rafmacalaba/datause-encoder-data huggingface dataset:
For example, the twinning arrangement between the Netherlands Embassy and DFID in the education and health sectors provided both agencies a greater influence over the directions of the policy dialogue in these two important areas.

**Figure 2. MDBS Contributions by Agency, 2003-2007 – US$ Million**

<!-- Start of picture text --> 160.0<br>140.0<br>120.0<br><!-- End of picture text --> <!-- Start of picture text --> France<br>Germany<br>Denmark<br><!-- End of picture text --> <!-- Start of picture text --> Switzerland<br>Canada<br>Netherlands<br><!-- End of picture text --> <!-- Start of picture text --> European Commission<br>AfDB<br>United Kingdom<br><!-- End of picture text --> <!-- Start of picture text --> AfDB<br><!-- End of picture text --> <!-- Start of picture text --> World Bank<br><!-- End of picture text --> <!-- Start of picture text --> 40.0<br>20.0<br>0.0<br><!-- End of picture text --> <!-- Start of picture text --> Canada<br>Switzerland<br>Denmark<br>Germany<br><!-- End of picture text --> <!-- Start of picture text --> 2006<br><!-- End of picture text --> <!-- Start of picture text --> France<br>2007<br><!-- End of picture text -->

**Source** : MDBS Secretariat at the Ghanaian Ministry of Finance and Economic Planning.

**The evolution of the contributions by the multilateral development agencies**<sup>**12**</sup> **was less straightforward, however** .  At first sight, it appears that the decision by multilateral agencies to scale up or not development assistance did not necessarily reflect a response to the new, more open and, necessarily, more complex structure of the MDBS.  For instance, the contributions from the World Bank, which was the largest contributor to the MDBS during this period, had to be scaled down in 2007 following the decision to extend multilateral debt relief to Ghana, since, at least initially, the Bank’s contribution to the Multilateral Debt Relief Initiative (MDRI) was partially funded by reducing the allocation of its concessional credits to the recipient countries.  The contributions from the African Development Bank (AfDB), on the other hand, rose consistently during the period 2003-2006, only to drop to zero in 2007 because of the transition from one program cycle to another.  The contributions from the European Commission were the only ones that continuously declined during this period, reflecting changes in overall funding schemes, as well as some concerns about how the agreements reached between the Government of Ghana and the MDBS were being enforced.

# **The issue that apparently held back the European Commission (EC) from channelling additional development assistance through the MDBS was, in part, a disagreement about how to best measure progress in the implementation of the**

> 12 African Development Bank, European Commission and the World Bank.

-  - 15
comparison sites and measuring the impact at different points in time, we are able to capture the _change in the return to the length of exposure._

# **4.2. Characteristics of Sites with Different Lengths of Exposure to the Program**

Since we focus on quantifying the return to duration of participation by comparing sites that enter the Seecaline program at different points in time, it is important to ask the question: “How different are sites that joined at different points in time?” Table 3 provides the group mean characteristics of the sites in each of the treatment categories. The comparison of these mean characteristics suggests that the program was progressively phased-in to the areas that are more isolated (both in terms of communes being more distant to the nearest urban center and further away from main roads, as well as in terms of more isolated communities within communes), with fewer infrastructure (connection to electricity and presence of markets), poorer (as indicated by the headcount ratio), and with a higher average initial malnutrition rate (measured at the opening date of the site). The differences reiterate the point that the phase-in aspect of the program is not made at random, and it is invalid to take a simple mean comparison approach that measure the difference in outcomes across the two treatment groups and attribute the difference to the length of exposure.

Importantly, the data also shows a substantial heterogeneity across sites that joined the program within the same period. Figures 2 and 3 show that the dispersion around the mean at different dates of joining the program is large along different dimensions, such as the poverty rate and the initial malnutrition rate. The extent of the dispersion is advantageous for our purposes because it gives us scope for finding sites that are “comparable” across different duration of exposure to the program with respect to the observed characteristics.

These data sources (the monitoring data, commune census, and poverty map) provide us with the key predetermined characteristics to us model the selection of communities/sites into different lengths of exposure within the intervention districts. We have information on the socio-economic characteristics of the communes such as remoteness, existing infrastructure and weather shocks that hit the commune in the three years prior to the census. The shock information helps us identify those communes in districts added only in a second phase in 2000 due to droughts and cyclones severely hitting the country. As the sequential selection of sites was done in 13
Table D.1: Scaling Factors used on Calibrated Parameters

|Location|Scaling|based on:|
|---|---|---|
||Rent|Wage|
|1: Low Population – Low Income|0.688|0.906|
|2: Low Population – High Income|0.995|1.004|
|3: High Population – Low Income|1.021|1.030|
|4: High Population – High Income|1.297|1.059|

Note: Scaling factors computed using the 2000 Census, country-level data.

