rafmacalaba/data-use-mentions-extended huggingface dataset:
39 percent compared to 26 percent among men). The proportion currently engaged in unpaid employment is low, among both males and females.

The average monthly income of a household in the sample is reported at 38,738 NPR.<sup>19</sup> Most households (80 percent) report having earned income from employment ( **<u>Table A.6, Annex A</u>** <u>). Other reported sources of</u> income in the month preceding the survey include international remittances (26 percent), loans (13 percent), and saving or sale of assets (10 percent). A small share of the sample (6 percent) reports receiving government transfers from safety net programs.

The average age of a respondent is 36 years (with an average gap of 5 years between men and women). Most respondents are Hindus (86 percent), followed by Buddhist (9 percent), but there is more diverse representation of ethnicities – 33 percent identify as Adibasi and Janajati, 29 percent as Khas and Aryan, 27 percent as Dalit, and 10 percent as Madheshi.

# Migrant Households

Migrant households in the sample mostly comprise husbands who migrated internationally (93.3 percent) to countries such as Saudi Arabia, Qatar, Malaysia, United Arab Emirates (UAE), and India. According to female spouses, work is the main reason for their husbands’ migration, with construction workers, security guards, and drivers being the most common occupations. Almost half of migrants have not completed basic education.

Migrant households in our sample differ on some important characteristics when compared to non- migrant households. Given the absence of the male spouse, migrant households are less likely to be headed by a male. They tend to be smaller and have more children and fewer household members of working age and that are in

> 18 Authors’ calculation using the Nepal Labor Force Survey 2017/2018.

> 19 This likely places the sample
generality, substitute _τ_ from (b) to gravity equation (1): Estimating equation (2) identifies _δ_ , the product of two parameters _σ_ and _θ_ . To recover the parameter _θ_ , one has to assume a value of _σ_ to calculate _θ_ = _δ/σ_ . For aggregate trade flows, typical values from the literature range between 5 and 10 (Anderson and van Wincoop 2003, 2004). Note that the impact of varying travel distance or travel time on trade flows is still informed by the combined elasticity _δ_ . However, if one is seeking to quantify the elasticity of trade costs to travel distance or time, the correct value is _θ_ .

The minimal data needed for this estimation is trade flows between locations within a country. Ideally, these locations should be spatially disaggregated so that most flows are across regions for which one can construct fine measures of distance or travel time. Otherwise if data is spatially not disaggregated enough and locations are fairly large, one has to take a stance on distance for within-regional shipments. One common approach is to use a proxy for internal distance (Head and Mayer, 2010). However, this ignores the distribution of economic activity within the region, and would be biased if it is concentrated in a few locations.

High quality data on domestic trade flows typically exists in developed or middle income countries. These are compiled from various sources. Some are due to specialized surveys, such as the _Commodity Flow Surveys_ in the U.S. or in Colombia ( _Encuesta Origen - Destino a Vehiculos de Carga_ ). While there is a voluminous literature making use of the former, few studies analyzed road transport costs using the Colombian data. Estimating a gravity framework, Duranton (2015) finds that a 10% increase in the travel time between Colombian cities reduces the value of trade between them by about 7%. To gauge whether within-city roads affect trade, he constructs a municipality-level road index summing up the log of the mileage of principal roads and the log of the
**_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

outsourcing; and the entrepreneurial agility displayed by Taiwanese firms to recognize and quickly seize new opportunities to specialize in narrow segments of the value chain.<sup>14</sup>

# **Upgrading: Pluses and Minuses for Developing Country Firms**

The advantages of incumbent lead firms with deep technological expertise, in terms of value extraction in GVCs, as well as the limitations for firms based in developing countries, are illustrated by the well-known case of Apple Computer Inc. Linden, Kraemer, and Dedrick (2007) estimate that only $4 of the $299 retail price of an Apple 30 gigabyte video iPod MP3 player is captured in China, where they are assembled and tested by the Taiwan-based ODM contract manufacturer Inventec. The share captured by domestic Chinese companies is even less; probably limited to packaging and local services. This is, in part, because iPods are assembled from components made mostly in other countries, such as the United States, Japan, and Korea. But more importantly, it is because Apple—which conducts high-level design work and software development in-house and orchestrates the product’s development, production, marketing and distribution—is estimated to capture $80 of the sale price. This study also estimates that $83 is captured in the United States by Apple’s technology suppliers and by retailers. Clearly, assigning the $183 per unit wholesale price of exported iPods (as would be reported in trade statistics) to the Chinese economy misrepresents where value is created in the global economy. Similarly, a “teardown analysis” of the recently released iPad tablet computer by the consulting firm iSuppli estimated Apple’s gross margin for the product (the $499 sale price less the component costs) to be $270, or 54 percent (Hesseldahl 2010). Assembly costs for the iPad may be higher than for the iPod, but it can still be assumed that very little of the product’s value is
# **2. Data**

