rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4916

# **Abstract**

Product standards can have a dual impact on production and trade costs. Standards may impose additional costs on exporters as it may be necessary to adapt products for specific markets (cost-effect). In contrast, standards can reduce exporters’ information costs if they convey information on industrial requirements or consumer tastes that would be costly to collect in the absence of standards (informational-effect). Using a new World Bank database of European standards for electronic products, the authors examine the impact of internationally-harmonized European standards on European Union imports. They find that European Union standards for electronic products that are harmonized to international standards have a positive and significant effect on trade. The results suggest that efforts to promote trade in electronic products could be complemented by steps to promote standards harmonization. This might include, for example, restarting talks to extend the Information Technology Agreement to non-tariff measures and commitments to harmonize national standards in electronic products.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to explore the linkages between trade costs, facilitation, and economic development. This work is aligned with the project “Trade Facilitation and Economic Growth: The Development Dimension” in the Development Economics Research Group with support from the governments of Norway, Sweden and the United Kingdom through the Multidonor Trust Fund for Trade and Development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jswilson@worldbank.org and aportugalperez@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_Raw measures of skills_ _Measures of cognitive skills._ The measure is based on a test of reading proficiency administrated with the survey by the Educational Testing Service (ETS) (ETS 2014; Pierre et al. 2014).<sup>6</sup> Reading proficiency refers to not only being able to read a text but also to understand it and be able to reason from it (OECD 2012). Instead of a single score, individuals have a set of 10 scores ranging from 0 to 500, called “plausible values,” which is an estimation of the plausible range of his reading proficiency level given his performance on the test and his background characteristics (Von Davier, Gonzalez, and Mislevy 2009; OECD 2013; ETS 2014).<sup>7</sup> The OLS/Logit regressions are repeated ten times for each plausible value and the average coefficients and standard errors of the ten estimations reported.

_Measures of socioemotional skills._ The survey provides measures of the Big Five personality traits (the most used set of personality measures including: agreeableness, conscientiousness, emotional stability, extraversion, and openness to experience (John and Srivastava, 1999), grit —perseverance and passion for long-term goals— (Duckworth _et al.,_ 2007), hostile attribution bias —tendency to interpret others’ intents as hostile, which in turn fosters one’s antisocial and aggressive behavior— (Dodge, 2003), and the Melbourne Decision Making Scale —coping strategies for decisional conflict— (Mann _et al.,_ 1997). Surveyed adults responded to 24 questions, designed by psychologists, corresponding to those domains. Response categories range from 1 (“almost never”) to 4 (“almost always”). Scores for each domain is the average of the responses to the two to four survey questions corresponding to it. Table 1 presents the definitions and items of the socioemotional skills of the survey. Correlations among socioemotional measures are often significant but modest in magnitude, ranging from 0 (emotional stability and grit) to 0.28 (decision making and openness to experience) (table 2). Correlations between socioemotional measures and reading proficiency —the cognitive measure— range from 0.02 (with grit) to 0.21 (openness to experience).

> 6 The reading proficiency test is comparable to the one produced by the Program for the International Assessment of Adult Competencies (PIAAC), another large-scale survey covering 24 OECD countries (OECD 2013). 7 Plausible values are multiple imputations, drawn after data collection, generated by combining test results with all available background information such as gender, age, and education. This procedure, based on item response theory, allows to reduce the measurement error inherent in large-scale surveys and to report comparable performance scales because survey participants respond only to a subset of the assessment items.

6
“Integrated Management of Childhood Illness” recommendations (WHO [2005]).

All subsidized clinical care was provided by medical professionals and staff trained in country. The clinics in this study were financially supported by Mali Health. For cost control purposes, Mali Health conducted spot checks on diagnosis and prescriptions using bills submitted to Mali Health and the clinic’s treatment records and accounting. As a result, per-visit value of care was unaffected by the interventions (see above). These quality control measures mean there may be differences in the level of care received relative to an unmonitored CSCom. However, the qualifications of care providers and the facilities and materials they can access are typical for peri-urban Mali.

# **3 Experiment and Data Collection**

The experiment took advantage of a planned roll-out wave for the Action for Health program in early 2013. Mali Health conducted a household census in 2012 and selected twice as many households as could have been enrolled in the absence of the experiment, using a proxy-means test for the poorest third of families.

All compounds with one or more eligible households were randomly assigned to either a subsidy-only, CHW-only, full-program, or control group.<sup>6</sup> The unit of randomization is the compound, since we expect spillover effects for CHW visits among households who share their living space. The randomization was stratified by average household assets in the compound, number of eligible children at baseline, and compound location. The CHW in the CHW-only and the full-program group were trained and managed in separate teams to reduce spillovers.