Table D.2: Proportion of Households with Available Relative Child Care.

|Location|Proportion|
|---|---|
|1: Low Population – Low Income|0.431|
|2: Low Population – High Income|0.391|
|3: High Population – Low Income|0.420|
|4: High Population – High Income|0.368|

Note: Values calibrated using proximity to grandparents by parents’ education levels estimated by Compton and Pollak (2015).

but using average wages instead of rents. They generate the scaling factors reported in the “Wage” column of Table D.1.

Fourth, we scale the the opportunity cost of non-relative care providers–set equal to the federal minimum wage of $5.15 an hour–so that it varies across locations. We use the same scaling factors as for transfers.

Finally, we allow the availability of relative care to vary across locations. As mentioned in Section 4.2.4, we use parental characteristics in our sample as well as estimates from Compton and Pollak (2015) to calibrate the aggregate sample’s proportion of households that have relative care available in close proximity. Those estimate show that not only parental education but also population density and labor market opportunities are related to this proximity. We therefore combine Compton and Pollak (2015)’s estimates with the distribution of parental education by location to obtain location-specific availability, as reported in Table D.2.

69
that losses are concentrated in about 100 (formerly) state-owned companies in Bulgaria during this period.

**Table 10 Bulgarian firms and the role of short-term credit and long-term debt.**

||_ST loans>_<br>_LTdebt>_|_O.58_<br>_O_|_ST loans_|_<O.58_|_ST loans_<br>_LTdebt_|_<O.58_<br>_=O_|
|---|---|---|---|---|---|---|
||Mean|Median|Mean|Median|Mean|Median|
|CF|-0.10|-0.13|0.07|0.04|0.10|0.06|
|IBK|0.26|0.21|0.24|0.19|0.24|0.19|
|LOANK|1.43|1.02|0.24|0.22|0.22|0.19|
|LTDEBTlK|0.34|0.11|0.08|0.00|0.00|0.00|
|TASSETYK|3.34|2.36|1.63|<br>1.46|1.65|1.46|
|NEMPL|751|406|749|283|462|259|

The findings in Section 5 and in particular in Tables 9 and 10 are evidence of the inefficiencies in Bulgaria in our view and they can only be explained if somehow SBCs are taken into account. First, firms which made losses were not liquidity constrained and thanks soft-budget constraints were still able to draw (increasingly) upon external funds. Second, firms with on average a positive cash-flow and relatively low levels of debt are liquidity constrained. In fact, what our regressions for the various sub-samples show is that imprudent (insolvent) borrowers had access to external finance which was not only due to SBCs. The continued access to external finance reinforced the problems associated with SBCs and increased the moral hazard problem which was at the root of financial crisis of 1996 and 1997. In this sense our empirical findings support the theoretical analysis of SBCs in Berglof and Roland (1998) and Dewatripont and Roland(1999).

# **6. Conclusions**

In this paper we have used a simple accelerator model of investment for a sample of 1003 Bulgarian firms for the period 1993-1995. This model is augmented by a liquidity variable in order to investigate whether or not firms are liquidity constrained. During our sample period (public) financial markets in Bulgaria were underdeveloped so that we expect that Bulgarian firms are liquidity constrained. The estimates for our entire sample confirm this prior. On the other hand, it is also well known that during these years 21
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9 adaptation of foreign technologies for use in domestic production processes. Such training is necessary to monitor technological trends, assess what is relevant for the firm or economy, and assimilate new technologies. A more educated population also tends to be relatively more technologically sophisticated. This generates local quality sensitive demand for advanced goods, which in turns tends to stimulate local firms to innovate and design technologically sophisticated goods and production techniques.

Most empirical cross-country studies of long-run growth now include some measure of human capital and recent studies of international differences in output per worker<sup>7</sup> and economic growth rates have focused the role of human capital in economic development<sup>8</sup> . Regardless of the underlying model, it is a fairly robust finding that a country’s human capital is almost always identified as an essential ingredient for achieving growth<sup>9</sup> . For example, Barro (1991), using cross-section data for 98 countries for the period 1960 to 1985 and the 1960 values of school enrollment rates at the secondary and primary levels as proxies for initial human capital, found that both school enrollment rates had statistically significant positive effects on growth of per capita real GDP. Similarly, Cohen and Soto (2001), using cross-country time-series data on educational attainment or average years of school, finds statistically significant positive effects of education on economic growth. Hanushek and Kimko (2000) take an alternative approach by focusing on the effects of educational quality on economic growth. Using international test scores as a proxy for the quality of educational systems, 10 they find that educational quality does exert positive effects on economic growth .

