rafmacalaba/data-use-mentions-extended huggingface dataset:
- Survey questions on skills acquired (or needed), as well as questions on economic mobility (including, relevant to women, information on respondents looking to move to paid work in the formal sector, and those transitioning from home and subsistence production into market work), are still rare and need further exploration in socioeconomic surveys.

- In addition to asking about desire to work, to better inform programs to improve economic mobility in rural areas, questions on constraints to seeking economic opportunities —  questions relevant to both wage and self-employment, across safety, market access, and time-related constraints —should be included regularly in surveys. These outcomes are also tied to non-cognitive factors affecting employment, an important area to examine as well.   As with constraints to seeking economic opportunities, understanding constraints to using technology are also important, given inequalities faced by women in areas such as mobile phone ownership and access to improved agricultural inputs and extension services.

# VI.   Conclusions

Policies can be better targeted with more accurate counting of rural women’s employment, as well as other important factors that affect their decisions to work and seek better economic opportunities. Shifts in international standards for data collection and measurement have also brought many work- and employment-related issues faced by rural women to the forefront — including underreporting of their 22
graduates work in the public sector. Vocational school graduates work almost exclusively in the public sector. Overall earnings are much higher in the public sector.

Economic restructuring policies, which began in the mid-1980s in Vietnam, accord a lead role to the private sector in the transition to a market economy and in overall economic development. Employment growth is highest in the private sector, which needs to absorb not only new labor market entrants but also the labor shed by state enterprises. During 1989-1993, about 4.7 million new jobs were created in the private sector. Given a reduction of about 900,000 jobs in the state sector, the net increase was 3.8 million additional jobs (World Bank 1995b). Private sector workers are paid less than those are in the public sector. In the public sector, workers with little or no education have relatively high earnings (Table 2). Remuneration rates in the public sector are compressed, resulting in apparent overpayment of workers with less than a primary education.

Table 2: Vietnam: Monthly Earnings by Economic Sector (VND per month)

|Educational level*|Public|Private|
|---|---|---|
|No education|187,514|117,641|
||(59)|(446)|
|Primary|154,690|146,558|
||(375)|(747)|
|Secondary|157,871|182,930|
||(111)|(75)|
|Vocational|146,891|187,137|
||(246)|(34)|
|University|227,452|247,837|
||(159)|(7)|
|Overall|167,259<br>_(950)_|140,385<br>(1,309)|
|Source: VLSS 1992-93|||
|Note: Numbers in parentheses|||

* Highest level attained

11
It is notable that Georgia, among these four countries, incurred the smallest reduction in the undervaluation of the BSA RER between 2000-04 and 2015-18.

If we are to better understand the long-term macroeconomic consequences of large migration and remittances on the FSU countries of CASC, two directions for further research would be worth pursuing. First, we need better estimates of the Balassa-Samuelson adjusted real exchange rates which can more accurately incorporate cross-country productivity differences in the production of traded goods than is possible by using GDP per capita as a proxy. Better estimates of the BSA RERs would enable us to have a clearer understanding of whether the receipt of remittances has undermined the price competitiveness of traded goods industries and, in turn, contribute to understanding whether price competitiveness or some other factors are the binding constraints to improving trade performance in these countries.

Second, we need a better understanding of why relatively high levels of human capital in the FSU countries cannot be fully translated into GDP per worker. One approach to this issue would be to utilize the information in the annual Global Competitiveness Reports which provide crosscountry data which score and rank countries on 12 different pillars which affect their international competitiveness. In the 2017-18 report, all four of the remittance receiving FSU countries in CASC had scores for the primary education and health pillar which were much higher than their overall competitiveness score but scored poorly on pillars such as market size, technological readiness, business sophistication and innovation (World Economic Forum, 2017).

# **References**

Asian Development Bank (2019), _Good Jobs for Inclusive Growth in Central Asia and the South Caucasus: Regional Report_ (eds Giovanni Capannelli and Ravi Kapur, Asian Development Bank, Manilla.