The baseline data was collected in Fall 2012, and the first follow-up took place in 2013. The 2013 data contains detailed health calendars covering 9 weeks, including all health care visits, treatments received, and symptoms observed. This data was used in Sautmann et al. [2020] to study the effect of the interventions on the targeting of primary care for children

> 6The per-household cost of the CHW and subsidy components is approximately the same, so Mali Health was able to provide just one arm of the program to twice as many households.

9
**Figure 17: Corporate governance and risk management framework**

## **17a. Key features By type**

<!-- Start of picture text --> Across granular regions<br><!-- End of picture text --> <!-- Start of picture text --> Commercial Development Benchmark Europe WEST ECA EAST ECA<br>Compliance systems in place Debt sustainability limits Compliance systems in place Debt sustainability limits<br>Periodic stress testing Documented risk framework Periodic stress testing Documented risk framework<br>Independence risk management Independence risk management<br>17b. Internal review functions<br>By type Across granular regions<br>Commercial Development Benchmark Europe WEST ECA EAST ECA<br>Internal audit Compliance Internal audit Compliance<br>Risk management Internal control Risk management Internal control<br>Risk Committee Risk Committee<br>100% 100%<br>80% 80%<br>Percent Yes 60% Percent Yes 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>100% 100%<br>80% 80%<br>Percent Yes 60% Percent Yes 60%<br>40% 40%<br>20% 20%<br>0% 0%<br><!-- End of picture text -->

## **17b. Internal review functions By type**

# **_5.6 Monitoring and evaluation_**

Monitoring appears to be similar across SOFI types, although Commercial SOFIs are less likely to be audited by the government (Figure 19). SOFIs in Non-EU countries have the lowest proportion of institutions with rated debt instruments.

38
# **3. Tariff per unit if specific tariff   = (specific tariff per unit) * ER * unit value**

The tariff per unit that applies is then the maximum of the ad valorem or specific tariff rate; dividing the tariff per unit by the unit value yields the actual tariff rate.

# **4. Actual MFN tariff rate per unit  = max {ad valorem tariff per unit, specific tariff per unit}/unit value**<sup>**16**</sup>

In the tables, we typically multiply the above actual tariff rate by 100 to report the rates in percentage terms.

Given that imports from the CIS typically enter duty free, we also calculate the estimated collected tariff rate by assuming that all CIS imports enter duty free.

# **5. Estimated collected tariff rate =(MFN tariff rate)*( value of imports from non-CIS countries)/( value of imports from all countries)**

We discuss this estimate below.

# **Weighted Average Overall Tariff Rates**

For the overall tariff rates, we take both a simple average of the tariff rates on the tariff lines as well as a weighted average, where the weights are the shares of the total value of imports of each tariff line. With the weighted average calculation the more important import categories receive higher weights. The problem with the weighted average approach is that very high tariffs discourage and may eliminate imports. Then the weight of these high tariffs will be zero. Although both approaches have their merits and problems, unless otherwise stated, our results are based on simple averages.

# **Estimated Collected Tariffs**

Collected tariffs are less than the MFN tariff because of a several exemptions in the Russian tariff structure. Most notably, CIS imports usually enter tariff free (although

> specific tariff is the ratio of the unit value in dollars to the specific tariff in dollars and the exchange rate is in both the numerator and denominator and thus cancels.

> 16 If the sum of the two tariffs is applied, then

> MFN tariff rate per unit = {tariff per unit if ad valorem  + specific tariff per unit}/unit value.

12
as exports per market. Also, resource mobility will facilitate the export of higher quality products, which will tend to a have a somewhat different input mix than traditional or lower quality products. Finally, resources need to be flexible enough to allow the emergence of new export activities. Hence, a key challenge for policy makers is to ensure that land, labor, capital and technology are moving to (a) sectors in which the country has a long-term capacity to compete and (b) to the most productive firms within sectors. This necessitates a clear understanding of how the macroeconomic stance, the business environment, and trade and tax policies interact to affect investment, output and trade decisions.

A major impediment to export growth is the strong anti-export bias in Nigeria’s trade regime. Nigeria has traditionally had very high levels of MFN tariff protection and numerous import bans, and despite recent reforms continues to be more protectionist than the averages for SubSaharan Africa and the world. High trade and transport costs to get products to export markets are another major impediment. A newly developed Logistics Performance Index (World Bank, 2007b), which is based on a world-wide survey of global freight forwarders and express carriers, makes it possible to compare the situation of countries across a broad set of transport and trade facilitation dimensions. The LPI provides information on several distinct dimensions of trade and transport logistics. It turns out that Nigeria’s performance is far from best practice, and mediocre even by the relatively low standards in Sub-Saharan Africa (Figure 3). Logistics experts see the most pronounced deficits in the areas of customs administration and logistics infrastructure. Moreover, with respect to logistics costs and timeliness of border clearance, Nigeria scores worse than the average in Sub-Saharan Africa.

