rafmacalaba/datause-encoder-data huggingface dataset:
the context is not constant in the long term and this raises the issue of whether a given training mode may be cost-effective for a limited duration and then be dominated by another, There is almost no literature addressed directly to this issue, but a micro-study of U.S. Air Force training is highly suggestive. Evans, Bolter and Stern (1976) report that when new tasks are created, as in the introduction of a new weapons system, there is a predictable sequence of types of training. First, the suppliers are responsible for providing training under contract; then the Air Force establishes mobile units to disseminate the training using formal methods; next, formal residential courses are established; and finally much of the training is gradually transferred to the on-the-job mode.

In this example formal methods are initially chosen primarily to accelerate the dissemination of the training, but other considerations would also favor their utilization: the innovative nature of the skill development and the opportunity to take advantage of the economies of scale associated with a high volume of training.

The same considerations would favor the initial utilization of formal methods for innovative skill development in a civilian context, but one should be cautious about a literal transfer of the model. As has been seen in Section 8, much innovative skiil development is provided by suppliers, partners in joint enterprises, and via other commercial links. Typically each individual intervention is on a small scale, but the cumulative effect of the interaction between skill development and industrial development can transform the labor force.

The South Korean experience is particularly instructive in this respect. Whether by accident or design, the acquisition of technological mastery may be summarized as a carefully-graduated learning-by-doing strategy with three key features. First, direct foreign investment apart, the introduction of new technology was heavily dependent on turnkey projects. These provided learning opportunities which allowed diminished future dependence: "In some industries, including synthetic resins and fibers, the first plants were often built on a turnkey basis and on a small scale, much smaller than either the size of the market or the size that would exhaust scale economies. Construction of the second and subsequent plants followed quickly with Korean engineers and technicians assuming an increasing role in project design and implementation, and at scales much closer to or equal to world scale.' (Westphal, Rhee and Pursell, 1981, p. 48).

Second, the Koreans concentrated on efficient plant operation rather than on innovation in product or plant design. This reflected the comparative advantage of its workforce, which, though relatively well-educated, was lacking in basic engineering know-how (Westphal, Rhee and Pursell, 1981, P. 51).

Third, the early industrialization focussed on non-proprietary technology which could be generalized to other applications: "In industries for which process technology is not product-specific, mastery has frequently

- 111 -
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34
## **Figure 7**

## **China as a standalone engine for growth (a)**

This figure presents weights from PC1 and PC2 from principal component analysis of G8 (G7+China) growth. Growth is proxied by real GDP annual log growth (q/q) and industrial production annual log growth (m/m). Source: IMF IFS Statistics, GEM World Bank.

<!-- Start of picture text --> 8%<br>0%<br><!-- End of picture text --> <!-- Start of picture text --> 8%<br>0%<br><!-- End of picture text --> <!-- Start of picture text --> 12%<br><!-- End of picture text --> <!-- Start of picture text --> 1%<br><!-- End of picture text --> <!-- Start of picture text --> 2%<br><!-- End of picture text -->

# **Industrial Production Growth**

<!-- Start of picture text --> 13%<br><!-- End of picture text --> <!-- Start of picture text -->
-8%  -15%<br><!-- End of picture text -->

<!-- Start of picture text --> 6%<br><!-- End of picture text -->
willingness to engage in corruption,<sup>8</sup> and that impact to vary with the degree of aversion to risk. Therefore, the results that women are less corrupt than men may merely reflect their higher (on average) aversion to risk. Eckel and Grossman (2003) for instance find that risk matters in their experiments because systematic behavioral differences between men and women tend to vanish with exposure to risk.

To really capture gender differences towards corruption one should either control for individuals’ aversion to risk, or observe them in a risk-free environment. We argue that Tajikistan health sector offers such an ideal setting. The accountability vacuum in Tajikistan’s health sector, one of the most corrupt in the country,<sup>9</sup> coupled with a strong presence of women in the sector as a whole but also in the top positions such as doctors generate the ideal setting for studying gender differences toward corruption. Indeed, the nearzero probability of detection and the complete absence of even a threat of sanction create a breeding ground for all forms of corruption. The ground becomes even more fertile for corruption when one accounts for the tremendous bargaining power health workers enjoy visà-vis their ill clients. It is in such a risk-free environment where men and women are given the same opportunities to engage in corruption _ceteris paribus_ that the study of gender differences is the most promising.

