rafmacalaba/datause-encoder-data huggingface dataset:
# **III.4. Efficiency Change Over Time**

To examine the evolution of input and output efficiency over time, we computed the efficiency scores in two different time periods: 1975-1980 and 1996-2002 for education study, and 1997-99 and 2000-02 for health study, the construction of which is driven by data availability. Appendix D reports the results on a regional basis<sup>14</sup> .

Comparison of different input-output bundles in different time periods has to be done carefully because the frontier can be shifting outward through time. In some cases the frontier displacement can be parallel (such as in the life expectancy case of Figure 9). In others, the frontier displacement can be very uneven (biased frontier shift in Figure 9) reflecting biased technological change (see Appendix D for detailed discussion).

<!-- Start of picture text --> Frontier Shift: Life Expectancy Frontier Shift: Gross Secondary Enrollment<br>LVA KOR BRB<br>CRI URY<br>KWT<br>CRI KWT<br>KWT<br>ARGIDN<br>GTM<br>KOR<br>OMN<br>KOR<br>600 800 1000 1200<br>350 400 450 500 550 600<br>Orthogonalized Public Expenditure on Education<br>Orthogonalized Public Expenditure on Education<br>Eff. Frontier 1975-80 Eff. Frontier 1996-02<br>Eff. Frontier 1997-99 Eff. Frontier 2000-02<br>Data Source: World Bank WDI<br>Data Source: World Bank WDI<br>Parallel Shift Biased Shift<br>78 100<br>80<br>76<br>60<br>40<br>Life Expectancy 74<br>20<br>Gross Secondary Enrollment<br>72 0<br><!-- End of picture text -->

**Figure 9. Efficiency Frontier Shift Over Time**

The detailed comparison between observed input-output combinations in different time periods distinguishes whether variations in the levels of input utilization or output production levels are due to changes in efficiency or changes in technology. This testing is possible with observed levels of inputs and outputs, and are based on the concept of a Malmquist Index (Fare, Grosskpof, Norris and Zhang, 1994). This method has been used to study productivity change in the OECD economies, as well as productivity in agriculture across the world (Coelli and Rao, 2003; Nin, Arndt, and Preckel, 2003). Appendix D describes details of the method that uses some of the efficiency scores calculated in previous sections, and the index that will facilitate the analysis of productivity change through time.

> 14 Scores for individual countries are available at the PRMED website indicated in footnote 1.

24
12

|Argentina<br>N|<br>16<br>ta72|34|||100|
|---|---|---|---|---|---|
|Brazil|21|43|57|91|100|
|Chile|14|28|51|74|100|
|Colombia|16|36|67|81|100|
|Ecuar &|20|37|83|98|100|
|Mexico|15|36|57|94|100|
|Venezuela|29|50|85|100|100|

# D. Plenty Out of Sample Testing: (Brazil 99 Ecuador 99 Colombia 98 Mexico 94)

Three crises (Brazil, Ecuador and Colombia) have taken place in our group of countries since we finished the first draft of our paper (July,1998). In this section we show how :he models predicted the vulnerabilities in each of them. Additionally, we'll see how ilhis methodology would have predicted the Tequila crisis. The methodology is based o:l estimating an ARIMA model for the IMVEQ, using a five year rolling sample, ; nd beginning two years prior to each crisis. A signal will be generated in each of the 24 months of the window prior to the crisis.

In the case of the Brazilian crisis (Jan/99), we estimated an ARIMA model for Ihhe IMVEQ beginning in January 97, with monthly information since Jan 92. For Febru:Lrv 97, the same procedure is repeated with information since February 92, and so on. 1 he signal is flashed when the six-month moving average exceeds zero. Graph I summariz es the evolution of the leading indicator derived from the Arima-residuals model, show:ing that 10 signals were issued in the 24-month window prior to the crisis. Graph 2 is -he leading indicator derived from the chartist model, showing that 13 signals were issuLe(d prior to the crisis.

