rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4: Summary of assessments on the feasibility of MDG4 by 2015**

||On current trends|On simulations|UNDP judgement|
|---|---|---|---|
|Guinea|off track|Policy package not enough|Possible to achieve if some<br>changes are made|
|Senegal|off track|Policy package not enough|Very likely to be achieved, on<br>track|
|Mali|seriously off track|Achievement close with policy<br>package|Possible to achieve if some<br>changes are made|
|Niger|on track|Achievement possible with policy<br>package|Very likely to be achieved, on<br>track|
|Kenya|seriously off track|Policy package not enough|Possible to achieve if some<br>changes are made|
|Uganda|almost on track|Achievement close with policy<br>package|Off track|
|Madagascar|on track|Achievement close with policy<br>package|Very likely to be achieved, on<br>track|
|Mozambique|on track|Policy package not enough|Very likely to be achieved, on<br>track|

Source: Authors’ calculation from DHS surveys. Note: UNDP judgment taken from <u>http://www.mdgmonitor.org</u> (April 27, 2009).

Niger is the only country where the simulated policy package would achieve more than is needed to reach MDG4 in 2015. Since it is the country with the second highest U5M in our sample, this could be explained the fact that there is still a lot of scope for improvement and, hence, strong policy leverage. Niger is also the only country where all three judgments converge to say that achievement of MDG4 is within reach. The other Sahel country, Mali, would achieve the highest reduction of almost 100 per thousand points, but would still come short of meeting the 2015 target. . Yet, the remaining gap would not be too large if it were to implement the full policy package, the underlying mechanism being similar to the Niger case. However, recent trends are not particularly encouraging or indicating that the country moves into the right direction.

In contrast to Niger, the other three of the four fairly successful countries in the past, Mozambique, Madagascar, and Uganda, would only make little additional progress. This is particularly worrying in the case of Mozambique where the U5M rate – despite recent progress – still stands at fairly elevated levels. All three countries seem to have used quite some of the policy space to reduce U5M in recent years, such that future reductions or a continuation of past trends would come only with more effort in improving other conditions relevant for the reduction of child mortality. Among such unobserved conditions, health infrastructure and the quality of health services are likely to figure most prominently. This holds in particular for Mozambique, where households seem to be less demand-side constrained (more supply-side constrained) than in other countries. For this country, our 39
**3.**

**swatiAtical methWology that** may constrain the analysis, and establish a baseline for 1988 against which **te mr sure future financial** trends.

**A second objective** of the studv is to identify SM policies and programs among the major otficial **sources** of external financi-g. In view of the four-month timetable to complete this study, **-t concentrates** on Official Developni. nt Assistance 'ODA) and does not include nongovernmental and **private** contributions **except** for official contributions to nongovernmental organizations (NGOs)<sup>3</sup> .

**The expectations** of this review are modest in view of four factors:

1. **The** wide variations in statistical methods for recording maternal health data make it difficult to compare data sets.

2. **New** projects started immediately after the conference are unlikely to have recorded expenditures in time to be incltded in this review.

3. **The absence** of international uniformity on the criteria for the initiative and the wide **range of** activities with direct and indirect maternal health effects means that much of the data were based on interpretations.

**4. The relatively** brief duration of the study.

# **B.** **_A nmptioiu_**

1. The study looks at financial flows rather than cost-effectiveness, and does not assume that magnitude of investment is directly correlated to improvements in maternal health.

2. Because there is no comprehensive global system to monitor health expenditures by developing countries or external sources, the data would have to be obtained from each financial source.

3. Because of variations in statistical recording systems and in definitions of health, full comparability would be difficult to assure.

4. It would be premature to expect identifiable new financing specifically addressing the initiative. Official statistical systems have not been adjusted to identify SM components. As a result, the data reflect unofficial estimates derived from multiple sources. The author, not the sources, is responsible for thc composite estimate.

_5._ To accomplish the study in the four-month period prior to the June 1989 Meeting of **Interested** Parties, direct interviews with the major bilateral and multilateral financial sources were considered preferable to risking a low response rate to a mailed questionnaire. For developing countries, however, the sources of information were resident WHO and UNDP representatives.

