rafmacalaba/datause-encoder-data huggingface dataset:
SOFES-recipients more often work on the side than students without a SOFES loan, and the treatment effect of student loans is about 8%-point. One the one hand, this is a counter-intuitive finding, since we expected that student loans would reduce part-time work and free up time for studying. On the other hand, it could reflect a change in the composition of student population, in the sense that the loan option provides access to a new group of students that are more likely to work on the side. Indeed, the family income of students _without_ loans is on average 48% higher than the family income of students _with_ student loans. This is consistent with the hypothesis that students in the SOFES program are still credit constrained.

|**Table 6: Impact of the loan program on de**|**cision to take a job on the side, survey data.**<br>DWORK|
|---|---|
|_Treatment effect_||
|DCREDIT|0.078<sup>***</sup><br>|
||(0.027)|
|_Socio-economic controls_||
|TUITION (×1000 pesos)|-0.006<sup>***</sup>|
||(0.002)<br>|
|INCOME (×1000 pesos)|0.002<sup>**</sup>|
||(0.001)<br>|
|DFATHERTECH|0.127<sup>**</sup>|
||(0.058)|
|DFATHERUNIV|-0.054<sup>*</sup>|
||(0.028)|
|DMOTHERTECH|0.015|
||(0.038)|
|DMOTHERUNIV|<br>-0.019|
||(0.032)|
|DFEMALE|-0.038|
||(0.027)|
|AGE|0.023<sup>***</sup>|
||(0.005)|
|_Educational background_||
|GPA_high school|0.012|
||(0.020)<br>|
|DPRIVATE_high school|-0.067<sup>**</sup><br>(0.026)<br>|
|DAREA_II|-0.090<sup>***</sup>|
|(Economics and Administration)|(0.034)|
|DAREA_III|-0.114<sup>***</sup>|
|(Engineering and Natural Sciences)|(0.035)|
|N<br>|1,266|
|R<sup>2</sup>|0.071|

Note: The regression method is PROBIT. Standard errors are between brackets. * indicates significance at the 10%-level, ** indicates <u>significance at the 5%-level, and *** indicates significance at the 1%-level.</u>

# _6.4 Impact on career choice_

Does the level of student support affect the field of study chosen by the students? According to the assignment matrix, students are eligible for different levels of credit across the subject categories. A student in 27
<!-- Start of picture text --> Rural Rice Net Buyers Rural Rice Net Sellers<br>40 Growth incidence curve 40 Growth incidence curve<br>35 Mean of growth rates 35 Mean of growth rates<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5<br>5<br>0<br>0<br>-5<br>-5<br>-10<br>-10<br>-15<br>-15 -20<br>-20<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Percentiles Percentiles<br>Annual growth rate % Annual growth rate %<br><!-- End of picture text --> Note: The horizontal axis shows per capita expenditure groups from poorest to richest, in 2 percent increments. The bottom 30<sup>th</sup> percentile is the poorest of the population in a specified category of households according to our definition. The vertical axis shows growth in expenditures between 1995-96 and 2000, in percent. The straight line shows the mean of the growth rates for all expenditure groups within a geographic area. Results are weighted by the individual weight. Source: calculated from household-level data from the 1995-96 and 2000 HIES.

This finding is reinforced by the results of a decomposition of growth in total expenditures. Using the income shares from the 2000 income structure and applying them to the expenditure growth figures in each of the six categories of households we analyze here, we examine the sources of growth.<sup>8</sup> This approach could be thought of as a reduced-form measure of direct first- and second-round effects of a price and productivity change and multiplier effects. Table 6 clearly shows that poor households in the bottom 30<sup>th</sup> percentile of total expenditures have experienced strong income growth, ranging from five percent for net sellers to 14 percent for rural non-farmers. Most of this growth among rural households is stemming from agricultural and non-agricultural wages and from other sources of non-agricultural income.