# **3. Data and Descriptive Analysis**

The data used in this paper come from the PETS carried out in Tajikistan’s health sector during the last two months of 2006. The main objectives of the PETS were to assist the government in improving the public financial system for the management of health resources, and improve service delivery, transparency and accountability in the sector. These

> 8 Abbink et al. (2002) show in an experimental bribery game that the threat of a severe penalty significantly reduces the probability of bribe-giving. Moreover, if the first agents still offers a bribe it is smaller in size and is more likely to be rejected by the second party. Olken (2007) also shows in randomized field experiment in Indonesia than increasing government’s audits reduce corruption.

> 9 Since 2000, Tajikistan has been consistently ranked amongst the most corrupt countries by several agencies such as Transparency International’s CPI or the Kaufman, Kraay, and Mastruzzi’s Control of Corruption or Voice and Accountability indices. Moreover, the health sector is perceived as the most corrupt sector in Tajikistan according to several public opinion and corruption surveys (see Lewis 2006, Government of Tajikistan 2006).

8
_Po&£h_ **_Fiscal CrLui_** 13 The actual fall in HCA profitability is very close to the lowe end of the cumulative effects of the above factors. Since the main source of uncertainty in the above estimates is the size of the change in the inflation bias, this suggests that the lower of the inflation bias estimates for 1990 may be the more accurate.

# V. A NOTE IN PASSING: INVENTORY **ACCUMULATON** IN **TllE NATIONAL AccouNT**

The national accounts produced by the Central Statistical Office are also severely affected by an inflation bias, especially the national accounts in current prices. This can be seen from Table 5, which compares changes in stocks from the national accounts with the estimates of real inventory accumulation in the enterprise sector described above (nb: the enterpr ise sector accounts for the bulk of inventories in the economy).

Table _5:_ Inventory accumulation and the national accounts

||1988|1989|<br>1990|<br>1991|
|---|---|---|---|---|
|Own calculations:|||||
|Inventory accumulation|||||
|as a % of nominal GDP|||||
|Assumed IL =1month|-1.9|-10.3|_-0.5_|-1.9|
|Assumed IL = 2 months|-1.8|-9.6|-2.6|-2.2|
|Assumed IL = 3 months|-1.7|-9.1|-3.3|-2.3|
|Inflation bias|||||
|as a % of nominal GDP|||||
|Assumed IL = 1 month|15.3|38.6|15.7|5.6|
|Assumed IL = 2 months|15.1|37.9|17.8|5.9|
|Assumed IL = 3 months|15.1|37.4|18.5|6.0|
|CSO national accounts data:|||||
|(From Rocznik Statystyczny 1991, p.|119)||||
|Changes in stocks<br>as % of nominal GDP|10.1|22.1|11.3|n.a.|
|Changes in stocks<br>as % of constant price GDP|5.1|6.9|2.5|n.a.|
# Introduction

The instruments and means needed to achieve the Millennium Development Goals (MDGs) have been subject to an intensive debate in academic and policy circles in the past decade. A wide variety of approaches and models have been proposed that have yielded a number of important insights into the key mechanisms, resource requirements, and the main bottlenecks for achieving the MDGs (see Website of the UN Development Group; Sachs and McArthur, 2005; Bruns, 2003; Glewwe and Zhao, 2006). This paper contributes to this debate by examining complementarities between different MDGs.

More specifically, its overall objective is to analyze the effect of progress in other MDGs, in particular the expansion of female education (MDG2 & MDG3), on child mortality (MDG4).<sup>2</sup> Several studies have analyzed the contribution of specific factors to the attainment of the MDGs or a subset of them, for example infrastructure (Fay et al., 2005) or population growth (Potts and Fotso, 2007). Complementarities between different MDGs are difficult to study because of the complex nature of interactions that exist between ‘inputs’ and ‘outputs’. For example, less income poverty may allow more people to send their children to school, which in turn reduces household income in the short run due to lost child labor but increases income prospects in the medium run. From this perspective, child mortality seems a particularly suited MDG outcome to study complementarities because there is no or little (immediate) feedback from child mortality to other MDGs.

Numerous studies have analyzed child mortality determinants in Sub-Saharan Africa (e.g. Brockerhoff and Derose, 1996; Brockerhoff and Hewett, 2000; Hill et al., 2001) with mixed outcomes in terms of socio-economic determinants, such as education and income. Unfortunately, many of these studies examine child mortality in one point in time and often for only one country. Most studies hence rely entirely on the cross-sectional within-country variation between households, mothers, and children typically using Demographic and Health Surveys (DHS). Moreover, they lack a common framework, which makes findings difficult to compare and to generalize. Finally, most studies spend little time at examining the transmission channels of socio-economic factors.