Exactly the same procedure was followed to study the crisis that took place in Ecuador in February,1999. Graphs 3 and 4 summarize the signals sent by the Arima residual moJ:LeL and the chartist one (chosen simply because of its computational ease): the first cne signaled 23 times and the second one 20 times in the 24-month pre-crisis period. For lihe Colombian crisis of September/98 the same approach is taken and the results , rc summarized in Graphs 5 and 6. The residuals model flashed 14 signals while the chart sil one did it 10 times.
|Gas FlaringReduction Project|Hannachi Morsli|
|---|---|
|Gas FlaringReduction Project|Hannachi Morsli|
|Gas FlaringReduction Project|Hannachi Morsli|
|Oil and Gas Sector Development Loan Project||
|Power Distribution and Transmission Project(02)|Sumith Pilapitiya|
|Power Distribution and Transmission Project(02)|Sumith Pilapitiya|
|Power Distribution and Transmission Project(02)|Sumith Pilapitiya|
|Power Distribution and Transmission Project(02)|Sumith Pilapitiya|
|Power Distribution and Transmission Project(02)|Sumith Pilapitiya|
|Power Distribution and Transmission Project(02)|Sumith Pilapitiya|
|Power Distribution and Transmission Project(02)|Sumith Pilapitiya|
|Child Survival and Safe Motherhood Project|AnthonyR. Measham|
|Child Survival and Safe Motherhood Project|AnthonyR. Measham|
|Child Survival and Safe Motherhood Project|AnthonyR. Measham|
|Child Survival and Safe Motherhood Project|AnthonyR. Measham|
|Child Survival and Safe Motherhood Project|AnthonyR. Measham|
|Child Survival and Safe Motherhood Project|AnthonyR. Measham|
|Structural Adjustment Loan and Credit Project||
|Structural Adjustment Loan and Credit Project||
|Maharashtra ForestryProject|Irshad A. Khan|
|Maharashtra ForestryProject|Irshad A. Khan|
|Maharashtra ForestryProject|Irshad A. Khan|
|Maharashtra ForestryProject|Irshad A. Khan|
|Maharashtra ForestryProject|Irshad A. Khan|
|Maharashtra ForestryProject|Irshad A. Khan|
|Maharashtra ForestryProject|Irshad A. Khan|
|Maharashtra ForestryProject|Irshad A. Khan|
|Maharashtra ForestryProject|Irshad A. Khan|
|Maharashtra ForestryProject|Irshad A. Khan|
|West Bengal ForestryProject|Jessica Mott|
|West Bengal ForestryProject|Jessica Mott|
|West Bengal ForestryProject|Jessica Mott|
|West Bengal ForestryProject|Jessica Mott|
|West Bengal ForestryProject|Jessica Mott|
|West Bengal ForestryProject|Jessica Mott|
|West Bengal ForestryProject|Jessica Mott|
|Power Sector EfficiencyProject|Argun Ceyhan|
|Power Sector EfficiencyProject|Argun Ceyhan|
|Power Sector EfficiencyProject|Argun Ceyhan|
|Power Sector EfficiencyProject|Argun Ceyhan|
|Power Sector EfficiencyProject|Argun Ceyhan|
|Power Sector EfficiencyProject|Argun Ceyhan|
|Power Sector EfficiencyProject|Argun Ceyhan|
|Power Sector EfficiencyProject|Argun Ceyhan|
|National Aids Control Project|Salim J. Habayeb|
|National Aids Control Project|Salim J. Habayeb|
|National Aids Control Project|Salim J. Habayeb|
To convey an idea of how the CPIA has changed over the years, it is instructive to compare the guidance given in 1999<sup>27</sup> and that in 2015.<sup>28</sup> For the sake of compactness, only the criteria for a rating of ‘2’ and of ‘5’ are given, and that only for Question 4 on trade.