The agricultural growth multiplier effect might be captured by two income categories: non-agricultural wages and other income sources. Even though we do not attempt to calculate consumption growth multipliers, it is clear that spillover effects of agricultural growth on other sectors of the rural economy in Bangladesh account for a large share of income growth between 1995 and 2000. Trade liberalization and the

> 8 This approach implicitly assumes that income growth has been homothetic, i.e. total growth can be decomposed simply in proportion to the share of each income source in the total income. This is a restrictive assumption, not likely to be satisfied in practice. However, this was the only feasible approach given the data constraints. A better approach would have been to examine the actual changes in income from each category, but we were not confident in the reliability of those results due to survey comparability issues, described in Appendix 2.

19
Brazil, which finds no effects of oil discoveries on migratory flows (Caselli and Michaels 2013).

# **7. Conclusion**

This paper examined the local impacts of Ghana’s offshore oil discovery in 2007. We find that the oil discovery increased real income and employment for households and individuals in districts associated with oil discovery compared to those without such discovery. Income increased by 4 percent for households living in the oil region compared to households in non-oil regions. The increase in income is stronger for workers with higher levels of education relative to less educated workers. We find similar effects when studying employment outcomes. Local employment in general increased by 4.5 percent, with the discovery having an indirect impact on other non-oil local sectors such as manufacturing and construction. There were no significant impacts on agriculture and service employment.

Though we find the effect of offshore oil production on income to be significant, we do not find a statistically significant impact on consumption. This may be because poor households that are mainly involved in the agriculture and service sectors did not benefit from the increase in employment or because of shifts in consumption from nondurable to durable goods.

We note that these results should be viewed as short-run effects because offshore oil discovery in Ghana is relatively recent, and thus we examine the impacts only a few years after the discovery.<sup>25</sup> In order to explore the longer-term effects of oil on local communities, an extension of the time period with a more recent wave of household survey data (GLSS 7) will be carried out. Later waves of household survey will provide in-depth insights on the long-term impacts of oil on local communities.

Overall, the findings suggest that the offshore oil discovery and production can create opportunities for local firms and workers in other sectors. They also indicate that

> 25 The findings are consistent with  Arezki, Ramey, and Sheng (2017) who argue that the news of large-scale oil resource discovery affects macroeconomic outcomes after 5 years due to the lag between discovery and production. However, our approach allows us to look at the distributional impacts at the household level.

18
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.4. Performance compared across Central African railways, 2005**

||Labor<br>productivity|Carriage<br>productivity|Locomotive<br>productivity|Wagon<br>productivity|Freight yield|Passenger<br>yield|
|---|---|---|---|---|---|---|
|Angola, CFM|580|4,045|30|950|||
|Cameroon, CAMRAIL|603|4,738|26|868|5|2|
|Congo, Dem. Rep. of, CFMK|18|64|10|257|14|4|
|Congo, Dem. Rep. of, SNCC|38|275|4|317|13|3|
|Congo, CFCO|221|3,212|27|300|11|6|
|Gabon, SETRAG|1,778|1,891|39|902|||
|Rail concessions|350|2,945|23|491|5|2|

_Source:_ AICD railways database. _Note:_ Labor productivity = ̳000s traffic units per employee; locomotive productivity = millions of traffic units per locomotive; carriage productivity = ̳000s passenger-km per carriage; wagon productivity = ̳000s net tonne-km per wagon.

# Ports

Central Africa has numerous ports, of which the two most significant ones are Douala and Pointe-Noire. Douala is the most important port from a regional perspective, since it handles transit traffic for the Central African Republic and Chad. Pointe-Noire is one of the best natural deepsea ports in Africa, and once played an important role in the region prior to the civil conflict affecting the Republic of Congo. But since that time, the quality of the country’s road and rail surface transport links has deteriorated markedly, preventing cargo from being channeled into Pointe-Noire. In the meantime, neighboring Gabon has developed its own integrated rail and port infrastructure, diverting traffic that would previously have gone to Pointe-Noire. The Democratic Republic of Congo, with its very limited coastline, lacks a natural deepsea port and could benefit from access to Pointe-Noire. But the absence of well-functioning surface transport systems prevents this connection, and the Democratic Republic of Congo continues to rely on the rather limited Matadi port as it explores the development of the port of Banana to accommodate larger vessels. The possible development of a road-rail bridge linking Kinshasa to Brazzaville, as well as planned improvements to road and rail infrastructure on the Brazzaville to Pointe-Noire corridor, could potentially change this picture. Improvements to river navigability on the Congo Basin could also potentially open up the route from Brazzaville to Bangui. But all this is still some years away.

