rafmacalaba/datause-encoder-data huggingface dataset:
captures how well country-level variables account for the cross-country variation in project performance, and is reported in the fourth column of Table 6. This between-country R-squared is quite respectable, averaging 40 percent. This indicates that the parsimonious set of country-level variables used here accounts for a close to half of the average cross-country differences in project performance. However, as we have seen, cross-country variation in project performance accounts for only about 20 percent of the variation in overall project outcomes. So, overall country-level “macro” variables can explain just 8 percent (40 percent times 20 percent) of the total variation in project performance.

As the first two columns of Table 6 indicate, differences between projects within countries account for the bulk of variation in project outcomes, and thus to account for this variation it is necessary to look at project-level explanatory variables. The within-country “micro” R-squared, , captures the explanatory power of the project-level variables in accounting for the micro” variation in project outcomes. While many of the project-level variables we have considered have been individually significant, collectively they account for only a very modest share of the “micro” variation in the data, as the “micro” R-squareds average just 6 percent. However, explaining just 6 percent of the 80 percent of the variation in project outcomes within countries nevertheless contributes importantly to the fit of the regression. Overall, the project level variables contribute around 5 percent (6 percent times 80 percent) of overall variation in project performance.

# _Good Countries or Good Task Managers?_

As noted above, a great deal of the variation in project outcomes occurs across projects within individual countries. So far, we have investigated the contribution of a range of project characteristics and early-warning signals. As shown in the previous section, our efforts in this respect have been only modestly successful, in that the project-level variables explain only a small part of the very substantial project-level variation in outcomes. In this section we investigate another potentially-important correlate of project performance, the identity of the task manager responsible for the project. We have obtained data on the staff identification number of the World Bank task manager at the time of completion, for a set of 3,921 projects evaluated since 1995. An important limitation of this data, however, is that task managers can change over the life of a project, but we only have information about the identity of the task manager at the end of each project.

In order to have a meaningful comparison of the relative importance of task manager characteristics and country characteristics in accounting for project outcomes, we need to restrict 20
- (6) Are specific forms of restrictiveness more relevant than others for manufacturing firms’ performance? To answer this question we replace the aggregate ‘rest’ indicator with restriction-specific indicators in equation (2).

# **5. Results**

## **5.1 Reform and Perception-Based Performance**

The firm-level data available from the World Bank Enterprise survey suggest that in the Indonesian service sector, foreign firms are larger than domestic firms. While 23 percent of foreign firms in the service sector have more than 100 employees, only 1 percent of domestic firms do. In addition, foreign firms tend to use better technologies and more sophisticated processes, as suggested by the fact that they are four times as likely to have international certifications than domestic firms within the same size class.

The performance of service sectors, as measured by the perceptions of their users, is negatively related to how restrictive they are toward FDI. We found that a one percent improvement in service sector performance as perceived by manufacturers was associated with a one percent reduction in the degree of restrictiveness toward FDI in those service sectors, as measured by the OECD restrictiveness index (see Figure 10, for visual inspection of the evolution of the two indicators).

Formally, we regress the index of restrictiveness on the perception based performance index, using a fixed effect model, which allows us to control for time-invariant sector specific factors that affect the perceptions of services performance, The point estimate for the elasticity of performance with respect to restrictiveness is -0.9, and significant at 1 percent. We cannot reject the hypothesis of the effect being different from -1.

The experience in freight and logistics services in Indonesia provides a concrete example of the link between policy restrictiveness and performance. If services related to freight and logistics services, for example, remain sheltered from foreign investment and competition, costs may remain high and service quality poor. This undermines the capacity of the private sector to benefit from business opportunities involving long-distance shipping logistics costs in Indonesia are estimated to be nearly double those in South Korea, and nearly three times those in Japan. Costs are significantly greater than in even neighboring comparator countries. For example, it costs $750 to transport a container from Cikarang to Tanjung Priok on the island of Java, but only $450 between Pasir Gudang and Tanjung Pelepas in Malaysia, a roughly similar distance (see Figures 11 & 12). These costs include road transport costs, warehousing and port handling charges.