# **2.1 River Salinity**

Our salinity data are drawn from river monitoring stations spread across the southwest coastal region in five areas (Figure 2a): Khulna (Rupsha River), Mongla (Passur), Sarankhola (Boleshor), Amtali (Paira) and Galachipa (Ramnabad). Figure 3 displays monthly mean salinity readings for [2020 – 2022], which display the same peak salinity in the spring. However, Figure 3 and the table in Figure 2b also show that typical annual readings vary greatly from west to east because varying flux conditions create different mixing ratios for fresh and salt water. Each location has minimum salinity below 0.25 ppt, but maximum salinity varies from 0.23 ppt (Amtali) to 12.75 ppt (Mongla).

**Figure 2:  River monitoring stations**

**(2a) Station locations                                                  (2b) Salinity readings**

|<br>Station<br>|Salinity (ppt)<br>Min Max|
|---|---|
|Khulna|0.13<br>8.89|
|Mongla|0.21<br>12.75|
|Sarankhola|0.13<br>1.18|
|Amtali|0.06<br>0.23|
|Galachipa|0.07<br>0.58|

Source: The Institute of Water Modeling, Bangladesh

## **Figure 3:  Monthly salinity readings**

**(3a) Khulna [Min 0.13; Max 8.89]                   (3c) Sarankhola [Min 0.13; Max 1.18]                   (3d) Amtali [Min 0.06; Max 0.23]**

4
expand exports, and whether this is especially true in less financially developed countries. These hypotheses are tested using detailed output data for 28 industries comprising the complete manufacturing sector in a sample of 45 emerging and developed countries that experienced sudden-stops in the last 30 years.

The results show that, indeed, output in sectors with higher external financing needs contract relatively more during sudden-stops episodes. The average decline in output growth among manufacturing industries is about 5 percent, but an industry with high external dependence (one standard deviation above the average) contracts 3 percent more than an industry with low external dependence (one standard deviation below the average). This larger decline among industries with high external dependence is significantly more pronounced among countries where firms are likely to experience higher financial frictions, such as emerging markets or countries with less developed financial markets, and among industries with little comparative advantages that are less likely to experience pressure to expand production.

Both the average and differential effect of sudden stops are much larger when considering only the episodes that coincide with an aggregate output contraction (recession), especially among financially underdeveloped countries. This shows that the simultaneous occurrence of a sudden stop and recessions are episodes when financing constraints are particularly tight. This is consistent with the hypothesis that the interaction of sudden stops and financing constraints at the micro level is behind the correlation between sudden stops and aggregate output declines.

The different effect of sudden stops across countries with low and high financial development is not purely due to differences in the size of the aggregate contractions associated with the sudden stop. Controlling for this size, all industries, and especially those with high external dependence experience a larger output contraction among less 5
inflows and investment in construction, offers greater returns than the more competitive export markets, where high production and trade costs undermine Lao competitiveness (World Bank 2011).

To put the characteristics of Lao exports into perspective, the World Bank’s Export Dynamics Database provides a useful framework for international comparison (Cebeci, et al. 2012). That analysis shows a high similarity between Laos and Malawi, another small, landlocked, developing economy, albeit without such large and developed neighbors. Overall, Laos is very close to trend on several measures given its GDP, including mean exports per exporter and exit rates. It has higher than average entry and survival rates, but its number of exporters (for its GDP) and of products and destinations per exporter is below average. In conclusion, it can be said that exporting is quite a difficult venture in Laos, in particular for small firms seeking to grow outside the dominant markets and products.