**While** NGOs are known to be actively and extensively involved in maternal health, financial data from **the estimated** 1,500 international NGOs on maternal health activities over the past few years would **be very difficult to** obtain.
# **IV. Estimation Results**

As a first step, we check whether our data sets are representative of the same underlying population (Assumption 1) by performing means difference tests across key predictors. Given that the PDM is a subsample of the Targeting or ProGres data sets, we need a test that is suitable for partially overlapping samples. Here we use the same test proposed by Verme at al. (2016, 58) applied to refugee ProGres data in Jordan. Table 1 provides the results. It shows that all the variables are not significantly different in terms of means, indicating that the two samples are representative of the same population.

To evaluate the performance of the welfare estimation model, we consider three models. Model 1 includes demographic and geographic variables (region of residence and country of origin). This is the most parsimonious model and uses the variables that are most readily available in the ProGres data set. Model 2 adds to Model 1 variables related to animal and asset ownership. Model 2 is richer than Model 1, but it is more demanding in terms of the control variables, which may also be less reliable or more likely to be missing in the census data. Model 3 adds to Model 2 variables measuring coping strategies. To test for multicollinearity, Table 2 reports the variance inflation factor (VIF) for the different models. It shows that no variable has a VIF that is over 5, and the mean VIF is smaller than this threshold. We conclude that multicollinearity is not an issue for any of the models considered.

Next, we test the out-of-sample performance and possible overfitting of the three models, using PDM data and the root mean square error (RMSE) and mean absolute error (MAE) as performance functions. To do so, the data set is split into five equal folds. In the first iteration, the first fold is used to test the model, and the rest are used to train the model. In the second iteration, the second fold is used as the testing set, while the rest serve as the training set. This process is repeated until each of the five folds has been used as the testing set. The performance function is obtained as the mean across the five iterations.

For the food consumption aggregate, the three models have similar measures of goodness-of-fit for both indicators. Model 1’s RMSE is 0.55, Models 2 and 3’s RMSE is 0.54. For the MAE, 10
# **Annex 5. Methodology: The Impact of Structural Reforms on GDP Growth**

For each policy variable, it is possible to compute the annual GDP growth that would be added if Serbia managed to close the gap with Germany in 20 years. In particular, assume that

- 0

- • 1 • zz = vvgglluuee ooff ssttssuussttuussggll vvggssvvggvvllee ffooss SSeessv gg zz = vvgglluuee ooff ssttssuussttuussggll vvggssvvggvvllee ffooss GGeessGGggllyy

- where Y is output per capita. yy = ln YY

The new level of GDP per capita for Serbia associated with the reform is computed as follows: 1 0 1 0)) where y0 is the level of GDP per capita in 2016 and yy = yy + ββ (zz is the weighted average of the estimated elasticity −zz 1 0 1 0)) for each structural variable along the relevant input channels, with weights as in equation (1) in the main text. ββ Therefore, g is the annual GDP growth rate that would be added by structural reforms if Serbia managed to close the gap with Germany in 20 years.

# **Annex 6. Details about Work-Time Regulation**

The CBR Labor Regulation Index Dataset (CBR-LRI) provides data on labor law for 117 countries from 1970 to 2013. A value between 0 and 1 is assigned to each indicator for each year covered, where 0 stands for absence of any protection for workers and 1 is assigned for the highest protection. Five areas of the law are covered: definition of the employment relationship and different forms of employment; work-time; dismissal; employee representation; and collective action.

The index is a measure of the extent to which a certain aspect is regulated (protected). Note that existence of a law regulating a certain aspect does not say anything about its actual application.

Table A6.1 reports on regulation of work-time for Serbia over time and in comparison to Germany.

**Table 13 Work-time Regulation, Serbia and Germany**

|**Regulation of work-time**||**Serbia**||**Germany**|
|---|---|---|---|---|
|Annual leave entitlement|1991: 0.6<br>2001: 0.67|20 days|1970: 0.5<br>1995: 0.67|24 work days if 6-day<br>week; 20 if 5 day<br>week: i.e., 4 weeks.|
|Public holiday entitlement|1991: 0.67<br>2007: 0.83|15 days|1970: 0.6|10.3 public holidays|
|Overtime premium|1991: 0<br>2001: 0.26|26% increase of<br>basic salary|1970: 0.25|25% general norm|
|Weekend work|1991: 0<br>2001: 1|At least one day<br>weekly rest|1970: 0.25|Sunday is not a normal<br>working day. Premium<br>25‒50%|