Differences in terms of openness and competition in Indonesia play a role in explaining these differences: Until recently, road freight was open only to domestic firms in Indonesia. One effect of these restrictions on competition relates to the lack of investment and the resulting poor quality of equipment, which is estimated to cause losses that, according to anecdotal evidence can account for about 7% of all transported merchandise. In Malaysia, road freight is relatively more 13
unemployed and poorer than successful applicants. Survey data show that only 40% of unsuccessful loan applicants reacted to the loss in access to HCST credit by either turning to informal sources of borrowing, such as loans from family and friends, or to other formal sources of borrowing, including high‐cost products, such as revolving credit and personal loans. 8% state that they had to cut expenditures or missed bills. The largest share, over one‐ third of respondents, did not take any action after their loan application had been declined. Overall, this suggests that for the majority of declined HCST loan applicants the cap prevented a rise in indebtedness, although in some cases at the expense of painful cut‐backs in expenditures.

**Figure 18: Reaction of declined payday loan applicants**

<!-- Start of picture text --> Something Used other<br>negative formal source of<br>Other or don't<br>3% borrowing<br>know<br>15%<br>13%<br>Cut expenditure<br>7%<br>Missed bill Used other<br>1% informal source<br>of borrowing<br>25%<br>Do nothing or<br>take no action<br>36%<br><!-- End of picture text --> _Notes:_ The Figure plots the answer to the following survey question: _When you were unable to get a payday loan on this occasion, what did you do? Source:_ FCA, Critical Research (https://www.fca.org.uk/publication/research/price‐cap‐research.pdf)

# Lessons from the country case studies

The case studies on Kenya, Zambia, Cambodia, the West African Economic and Monetary Union (WAEMU), India, and the United Kingdom serve as examples for different variations of interest rate regulations and highlight several aspects of their design and effects. Although the impact of caps varies substantially based on these differences and causal statements of effects are complicated by simultaneous developments, the case studies point to the following general effects of interest rate ceilings, which are broadly in line with the consequences identified based on economic intuition: 34
evidence of reductions in child labor, especially in domestic work, outside the household; however, “the time freed seems to be replaced with greater involvement in within-household tasks”. Participation in household chores, for instance, increased significantly by 8 percentage points according to the authors’ propensity score matching estimates. Although participation in work in the family farm did not change significantly, _hours_ worked in the family farm or family business increased.<sup>17</sup> Miller and Tsoka (2012) provide difference-in-difference estimates using the same data, confirming that participation in household chores increased significantly, whereas participation in work for pay decreased.

# **<<B>> 2.2. Old Age Pensions and the Role of Credit Constraints**

In our theoretical discussion, we assumed that households are credit constrained. If they are not credit constrained, then household investment in education and productive assets (and, concomitantly, child labor) should not change upon the receipt of a fully anticipated unconditional cash transfer. Two studies examine whether credit constraints matter for child labor by studying the impact of old-age pensions. Pensions are highly institutionalized and represent an _anticipated_ and relatively certain future income stream for the household. Economic theory suggests that in the absence of credit constraints, households will follow the optimal smooth path of consumption and investment. Therefore, we should not observe any discontinuity in the behavior of households, and the fraction of working children in particular, just above and just below the pension age.

Edmonds (2006) uses data from South Africa’s old-age pension scheme to test this proposition. South Africa’s pensions are means tested and, as a result, primarily cover the comparatively deprived black population of South Africa. The benefits provided by the pension scheme are large: in 1999, they represented approximately 125% of median per capita income of South Africa’s black population. School participation of 13- to 17-year-old children in the household increases substantially when an eligible elderly person reaches the pension age, an effect that is especially relevant for male pensioners. Children’s participation in economic activities does 8
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eventual reversal of beliefs. In our 3-period setting, this doesn’t say much, but in a multi-period setting it would have a potentially important consequence on which we will return. The third term, finally, is the difference in expected gains between a good and a bad supplier, which can be written as ~~~~ _G_  ~~~~ _B_   _G_   _B_  1   0  . (10) The first factor on the RHS of (10) is the difference between the prospects of a good and a bad supplier, a measure of their heterogeneity; the second is the effect of quality differences on profit, a measure of the industry’s characteristics (quality-sensitivity). Thus, the value of information, which in our setting drives the search for quality, is increasing in their heterogeneity and in the sensitivity of buyers to product quality.

In order to get a better feel for what our simple model suggests empirically, we now turn to a few extensions of its baseline version.

# **2.2 Extensions**

## **2.2.1 More than three periods**

With more periods, the revision of beliefs (i.e. the difference between posterior and prior from one period to the next) becomes smaller over time as beliefs approach asymptotically zero or one, but how fast the process of revision converges depends, of course, on the parameters of the two processes. If the two distributions (good and bad) have similar parameters, it takes, in expectation, more time to tell apart the two types, which requires longer sampling. This illustrates how the rate of convergence varies with the parameters. In the LHS panel the two distributions are characterized by sharply different parameters and beliefs converge after twenty periods; in the RHS panel, the two distributions have similar parameters and the beliefs take almost a hundred periods to converge.

