rafmacalaba/datause-encoder-data huggingface dataset:
Parents may therefore _overuse_ care relative to the policymaker’s assessment by going to the doctor too early, before care is required, or _underuse_ it, by going too late. Subsidies will lower the parents’ cost threshold and lead to earlier (more) care, which can represent either an reduction in underutilization or an increase in over-utilization. Providing information about the child’s health through the CHW has the potential to improve the allocation of care, but only if the parents’ cost-benefit assessment is aligned with that of the policymaker. This may make the CHW and subsidy interventions complements in achieving better curative care: essentially, the CHW provide advice on when to seek care, and the subsidy allows the parent to follow through on that advice.

– – We observe 3747 illness spells more than two per child with an average length of 6.4 days, of which 3.0 are “care-required” days. We find that overuse according to WHO recommendations is rare in the control group, even though there is surprisingly large scope: about half of all illness spells end without ever requiring care. By contrast, underuse is rampant, with a probability of care-seeking on a care-required day of five percent or lower. As a result, only 11 percent of spells that reach “care required” status actually receive formal care.<sup>3</sup> Overall, there is evidence that cost constraints are more binding than information constraints: the probability of care is on average more than five times higher on days when care is required than when it is not, suggesting that parents are able to differentiate illness severity as embodied in the WHO care recommendations, but they delay the doctor visit longer than recommended.

The subsidy increased the probability of seeking care on any day of an illness spell by 270 percent and the total value of care received by 77 percent. Importantly, this increase does not crowd out private spending on acute care, and the bulk of the increase occurs

> 3Note that the idea of seeing the doctor too early hinges on the possibility that an illness may pass harmlessly without entering “care required” status. Nonetheless, a spell that enters “care required” status may end without treatment. “Care required” should be interpreted to mean that the risk of harm or death is sufficiently high to warrant the cost of care, even if the illness may ex post pass without apparent harm. Indeed, more than 85% of illness spells in our sample end despite not receiving any formal care, including those that would have required care according to the IMCI guidelines by the WHO. This is not a sign that care was not required based on ex ante risk, only that it was not necessary for recovery ex post.

5
# 4.2.2.- Services to airlines

Service directly provided to airlines also has to be taken into account in order to reach a complete quality assessment. Although BAA is not subject to quality standards, some airlines have already requested the Monopolies and Mergers Commission to establish standards regarding the availability of key operational equipment such as baggage belts, jetties, stands, moving walkways and lifts.

In spite of a lack of quality regulation, BAA makes direct measurements of its service delivery by recording objective data on the availability of critical equipment. Table 4.5 shows the 24 hour availability data for passenger-sensitive equipment from April 1995 to March 1996. Other performance indicators developed by BAA are: number of faults per unit (as a measure of the effectiveness of preventive maintenance), and time to site and time to repair (as measures of reactive maintenance). A target of repairing 95% of faults within four hours was set. Table 4.6. shows average fault repair time for passenger sensitive equipment. Other aspects considered are: percentage of passengers boarding /disembarking via jetty, coach or steps (see Table 4.7), planned and unplanned stand outage (in terms of hours per month) and maximum baggage handling delivery times (see Table 4.8).

**Table 4.5. Percentage availability of critical equipment at selected BAA airports. April 1995 to March 1996**

||97.8|99.2|99.0|99.1|98.9|
|---|---|---|---|---|---|
||98.8|99.5|99.3|n.a.|99.3|
||98.4|99.4|98.7|98.5|99.3|
|.~|98.6|99.5|99.4|99.7|99.8|
||98.1|99.4|98.5|99.2|99.5|
||97.5|99.2|97.9|98.8|99.2|
||99.4|99.4|99.5|n.a,.|99.9|

n.a.: Not available. Source: Monopolies and Mergers Commission (1996).

41
report food inflation to be a strong concern related to access to food, which is in line with high food inflation rates in 2016–17.

# 5.2.   Consumption

Table 4 shows the results for estimating the relationship between conflict and (log) consumption. We can see that one additional conflict events decreases consumption by 3.2– 3.8 percent (columns 1–3). In column 4, we removed households that reported having received any assistance after the most recent event, similarly as in table 2. We can see again that the magnitude drops slightly, to 2.3 percent. This is consistent with the food insecurity results; it seems that households that received any assistance at some point had been harder hit. Columns 5–8 in table 4 break the results between food and nonfood consumption. We can see that both forms of consumption are affected by victimization, with a slightly stronger decrease in nonfood consumption.