39
|**Table 2**<br>**Variable**|**Definition**<sup>**~~*~~**</sup>|**Mean**|**Standard**<br>**error**|**Minimum**|**Maximum**|
|---|---|---|---|---|---|
|gr|Annual growth of log per capita<br>exp, 92-99:<br>Ln(pcx99) – Ln(pcx92)/7|0.04|0.04|-0.15|0.15|
|y|Log expenditure pc 92: Ln(pcx92)|9.46|0.23|8.74|10.20|
|gini|Expenditure Inequality: Gini<br>coefficient wrt pcx|0.31|0.03|0.20|0.50|
|gi_hyredu|Education Inequality: Gini<br>coefficient wrt household head<br>education years|0.58|0.09|0.34|0.99|
|n_hyredu|Household head’s education,<br>number of years|3.59|1.00|0.04|8.41|
|n_spyredu|Spouse’s education, number of<br>years|1.36|0.54|0.00|3.67|
|hage|Age of household head|42.17|2.18|30.89|47.75|
|hfem|Gender, equals 1 if household<br>head female|0.28|0.08|0.11|0.75|
|aesize|Adult equivalent household size|3.90|0.55|2.16|8.09|
|pchild<br>*Note: all o<br>exception of|Percentage of children in<br>household<br>bservations are sub-county means of t<br>the Inequality measures. No. of obse|0.44<br>he household<br>rvations: 719|0.06<br>values of th<br>|0.12<br>e variables me|0.60<br>ntioned, with the|

# **4. Estimation**

The properties of estimators obtained from downstream<sup>3</sup> regressions using imputed values for welfare indicators are investigated in Elbers et al. (2005). Their main proposition is that coefficients from regressions involving imputed welfare indicators which have been derived with small area estimation techniques, either on the LHS or on the RHS, do not differ systematically from regressions with ‘real data’. The 10
found similar effects in the 2014 follow-up survey that suggest measurement error.<sup>14</sup>

# **3 IMCI Classification and Illness Spells**

Throughout the paper, we identify “overuse” and “underuse” of care _relative to the IMCI guidelines_ proposed by the WHO. The C-IMCI is part of the community and family arm of the IMCI itself, the WHO’s and UNICEF’s primary children’s health program, which has been adopted by over 80 countries (Bryce et al. (2004)). The C-IMCI goes through a set of simple checks in order to spot danger signs and classify symptoms into gastrointestinal illness, respiratory illness, malaria, and so on. It targets the causes of about 70% of child deaths.<sup>15</sup> All diagnostic tests can be operationalized in the field. The effectiveness of these checks in identifying the underlying disease was rigorously tested (see Gove (1997) and the studies therein). Based on the duration and severity of symptoms, the C-IMCI algorithms make recommendations for when formal medical care should be sought (see below). The C-IMCI is explicitly tailored to a developing-country context, and we assume that its careseeking recommendations take into account factors like the expected quality of care and resource constraints.<sup>16</sup> What justifies the use of the IMCI to assess need for care? Ultimately, we are interested in assessing the allocation of acute care. However, the optimal allocation is unobserved.

> 14In 2014, we prompted parents to report visits from any NGO workers who measured the child’s weight or height in the last 2 weeks, and we asked whether the child had a card entitling them to receive free health care at the clinic (still avoiding direct mention of Action for Health). We found that 12% more CHW visits were reported in the full program group than in the CHW-only group, but we also found that 10% of families reported visits in the subsidy-only group, and we cannot reject that the sum of the CHW-only and subsidy-only effects equals the full program effect. Moreover, we found both a greater share of families reporting the subsidy card in the full-program group than the subsidy-only group and a share of respondents reporting the subsidy in the CHW only group (Dean and Sautmann, 2023). Together, these numbers suggest misreporting or misidentification of aspects of the different program components of Action for Health. It is possible that some of the reported CHW visits in the full program group were clinic staff making home visits, which suggests some measurement error in formal care received.

> 15The five main causes are diarrhea, pneumonia, measles, malaria, and malnutrition. According to Unicef estimates, around 50,000 children under five died in Mali in 2017, 17% of whom died of pneumonia, 10% of diarrhea, and 14% of malaria.

