rafmacalaba/data-use-mentions-extended huggingface dataset:
# 4. Applying the Macro-Micro Simulations to Potential Trade Policy Reforms in Sri Lanka

**To illustrate the use of this methodology, the CGE-GIDD approach is used to assess a set of ex-ante trade policy simulations for the case of Sri Lanka.** Sri Lanka is a country that has some key characteristics that make the country a good candidate to showcase the methodology. First, it is a small open economy and effectively a price taker in international markets. Second, as other developing countries, its export basket is dominated by a primary commodity – in this case exports of tea. Lastly, after a period of political instability that ended in 2009, the economy grew at an average 5.8 percent during the period of 2010-2017. This reflects a determined policy momentum towards reconstruction and rapid structural transformation, despite signs of a slowdown in the last few years. This paper studies the impacts of stylized policy scenarios including assessments of unilateral para-tariff liberalization, free trade agreements with China and India, and a full reform scenario (para-tariffs + FTAs). Section 3.1 discusses the current structure of the labor market in Sri Lanka while Section 3.2 describes the details of the baseline and trade policy scenarios. Simulation results are discussed in Section 4, first focusing on the sectoral level and then on the sub-national distribution of those effects.

**The model can be used to assess the sub-national, localized effects of trade** . The impact of reforms is differentiated across types of households and workers. Such heterogeneity is key in determining the welfare (consumption, poverty, inequality, etc.) and distributional impacts of any reforms to be undertaken. The GIDD distributes the macroeconomic results of the CGE model to households in line with the Sri Lanka Household Income and Expenditure Survey (HIES 2016). The microeconomic model distributes shocks while keeping the consistency with the aggregate results obtained from the macro CGE model.

# 4.1.Employment and skills profile

**The Sri Lanka Household Income and Expenditure Survey (HIES)
# **3. Data**

We build a data set of occupational skills profiles for four Southeast Asian countries that we call the Southeast Asia Digital [SEAD] data set. The SEAD is created from online job postings data collected in Malaysia that we link to employment data from household and labor force surveys in Malaysia, Cambodia, Thailand, and Vietnam. The online job postings data are used to identify the types of skills (digital, cognitive, socioemotional, and others) associated with each occupation in Malaysia. These occupational requirements are then extrapolated to the employed population of Malaysia and the three other countries selected because of similar interests in digital development, potential synergies from being in the same sub-region, and the availability of data.<sup>5</sup> The household and labor force surveys provide information on the distribution of employment by occupation in each country.

## **3.1. Deriving skills requirements from online job postings data**

_The World Bank-Burning Glass Online Job Postings data in Malaysia_ We begin with a data set of more than half a million online job postings collected by the firm Burning Glass Technologies (Burning Glass hereafter) in Malaysia from May 2016 to December 2018.<sup>6</sup> In collaboration with the World Bank, Burning Glass collected more than 600,000 unique online job postings.<sup>7</sup> More than 95 percent of vacancies listed at least one skill requirement for a total of 8,221 unique skills. The raw data, in the form of job postings, include the job title, a free-text description field that lists required skills, and other job characteristics. Burning Glass parsed the text of each job vacancy, coded keywords and phrases as skills, and categorized the job titles into the 2013 Malaysia Standard Classification of Occupations (MASCO).<sup>8</sup> Burning Glass grouped individual skills into 633 clusters (groups of similar skills commonly learned together or substitutable),<sup>
947|0.769|0.884|0.919|1.000|||
|_P _<sup>_M_</sup>|0.581|0.579|0.289|0.425|0.550|0.730|1.000||
|_P _<sup>_M′_</sup>|0.533|0.318|0.278|0.331|0.090|0.252|0.369|1.000|

* Authors’ calculations, using data from the WDI, DOT, IFS, Global Migration, Patentscope, and NSF databases. Growth rates calculated from GDP data in real 2000 U.S. dollars.

> _†_ Main measures of trade, finance, technology, and migration computed from imports, financial outflows, patent citations, and immigrant stock.

> _‡_ Secondary measures of trade, finance, technology, and migration computed from absorption, bilateral FDI, scientific articles, and skilled labor emigration.

measure, calculated with and without the inclusion of migration, and with both real and PPP growth rates, are reported in Table 3.<sup>12</sup> The computed measures in this case are far more stable, and the two clearest poles to emerge are the United States and China. By and large, however, it appears that in addition to these two economies, a mix of advanced (Germany, Japan, the United Kingdom) and emerging (India, Russia, South Korea) economies share the role of being growth poles for the global economy, at least based on the 2004–08 period.