Table 5 reports the results from a model with events split by the type (columns 1–4) and the perpetrator of the event (columns 5–10). We can see from columns 1 and 2 that the decrease in consumption is driven by property-related events more than by violence, with a 6.6–8.7 percent decrease in consumption for an additional conflict event. The coefficient estimates for the violent events are negative, but they are not statistically significant.

Columns 5–10 show the same models, splitting the conflict events by the main type of perpetrator. We can see that the results are clearly the strongest when the perpetrator is reported to have been an insurgent. An event perpetrated by insurgents leads to a decrease in consumption by more than 4 percent, and the result is statistically significant at the 1 percent level. The results are slightly stronger when controls and region-survey wave fixed effects are added. Although the events in which the perpetrator is reported to be a bandit/criminal or a pastoralist/nomad are not statistically significant, they are of the negative sign. The result indicates a stronger effect of victimization in areas where Boko Haram has been active.

For reference, Serneels and Verpoorten (2013) find that households that experienced high violence in their localities during the Rwandan genocide in 1994 had at least 28 percent lower consumption levels in 2000 than did households that experienced no conflict. Mercier, Ngenzebuke, and Verwimp (2016) find that the exposure of a locality to conflict during the Burundi civil war (1998–2007) is associated with at least a 9 percent decrease in household consumption in 2012. Our coefficient estimates are smaller in magnitude than in Rwanda and Burundi, countries that experienced a genocide and a prolonged civil war, respectively.

19
Figure 2: Public Spending by Sector (including subsidies, in percentage of 2013 GDP) <!-- Start of picture text --> 30<br>24.4<br>25<br>19.2<br>20 18.1<br>15 12.8<br>10.6<br>10<br>3.5 5.2 4.1 5.2 4.9 5.2<br>5 1.1 2.2 1.5 1.2<br>0<br>Percentage in Government Budget<br><!-- End of picture text --> _Source:_ WB staff calculations using data from Tunisian Ministère des Finances (October 2013). _Notes:_ Education includes all levels; subsidies refer to explicit subsidies; social assistance includes cash transfer programs and health cards; other social services includes programs and services of the Ministère des Affaires Sociales, de la Jeunesse, and de la Femme et l’Enfance; health does not include health insurance. Subsidies are further disaggregated into three categories, basic products, transportation, and energy.

These changes in the composition and magnitude of subsidies reflect recent specific changes in the policy of energy subsidies. In effect, after the immediate and deliberate use of subsidies to appease post-revolution social tensions, the government of Tunisia began implementing a gradual strategy of subsidy reduction and improvement in public spending targeting. As reported by IMF (2014), the prices of gasoline, diesel, and electricity increased by 7% in September 2012, followed by similar increases in March 2013. Energy subsidies to cement companies were halved in January 2014 and fully removed in June 2014. Electricity tariffs on low- and medium-voltage consumers were increased in a two-step process, by 10% in January 2014 and another 10% in May 2014. The government introduced a lifeline electricity tariff for households consuming less than 100 kWh per month in 2014. Also in January 2014, the government established a new automatic price formula for gasoline to align domestic price convergence to international prices over time, but without a smoothing mechanism or a clear calendar. In parallel, the government launched a new social housing program; increased income tax deductions for the poorest households; and committed to creating a unified registry of beneficiaries of social programs and to improving social spending targeting (to be finished in 2015). In addition, plans are also in the works to expand the current cash transfer program (PNAFN) to 250,000 beneficiaries and to reduce its exclusion errors.

This brief history of energy subsidies in Tunisia shows that the country is striving to achieve a difficult balance. That balance aims to improve fiscal and equity concerns by reducing subsidies, while also trying to appease social tensions by maintaining subsidies as a cornerstone of its social protection strategy. The transition administration has attempted to maintain that balance through a progressive reduction of subsidies that started well into the post-revolution period and by beginning an expansion of a social protection system less reliant on subsidies. Not pursuing a more ambitious future energy subsidy reform would represent a big missed opportunity for Tunisian development. According to IMF, Tunisia is the only country in the 5
4 however, the continued decline of public investment followed naturally from the government's new economic strategy, which was oriented toward creating a more private sector-based, market-friendly economy.

**Table 1: Mexico** -- **Gross Fixed Investment**

|(con|stant prices, as|% of GDP)||
|---|---|---|---|
|**Years**|**Total**|**Private**|**Public**|
|1960-1964|16.8|12.6|4.2|
|1965-1970|19.2|13.1|6.1|
|1970-1977|21.2|13.5|7.7|
|1978-1981|23.8|13.3|10.5|
|1982-1987|17.7|11.0|6.7|
|1988-1994|17.7|13.8|3.9|
|1995-1996|15.4|12.0|3.4|

Source: INEGI, Sistema de Cuentas Nacionales, Serie 1960-93 and Serie 1988-96. World Bank, "Mexico After the Oil Boom" (Report # 6659-ME), 1987, for data in 1960-70.