# **An Effective Innovation System**

Economic theory (Solow, 1957; Romer, 1986, 1990b) indicates that technical progress is a major source of productivity growth and an effective innovation system is key for such technical advancement. An innovation system refers to the network of institutions, rules and procedures that influences the way by which a country acquires, creates, disseminates and uses knowledge. Institutions in the innovation system include universities, public and private research centers and policy think tanks. Nongovernmental organizations and the government are also part of the innovation system to the extent that they also produce new knowledge.

> 7 See Temple (1999), Krueger and Lindal (2000).

> 8 See Mankiw et al. (1992), Benhabib and Spiegel (1994), Hall and Jones (1999).

> 9 However, the quantitative impact of human capital on growth has not been precisely estimated up to now (See Gundlach et al., 2001).

> 10 Cohen and Soto (2001) and Hanushek and Kimko (2000) will be discussed in greater detail below.
as where _x_<sup>_b_</sup> _mt_<sup>and</sup><sup>_xs_</sup> _mt_<sup>areappropriatelydefinedvectorsofdummiesusedtoestimateorder</sup> biases (see below), the constant _α_ provides an estimate of the average willingness to pay, and the term _ui_ accounts for individual variation in WTP. Even after controlling for preference variation and order biases, the _eit_ may be correlated within MPL, and we recommend clustering standard errors at the subject level.<sup>19</sup>

# **3.3 Identification of the WTP probit model**

**Average willingness to pay** _α_ **.** In a probit, utility is taken to be ordinal and all terms are scaled so that the variance of the error term equals 1. However, here we would like to express all terms, and in particular willingness to pay _α_ , in terms of money. We achieve this by restricting the coefficient on ∆ _t_ to be 1 and letting the error variance _σe_ be identified off of the data. Equivalently, we can estimate a (standard normal) probit model without the coefficient restriction and re-scale all coefficient estimates. The coefficient estimate for ∆ _t_ represents the inverse of _σe_ in this case.

**Individual willingness to pay** _α_ + _ui_ **and choice error** _eit_ **.** In most latent utility models, the error term is assumed to capture unobserved variation in true underlying utility. Here, we differentiate between the individual-level willingness to pay _α_ + _ui_ , and error _eit_ due to inattention, lack of comprehension, or near indifference (see section 2.2). The two distributions are separately identified because we observe a panel of _T_ choices for each subject, as long as preferences are stable during the administration of the MPL, so that inconsistencies in expressed preferences within the same MPL are the result of choice errors. Both terms are assumed here to have a normal distribution, but other distributions are in principle possible; the most obvious being a logistic distribution for _eit_ which leads to a random effects logit.

The distributional assumptions on _ui_ and _eit_ discipline the distribution of WTP within each WTP interval. In the probit, the assumed distribution of latent indifference points across MPL intervals is normal with mean _α_ . The location of the sample average will thus partly determine where the mass of indifference points within a given MPL interval is placed. If this seems restrictive, note that any method deriving point estimates for WTP from MPL data must make distributional assumptions. For example, when assigning the

> 19For datasets with multiple MPL per subject, we recommend clustering at the subject-by-MPL level.

19
sufficiently optimistic view of the long-term prospects for good health in Kenya. The mortality profiles in the intervening period are obtained by simple linear interpolation. The pyramid for the year 2000 is generated by applying the corresponding mortality profile to the cohorts aged 5 to 54 in 1990, and then inserting the cohort sizes for children under 15 and adults over 64 taken directly from the USCB ( _D_ = 0) series itself. This process is then applied to the resulting pyramid for 2000 in order to yield the pyramid for 2010, and the procedure is repeated up to 2050. The pyramids for 2060 and 2070 are obtained by making the assumption that the cohorts of 0-4 and 5-14 year-olds, as well as those over 64, remain at their sizes for 2050, which is in keeping with the projection that the population will have almost reached a stationary configuration by that date. It is readily admitted that this mixing of cohort sizes at the top and bottom of the pyramids generated by one set of assumptions with mortality profiles generated by quite another is inconsistent, but inspection of Table 3 in relation to the USCB’s counterfactual does not reveal any departures so egregious as to call into question the implicit assumption of identical fertility.

The corresponding series for _D_ = 1 from 2000 onwards is obtained in similar fashion, except that the USCB’s mortality profiles are taken over almost in their entirety. The sole amendment concerns 10 _q_ 10 in 1990, which appears to be implausibly high in view of the fact that among all age groups, children of school-going age are the least likely to die of AIDS. The said statistic is set instead at its level in 1980. The pyramids for 2060 and 2070 are generated using the mortality profiles for 2040. In this connection, it should be remarked that the mortality rates for adults between 15 and 44 in the year 2040 are between 50 and 100 percent higher than those that would rule in the absence of AIDS: the USCB’s projections are based on the assumption of a continuation of the epidemic beyond the middle of this century, albeit on a greatly weakened scale.

