooperation and installing greater economic rationalization in the domestic electricity sectors is indeed challenging but certainly possible.

# **References**

- Asian Development Bank (ADB) (1999). Report and Recommendation of the President to the Board of Directors on a Proposed Loan to the Kingdom of Nepal for the Rural Electrification, Distribution and Transmission Project, Asian Development Bank, Manila.

- ADB (2000). Report and Recommendation of the President to the Board of Directors on Proposed Loans to the Islamic Republic of Pakistan for the Energy sector Restructuring Program, Asian Development Bank, Manila.

- ADB (2004). Project Completion Report on the Second Power System Expansion Project in Sri Lanka, Asian Development Bank, Manila.

- ADB (2013). RETA 7529 – Study on South Asia Regional Power Exchange, Asian Development Bank, Manila.

- Alam, M.S.,Kabir, E., Rahman, M.M. and Chowdhury, M.A.K. (2004). Power Sector Reform in Bangladesh: Electricity Distribution System, _Energy_ , Vol. 29, pp. 1773-1783.

- Amarawickrama, H.A. and Hunt, L.C. (2004). Sri Lankan Electricity Supply Industry: A Critique of Proposed Reforms, Surrey Energy Economics Centre, School of Economics, University of Surrey.

22