The role of public investment under the new economic strategy is very different from what it had been before. To start, the decline in public investment is a natural consequence of the privatization of many state enterprises. Also, the new strategy sees the private sector as taking the lead in overall economic investment decisions and limits the state to a facilitating role in providing public infrastructure that complements private initiatives and to the correction of market failures. The latter calls for a refocusing of public investment in sectors considered to have important externalities (education, health, environment) and whose services are traditionally under-provided by the private sector. To permit a better prioritization of the public investment program in this sense, an "Investment Unit" was created in the Finance Secretariat in the early 1 990s with broad review and clearance.

The change in strategy described above can be seen in Table 2 by noting the decline of public investment in the two productive sectors, Agriculture and Industry, Tourism & Commerce. Public investment in both sectors used to account for around 20% of total public investment in the early 1980s and currently accounts for around 7% of the total. Also, the falling share of the communication and transport sectors after 1990, reflects divestitures in the telecommunications sector. Conversely, the share of public investment in the social sectors (education, health, social security, labor, nutrition and social assistance, and urban and regional development) increased from less than 30 percent in the early 1980s to around 40 percent in the early 1990s. This share fell again in the latter half of the 1 990s, but still remains significantly above the level in the 1 980s. The sector that accounts for the greatest share of public investment is the energy sector, which is dominated by the two parastatals, PEMEX and CFE.<sup>5</sup>

> 5Table 2 does not include public sector investments at the state and municipal levels, which represent between 15 and 20 percent of total public investment. INEGI breaks down total fixed investment into Construction and Equipment & Machinery, each of which is again sub-divided into public and private
safely take system quality as given in its decisions. In contrast, there is no particular reason to expect the causal impact of quality of country systems on use of country systems to vary by donor size. Therefore, if coefficients on control of corruption in Tables 1 and 2 mostly reflect endogeneity bias, the strength of association should vary positively with donor size. We add the appropriate interaction term in equation 2.5 to test this hypothesis.<sup>16</sup> Results are highly inconsistent with the argument for reverse causation: the coefficient on the interaction term is negative (and significant at the .10 level for a two-tailed test), not positive.

In our second variant of this identification strategy, we distinguish donors by selectivity in aid allocations. Specifically, we posit that donors that are more sensitive to quality of country policies and systems when allocating aid will also be more sensitive to the quality of systems when deciding whether to rely on them for managing their aid. In contrast, there is no particular reason to expect the strength of any reverse causation from use of country systems to vary with donors‟ sensitivity to policy in allocating aid. An empirical finding of heterogeneous impacts in this instance can be explained better by the causal argument than by reverse causality (from use of systems to system quality). Equation 3.1 includes an interaction term, to test the hypothesis that Control of Corruption will affect use of country systems more for donors that are more policy selective in allocating aid among recipients.<sup>17</sup> For this purpose, we use the donor policy selectivity scores from Knack, Rogers and Eubank (2011), based on aid disbursements in 2007. The interaction coefficient is positively signed, as hypothesized, and significant at the .05 level.

> 16 The interaction term is equal to the product of the deviations of Control of Corruption and donor aid share from their respective means.  The coefficient on Control of Corruption thus indicates its marginal effect conditional on the mean value of donor aid share.

> 17 The interaction term is equal to the product of the deviations of Control of Corruption and policy selectivity from their respective means.  The coefficient on Control of Corruption thus indicates its marginal effect conditional on the mean value of policy selectivity.  The coefficient on policy selectivity is subsumed by the donor-year fixed effects, because it is donor-invariant.

24
collected in 2013 the These data include information on the during matching process. trainer’s background; the number of workers and apprentices employed; assets, sales, and profits; and management practices. We also conducted cognitive and noncognitive assessments with the trainers.

The endline survey of apprentices was launched in August 2017 and continued through May 2018. The endline apprentice survey covered topics similar to those in the baseline but included more details about labor market outcomes. For example, for self-employed workers the survey captured firm management practices. The survey also used survey questions comparable to those in other impact evaluations conducted on youth labor markets (e.g. Hicks, Kremer, Mbiti, and Miguel (2013)), as well as those in large-scale labor market in Ghana such as the World Bank STEP and the Ghana surveys survey Living Standard Survey.<sup>10</sup> The timeline of program and evaluation activities is summarized in Figure 1. In our analysis we use data primarily from the endline survey collected in 2017-18, complemented by baseline measures for heterogeneity and balance analysis. Note that apprentice placement occurred between October 2013 and January 2014, between 42 and 52 months before the endline survey data collection.