9
repayment and borrowing decisions of both countries. The equilibrium strategies of repayment and borrowing _{b_<sup>_′_</sup> _i_<sup>(</sup><sup>_S_)=</sup><sup>_bBR_</sup> _i_<sup>_′_</sup> ( _S, b_<sup>_BR_</sup> _i_<sup>_−′, hBR_</sup> _i_<sup>_−′_),</sup><sup>_h′_</sup> _i_<sup>(</sup><sup>_S_)=</sup><sup>_hBR_</sup> _i_<sup>_′_</sup> ( _S, h_<sup>_BR_</sup> _i_<sup>_−′, bBR_</sup> _i_<sup>_−′_)</sup><sup>_}∀i_arecomputed</sup> by solving a Nash Equilibrium, thus they reflect the best response of country _i_ given the best response of country _i_<sup>_−_</sup> .

The best response for the repayment strategy of country _i_ , given the arbitrary current strategies _{h′i_<sup>_−, b_</sup> _′i_<sup>_−}_isdefined:</sup> The best response for the debt strategy of country _i_ , given the arbitrary current strategies _{h′i_<sup>_−, b_</sup> _′i_<sup>_−}_isdefined:</sup> Moreover, _V_<sup> ̃</sup> _i_<sup>_E_(</sup><sup>_S_;</sup><sup>_{b_</sup> _′i_<sup>_, h_</sup> _′i_<sup>_}∀i_)isthepayofffunctionofcountry</sup><sup>_i_,giventhearbitrarycurrent</sup> strategies _{b′i_<sup>_, h_</sup> _′i_<sup>_}∀i_</sup>

**Definition 2:** Given the future value functions _{Vi_<sup>_E_(</sup><sup>_S′_)</sup><sup>_, V_</sup> _i_<sup>(</sup><sup>_b′_</sup> _i_<sup>_, y_</sup> _i_<sup>_′_)</sup><sup>_, V_</sup> _i_<sup>_def_</sup> ( _yi_<sup>_′_)</sup><sup>_}_and the prices</sup> _{qi_ ( _b_<sup>_′_</sup> _i_<sup>_, yi_)</sup><sup>_, qE_(</sup><sup>_b_</sup> 1<sup>_′, b_</sup> 2<sup>_′, y_1</sup><sup>_, y_2)</sup><sup>_}_,theintra-periodNashEquilibriumconsistsofthebestresponse</sup> strategies for borrowing and repayment _{b_<sup>_BR_</sup> _i_<sup>_′_</sup> ( _S_ ; _b_<sup>_BR_</sup> _i_<sup>_−′, hBR_</sup> _i_<sup>_−′_)</sup><sup>_, h_</sup> _i_<sup>_BR′_</sup> ( _S_ ; _h_<sup>_BR_</sup> _i_<sup>_−′, bBR_</sup> _i_<sup>_−′_)</sup><sup>_}∀i_s.t.:</sup>

1. The best response strategies for repayment and borrowing are the solutions to maximization problem 12 and 11

> 2. The equilibrium pay-off value function _Vi_<sup>_E_(</sup><sup>_S_)isderivedbytheequilibriumstrategies</sup> _{b_<sup>_BR_</sup> _i_<sup>_′_</sup> ( _S, b_<sup>_BR_</sup> _i_<sup>_−′, hBR_</sup> _i_<sup>_−′_)</sup><sup>_,hBR_</sup> _i_<sup>_′_</sup> ( _S, h_<sup>_BR_</sup> _i_<sup>_−′, bBR_</sup> _i_<sup>_−′_)</sup><sup>_}∀i_andequation13s.t.:</sup>

9
7 they actually receive in practice are often different, due to health worker absenteeism and drugs being unavailable (Lavy et al. 1996; Chaudhury et al. 2006; Lindelow 2008). Further, we want to know not just whether the patient received a particular intervention but also whether the intervention was needed. Ideally, in fact, we would like to know as well the degree to which the (received and needed) intervention had its intended effect on the patient’s health, so we can get at ‘effective coverage’, i.e. patients in need of an intervention getting it and the intervention achieving its intended effect on the patient’s health (Shengelia et al. 2005; Boerma et al. 2014a). A patient may not seek treatment and go undiagnosed and never receive an intervention even though it is needed. A patient may seek treatment but be incorrectly diagnosed, or a provider, having made the correct diagnosis, may prescribe the wrong treatment, not necessarily due to a provider’s ignorance but because providers often do only part of what they know they should do (Das et al. 2012); either way, the patient ends up with an intervention that is inappropriate given their need. Finally, the patient may be correctly diagnosed and prescribed the appropriate intervention, but may fail to see the full health benefit, because of either supply-side factors (e.g. clinical standards were not adhered to) or demand-side factors (the patient failed to adhere to the treatment).