The above procedure above somewhat understates the mortality due to AIDS, as estimated by the USCB at least. For the level of mortality in Bell _et al._ ’s (2004) ‘revised’ 29
|**Variable**|**Description**|**Data Source/ Method**|
|---|---|---|
|Informal<br>Modern Labor<br>(LI)|The total number of workers<br>employed<br>in<br>informal<br>firms<br>operating in the modern economy.|We estimate informal employment in the modern<br>economy as the residual share of the labor force<br>(from WDI) that is not part of the formal sector or<br>the informal rudimentary economy.|
|Legally<br>mandated<br>labor cost, i.e.<br>minimum<br>wage (W)|The legally mandated cost of labor<br>paid by firms in the formal sector.|We use the equation for the aggregate capital‐labor<br>ratio in the modern economy to derive an estimate.|

31
<u>2015). Despite this, international assessments show that the level of achievement of CABA students in</u> Science is still well below the OECD average (OECD 2016). Problems with teaching and learning Science in CABA were also highlighted by the last wave of the Trends in International Mathematics and Science Study (TIMSS), according to which CABA students (fourth and eighth graders) place at the bottom of the world ranking, just above Egypt and South Africa (Martin et al. 2016). These results are not surprising given the reality of Science education in Argentina, where lessons are mostly teacher centered and focused on the transmission of encyclopedic content, far from competency-based international learning standards, such as those assessed by TIMMS and PISA (Argentine Ministry of Education 2007). Countries with high levels of scientific literacy tend to implement inquiry-based approaches that position students as active knowledge producers in a classroom community of practice, placing importance on the development of specific Science skills and deep understandings (OECD 2016).

# 3. Experimental Design

A randomized controlled experiment was carried out to assess the effect of different teacher professional development approaches on student learning in Science. The intervention focused on a compulsory unit of the seventh-grade Science national curriculum: the Human Body.

The intervention consisted of a random allocation of 70 CABA state primary schools to one of three experimental groups (Appendix 1). Thus, the unit of randomization was the school.<sup>10</sup> All participating teachers involved received one in-service four-hour training session, and were then asked to teach the Human Body Science unit according to national curriculum guidelines, over the following 12 weeks. During the training session, teachers discussed and took part in inquiry-based activities related to the teaching of the

> 10 The randomization was at the school level and not at the classroom level because 46 percent of the schools in our sample shared the same Science teacher for at least one of their classrooms. Therefore, assigning classrooms to different treatments was operationally impossible.

11
In addition to informing decisions on cost reduction, the efficiency literature is valuable in that it seeks to explain other possible reasons for inefficiency at the government and service level. These causes of inefficiency must be taken into account when evaluating whether or not amalgamation will improve government performance. The way in which local governments are financed, income and education levels, and political structures have implications for efficiency, but may also impact other objectives of consolidation. Political and equity issues are considered below.

## _<u>Transition Costs</u>_

The economies of size literature can be useful for establishing a reasonable expectation of the costs before government is consolidated and the costs that can be expected after a consolidated government has fully transitioned to an operating structure. The problem is that there may be a significant transition between these two and there may be significant transition costs that must be borne in moving the existing governments to consolidated governments. The obvious point must be made that consolidation only reduces costs if expenditures of the combined governments are lowered. Local governments are frequently very labor intensive, meaning the number of staff must be reduced if consolidation is to have a significant effect on expenditures. The number of employees can be reduced through layoffs or by attrition. The latter is politically less difficult but means the cost savings will only accrue over a significant time (the transition is long) and the staff losses may be inconsistent with where redundant people are located. Layoffs are faster but much more politically difficult to undertake and more likely to engender political opposition to consolidation. Also, significant costs of eliminating staff may exist, such as the need to pay severance or retirement benefits.

The transition may also include many new costs. For example, office systems must be combined, including the monetary costs of merging systems (computer, phones, filing, etc). The physical costs can be large in some situations, such as if there is need to link sewer and water systems or new government buildings. The costs of new elections for city council are another example.

A more subtle and difficult task is to take two (or more) groups of people in each of the many offices of government with different working cultures and move them to where they work as one coherent team. Significant time and monetary costs may be borne to develop a government that operates as a coherent single unit. The mergers of many large businesses have demonstrated the difficulties of combining office cultures.

# **4. Do Consolidated Governments Meet Service Demands?**

The effects of amalgamation on the ability to deliver the services that people demand must also be understood. A loss in economic efficiency exists to the extent that the services provided are inconsistent with the services that people want. Proponents of more fragmented governments argue that smaller governments and governments that are closer to the people should be better able to provide the tax/service delivery package that 20