Balassa, Bela (1964), “The Purchasing Power Parity Doctrine: A Reappraisal”, _Journal of Political economy_ , Vol 72, No 6, pp584-596.

Choudri, Ehsan U, and Moshin S. Khan (2005), “Real Exchange Rates in Developing Countries: Are Balassa Samuelson Effects Present?”, _IMF Staff Papers_ Vol 52
1967 New York Blackout and the impending federal actions over concern about the reliability of electricity service provision. Other examples are self‐reporting of pollution emissions and green labeling so as to ward off government imposed requirements that would be more stringent. In our case, it pertains to businesses taking into account positive and negative co‐benefits of DRM.

# **7.3. Broader Approaches**

Long‐run considerations have been increasingly incorporated into decision‐making relating to business environmental management decisions now for decades, and more recently have been integrated into sustainable planning. In the US, in more than 300 Standard and Poor’s 500 Stock Exchange Index businesses report their greenhouse gas emissions. The Brazilian firm Natura chooses suppliers on the basis of economic, social and environmental performance. Alcoa bases 20% of its executive compensation to progress on sustainability (Perera et al., 2013). Moreover, NGOs, such as the World Resources Institute, have worked with the private sector to evaluate and improve environmental performance. These initiatives in the environmental area could be extended to DRM, especially in light of recent findings in general and risk management. A survey by EIU (2007) found that protecting and enhancing the firm’s reputation was ranked even higher than preventing financial loss. Still, terrorism and natural weather events ranked 8<sup>th</sup> and 9<sup>th</sup> as the most important external drivers of improvements and risk management in the EIU survey of firms. The study found that the major hindrance to action in DRM was the difficulty in measuring its benefits, a finding that makes improving measurement all the more important in promoting DRM.

Liability considerations in the environmental area already have overlaps with DRM. In 1990, the Chairman and CEO of Bank of America formed an environmental task force in response to increased shareholder interest in such issues. The move was also in response to the fact that, since the 1980s, courts in the U.S. were holding
transformation and growth, as long as the economy has been able to build up human capital and accumulate fundamental capabilities that transform those services into high-productivity activities (like finance and business services). However, this typically happens rather late in the development process, after industrialization runs its course, and high-productivity (tradable) segments of services cannot absorb as much labor. As for labor-intensive tradable services (like tourism), they have typically spawned few links to the rest of the economy and have not produced much diversification.

One prominent exception is the success of Hong Kong. Its structural transformation picture looks just like that in Vietnam, except that the roles of agriculture and manufacturing are reversed (Figure O.10). In Hong Kong, it is manufacturing that has rapidly shrunk since 1990, releasing more than 20 percent of the economy’s labor force to other sectors. The displaced labor found employment in services (wholesale and retail trade, finance, insurance and business services, and so on), but at even higher levels of productivity. So deindustrialization was growth promoting. The difference with other countries is that Hong Kong first achieved significant levels of industrialization

**FIGURE O.10** A move from manufacturing to services in Hong Kong

<!-- Start of picture text --> Correlation between sectoral productivity and change<br>in employment shares in Hong Kong (1990–2005)<br>2<br>β = 2.8359; t -stat = 2.51 pu<br>1 fire<br>tsc<br>0<br>min wrt<br>cspsgs<br>con<br>–1 man<br>agr<br>–2<br>–0.2 –0.1 0.0 0.1<br>Change in employment share (∆Emp. Share)<br>Fitted values<br>productivity<br>productivity/total<br>sectoral<br>of<br
is based on two digit codes from the North American Industry Classification System (NAICS), plus two more digits specific to the ENOE. There are nine industry codes that correspond to durable goods manufacturing. Appendix Table 2 lists these nine codes along with their names and shows how I classified them into eligible vs. ineligible for the wage subsidy program.