Even though systematic data for the growth of containerized and cargo traffic in Central African ports are not available, a number of indicators point to capacity constraints (figure 2.9). This is most notable in the cases of Douala (Cameroon) and Luanda (Angola), where the general-cargo traffic handled significantly exceeds design capacity. The port of Luanda (Angola) is also experiencing capacity constraints with respect to container traffic. There is some scope for easing capacity constraints by improving the efficiency of port performance although, ultimately, new investments will be required.

21
WPS4191 <mark>P</mark> OST <mark>-C</mark> ONFLICT <mark>T</mark> RANSITIONS <mark>W</mark> ORKING <mark>P</mark> APER <mark>N</mark> O <mark>. 5</mark>

# Post-Conflict Justice and Sustainable Peace

## Tove Grete Lie, Helga Malmin Binningsbø, and Scott Gates

Centre for the Study of Civil War, PRIO and Norwegian University of Science & Technology (NTNU)

### Abstract

No systematic study has examined the effect of post-conflict justice on the duration of peace on a global basis. This paper attempts to fill that void by building on a newly constructed dataset (Binningsbø, Elster & Gates, 2005), which reports the presence of various forms of post-conflict justice efforts (trials, purges, reparation to victims, and truth commissions) as well as processes associated with abstaining from postconflict justice (amnesty and exile). The long-term effects of post-conflict justice on the duration of peace after conflict are investigated. A Cox proportional hazard model is used to analyze the influence of the various types of post-conflict justice on the length of the peace period before the recurrence of violent conflict. Post-conflict trials as well as other types of justice do lead to a more durable peace in democratic as well as non-democratic societies, but the results are weak and are therefore difficult to generalize. Forms of non-retributive justice (i.e. reparations to victims and truth commissions), however, are strongly associated with the duration of peace in democratic societies, but are not significant for non-democratic societies. Amnesty tends to be de-stabilizing and generally associated with shorter peace duration, but exile tends to lead to a more durable peace.

### World Bank Policy Research Working Paper 4191, April 2007

_The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post-Conflict Transitions Project can be found at http://econ.worldbank.org/programs/conflict_ ). _An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org ._
43 6. CONCLUSIONS How the poor fared during the structural adjustment programs of the 1980s has been<sup>-</sup> and continues to be - the subject of debate. Some argue that the poor suffered a great deal and that their fate depended on adjustment. Others argue that the decline in living standards was not so severe or pervasive, and that the fate of the poor would have been much worse had adjustment measures not been taken. The purpose of the OECD project was to bring evidence to the debate by addressing<sup>two</sup> key issues: What happened to the poor, and what might have happened to them with adjustment packages other than those adopted?

We started with an analytical discussion stressing that adjustment involved, among other things, the need to cope with an adverse external shock involving a permanent loss in the income of the poor. Under those circumstances, the marginal value of income to the poor rises, justifying an income transfer to the poor. However, because the initial conditions characterizing many economies on the eve of adjustment implied that they were operating well within their production frontier, the possibility existed of improving the situation of the poor by successfully undertaking growthaugmenting structural reforms. We concluded that, ultimately, the issue remains an empirical matter that can be resolved only by recourse to careful analysis of countries that carried out adjustment programs. Because of the enormous difficulty of identifying the poor and of attributing changes in their well-being to policies or states of nature, the OECD project relied on case studies, complemented by counterfactual simulation analysis.