_A workable definition of UHC_ This points to a broad definition of UHC that captures the payments patients _actually make_ and the interventions they _actually receive_ . The World Bank and the World Health Organization (WHO) (Boerma et al. 2014b; Boerma et al. 2014c) 7
8 entire period to the replacement year not covered by the other phases. All countries with initial TFR under 3.0 exhibit only this phase. All other countries also go through this phase, though it may begin later.

The age pattern of fertility was allowed to change with its level. The age patterns when TFR is 7.6, 4.1, or 2.8 are shown in Figure 4. The - mean age at childbirth declines from 28.86 years when TFR 7.6, to 28.45 years when TFR - 4.1, to 27.98 years when TFR - 2.8. For intermediate TFRs, the age pattern falls between these curves.

# Hijration level and trend

Estimates of international migration come from the U.N. 1984 assessment and the U.N. 1988 revision, documents prepared by the U.N. for the 1984 International Conference on Population, the U.S. Bureau of the Census (1985), Eurostat (1987), :.-d Bank sources. To deal with the volatility of refugee movements in various parts of the developing world, only those are counted as migrants who are believed likely to remain permanently in the receiving countries.

Available data seldom include age-sex distributions of migrants, which were instead chosen in this manner. For each country, some estimate was made of the sex ratio among migrants. Based on this sex ratio, an appropriate age-sex distribution was chosen from among alternative models. This distribution was assumed to be constant over time, regardless of the total number of migrants.

Trends in the volume of migration are based on past trends, but are still essentially speculative. For most countries, net migration is assumed to decline to zero by the year 2000; for the remainder, net migration declines to zero by the year 2025.

# Summary of Results

Summary projection results are provided in Tables 2-12. (Tables 4-12 appear at the end of this Introduction.) In discussing these tables, we will summarize world population growth; note changes in these projections from previous versions; compare the four World Bank regions and 19 country departments; note other comparisons among geographic regions and groups of countries by income level; observe the results for some individual countries; and essay a brief conclusion.

# World ponulation

The projections show world population rising from 4.843 billion in 1985 to 6.205 billion in 2000 and 8.262 billion in 2025 (Table 2). From 1985 to 2025, the birth rate per thousand population will decline by 10 points, but the death rate will decline by less than 2 points. As a consequence, the annual growth rate will fall from 1.7 percent to less than 1 percent.
invested in developing transparency measurement criteria such as the IMF _Code of Good Practices on Fiscal Transparency_ , which has been used to produce Reports on Observation of Standard and Codes (ROSC) for a large number of countries. Using information from these reports Hameed (2005) finds that transparency matters to delivering fiscal discipline, controlling corruption and achieving better credit ratings. (See also Debrun, and Kumar, (2007) on the disciplining role of transparency. Alesina (2010) is of the view that transparency in the budget and outcomes is the most important element in delivering good fiscal outcomes because it is more difficult for pressure groups to hide wasteful programs in an environment of greater transparency.

The traditional focus on (primary) deficits and debt to GDP ratios has been shifting to the pro-cyclical fiscal behavior of governments, something which seems ubiquitous in developed, transition and developing economies. Fragmentation and lack of transparency are found also to explain pro-cyclical fiscal behavior. Alesina et al. (2008) indicate that in developing countries pro-cyclical behavior is likely to be linked with a lack of transparency. Given that pro-cyclical behavior occurs even in European economies ranked high on transparency standards, other factors are likely to be at play. Complementary explanations therefore point to the inability to make credible inter-temporal commitments to the future allocation of resources. Balasone, and Kumar (2007) review the challenges of cyclical behavior for fiscal institutional design.

In countries around the world, considerable attention has been given to improving fiscal institutional designs anchored on the emerging consensus that institutions matter for fiscal outcomes. Fiscal institutions of various types have been adopted to counter budgetary fragmentation and non-transparency in fiscal policy. The underlying presumption is that certain budgetary procedures could reduce institutional fragmentation, increase transparency and Page | 5