The mapping to IMSS industry codes, on which eligibility for the program was based, is not always straightforward. For example, code 3340 in the ENOE includes manufacture of computers, communication and measurement equipment, as well as other electronic equipment. While most of these activities correspond to eligible industries in IMSS industry groups 36 and 37, IMSS classifies measurement equipment in an industry group that was not eligible for the wage subsidy program (group 39). That is, due to mismatches between the ENOE and IMSS classifications, some workers classified as eligible in the ENOE data may in fact not be eligible.<sup>10</sup> 10 In general, I tried to classify the ENOE industry codes as eligible (or ineligible) as accurately as possible based on whether the majority of industries listed in the name were eligible or not under the IMSS classification. For the example given above, this method seems to work relatively well. Data on employment in NAICS industries is available from the Monthly Manufacturing Industry Survey (EMIM) conducted by INEGI. This data shows seven subindustries in code 334 (which has the same name as ENOE industry 3340), one of which is manufacture of 11
of development: What happened, what follows?" _Journal of Monetary Economics,_ 53 (5): 981– 1019.

<mark>Lillard, L, and R. J. Willis, 1994. "Intergenerational Educational Mobility: Effects of Family and State in Malaysia,"</mark> _<u><mark>Journal of Human Resources</mark></u>_ <mark>, vol. 29(4), pages 1126-1166.</mark> Lindahl, L. 2008. ―A Comparison of Family and Neighborhood Effects on Grades, Test Scores, Educational Attainment and Income—Evidence from Sweden‖. Essay III in L. Lindahl, _Family Background and Individual Achievement,_ dissertation no.75, Swedish Institute for Social Research.

- Lindahl L (forthcoming) ―Does the Childhood Environment Matter for School Performance, Education and Income? - Evidence from a Stockholm Cohort.‖ _Journal of Economic Inequality._

- Manski, C (2011), ―Genes, Eyeglasses, and Social Policy‖, _Journal of Economic Perspectives_ , Fall 2011.

- Mazmuder, B (2012), ―Is intergenerational economic mobility lower now than in the past?‖, _Chicago Fed Letter_ , April 2012.

34
or proposed customs unions (Tanzania in EAC, COMESA which includes all SADC members except for South Africa, Botswana and Mozambique) is inconsistent with the proposed formation of a SADC Customs Union. This dilemma of multiple memberships also extends to other areas such as infrastructure, where different harmonization options and strategies are being pursued (Kritzinger-van Niekerk and Moreira 2002).

The second type of tariff reform is multilateral or unilateral liberalization. On this front, SADC countries have made considerable progress in reducing import barriers since the early 1990s. Table 5 presents summary statistics of the 2008 MFN tariff rates applied by each SADC country. Simple average tariffs range from 2.9 percent in Mauritius to 25.5 percent for Zimbabwe.<sup>10</sup> For most countries, average MFN rates

> 8 This is the well known Lerner Symmetry theorem. Edwards and Lawrence (2008) find that reductions in the export bias associated with tariff liberalisation accounted for approximately 40 percent of South Africa's export growth after 1994. Similarly, Edwards (2010) finds that liberalisation of tariffs on intermediate inputs is associated with improved manufacturing export performance in Africa. 9 South Africa, for example, reduced most tariffs on SADC imports to zero in 2000 (Edwards 2005), while other countries phased their tariffs down at a slower rate (http://www.sadc.int/fta).

> 10 The average rate for Zimbabwe appears to be particularly high even compared to earlier period. For example, the WTO World Tariff Report for 2007 estimates average protection in Zimbabwe to equal to 14 percent. The 2006 WTO World Tariff Profiles indicate a simple average of 16 percent for Zimbabwe for 2003. This highlights the difficulty in estimating average protection for countries. Many of the countries, SACU in particular, used non-ad valorem rates such as specific rates, mixed rates and compound rates. SACU also used formula duties which we find a reservation price. If import prices fell

13
parable before the influx, and night light density increased after the Rohingya arrived in Bangladesh. We calculate the distance to the camps as a weighted average, considering each individual’s or area’s distance to each of 33 camps with weights based on each camp’s population. This approach enables a nuanced comparison between individuals living closer to and farther from the most populated camps in Cox’s Bazar both before and after the Rohingya arrived.