In 1999, the guidance for question 4 on trade, for ratings of ‘2’ and ‘5’ was: “2. Average tariff (weighted by global trade flows) is high (over 30%). High and erratic import and/or export barriers, including quantity restrictions and duty exemptions. Customs authorities make discriminatory or <u>ad hoc</u> exemptions and valuations. Administered foreign exchange regime with multiple exchange rates.

“5. Average tariffs (weighted by global trade flows) is low (10% or less), with low dispersion and insignificant or no quantitative restrictions or export taxes. No trading monopolies. Indirect taxes (e.g. sales, excise, surcharges) do not discriminate against imports. Efficient and rule‐bound customs administration. IMF Article 8 status. Minimal or no foreign exchange restrictions on long‐term investment capital inflows.” In 2015, the guidance for question 4 on trade, for ratings of ‘2’ and ‘5’ was: “2. a. Very discretionary and discriminatory trade regime with widespread and discretionary use of highly trade‐restrictive NTBs (e.g. quantitative restrictions); no consultation for NTMs; high and variable taxation of imports or exports (e.g., average MFN tariffs plus all other taxes collected at the border above 15%), with discretionary and nontransparent exemptions; haphazard application of special import regimes, and refunds (e.g., duty drawbacks) rarely made; several changes in export regulations each year; severe limitations on service trade with high level of discretion (e.g., STRI between 60 and 80); very limited data transparency (e.g., through collection and publication of statistics).

“b. Some trade facilitation strategy at national or agency level, some one‐way dialogue with stakeholders. Some ad hoc data available on clearance times. Difficult to obtain latest and complete information on trade‐clearing procedures and formalities. Appeal mechanism established but difficult to access. Widespread corruption in border management agencies, but visible attempts to address the issues. Heavy reliance on physical inspection of goods, but some risk‐based selectivity (e.g., over 75% Customs physical inspection). Frequent delays and unreliability in the clearance of goods. Customs declarations submitted in paper form but processing supported by IT system in Customs. Limited access to foreign transport service providers (e.g., freight for both domestic and transit movement is allocated by quota system).

“5. a. Low barriers and transparent trade regime with limited use of NTBs; formal review process for NTMs, low taxation of imports or exports (e.g., average MFN tariffs plus all other

> 27 See International Development Association, 2000. _IDA Country Performance Rating Process: Annual Report 1999._ February.

> 28 See The World Bank Group, 2015. _CPIA 2015 Criteria._ September 24, 2015.

33
in various low-income countries. The relative merits of establishing such an institution, however, would go beyond the scope of this paper.

# **4.  Conclusion**

The results of recent trade finance surveys conducted, among others, by the World Bank and the IMF suggest that emerging markets have been impacted severely by the lack of available trade financing options during the financial crisis. Both bankers and the international community have since called upon state-backed ECAs to expand their operations to mitigate credit risk and keep trade finance markets in developing countries from drying up.

Most ECAs have been founded with the stated objective of promoting foreign trade and/or alleviating financial sector related constraints in the provision of the necessary financial products to domestic firms involved in foreign trade. Given the renewed interest in these institutions and the fact that such institutions have long been part of the financial sector landscape in many advanced and emerging countries with relatively large foreign trade sectors, the question arises whether less developed countries should follow suit and establish their own agencies to support exporting firms and to shield their economies against complete trade finance shortages in times of crisis.

A number of issues require attention when deciding whether a country should establish a specialized financial institution to finance exports as well as what shape, form and modus operandi it should have. Any decision to establish such a specialized financial institution to finance exports should be made only after a careful evaluation of its potential impact on both the financial and the real sector of the economy. In addition, the choice of a sustainable business model is shown to be crucial. We argue that a sustainable business model involves a strong governance structure as well as the availability of a sufficiently large capital stock for the institution to be capable of acting independently and to have a strong enough operational footprint to achieve its objectives. These are two preconditions that are seldom met in lowincome economies, which often suffer from sovereign debt problems, weak institutional capacity, poor governance practices and difficulties in applying the rule of law.