Table 3 captures several other interesting facts. First, it is important to recognize that the ordering of economies above are not one-for-one matches with economic size. Indeed, several large developed economies—such as Germany and France—appear several notches lower than their economic sizes alone would suggest; likewise, several emerging
school.<sup>52</sup> Overall, these results contribute to the existing body of research that shows the positive effects of mindfulness on an array of behaviors and outcomes of adults (Keng et al., 2011). The main feature of the _Mindful_ intervention is that it is low cost, which is an attractive feature of public policy. Based on the impacts estimated from the ASP, an ASP should also include an evidence-based curriculum oriented toward improving student outcomes. We find that the _Mindful_ intervention is the best curriculum to add to _Clubs_ to help vulnerable youth in poor and highly violent countries.

# **5.5 ASP Attendance**

We first explore the differences in ASP attendance by ASP variation using data obtained from reports from volunteers. Table 8 shows the results from estimating a version of specification (2), which excludes the interactions with the _Post_ variable. Column (1) presents the effects on attendance (as a percentage of the total sessions scheduled), column (2) shows the effects on an indicator of whether the student dropped out of the ASP, and column (3) presents the effects on the number of sessions scheduled. We find that, on average, students assigned to _Clubs_ attended 61% of the scheduled sessions. The attendance rate was greater for the _Virtue_ intervention relative to both _Clubs_ and _Mindful_ (column (1)). Moreover, students in the _Virtue_ ASP are 11 pp less likely to abandon the intervention relative to students assigned to _Clubs_ (column (2)). We show that these differences in attendance are not driven by differences in number of sessions scheduled in each type of ASP variation (column (3)). Considering the ASP attendance and dropout results together, we show that the _Virtue_ ASP was more attractive to the students assigned to it since they attended a greater share of sessions relative to both _Mindful_ and _Clubs_ and they were less likely to abandon the intervention relative to _Clubs_ .

> 52 Overall, these results are robust
only allows comparisons of weight across cohorts, the availability of multiple NFHS rounds allows for estimation of average weight gain with age for adults _within cohort_ . In Appendix Table S.8, the average weight gain is estimated controlling for cohort (birth-year) fixed effects across survey rounds, with data availability allowing for measurement of average weight gain between 2005 and 2015 for men and women, and between 1998 and 2005 for ever married women. Estimates controlling for height are also reported, which may capture some non-random attrition within a cohort related to nutritional status. The data imply an average weight gain of between 0.5KG and 0.6KG per year within cohort, with a slightly higher rate between 2005-2015 than 1998-2005 for ever married women. A weight gain of 1 kilogram is associated with a caloric surplus of 7700 for adults ((St. Jeor and Stumbo 1999)), so the reported level of weight gain translates into between 10.5 (= 0 _._ 5 _∗_ 7700 _/_ 365) and 12.7 (= 0 _._ 6 _∗_ 7700 _/_ 365) surplus calories per day above TEE and the caloric cost of disease. This suggests that, on average, the Indian (adult) population has been in caloric surplus since at least 1998 and even more between 2005 and 2015, even as official NSS caloric intake estimates using 30-day recall plummeted during the 2000s to reach levels well below the TEE estimates. Given that the analysis above suggests a limited scope for such a large decrease in population TEE, and that most of the improvement in the disease environment occurred before this period, the overall evidence is consistent with the view espoused in (Smith 2015) that the steep decline in official caloric intake estimates beginning around 2000 is likely the result of substantial measurement error.

# **4 Conclusion**

In this paper, population level Total Energy Expenditures (TEE) for India over the 1983-2012 period are quantified using a combination of time-use data, anthropometric
This version: 15 Feb 2024 21

# **3. Focus Group Analysis**

# **3a.  Methodology**

We conducted 56 focus group discussions (FGDs) in 2019 with a total of 473 respondents representing three ethnic groups, spread across three regions, an exercise we call the Malaysia Living Standards Qualitative Survey (henceforth, MLSQS). The FGDs were organized using a semi-structured discussion format which was built on five broad questions to generate open and free discussions (see **Table 2** ). We also asked a question on people’s preferences for relative versus absolute higher incomes that also resulted in a discussion that we analyzed. There was also a short quantitative exit survey collecting basic socioeconomic data on respondents.

Fieldwork was conducted in three states: Selangor (which covers the Kuala Lumpur suburbs), Terengganu, and Sabah on Borneo island. For respondent recruitment in study states, we relied on a snowball sampling approach and selected people with self-reported incomes that placed them in middle-class, lower middleclass and poor categories. Thus, our qualitative data reflect the views of people in the lower half of the Malaysian income distribution.