7
# Floods and Their Impacts on Firms: Evidence from Tanzania

**<mark>Jun Rentschler</mark>**<sup>**1**</sup> **<mark>, Ella Kim</mark>**<sup>**1**</sup> **<mark>, Stephan Thies</mark>**<sup>**1,2**</sup> **<mark>, Sophie De Vries Robbe</mark>**<sup>**1**</sup> **<mark>, Alvina Erman</mark>**<sup>**1**</sup> **<mark>, Stéphane Hallegatte</mark>**<sup>**1**</sup>

_1The World Bank, Washington DC, USA 2School of International and Public Affairs, Columbia University, New York, USA_

**Acknowledgments:** This working paper is based on firm survey data collected as part of a larger analytical program on the disaster resilience of infrastructure systems. Data collection was conducted by UDA, led by Hakan Demirbüken, Sadick Nassoro, and Samet Sahin. It has benefited from helpful comments, feedback, and inputs by Edward Anderson, Celian Colon, Elwyn Davies, Eric Dickson, Yohannes Yemane Kesete, Julie Rozenberg, Eugene Tan, and Nora Weisskopf. This study was made possible with the financial support from the Japan-Bank Program for Mainstreaming Disaster Risk Management in Developing Countries, which is financed by the Government of Japan and receives technical support from the World Bank Tokyo Disaster Risk Management Hub. It is a product of the Global Facility for Disaster Reduction and Recovery (GFDRR).

**JEL classification:** D22, Q54, O18

**Keywords:** Firms, business, floods, disasters, transport, resilience
zone fixed effects. Controls: Inputs - Plot area, total labor p. ha, any hired labor dummy, seed quantity p. ha, improved variety dummy, inorganic fertilizer quantity p. ha, any organic fertilizer dummy; Household - dependency ratio, any HH member has primary education dummy, agricultural asset index; Manager - female dummy, age, primary education dummy; Plot - intercropped dummy, precipitation of wettest month, soil fertility index; Zone fixed effects. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

Table 7 and Table 8 explore the alignment of expectations with harvest realizations by regressing realized harvest on expected harvest, first in a bivariate regression and later including a full set of controls. Expectations prove a significant predictor of self-reported realized harvest with the association much stronger in Malawi. Furthermore, consistent with the notion of systematic overexpectation, coefficients are positive but less than one. Adding the planted area- and plant productivity loss measures as additional covariates to the model, we gain some but not much explanatory power with the coefficients carrying the expected signs.<sup>45</sup> This again indicates that the two measures (one in Malawi) are associated with losses but do not provide exact point estimates.

> 45 We verify this result graphically by comparing the mean (positive) expectation gap to the mean estimated loss from Question 1 by country, crop, and survey round and by plotting the expectation gap against the two different losses measures (area and plant productivity losses in percent), respectively. Alignment is imperfect and the general pattern consistent with the finding of Sohnesen (2020) that farmers’ shock (loss) reports and realized yields (and thus the expectation gap) tell two different stories.

29
international trade described in Subsection 3.3.<sup>8</sup> To ensure the comparability of different measures capturing different aspects of financial vulnerability, we compute the measures in a unified way and normalize them to be between zero and one.<sup>9</sup> Following Rajan and Zingales (1998), we compute _Investment needs_ as the difference between capital expenditures (Compustat item 128) and cash flow (Compustat item 110), divided by capital expenditures.<sup>10</sup> This proxy captures the share of the necessary investment into physical capital that cannot be financed from internally generated funds and therefore requires external financing. The reliance on large US firms covered in Compustat in the computation assures that the resulting share reflects the investment that a representative firm in the industry truly requires but cannot finance itself. For smaller US firms and most other firms around the world, the observed use of external financing represents an equilibrium outcome between the firm’s demand and the available supply from local banks and stock markets. By contrast, large publicly traded firms in the US face only minimal frictions in accessing external finance so that the observed amount of their external funding represents a good measure of their actual financing needs (Rajan and Zingales 1998).

Following Raddatz (2006), we compute _Liquidity needs_ as the median ratio of total inventories to sales (i.e. dividing Compustat item 3 by Compustat item 12). A higher

> 8 The use of more broadly defined sectors in the original papers fits with the prevailing aggregate focus of the finance-growth literature at that time. Existing trade literature has mostly followed these broader industries when using the difference-in-difference identification strategy adopted in this paper. More recently, both finance and trade papers using disaggregated data have started to recompute some of these measures for narrowly defined industrial sectors. Crin`o and Ogliari (2017) recompute investment needs and asset tangibility for 273 SIC industries in their study on financial frictions and product quality in international trade. Tong
# **1. Introduction**

Employment is at the center of multiple debates across South Asia. Are sufficient jobs being created to absorb the numerous young entrants to the labor force? To what extent is economic growth translating into employment growth? Is all employment growth happening in the services sector, or are manufacturing jobs growing too? How many of the jobs being created involve regular wage employment? And is the female share of employment declining with economic development?