ECAs are institutions that can – under the right circumstances, rules, and discipline – help alleviate market failures in the effective provision of export credits as well as insurance and guarantees. This is especially true in times of crisis. However, conditions for an ECA to be effective are demanding in terms of economic environment, institutional design, and governance structure. Such pre-requirements make ECAs more suitable for more matured economies than for low-income countries that are still often confronting many basic development challenges.

17
Table 2.6o Rice Production. Are., and YivIde, 1966-1978 to 1976-1930

||Average<br>of|Production<br> <br>Paddy|**Averae.**<br>Annual<br>Rate<br>of<br>Growth|Averago<br>(thousand|<br>Area<br>**hectares)**|**Aver-go**<br>Annual<br>Rate<br>of<br>Oroeth|Ae.rago<br>yiel|Paddy<br>d|Average<br>Annual<br>Rate<br>of<br>Groeth|
|---|---|---|---|---|---|---|---|---|---|
|Country|(thousand|estric<br>tons)|1966-75<br>to<br>1976-S0|||1966-75<br>to<br>1976.-0|Cmetric<br>hecta|tons<br>per<br>rt)|1966-75<br>to<br>loPs-SO|
||1966-75|1976-80|(percent)|1966-75|1976-80|**(percent)**|1966-75|1976-80|(percent)|
|East<br>AELL|137.946|164.149|2.3|38.022|40,988|1.0|3.63|4.01|1.3|
|China|110.164|134.400|2.7|32,420|35.640|1.3|3.39|8.77|1.4|
|Japan|16.090|14.793|-1.1|2.944|2.592|-1.7|5.48|5.71|0.6|
|South<br>Korea|5.5s0|7.135|S.4|1,203|1,222|0.2|4.62|6.83|3.2|
|Southasat<br>A;a<br>b|<br>49.391|62.966|J.3|24.035|28.032|1.6|1.98|2.24|1.7|
|Indonesia|18.736|25.696|4.3|7,979|8,906|1.5|2.34|2.89|2.9|
|Malaysia|1.679|1.862|1.4|679|709|0.6|2.46|2.62|0.6|
|Philippines|5 060|7 .221|4.9|3,287|3,524|0 .|154|2<br>05|3.9|
|Thailand|13.182|16.400|3.0|7,476|8.990|2.5|1.76|1.82|0.4|
|South<br>RAia|9;<br>328|110.900|2.3|55.321|56.375|0.7|1.69|1.90|1.6|
|Sangladosh|16.700|19.230|1.9|9,793|10,071|0.4|1.71|1.91|1.5|
|autoe|8.054|9.579|2.3|4755|4.930|0.5|1.69|1.93|1.6|
|India|60.773|73.475|2.6|57,404|39,458|0.7|1.62|1.86|1.9|
|Nepal|2.268|2.306|0.2|1,184|1,264|0.|1.91|1.02|-0.6|
|Pakietan|3.212|4.589|4.9|1.627|1,941|J.3|2.09|2.36|l$|
|Sri<br>Lanka|1.321|1.721|8.6|6t5|702|0.9|2.00|2.41|2.7|
|TOTAL<br>ASIA.|279.666|338.015|2.6|118,276|127,375|1.0|2.o7|2.66|1.6|

*Includes North Korea and Taiwan. blncludea Laos and Vietnam. Clncludes North Korea, Teiwan, Laos *nd Vietnam.

So.see: Adapted from Randolph Barker and Robert W. Mlrdt, with Seth Rose. ThRica Econosu of A-i(Wehington, Resowrces for the Future, 1985), pp. 45-47.