The baseline characteristics of program applicants as well as the estimated differences between the treatment and control groups are reported in Table 1. On average applicants were 23 years old at the baseline and had completed just over seven years of schooling. The education levels of both mothers and fathers were lower than the schooling of our primary respondents, and mothers had almost 2.5 years less education than fathers. Among measures of labor market attachment, a quarter of the sample had ever started an apprenticeship and just over 40 percent were working. Only 5 percent of the worked for a and under 20 were sample wage, just percent self-employed. Applicants were working about nine hours a week and earning 15 GHS a month from all sources. Garment making and cosmetology were the two most popular trades, which is unsurprising given the gender composition of our sample. Of the applicants, 44 percent expressed interest in an apprenticeship in garment making, 35 percent were interested in cosmetology, and the rest were interested in construction trades (welding, masonry, and carpentry).

In order to provide evidence for the internal validity of our randomization, we test whether our treatment and control groups are similar on observable characteristics measured at baseline. For a given characteristic we run an ordinary least squares (OLS)

> 10Additional details of the World Bank STEP survey can be found at `http://microdata.worldbank .org/index.php/catalog/step/about` .

10
# **Exhibit 2b: Evidence from cross-national studies (II/II)**

|**Study**|**Brief**<br>**description**|**Time**<br>**period**|**Key finding**|**Implications for market vs. intervention**<br>**question**|**Implications**<br>**for centre vs.**<br>**region**<br>**question**|
|---|---|---|---|---|---|
|Beugelsdijk<br>and<br>Eijffinger<br>(2005)|• Investigation of<br>convergence<br>across EU-15<br>• Assesses<br>impact of<br>Structural<br>Funds,<br>presence of<br>moral hazard|1995-2001|• Convergence across EU-15<br>• Positive impact of structural funds on<br>convergence rates<br>• Corrupt countries do not gain "less"<br>economic growth from structural<br>funds|• Evidence in favor of interventionist policy<br>at country level. But may mask regional<br>differences within countries - as the richer<br>regions in poor countries may be<br>responsible for most of their growth.<br>• Corruption finding can be loosely<br>interpreted as absence of evidence for<br>moral hazard|• NA|
|Ederveen<br>et. al (2006)|• Impact of EU<br>funds on 13 EU<br>countries (EU-<br>15 minus<br>Luxembourg<br>and Germany)|Seven five-<br>year<br>periods<br>from 1960-<br>1995|• European support as such does not<br>improve countries’ growth<br>performance<br>• However, enhances growth in<br>countries with the right institutions,<br>such as openness and institutional<br>quality (different measures such as<br>corruption, government<br>effectiveness etc.)|• Aid works if directed to regions with the<br>right institutions, and who are open to<br>competition|• **NA**|

**Evidence for market         Evidence for interventionist policy         Evidence for decentralized approach Evidence for centralized approach**

19
# **Table 5: Participation rates and targeting by caste**

||Scheduled<br>Tribes|Scheduled<br>Castes|Other<br>Backward<br>Castes|Weighted<br>mean for<br>ST, SC<br>and OBC|Others|Targeting<br>differential for<br>ST/ backward<br>castes|
|---|---|---|---|---|---|---|
|Andhra Pradesh|0.567|0.434|0.382|0.412|0.150|0.262|
|Assam|0.192|0.179|0.163|0.174|0.191|-0.017|
|Bihar|(0.087)|0.185|0.089|0.116|0.016|0.100|
|Chhattisgarh|0.519|0.435|0.504|0.500|0.214|0.286|
|Gujarat|0.340|0.289|0.180|0.252|0.070|0.181|
|Haryana|(0.000)|0.105|0.044|0.071|0.018|0.054|
|Himachal Pradesh|0.392|0.413|0.294|0.376|0.298|0.077|
|Jammu and Kashmir|(0.054)|0.134|0.109|0.114|0.090|0.024|
|Jharkhand|0.204|0.268|0.155|0.197|0.149|0.048|
|Karnataka|0.186|0.160|0.042|0.089|0.054|0.035|
|Kerala|(0.168)|0.238|0.098|0.123|0.088|0.035|
|Madhya Pradesh|0.567|0.442|0.334|0.433|0.211|0.222|
|Maharashtra|0.063|0.017|0.074|0.058|0.015|0.044|
|Orissa|0.323|0.220|0.224|0.253|0.100|0.153|
|Punjab|(0.000)|0.104|0.016|0.082|0.009|0.074|
|Rajasthan|0.816|0.654|0.581|0.644|0.444|0.200|
|Tamil Nadu|(0.286)|0.523|0.279|0.338|0.069|0.269|
|Uttar Pradesh|(0.140)|0.325|0.118|0.191|0.044|0.146|
|Uttarakhand|0.388|0.513|0.082|0.321|0.278|0.043|
|West Bengal|0.656|0.507|0.449|0.521|0.362|0.159|
|All India|0.415|0.336|0.214|0.279|0.155|0.124|