In this paper, we attempt to overcome some of these weaknesses by conducting a multicountry study using two rounds of DHS per country and a consistent micro-econometric methodology. We hence combine rigorous micro-econometric work and a comparative

> 2 MDG 4 calls for a reduction of the under-five (child) mortality (U5M) rate by two thirds between 1990 and 2015.

2
# 5.2 Short-run dynamics

In this section, results of the VAR analysis are reported. The number of lags in the VAR is determined through the standard information criteria (see Annex 3). The results were not sensitive to the ordering of the variables. In general, the results show that pass-through to headline inflation is feeble but it is high to food price inflation (estimated around 29% after 6 months and around two-thirds after a year). The spillover effect to core inflation is almost negligible.

# 5.2.1 Impulse response functions

Table 4 reports the pass-through coefficients for horizons j = 1, 3, 6, 12, 18 and 24 months. Impulse responses suggest that movements in world food prices have a feeble impact on domestic CPI inflation. A 1-standard deviation initial shock (estimated at around 31.15 percentage points (ppts)) in world food prices leads to an estimated cumulative increase in CPI by 4.5 ppts after 6 months. Another measure of the size of the pass-through in month _j_ is to look at the ratio of accumulated responses of domestic inflation to one standard deviation shock to the international food price inflation _j_ horizons after the shock. This way of measurement, usually used in the exchange rate pass-through literature, would suggest that the pass-through to CPI is weak, with only around 9% of the external shock passing through to CPI inflation after 6 months and around 10% after 8 months.

Shocks from international food price inflation to domestic food inflation have a stronger effect. A hike in world food inflation by 27.5 ppts is estimated to raise domestic food inflation by 10.7 percentage points after 6 months (table 5). In the same way of measurement as above, pass-through to food prices is high: more than a quarter of the external shock (29%) passes-through to food inflation after 6 months and around two-thirds after a year. This is in line with Kraay (2007) who found that pass-through to food items is higher than other items. While our results are not strictly comparable with others (World Bank, 2011), they confirm that pass-through to domestic food prices is high in Egypt.

16
network effects. Using the notation of Chiquiar and Hanson (2005), the wage equation in Mexico (subscript 0) may be written as: ln _w_ 0 = μ 0 + δ 0 _s_ (1) where _w_ is the wage, μ > 0 is the minimal wage level paid in the absence of schooling, δ> 0 is the return to schooling and _s_ is the level of schooling.

Similarly, the wage equation in the U.S. (subscript 1) may be written as: ln _w_ 1 = μ 1 + δ 1 _s_ (2) As minimum wages are higher in the U.S. and relative returns to schooling are higher in Mexico, we assume μ 1 > μ 0 and δ 0 > δ 1 .<sup>2</sup>

# **2.2 Migration Costs**

Let _C_ be the migration cost. In line with the migration networks literature (Massey et. al., 1987, Carrington et al., 1996, Bauer et al., 2002, Munshi, 2003, Kanbur and Rapoport, 2005), we assume that it is decreasing with the size of the community migration network, _n_ : _C_ = _C_ ( _n_ ),C' < 0 Expressed in time-equivalent units, the migration cost<sup>3</sup> may be written as: Then, a resident of Mexico will find it beneficial to migrate to the U.S. if<sup>4</sup> ln( _w_ 1) − ln( _w_ 0 + _C_ ) ≅ ln( _w_ 1) − ln( _w_ 0 ) − π > 0 (5) 2 Chiquiar and Hanson (2005), Ibarraran and Lubotsky (2005) and Cuecuecha (2005) all provide evidence supporting these assumptions.

> 3 To explain why people with similar networks and schooling levels may make different migration decisions, we could add a source of unobserved heterogeneity such as preferences for consuming in one's origin country. This may be captured for example by adding a psychological component to the migration cost in the form of an individual taste parameter _ki_ distributed on [0,1] with density _g(k)_ such _that C=C(n,k), C'<0_ and π = _e_ μπ −γ 1 _s_ −γ 2 _n_ −γ 3 _k_ , with _s_ and _k_ independently distributed. Note also that _k_ could be seen as positively depending on _n_ as having _landsleit_ (people from the same town or village) certainly makes it not only cheaper to move, but also easier to adapt to the new destination, thus reinforcing the role of networks. However, this would not change the qualitative predictions of our results (which may be seen as valid "for a given _k_ " and easily generalize to all _k_ ) and we therefore keep this additional component implicit.