O.C.
**Figure 2. Nutrition Outcomes over Time**

<!-- Start of picture text --> 25<br>23<br>20<br>20<br>15<br>15<br>2003/4<br>12<br>10 2006/7<br>10<br>2011<br>5<br>3<br>2<br>0<br>Stunted Underweight Wasted Iodized salt<br>Percentage<br><!-- End of picture text --> _Source:_ Authors’ calculations based on DHS 2003/04, MICS 2006/07, and ENPSF 2011.

- 4.1.3 Cognitive, Emotional, and Social Development

Moroccan children face a number of challenges in terms of their cognitive, emotional, and social development; relatively little progress has been made over time (Figure 3). In 2006/07, approximately 51 percent of children aged five received early childhood care and education (ECCE). By 2012, this rate had risen to 58 percent. However, over a similar period, the percentage of children engaged in developmental activities fell from 48 percent (in 2006/07) to 34 percent (in 2011).<sup>23</sup> The low level of engagement in developmental activities is of particular concern, as it means that two-thirds of the children are missing out on these important opportunities.

Most concerning are the high chances of violent discipline, with 90 percent of children experiencing violent discipline in the past month, substantially endangering their development. However, once again, we lack the data to examine trends among this proportion of children over time. Work or domestic work done by children aged 5 is also a potential problem, with 20 percent of children engaged in such work. This may make the transition to school more difficult and could be potentially hazardous to children’s well-being. Here too, available data do not allow for comparisons over time, given that the outcomes are from a single survey.

> 23 In the 2006/07 data, the question was asked about all children under the age of five. In the 2011 data, the question was asked about the youngest child only. Additional analyses showed that at every age children were substantially less likely to be engaged in development activities in 2011 as compared to 2006/07. Thus, differences over time are not driven by differences in who the question covers.

10
We next analyze periods of under- and overestimation separately. While statistical capacity reduces both, voice and accountability only reduces overestimation. The nighttime light-adjusted measure suggests a smaller decline in economic activity than reported in the national accounts for countries with a higher share of value added by industry and especially services (and a lower share of value added by agriculture). COVID-19 hit economies with a large service sector especially hard, because the sector relies heavily on human-to-human contact and it was partially shut down to prevent the transmission of the virus. Table I.1 in the appendix shows that both reported as well as nighttime light-adjusted GDP growth during the pandemic declined more with higher shares of services. However, the deviations between the two measures suggest that these countries may have overestimated the impact of COVID-19 (and hence underestimated GDP growth more).

It might seem that the result could reflect a different elasticity of value added in manufacturing and services compared with agriculture. However, we showed in table 5 that the elasticity between countries with above and below median agriculture is nearly the same. Moreover, the elasticity was higher for countries with an above-median share of services, suggesting that a possible bias would not weaken but strengthen the finding.<sup>24</sup>

# **7 Heterogeneous Subnational COVID-19 Impacts**

With our estimated elasticity, we can quantify different impacts of COVID-19 at the subnational level. Subnational data on economic activity are not often available for EMDEs, especially not at quarterly frequency. Nighttime lights provide an alternative way of measuring spatially disaggregated economic activity (see Section 5). Our elasticity estimate from Section 5.2 allows us to translate changes in nighttime lights into changes in economic activity.

We regress regional changes in nighttime lights at quarterly frequency on population density and a region’s average nighttime lights per capita, which is a proxy for its level of development. We estimate the following regression model:

> 24To make the argument work would require arguing that the elasticity between nighttime lights and value added in industry and services declined much less (or increased much more) than the elasticity between nighttime lights and value added in agriculture. However, we are not aware of any reasons to think that this happened.