_Notes:_ TD for ST/backward castes is defined as the difference between the (weighted) mean participation rate of ST, SC and OBCs and that for others. ST figures in parentheses had less than 100 sampled ST households and so might be unreliable. All-India figures include states not shown in the table. _Source:_ Authors‘ calculations from NSS (2009/10).

28
**TABLE 2: ROBUSTA COFFEE PRICES IN UGANDA**

||**_Averag _**|**_e Price_**|**_Producers_**|||**_SC 15 as_**|**_Exchange_**|
|---|---|---|---|---|---|---|---|
||**_F.A.Q._**|**_SC 15_**|**_Share_**|**_ICO Robust_**|**_a Indicator_**|**_ICO Share_**|**_Rate_**|
||**_(Ush/kg)_**|**_(Ush/kg)_**|**_(percent)_**|**_(US$/kg)_**|**_(Ush/kg)_**|**_(percent)_**|**_(US$/Ush)_**|
|1991/92|420|927|45|0.95|1,005|92|1,058|
|1992/93|568|1,092|52|1.08|1,300|84|1,204|
|1993/94|1,292|1,684|77|2.12|2,203|76|1,039|
|1994/95|1,685|2,343|72|3.02|2,842|82|941|
|1995/96|1,254|1,606|78|2.01|2,082|77|1,036|
|1996/97|1,146|1,480|77|1.69|1,800|82|1,065|
|1997/98|1,430|1,900|75|1.80|2,142|89|1,190|
|1998/99|1,433|1,899|75|1.59|2,250|84|1,415|
|1999/2000|1,100|1,580|70|1.08|1,691|93|1,566|
|2000/01|700|997|70|0.66|1,173|85|1,778|
|2001/02|520|778|67|0.59|1,045|74|1,771|
|2002/03|1,080|1,386|78|0.82|1,583|88|1,930|
|2003/04|1,090|1,420|77|0.80|1,495|95|1,869|

**Notes:** F.A.Q. refers to fair average quality and represents the price paid by exporters to the “F.A.Q.” traders. SC 15 refers to screen 1500, the grade accounting for two thirds of Uganda’s robusta output; these are the prices received by exporters (f.o.t. Kampala). Producers share is the ratio of F.A.Q. over SC 15 (i.e. 1090/1420 = 77%). In reality, producers receive a smaller share when marketing costs are accounted for. All variables reflect October‐September averages.

**Source:** Uganda Coffee Development Authority, _2003/04 Annual Report_ (Table 2.9, p. 9), World Bank, _Commodity Price Data_ , and International Monetary Fund, _International Financial Statistics_ .

**TABLE 3: COMPOSITION OF COFFEE PRODUCTION BY TYPE AND QUALITY (PERCENT)**

||**_ARABICA_**|**_ROBUSTA_**|**_> SC 15_**|**_SC 15_**|**_SC 15 >_**|**_OTHERS_**|
|---|---|---|---|---|---|---|
|1997/98|11|89|12|63|20|5|
|1998/99|10|90|6|56|32|5|
|1999/2000|18|82|9|63|24|4|
|2000/01|15|85|12|60|23|6|
|2001/02|14|86|14|60|21|5|
|2002/03|17|83|13|56|23|8|
|2003/04|19|81|14|58|16|12|

**Notes:** The shares of arabica and robusta refer to total coffee and add to 100; the remaining 4 shares refer to robusta coffee and they also add to 100. ‘> SC 15’ corresponds to quality greater than screen 1,500, the highest robusta quality. ‘SC 15’ is the average quality, while ‘SC 15 >’ corresponds to lower quality ro‐ busta, less than screen 1,500. Others include various other grades including specialty/organic coffee. **Source:** UCDA

**_— 20 —_**