25
school for children aged 6-10 was 58 percent, enrollment at the middle school level for children aged 11-14 was 29 percentage points lower (Multiple Indicator Cluster Survey – MICS, 2003).

Education levels of females are low, both in absolute and compared to boys, especially in rural areas. At baseline, girls in program areas were less likely than boys to be enrolled in school. While enrollments in primary and middle school were very similar for boys and girls in nonstipend districts, large differences are observed in stipend districts – up to 8 percentage points (MICS, 2003). Such gaps in enrollment by gender are even wider when the samples are restricted to rural areas of Punjab. In terms of school attainment, numbers for 2001 (before the program started) show that women in Pakistan achieved on average 4.9 and 1.3 years of schooling in urban and rural areas, respectively – about 1.9 and 2.8 fewer years of education than men. These disparities by gender are also evident for Punjab, where the levels of enrollment and educational attainment are systematically lower for women in rural and urban areas (Table 1).

Finally, there are also marked differences in school enrollment and access between districts in Punjab covered and not covered by the GSP program. Enrollment levels in program districts were well below those of non-stipend districts. In 2003, for instance, enrollment in primary school for children aged 6 to 10 was 20 percentage points lower in stipend districts; similarly, only 20 percent of children 11-14 years old were in middle school compared to 34 percent in non-stipend districts (MICS, 2003). As shown in Table 2, access to school was also more difficult in stipend districts before the program began, where higher fractions of households live in villages without a school and, thus, their children had to travel longer distances to go to school (MICS, 2003; PSLM, 2004).

# **_The Female School Stipend Program_**

The FSSP was designed as part of a broader educational reform of the Provincial Government of Punjab (GoP) to improve the educational attainment among girls and decrease gender inequities, particularly at the middle school level.<sup>5</sup> Prior to the start of the program, as noted before, female enrollment in primary and secondary schools was low, both in absolute terms and relative to that

> 5 The Punjab Education Sector Reform (PERSP) seeks to address flaws identified in the educational system, including: lack of infrastructure or substandard quality of infrastructure, which limited access to schools; bad quality of service delivery and financial management; limited capacity of staff at all levels of the system; weak community and parental involvement; and a rise in the private sector provision of schooling with weak oversight for standards and quality.

6
28 On the first concern, we note that the average GDP growth registered during 2016–17<sup>28</sup> is 4.9%. To reach the target of 5.8% forecast in the five-year plan, the economy must grow at the rate of 6.3% for the remaining years, a growth rate that Malaysia achieved in the 1980s and 1990s. We take cognizance of the limitations in using the official forecast as our baseline, but we contend that our simulations focus on deviations, and less so on the actual growth rates. Furthermore, reaping productivity gains from a higher allocative efficiency is still possible in Malaysia’s case as it is still some distance away from the efficiency frontier. A more focused and purposeful removal of distortions in the domestic economy should allow the economy to reach a higher level of efficiency.

To address the concern about the optimistic baseline, we have also used a simple moving average model (ARMA) [MA (q)] to project a second baseline GDP growth.<sup>29</sup> Our ARMA (0,3) model with a dummy to represent the periods in recession and growth trend produces an average growth of 5% during the 11MP period. This forecast growth is assumed to be the baseline growth for the second set of simulations (equation 24). Apart from the change in baseline growth, the other assumptions in table 4 apply.

GDP= c+ α1dummy+ α2t+ θ1εt−1 + θ2εt−2 + θ3εt−3 |θ| < 1, (24)

where GDP represents GDP growth rate; the dummy represents recession years in 1998 and 2009; and t represents trend.

Our assumption on the unchanged contribution of capital and labor to overall GDP growth in the three scenarios may be slightly conservative because a higher TFP often leads to a higher demand for capital. In other words, we are not accounting for the possible productivity spillovers that could result in a higher, and perhaps permanent, increase in growth. This could happen if such gains result in investments in production-enhancing technologies by firms in the manufacturing sector and its supporting sectors.

The simulation of the three scenarios based on a 10% incremental realization of productivity gains show an additional increase of GDP growth of between 0.4ppt and 1.3ppt (figure 19). With improvements in allocative efficiencies, the TFP component becomes the single most dominant component in growth, accounting for as much as 50% of the contribution to GDP growth (table 5). The simulations also show that TFP will reach its highest growth, comparable to the Republic of Korea’s average productivity growth of 3.4% from 1990 to 2011 (figure 20).<sup>30</sup>

> 28 Based on a simple average of the official GDP growth rates from the first quarter 2016 to the third quarter 2017. 29 We used the Box Jenkins method to identify a suitable ARMA(p,q) model, and found ARMA to be (0,3).  In other words, a simple MA(3) that  yields the lowest Akaike Information Criterion  (AIC) appears to be the best model for GDP growth. 30 OECD estimates.  Data are available on the OECD website: //data.oecd.org/lprdty/multifactor-productivity.
education, earn about 20% lower wages, and have about 8 months less tenure at the firm than white incumbents.