The FGD sites were selected in consultation with government bodies, community leaders, and NGOs. Several pilot sessions were conducted to ensure a level of standardization in how questions were posed, clarified and probed (SattinBajaj 2018). To ensure diversity among participants, the team recruited from a variety of locations: workplaces such as schools, private companies and hospitals, religious institutions, low-income housing complexes, vegetable and fish markets, street 21
may be because weak institutions make a credible commitment to price stability difficult without a credible anchor in the form of a fixed exchange rate.

# 5.5 Contributions of the shocks to core inflation variation

Variance decompositions of core inflation were examined for the three country groups, using within-group medians (Figure 6). The key differences were found between advanced economies, on the one hand, and LICs and other EMDEs on the other. Consistent with substantially stronger anchoring of domestic inflation expectations in advanced economies, more than three-quarters of the variance of core CPI inflation rates in these economies is explained by shocks to core inflation. In LICs and other EMDEs domestic core inflation is overwhelmingly explained by shocks to global core inflation. The variance share of global core price shocks in the total variation of domestic core inflation is around 60 percent for both these income groups. The contribution of shocks to domestic core inflation, by contrast, is much smaller. The share of domestic core inflation explained by global food and energy shocks is moderately larger for LICs than for advanced economies and other EMDEs. In LICs, global food and energy price shocks account for 12 percent of core inflation variation—half more than in advanced economies and one-fifth more than in nonLIC EMDEs. In line with the results from the IRFs, this result may suggest that central banks in LICs have not succeeded in anchoring inflation expectations in the face of shocks to inflation rates, and that much of LIC inflation seems to have been driven by spillovers from advanced economies and other EMDEs. This is discussed in more detail in the next section.

16
wave of the HFPS ( _Internet_ ). Formally, (1) Where _i_ labels the individual and _j_ her occupation. Notice that by adjusting for home internet access the index of _WFH_ at the occupation level _j_ becomes an index defined at the individual level. Figure 5 shows the distribution of the WFH index at the individual level for each country before the pandemic. The distributions differ across countries because of the differences in their occupational structures. For instance, the occupational structure in Ecuador or Guatemala is much less compatible with remote work than that of Argentina or Chile.

# **4 Job Losses, Work from Home and Childcare**

Our main interest is to assess the relationship between job losses and the potential for working from home of the pre-pandemic occupation and whether this relationship differs by gender. Figures 6 and 7 show suggestive evidence on this regard. Figure 6 shows the correlation across countries between the share of workers who lost their jobs and the WFH index. In the first panel—wave 1—a clear negative association between WFH and job losses is observed for both women and men.<sup>10</sup> In other words, the initial impact of the crisis implied more job losses for countries where the occupational structure was less compatible with remote work, both for male and female workers. While this pattern persists for women in the following waves of the survey, for men the relationship between job losses and WFH loses strength and virtually disappears in the third wave. Figure 7 shows that a similar pattern arises from an individual-level analysis, i.e., the correlation between the probability of job loss and the individual WFH index is stronger for women, suggesting that the possibility of working from home was more decisive in preventing job losses for women than for men.

At least two mechanisms can account for the differential importance of WFH between men and women. One is related to gender differences in childcare responsibilities, which fall mostly on women as we mentioned above. Given the allocation of these responsibilities and
**Figure 9.3. Percent of those visiting agents who experienced a problem, by gender**

<!-- Start of picture text --> 100%<br>88%<br>90% 85% 86%<br>82% 83%<br>79%<br>80%<br>70%<br>62%<br>60%<br>48% Male<br>50%<br>Female<br>40%<br>30% 31%<br>30% 27% 26% 27%<br>21%<br>20%<br>10%<br>0%<br>Kenya Uganda Tanzania India Bangladesh Nigeria Pakistan<br><!-- End of picture text --> Note: Financial Inclusion Insights data 2016, weighted.

# **_References_**

<mark>Agarwal, Sumit; Alok, Sashwat; Ghosh, Pulak; Kanti Ghosh, Soumya; Piskorski, Tomasz; and Seru, Amit. 2018. “Banking the Unbanked: What do 255 Million New Bank Accounts Reveal about Financial Access?" Working Paper.</mark> <mark>Agent Network Accelerator. 2016. “Successful Agent Networks” Helix Institute of Digital Finance.</mark> Ahlin, Christian and Godwin Debrah. Forthcoming. “Group Lending in Theory and Practice.” In _Handbook of Microfinance, Financial Inclusion and Development_ , edited by Valentina Hartarska and Robert Cull. Elgar Handbooks in Development Series.

Allen, Franklin, Elena Carletti, Robert Cull, Jun QJ Qian, Lemma Senbet, and Patricio Valenzuela, 2021. “Improving Access to Banking: Evidence from Kenya.” _Review of Finance_ March 2021: 403-447.