24
# **Table D2: The Impact of Reference Period on Weekly Labor Market Reporting – with controls**

<mark>The impact of the reference period on labor market reporting Phone triweekly vs phone weekly</mark> <u><mark>(omitted category)</mark></u>

|Phone tr|iweeklyvsphon|e weekly (o|mitted category<br>Days-|)|Hours-|
|---|---|---|---|---|---|
||Working|Days|conditional<br>on work|Hours|conditional<br>on work|
|Panel A: Main effects|(1)|(2)|(3)|(4)|(5)|
|Phone triweekly|0.08***|-0.03|-0.52***|-0.14|-3.84***|
||(0.03)|(0.18)|(0.11)|(1.73)|(1.48)|
||[0.02]|[0.93]|[0.00]|[0.93]|[0.02]|
|Mean phone weekly|0.74|3.84|5.18|32.96|44.50|
|N|2,925|2,925|2,284|2,925|2,284|
|Adjusted R2|0.066|0.119|0.164|0.252|0.334|
|Panel B: Heterogeneity by self-empl|oyment status|||||
||(6)|(7)|(8)|(9)|(10)|
|Phone triweekly|-0.01|-0.47*|-0.56***|-3.93|-4.89**|
||(0.05)|(0.28)|(0.17)|(2.91)|(2.36)|
||[0.88]|[0.14]|[0.01]|[0.24]|[0.09]|
|Self-employed|-0.10**|-0.57**|-0.13|-7.28**|-5.46**|
||(0.05)|(0.28)|(0.14)|(3.20)|(2.63)|
||[0.09]|[0.09]|[0.44]|[0.09]|[0.09]|
|Phone triweekly*Self-employed|0.15***|0.76**|0.07|6.52*|1.82|
||(0.06)|(0.35)|(0.21)|(3.76)|(2.99)|
||[0.08]|[0.09]|[0.80]|[0.14]|[0.63]|
|Mean wage employed phone||||||
|weekly|0.79|4.15|5.23|36.95|46.57|
|Triweekly effect self-employed|0.15|0.29|-0.49|2.60|-3.07|
|p|0.00|0.21|0.00|0.25|0.10|
|Number of observations|2,925|2,925|2,284|2,925|2,284|
|Adjusted R2<br>Note: *** p<0.01, ** p<0.05, * p<0.|0.073<br>10, standard er|0.124<br>rors clustere|0.164<br>d by household|0.255<br>are present|0.335<br>ed in|

Note: *** p<0.01, ** p<0.05, * p<0.10, standard errors clustered by household are presented in parentheses. Multiple testing adjusted FDR-adjusted q-values are presented in square brackets. The omitted category are individuals who were working at baseline and completed weekly in person interviews. Regression are attrition weighted with weights corresponding to 1/number of weeks for which we have data for each individual. All regressions control for gender, age, marital status, education, days worked per week at baseline, hours worked per week at baseline, pay per week at baseline, self-employed at baseline, wage-employed at baseline, dummies for being employed in manufacturing, trade, services, the public sector, household dependency ratio, and log household assets, and week of interview dummies.

39
a U-shape. Second, adding cohort dummies to the regressions has little effect on the age coefficients and, therefore, minima and maxima remain fairly unchanged.

**Figure 2. Average marginal effects of subjective well-being over the lifespan with cohort dummies:**

<!-- Start of picture text --> IVS data (1981–2009)<br><!-- End of picture text --> <!-- Start of picture text --> ECA Western Europe<br>(1) squared (2) squared<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93<br>(3) cubic (4) cubic<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93<br>(5) non-parametric (6) non-parametric<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93<br>Age Age<br>Note: The panel numbers correspond to the regressions in Table 2<br>Linear Prediction<br>Linear Prediction<br>Linear Prediction<br><!-- End of picture text --> 12
<!-- Start of picture text --> Log OECD<br>measure of<br>regulation<br>(v3)<br><!-- End of picture text --> <!-- Start of picture text --> fun’<br>«dia= «@ bdasg ee. ° measureregulationLog ILO of<br>. = (eplex)<br>2 a<br>Fe nae « 90 OLD be<br><!-- End of picture text --> <!-- Start of picture text --> ite gor? oo fee » tos? (p 50)<br>i] °° oe “ss ee a5 « . lep_db<br>o1* yt 2<br>a a<br>0 5 1 1.62 -1 0<br><!-- End of picture text -->