An interesting feature of the six country studies in this symposium is their diversity. ';wo of the countries, Indonesia and Malaysia, managed to adjust without any apparent adverse impact on the poor, despite some cuts in social expenditures in Indonesia. This superior performance on the distributional front was attributed to a number of factors, including relatively favorable initial
# **Highlights**

- We estimated outpatient caseloads and provider competence using nationally representative facility-level survey data from 10 Sub-Saharan African countries.

- The median primary health care provider sees 10.9 patients every day, but the median outpatient visits a provider who sees 25.7 patients daily.

- There is almost no correlation between caseloads and competence in most countries: low-competence providers are nearly as busy as the most competent.

- Hypothetical reallocations that match underutilized high-competence providers to larger caseloads could meaningfully improve the quality of care in half of the sampled countries.

# **1 Introduction**

A prominent consensus in the voluminous literature on “human resources for health” is that there is a global workforce crisis in primary health care (PHC) – a general shortage of providers, which is far worse in poorer countries and especially acute in Sub-Saharan Africa (SSA).<sup>1, 2</sup> The _prima facie_ case for this consensus is unassailable. Even in the OECD countries, doctors and nurses are extremely busy and visibly overworked, with frequent reports of provider burnout. There are 4.5 physicians per 1,000 people in Germany and 3.6 in the United States,<sup>3</sup> but only 1.55 physicians, nurses _and_ midwives for every 1,000 people across the WHO African Region.<sup>4</sup> Given that there are far fewer physicians per population in SSA than in OECD countries, it does not require a huge leap of faith to assume that the workforce crisis must be much worse in SSA compared to the United States or the European Union.

On closer consideration, however, these statistics present a puzzle. Assume each person in SSA seeks outpatient primary care 6 times per year, which is the OECD average for in-person physician consultations in 2021.<sup>5</sup> In the African Region, there are 1.55 providers per 1,000 people,<sup>4</sup> and if each provider worked 250 days a year, this would imply that the average provider would therefore have to see just 15.5 patients per day. Even at the highest LMIC observed PHC consultation length of 9 minutes,<sup>6</sup> the average provider would see patients for 140 minutes each day, or two hours and twenty minutes. After paperwork and administrative duties, the typical PHC provider in SSA must still spend substantial time not seeing patients, or “excess capacity”. Published statistics are therefore consistent with the idea that the workforce is in fact _under_ -utilized.

The first part of our study investigates this apparent contradiction: Academic consensus, personal observation, and patient reporting all indicate that SSA primary care facilities are highly overcrowded, but published national and regional statistics are instead consistent with substantial underutilized time for PHC providers.

Our investigation uses nationally representative PHC facility survey data covering 7,915 health facilities in 10 Sub-Saharan African countries, collected between 2014 and 2019 through the World Bank’s Service Delivery Initiative (SDI).<sup>7, 8</sup> We first show that,

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Table 2: Difference-in-Difference - Log individuals

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Outcome:|∆ln_Li_|∆ln_LiF_|∆ln_LiI_|∆ln_Li_|∆ln_LiF_|∆ln_LiI_|
|||_Panel A: Poo_|_l of workers_||||
|_Ti_|0.017*|0.057***|-0.010|-0.006|0.030***|-0.034***|
||(0.009)|(0.010)|(0.013)|(0.009)|(0.011)|(0.013)|
|Observations|3,192|3,192|3,192|3,192|3,192|3,192|
|R-squared|0.310|0.417|0.177|0.365|0.458|0.251|
|||_Panel B: Low-s_|_killed workers_||||
|_Ti_|0.022***|0.067***|-0.002|-0.001|0.039***|-0.026**|
||(0.008)|(0.011)|(0.013)|(0.009)|(0.011)|(0.013)|
|Observations|3,192|3,192|3,192|3,192|3,192|3,192|
|R-squared|0.440|0.489|0.220|0.465|0.513|0.280|
|||_Panel C: High-s_|_killed workers_||||
|_Ti_|0.008|0.022|-0.014|-0.011|0.004|-0.038**|
||(0.013)|(0.014)|(0.017)|(0.012)|(0.013)|(0.017)|
|Observations|3,192|3,192|3,192|3,192|3,192|3,192|
|R-squared|0.446|0.442|0.375|0.497|0.492|0.427|
|Controls|X|X|X|X|X|X|
|State fe|X|X|X||||
|Municipality fe||||X|X|X|