4 The approximation is valid if π is small, which appears the case if _w_ is defined as the present value of a flow of future incomes.

5
Control (no intervention). After the initial sample selection, the district of Massasi experienced a re-districting process through which 9 wards were reassigned to a neighboring district which was not part of the program and were dropped from the sample. The reassignment was balanced across treatment arms and included 3 wards from the sanitation only group and 2 wards from each of the other study arms resulting in a final experimental sample of 181 wards.

We implemented a block randomization procedure within districts with the objective of balancing the population sizes of treatment and control wards within each district. First, wards were ordered by population size within districts and blocked into groups of four, starting with the four most populous wards, second most populous, and so on. A random number was then assigned to each ward, and a group number between 1 and 4 was given to each ward based on the ordering of the random number within its respective block. Finally, group numbers were randomly assigned to represent one of the three treatment or control groups.<sup>26</sup>

# **_3.2._** _Compliance_

Table 3 presents treatment compliance at the ward level. Of the 181 wards selected for the sample, 45 were assigned to handwashing, 44 to sanitation, 46 to the combined intervention and the remainder to control. According to administrative records, the implementing agency accidentally conducted handwashing promotion in one of the sanitation wards, resulting in actual delivery of TSSM only to 43 wards and combined TSSM and HWWS to 47 wards. There were no reported deviations from the planned implementation of mason or FLA training, and no information was available to assess the actual delivery of village-level media (wall drawings, posters, etc.), although, based on information from the field managers, we expect that a majority of the print material dissemination was implemented as scheduled. Within each ward, the two largest villages were targeted for program implementation.

Administrative records of program implementation determined that some wards received only partial treatment. 2 of the 45 HWWS wards were not exposed to the DCC event, 7 of the 43 TSSM wards did not have a CLTS triggering, and 5 of these 7 wards did not receive the DCC roadshow event. Of the 47 wards that were assigned to both TSSM and HWWS, 4 wards did not receive a CLTS triggering or DCC exposure.

# **_3.3._** _Balance_

Table 4 presents balance statistics. Since we do not have a baseline survey, this consists of a combination of time-invariant indicators and retrospective responses asked in the endline dating to February 2009 – before the intervention had started. Of the 87 variables we are able to present for each of the three groups (resulting in 261 comparisons to the control), we find a statistically

> 26 The randomized selection procedure can be obtained by the authors on request

14
# **1. Introduction**

In response to the global financial crisis, a number of countries substantially increased the coverage of their financial safety nets in order to restore confidence and to avert potential contagious runs on their banking sectors. This has rekindled the debate on the impact of deposit insurance on banking sector stability and performance. While previous studies suggest that deposit insurance exacerbates moral hazard problems in bank lending and is associated with higher likelihood of banking crisis (Demirguc-Kunt and Detragiache 2002), to the best of our knowledge, there is no study that examines the impact of deposit insurance on bank risk and stability during a period of global financial instability. This is an important gap in our knowledge since economic theories suggest that deposit insurance brings both benefits and costs that are likely to vary with economic conditions. Hence while deposit insurance can increase moral hazard and make financial systems more vulnerable to crises during good times, it can also enhance depositor confidence and reduce the likelihood of contagious bank runs during turbulent periods such as the recent global financial crisis. The net effect of deposit insurance on bank risk and stability, therefore, depends on whether the benefits of deposit insurance can outweigh its costs.

In this paper, we take advantage of the global financial crisis and study whether deposit insurance schemes have a stabilizing effect during financially turbulent periods. Specifically, using a sample of 4,109 publicly traded banks in 96 countries, we examine the impact of deposit insurance on bank risk and systemic stability separately for the crisis period from 2007 to 2009, as well as the three years from 2004 to 2006 leading up to the global financial crisis. We use z- score and stock return volatility to measure standalone risk of an individual bank, and the conditional value at risk measure (CoVar) of Adrian and Brunnermeier (2010) to measure the risk posed by an individual bank to the banking system as a whole. We find that generous financial safety nets increase bank risk and reduce systemic stability in non-crisis years. However, bank risk is lower and systemic stability is greater during the global financial crisis in countries with deposit insurance coverage. Nevertheless, the overall effect of deposit insurance over the full sample we study remains negative since the destabilizing effect during normal times is greater in magnitude compared to the stabilizing effect during global turbulence. Consistent with the prior literature, we also find that good supervision can enhance the positive effects of deposit insurance during turbulent periods and dampen the negative effects due to moral hazard 2