> 16Mayo Clinic recommendations for children in the United States, for example, have lower thresholds for seeking medical advice. They advise to call a doctor after 24 hours of diarrhea, and state on fever: “When in doubt, go ahead and call your child’s doctor, whether you think your baby’s temperature is abnormally high or abnormally low.” (Mayo Clinic (2014, 2016)).

16
**Table 8: Africa-wide impacts from uniform climate scenarios**

|**Impacts**|**2.5°C**<br>**warming**|**5°C**<br>**warming**|**7%**<br>**decreased**<br>**precipitation**|**14%**<br>**decreased**<br>**precipitation**|
|---|---|---|---|---|
|**Dryland**|||||
|ΔNet revenue|-72.2|-120.4|-14.1|-28.3|
|($ per ha)|_(-16%)_|_(-30%)_|_(-6%)_|_(-11%)_|
|ΔTotal net revenue<br>(billions $)|-22.6|-37.7|-4.4|-8.9|
|**Irrigated**|||||
|ΔNet revenue|110.3|258.8|-15.9|-31.5|
|($ per ha)|_(9%)_|_(23%)_|_(-1.4%)_|_(-2.7%)_|
|ΔTotal net revenue<br>(billions $)|1.4|3.4|-.21|-0.41|
|**Total (Africa)**|||||
|ΔNet revenue|-49.2|-95.7|-18.3|-37.2|
|($ per ha)|_(-11.3%)_|_(-21.9%)_|_(-4.2%)_|_(-8,5%)_|
|ΔTotal net revenue<br>(billions $)|-16.0|-31.2|-5.96|-12.1|

_Note:_ Using coefficients in Table 6 and changes to climate that are uniform across Africa. The numbers in brackets represent the percentage change in net revenue per hectare relative to the mean of the sample.

34
inherent in privatization.<sup>13</sup> The success of the IV estimation depends heavily on finding adequate instrumental variables that satisfy the exogeneity condition. As suitable instrumental variables are often difficult to obtain, the fixed effects estimation has been frequently used, especially in earlier studies.

In our evaluation, we use only estimates from the set of studies that employ fixed effects or IVs because they are less likely to suffer from selection bias. In the case of privatization, private (especially foreign) owners are naturally interested in acquiring firms that have (at least potentially) superior performance. Hence, studies that do not account for selection may erroneously attribute potentially superior performance of privatized firms to the new owners rather than to the inherently superior performance of firms selected for privatization.

In view of these issues, we consider 14 privatization studies covered by D-M that handle the selection/endogeneity problem and we add 20 studies that have been published or circulated as working papers by December 2007. In Table 2 we list these 34 studies, together with information on their region and performance indicator.

In assessing the effects of privatization, we focus on total factor productivity (TFP) and TFP growth (Figure 1a and 1b, respectively), profitability and growth in profitability (Figure 2a and 2b, respectively), and revenue level and growth in revenue (Figure 3a and 3b, respectively). We also discuss the main findings of studies dealing with labor productivity, employment, wages, and other indicators of performance (not reported in figures or tables). In the figures, we report separately results from studies dealing with Central-East Europe (CEE), including the Baltics and Balkans, and studies dealing with the Commonwealth of Independent States (CIS), which started the transition later and placed less emphasis on the development of a strong, market-oriented legal framework and institutions.<sup>14</sup> As could be expected, even within each category of performance (e.g., TFP), the various studies employ a variety of measures (e.g., revenues, sales, or value added). Since there are very few studies that use a homogenous measure of performance, we have

> 13 Alternatively, other suitable techniques such as difference in difference estimator and matching-type estimator can be employed, provided that adequate data are available.

> 14 For a more detailed discussion of the results of these studies, see Estrin, Hanousek, Kočenda and Svejnar (2007), which contains detailed tables listing region, time period, performance measure, types of ownership, and resulting effects separately for each available study from the CEE, CIS and China.