_Notes:_ This table reports the results of a regression relating changes in the log of the number of individuals in each location and sector with the line B of the subway. Panel A reports the results for the pool of workers, panel B for low-skilled workers, and panel C for high-skilled workers. In the first three columns, I include state-time fixed effects, and in the fourth column to the sixth column municipality-time fixed effects. The first and fourth column reports the results for the overall number of individuals, the second and fifth column for individuals in the formal sector, and the third and sixth column for workers in the informal sector. The regressions are weighted by the population in 2000. Standard errors are clustered at the census tract level and reported in parentheses. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.

49
Using this methodology, one finds that African countries have experienced numerous episodes of growth acceleration in the past 30 years, but also a comparable number of growth collapses. In short, Africa’s long-run record of slow and volatile growth reflects a pattern of alternating, identifiable accelerations and declines, rather than random variations of growth rates around the long-run trend. In addition, growth volatility—when viewed as the product of accelerations and declines—is not neutral and indeed matters for economic and social outcomes. To begin to address the public policy questions posed by these results, one must look for correlates associated with acceleration and deceleration episodes and examine the probability that an economy will undergo a growth acceleration or deceleration.

**_Growth acceleration and deceleration over time._** Table 4 shows the unconditional probability of accelerations and decelerations and their respective growth rates for different periods. For the entire period from 1975 to 2005, there is a slightly higher probability of growth acceleration than deceleration: 25 percent of the 1,243 country-year observations (total of valid observations per country per year) refer to countries experiencing growth acceleration, while 22 percent refer to countries experiencing growth decelerations.<sup>23</sup>

|**Table 4: Likelihood and growth**|**rates of econo**<br>**All cty ye**<br>**in the pe**|**mic accele**<br>**ars**<br>**riod**|**ration and de**<br> <br>**Cty year**<br>**growth acc**|**celeration in**<br>**s with**<br>**eleration**|**Africa, 197**<br>**Cty year**<br>**growth dec**|**5-2005**<br>**s with**<br>**eleration**|**Cty years**<br>**with trend**<br>**growth**|
|---|---|---|---|---|---|---|---|
|**Period**|**Observations**<br>**(country-**<br>**years)**|**Growth**<br>**rate**|**Frequency**<br>**(of cty**<br>**years)**|**Growth**<br>**rate**|**Frequency**<br>**(of cty-**<br>**years)**|**Growth**<br>**rate**|**Frequency**<br>**(of cty-**<br>**years)**|
|1995-2005 (after trend break)|494|1.88|0.42|3.76|0.12|-1.29|0.46|
|1975-1994 (before trend break)|749|-0.07|0.14|3.39|0.29|-3.14|0.57|
|1985-1994|433|-0.23|0.21|3.21|0.36|-3.18|0.43|
|1975-1984|316|0.13|0.04|4.61|0.18|-3.06|0.78|
|1975-2005 (Allyears)<br>Source: Arbache and Page(2007a)|1,243|0.7|0.25|3.64|0.22|-2.74|0.53|

Between 1975 and 2005, countries in Africa that experienced growth accelerations managed to grow on average by 3.6 percent during those episodes, compared with the regionwide average of 0.7 percent. During decelerations, countries contracted on average by −2.7 percent. Given the almost equal probabilities of growth

> 23 As a means of checking the robustness of the results, growth accelerations and decelerations were also identified by replacing 0 with +1 percent and −1 percent for acceleration and deceleration, respectively, in condition 1, but the results did not change substantially. Therefore, only the base-case results are reported, because they are less restrictive

12