Our main outcome variable is incumbents’ retention at the establishment. Only about 50% of the incumbents in our sample remain in the establishment three years after the death. Figure 1 reports estimated Kaplan-Meier curves, depicting how retention rates evolve over time and vary by race of the incumbent. In line with evidence from the U.S. (Linos _et al._ , 2024; Sorkin, 2023), we observe a racial gap in retention rates: non-white workers have a 4.6 percentage points lower survival probability than white employees at the end of our 3-year observation period. Appendix Figure 4 distinguishes between employer-initiated separations (“layoffs”) and employee-initiated separations (“quits”). More than 40% of incumbents are laid off three years after death, while only about 10% of jobs are quit by incumbents. Interestingly, we find that the racial gap in overall turnover is entirely driven by layoffs which are more likely to occur among non-whites. In contrast, non-white workers quit less often than white workers.<sup>10</sup>

# **3.2 Identification and Estimation**

Our empirical strategy exploits shocks to the racial composition of incumbents’ coworkers that arise from the death of a non-white vs. white coworker. Causal identification relies on the race of the deceased worker being independent of all other factors that drive incumbent workers’ retention.

**Balance checks.** To examine the exogeneity of the deceased worker’s race, we start by regressing a dummy for the deceased worker being non-white on various observable characteristics of incumbent workers, such as race, gender, education, age, wages, tenure, and firm size. Results are reported in Table 2, separately for all incumbents, non-white incumbents, and white incumbents. As can be seen in columns (1), (3), and (5), several characteristics are significantly correlated with the race of the deceased worker. A major difference between peer groups in which a non-white or white worker dies lies in their initial racial composition: as expected, there is a one-to-one relationship between the initial non-white share in the peer group and the likelihood of the deceased worker being non-white (see Appendix Figure A.2). Due to racial workplace segregation, the race of the deceased worker is also highly correlated with the race of incumbent workers. Column (1) shows that non-white incumbents are 36 percentage points more likely to be in a peer group with a non-white deceased worker.

> In columns (2), (4), and (6) of Table 2, we flexibly control for the initial racial composition

> 10Search models with employer discrimination predict lower quit rates among racial minorities due to their lower outside options and higher search costs (Black, 1995; Whatley and Sedo, 1998).

10
Table 16. Indonesian Exports, ICT use, and Log of Skills Measures. All workers. 2011-2015

||||||N|on-routine<br>|skill measure<br>|s|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||Ana|lytical|||Interp|ersonal|||Ma|nual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.000353||0.000343|0.000238|0.000302||0.000296|0.000204|-0.000251||-0.000248|-0.000163|
||(0.000170)**||(0.000169)**|(0.000179)|(0.000152)**||(0.000151)**|(0.000157)|(0.000144)*||(0.000144)*|(0.000148)|
|Share of hhlds with computer||-0.133|-0.132|-0.145||-0.0899|-0.0889|-0.1000||0.0563|0.0552|0.0654|
|||(0.0553)**|(0.0565)**|(0.0578)**||(0.0449)**|(0.0458)*|(0.0467)**||(0.0414)|(0.0418)|(0.0426)|
|IDN exports per worker *||||0.00251||||0.00219||||-0.00202|
|Share of hhlds with computer||||(0.000795)***||||(0.000706)***||||(0.000682)***|
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|
|R-squared|0.847|0.847|0.847|0.847|0.487|0.488|0.487|0.487|0.878|0.878|0.878|0.878|

|||Ma|nual|Routine skil|l measures|Cog|nitive||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|-0.000143||-0.000134|-7.84e-06|-0.000156||-0.000153|-7.63e-05|
||(0.000327)||(0.000325)|(0.000333)|(0.000144)||(0.000144)|(0.000144)|
|Share of hhlds with computer||0.123|0.122|0.138||0.0452|0.0444|0.0536|
|||(0.0680)*|(0.0688)*|(0.0694)**||(0.0430)|(0.0433)|(0.0435)|
|IDN exports per worker *||||-0.00301||||-0.00183|
|Share of hhlds with computer||||(0.00104)***||||(0.000685)***|
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|
|R-squared|0.976|0.976|0.976|0.976|0.877|0.877|0.877|0.877|

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and province linear time trends. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

35