17
-12-

Table **4: Distribution of Annual Household Income Per Capita (%, Leva)**

||Na|tional|U|rban|Ru|ral|
|---|---|---|---|---|---|---|
|Income<br>Decile|Share<br>(%)|Average<br>(Leva|Share<br>(%)|Average<br>(Leva)|Share<br>(%)|Average<br>(Leva)|
|Bottom|4.2|6,941|4.5|6,816|3.8|7,507|
|Second|5.6|9,361|5.9|8,882|5.6|11,020|
|Third|6.5|10,900|6.7|10,101|6.6|13,037|
|Fourth|7.4|12,387|7.6|11,359|7.5|14,789|
|Fifth|8.3|13,928|8.4|12,654|8.5|16,622|
|Sixth|9.3|15,643|9.4|14,095|9.5|18,663|
|Seventh|10.6|17,778|10.5|15,844|10.5|20,856|
|Eight|12.1|20,380|12.1|18,192|12.0|23,608|
|Ninth|14.3|24,121|14.4|21,598|14.2|28,265|
|Top|21.8|36,653|20.7|31,357|21.8|42,784|
|Decile Distr|ibution Ratio 0.6|6|0.|70|0.65||
|Gini Coeffi|cient<br>25.|8|2|4.6|26.|5|

Source:World Bank staff estimates from 1992 Individual Budget of Households Survey, NSI.

in the definition of income employed by the NSI survey. We assessed, therefore, the effect that such an inclusion might have on income levels and income distribution data. We made a number of adjustments to income as defined by the survey. Savings withdrawals are excluded as they do not belong to current income. In 1992, they accounted for about 9 percent of total household income as defined by the NSI. Theoretically, one should include imputed income which represents income that would be received if an asset were rented - rather than sold - in the marketplace instead of being used by the owner, although in practice, this is extremely difficult. In general, income is not easily observable and measurable, especially during periods of radical changes in the structure of remuneration and
**_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

**Table 3** Average Annual Change in United States Imports and Exports, Final and Intermediate Goods in Three Industries, 1997–2009

||**Average annual change (percent) — value of**<br>**trade**<br> <br>|
|---|---|
|**Imports**|**1997–2007**<br>**2007–2009**|
|Electronics final goods|8.7<br>–3.8|
|_Electronic components_|–0.4<br>–11.0|
|Motor vehicles|5.8<br>–23.0|
|_Motor vehicle parts_|7.1<br>–20.2|
|Apparel|4.9<br>–7.4|
|_Textiles and fiber_|1.6<br>–15.8|
|**Total non-petroleum Imports**|**7.3**<br>**–10.1**|
|**Exports**||
|Electronics final goods|8.0<br>–17.3|
|_Electronic components_|6.7<br>–8.9|
|Motor vehicles|0.5<br>–9.5|
|Motor vehicle parts|0.6<br>–14.7|
|Apparel|–8.7<br>–3.8|
|_Textiles and fiber_|4.2<br>–11.7|
|**Total non-petroleum exports**|**4.8**<br>**–6.2**|

_Source:_ U.S. International Trade Commission, http://dataweb.usitc.gov Because electronic hardware and systems are rightly perceived as having a “propulsive” effect on other industries, and because deep expertise has tended to be concentrated in only a few places (for example, in Silicon Valley, California, and in large firms based in the United States, Europe, and Japan), politicians and policy makers have been loath to put too much pressure on firms to produce locally or to put up barriers to trade, even during economic crises. Intense competition, at first between American and Japanese producers, is what pushed early fragmentation of electronics GVCs, rather than trade barriers and local content rules. Producing electronic hardware in low-cost locations lowers prices, which speeds adoption of information technologies at home and leads to productivity spillovers (Mann and Kirkegaard 2006). Because trade barriers have been minimal in this industry worldwide, the main impact of the economic crisis has been to sharply reduce demand, driving the full absorption of operating inventories and accelerating existing trends toward consolidation and low-cost geographies discussed throughout this paper. However, the crisis may have hastened the longstanding trends of consolidation and supplier learning and GVC upgrading that will be discussed at length in subsequent sections of the paper.

# **_Value Chain Modularity_**

Why is it that before and likely after the crisis, GVCs in the electronics industry are more extensive and dynamic than in any other goods-producing sector? One reason is that electronic parts and most final products have a high value-to-weight ratio that makes long-distance shipping relatively inexpensive. For the high-value components and some final products, such as notebook computers and mobile phone handsets, air shipment is common. Obviously, low transportation 9