We use two distinct data sources to investigate the impact of refugee camps on local communities in Cox’s Bazar. First, the study incorporates annual grid-level remote sensing data that tracks night light density as a proxy for economic growth, deforestation, and land use (including grassland, cropped areas, and dense-open forest). We use this data to assess the development effects of the Rohingya’s presence on individuals living around each of the 33 refugee camps. The analysis addresses a grid level of one square kilometer, encompassing the 2,392 cells observed annually from 1992 to 2021 in Cox’s Bazar.

The second data source is the Cox’s Bazar Panel Survey, conducted in 2019 and 2023 after the arrival of the displaced Rohingya. This data represents the Rohingya population plus non-Rohingya individuals living outside the refugee camps. Since data collection began after the Rohingya arrived, only distance to the camps is used to assess the impact on individual outcomes. We enhance our analysis by incorporating survey fixed effects, leveraging the longitudinal nature of the data.

We find that hosting displaced populations in camps produces complex mixed effects on nearby hosting communities. The study reveals that the influx of displaced Rohingya and the subsequent humanitarian aid prompted greater night light density and more deforestation. The increments in night light density, however, are substantially greater than the gains in deforestation. Specifically, the results suggest that when the proximity of a grid to the refugee camps increases by 30 km (18.6 miles), night light density increases by 1.7 percent and deforestation increases by 0.
allocative wedges in the economy identified jointly from the firm-level data and the model’s equilibrium (Fattal-Jaef, 2022). While less informative than studying how a specific distortion within a class of distortion shapes informality and aggregate productivity, as is the case in most of the literature, our approach subjects the entire class of distortion to a higher hurdle, namely its ability to generate a missing middle in the size distribution.<sup>6</sup><sup>_,_7</sup>

# **3 Data**

## **Firm-Level Data in Sub-Saharan Africa**

The lack of suitable data constitutes one of the main challenges associated with testing the missing middle conjecture. Even though the information required for this type of exercise is extremely basic—the power of looking at the size distribution to assess broader macroeconomic patterns precisely lies in its simplicity and low data requirement—the availability of such information proves to be an extremely binding constraint in developing countries. Such data availability is particularly challenging for most Sub-Saharan African countries, where informality is larger. Additionally, in view of the pervasiveness of informal economic activities in these countries, it is necessary that revisiting the question of a missing middle should use census data of both formal and informal firms across the entire spectrum of sizes.

Fortunately, over recent years, a number of Sub-Saharan African countries have engaged in establishment censuses as part of wider programs aimed at informing economic policies. Crucially for our purpose, the census is by definition taken of all establishments, including unregistered ones, so the ensuing data is representative of the true size distribution. These censuses are typically nothing more than business registers and essentially include contact and location information, legal form, records of business registration, tax and other administrative data, type of economic activity, and the number of workers, which is the crucial element for our purpose.

For this analysis, we obtained establishment censuses from the statistical agencies of some selected Sub-Saharan African countries, namely Burkina Faso, Cameroon, Ghana,
(particularly investment) and assets if they were headquartered in municipalities with high exposure to banks that were more impacted by the decline in energy prices.