<mark>Alliance for Financial Inclusion. 2012. Agent Banking in Latin America. AFI Discussion Paper, March.</mark> <mark>Annan, F. (2019). Gender and Financial Misconduct:
have sharply reduced activity in officially designated “excess capacity” or polluting industries, such as coal and steel production. These cuts are likely to continue in the medium‐term. In February 2016, additional capacity reduction targets were announced for coal and steel and a fund was established to re‐employ or compensate affected workers. Capacity cuts were accompanied by other measures to strengthen SOE efficiency, including ten pilot programs for SOEs introduced in September 2015 and February 2016. Some provinces began in June 2016 to restructure unviable SOEs.

**Private enterprises: Falling returns.** Just over a third of the deceleration in private investment growth thus far in 2016 can be attributed to the slowing manufacturing sector (Qu and Wang 2016). Weakness in manufacturing investment reflects deteriorating business confidence and rising funding costs amid weak return prospects. Slowing export and domestic demand growth and persistent producer price deflation have weighed on return prospects. Between 2011 and 2015, the annual return on investment of private industrial enterprises has been estimated to have fallen by 3 percentage points to 8.5 percent, according to official data. Despite recent efforts to cut red tape, private enterprises still face high entry barriers, sales taxes, and surcharges by comparison with other countries in the region (Ernst and Young 2016).

# **Spillovers from China’s investment growth slowdown**

While the investment growth slowdown is an integral part of ensuring sustainable growth in China in the medium to longer term, it has had significant negative repercussions on activity both domestically, given investment’s large share in China’s GDP (about 43 percent in 2015), and globally because of China’s large role in the global economy. A slowdown in investment spills over to other sectors of the domestic economy through industry and financial linkages.<sup>37</sup> Since investment is more import‐intensive than other components of demand, adverse external spillovers from China’s investment slowdown have been particularly pronounced. For example, China imports large volumes
(1,779.81)|(1,152.31)|
|p-value|0.015|0.003|0.020|0.002|0.163|0.066|0.145|0.061|
|Endline control mean|2,05|7.38|2,0|64.98|16,77|0.18|16,6|08.65|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.035|0.389|0.034|0.385|0.012|0.389|0.014|0.392|
|Observations|552|552|454|454|552|552|454|454|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Outcomes refer to productivity and income obtained from the main crops maize, wheat, teff, barley and sorghum. Sample includes treatment farmers adopting the full integrated ISFM package on at least one plot, and their matched controls. T subsumes T1 and T2. Additional control variables identical to those listed in notes of Table 2. One-sided equality tests of T1 and T2 are F-tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

36
more reactive to economic incentives.

The presence of a producer association widens the opportunity cost, suggesting that it raises the return to commercial farming (possibly by improving the farmers’ bargaining power vis-`a-vis middlemen) without making it easier for subsistence farmers to switch. Access to credit also reduces negative earnings differentials, suggesting that credit does help to overcome barriers to entry in the market. However as discussed elsewhere in this paper one shouldn’t make too much of this given the very low proportion of farms with access to credit. Finally, the presence of a national road in the village raises the opportunity cost of being in subsistence, suggesting that it raises the return to commercial farming (as one would expect) without helping subsistence farmers to do the switch. Taken together, the combination of the effects of credit and transportation leans more in the direction of private than public entry barriers.

# **4 Concluding remarks**

As discussed in the introduction, so far empirical work on entry barriers has largely left out agricultural markets. Yet, these markets are important in poor countries where tradeadjustment assistance mechanisms can be expected to be weak; moreover, the extreme poverty of rural households in least-developed countries like Madagascar makes them particularly vulnerable to even small deteriorations in their terms of trade. Recent work on trade and poverty has also highlighted the fact that agricultural markets themselves are fragile and can be destroyed when institutions (like intermediation) or infrastructure (like rural roads) are left to decay, which can happen when changes in relative prices are perceived to last. Restoring those markets can be long and costly.

This suggests that rural households in the grey zone between market participation and subsistence should receive particular attention in the design of reforms. Of course, the incentives faced by those households can be assessed only indirectly using the household surveys’ noisy information, and there is anyway no natural definition of subsistence farming.

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- Engel, E. 1895. “Die lebenskosten belgischer arbeiter-familien fruher and jetzt.” _International Statistical Institute Bulletin,_ 9: 1–74.

- FAO/WHO/UNU Expert Consultation. 2001. “Human energy requirements.” _Food and Nutrition Technical Report Series_ .

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- Fiedler, J. L., and S. Yadav. 2017. “How can we better capture food away from home? lessons from india linking person-level meal and householdlevel food data.” _Food Policy_ 72: 81–93.

- Gibson, J. 2002. “Why does the engel method