In addition to these firm-level results, we analyze the impact of the collapse of energy prices at the more aggregated state level. We run a similar specification using state-quarter-level data. In this exercise, we relate quarterly state GDP to the average ex-ante exposure to the energy sector of banks operating in a state. We use the following specification: where _ys,q_ is the total GDP of state _s_ in quarter _q_ . Furthermore, we study the decomposition of the GDP in the three sectors: primary, secondary, and tertiary. The regressor— _AvgExposureEnergys,Aug14_ —is the average ex-ante exposure to the energy sector of banks operating in state _s_ , weighted by loan value. Finally, we include state _γs_ and quarter _γq_ fixed effects to control for state-specific, time-unvarying variation as well as aggregate time variation affecting all states, and errors are clustered at the state level. Our coefficient of interest is _β_ , which indicates whether the aggregate production of a given state was differentially affected by the drop in energy prices as the average ex-ante exposure of its banks to the energy sector increased. To isolate the impact of the contraction in bank lending from the drop in energy prices, we present the results for all 32 states in Mexico and the 30 states in Mexico that do not produce energy.<sup>14</sup> Table 6 presents the findings. Focusing on the non-energy-producing states, an increase of one standard deviation in the exposure of a state to the energy sector reduces the state’s GDP by 1.8 percentage points. We interpret this finding as evidence that the reduction in output was caused by the contraction in lending of banks that were highly exposed ex-ante. Furthermore, as the results in the table show, the brunt of the impact was on the GDP of the secondary sector. Relative to the tertiary sector, the secondary sector tends to be more
following the same households over time. As a result, there is a significant panel dimension to the household sample.<sup>12</sup> The scope of the survey is quite broad. Households are asked a range of questions regarding political status (e.g., household cadre status), education, sources of income, labor supply, land use, asset ownership, occupational choice and other household characteristics. Respondent households keep daily diaries of income earnings and expenditures and a resident survey administer/enumerator living in the county seat visits with households once a month to collect information from the diaries.

The data set used in our analysis comes from part of the complete RCRE survey.<sup>13</sup> It covers ten provinces (Shanxi, Jilin, Jiangsu, Zhejiang, Anhui, Henan, Hunan, Guangdong, Sichuan and Gansu) and spans the period 1986-2003 except 1992 and 1994 as RCRE was unable to conduct the survey in these years because of funding difficulties. As a result, the data set includes 14,417 households and has a total of 123,867 householdyear observations. RCRE‘s sampling is not proportional to provincial rural population. For example, the number of households surveyed in Sichuan is nearly the same as that surveyed in Gansu, despite the fact that Sichuan has a rural population that is nearly five times larger. Thus, in the descriptive statistics presented, we weight by rural population

> 12Despite the significant panel dimension, nearly one third of originally selected households were lost to attrition during the period 1986-1999.  This is mainly due to village attrition that occurred during two two-year gaps when RCRE was unable to conduct the survey in 1992 and 1994 because of funding difficulties.  To supplement the sample, RCRE replaced lost villages by comparable villages in the same counties.  Households lost through attrition were replaced (at least in principle) on the basis of random sampling. For a detailed discussion of the RCRE panel data set, including discussions of survey protocol, sampling, attrition
<mark>treatment arm. We anticipate monitoring to lead to an increase in inspector effort and improved customs outcomes as it lowers the expected return to colluding with importers to lower the customs value.</mark>

# **3. Data**

## **3.1. Data sources and representativeness**

The analysis combines a number of unique data sets from different providers.

## **Administrative customs data**

From Madagascar’s customs administration, we obtained highly disaggregated administrative data tracking imports at the transaction level spanning the period January 2016-February 2019 (extracted through the customs management system (ASYCUDA)). For each import declaration, ASYCUDA data cover several standard variables: the port of entry, the HS 8-digit products included (designated as items) and for each item the import value (in Madagascar Ariary), taxes paid (tariff and value added tax as well as exemptions, all in Madagascar ariary) and weight (in kilograms), the country of origin (transit). The data also include more unique and novel variables such as the time stamps for date of registration, date of assessment, and other relevant dates in the customs clearance process (day/month/year/hour), the import value initially declared by the broker on the behalf of the importer, the inspection channel initially recommended by: (i) the risk management system; and (ii) by the RMU (documentary control/yellow channel, physical frontline inspection/red channel or no inspection/blue channel), and unique numeric identifiers for the broker, the importing firm and, importantly, the customs inspector assigned to handle the declaration, which allows us to study inspector performance.

## **Internal control and fraud data**

We merge these ASYCUDA data with additional data sets from the customs administration’s internal control systems. First, we combine these data with information on modifications made by the customs inspector during the customs clearance process to the inspection channel (upgrade in