If anything, the debate is made more acrimonious by the scarcity of reliable employment data. Highfrequency labor force surveys are still uncommon in the region. Employment data is typically generated out of statistical instruments that are better geared to measuring population, economic activity, poverty or human development. These instruments rely on different definitions of key employment indicators, making comparisons difficult. And often the data is released years after it is collected.

In this paper we try to address these shortcomings by generating comparable employment figures across South Asian countries from 2001 to 2017, out of over 60 primary data sources.<sup>1</sup> We curate the data directly from population censuses, household surveys, and labor force surveys, in a transparent and replicable way. Each individual respondent to these censuses and surveys is classified as employed, unemployed or inactive based on standard labor definitions, matched as closely as possible to the corresponding questionnaires. In addition, we include information on the type of the employment, the sector of activity, and the institutional sector – public or private.

Part of the paper is devoted to explaining in some detail the data harmonization process on which our dataset is built. We also assess differences between the resulting indicators and the readily-available national estimates and model-based estimates reported by the International Labour Organization (ILO). By going into this detail, we hope to reassure readers and potential users about the reliability of the dataset, while at the same time encouraging researchers to further improve it.

The paper also aims to show the usefulness of the dataset to analyze
Table A6: Early Life Exposure to Coffee Mills and Test Scores: Effects by Location

|||All Subjects|||STEM|||Non STEM||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||||||Rural|||||
|CatchmentArea X Post|0.0459**|0.0505***|0.0447**|0.0342**|0.0353**|0.0306*|0.0493**|0.0557***|0.0495***|
||(0.0187)|(0.0179)|(0.0175)|(0.0164)|(0.0167)|(0.0163)|(0.0197)|(0.0186)|(0.0182)|
||[ 0.029 ]|[ 0.014 ]|[ 0.023 ]|[ 0.049 ]|[ 0.048 ]|[ 0.077 ]|[ 0.026 ]|[ 0.013 ]|[ 0.015 ]|
|R-squared|0.3112|0.3587|0.3588|0.3017|0.3450|0.3450|0.2925|0.3422|0.3423|
|Observations|1314120|1313807|1313807|1314123|1313810|1313810|1314119|1313806|1313806|
||||||Urban|||||
|CatchmentArea X Post|0.0432|0.0369|0.0415
magnitude, statistically insignificant, and in some instances have a negative sign. The results are similar if we use alternative specifications for the consumer protection variables. We have also tested for correlations between policy variables and levels of financial access, controlling for income per capita and other country level characteristics, but also do not find consistent and statistically significant correlations.

These statistical results are not entirely surprising due to a number of challenges discussed above. While the data collected in the survey provides a useful comparison of information on financial consumer protection, it is not sufficient to conduct any serious empirical analysis or design policy indicators. Further refinement of the methodology requires micro-level studies, collection of more granular outcome data, and availability of time series data.

# **4 Conclusion**

This paper introduces and describes a new cross-country data set on financial consumer protection based on a survey of financial regulators in 142 countries. This is the first effort of its kind to collect information on the details of existing financial consumer protection legal and institutional frameworks using consistent methodology.

The analysis of the data provides a number of insights. First, most countries have some form of consumer protection legislation in place, though it often does not include provisions specific to the financial services industry. Second, enforcement powers and monitoring ability of supervisors are often limited. Third, unregulated financial institutions are rarely covered by existing financial consumer protection legislation. Fourth, regulations on financial consumer protections are often recent and many countries are actively reforming in this area. In sum, this is a new area of focus where much further analysis is needed to inform the policy making process and develop effective solutions.

To assist future research we also compile a comprehensive list of laws and regulations relevant for financial consumer protection reported by the countries in the survey in Annex I.

This analysis presents the first step in a systematic assessment of financial consumer protection across a large set of countries. Further refinements in methodology are necessary to better capture the effectiveness of the implementation of the existing financial consumer protection regulations.
vestment share of SWFs on rate of return, risk, and networking indicators using cross-country data over the period 1985-2013 for 26 SWFs. Empirical results show that rate of return has a positive and significant effect on SWF investments, while risk exerts a negative and but not significant effect. Most importantly, we find that political SWFs are more likely to invest in Africa. These results are robust to the introduction of the following control variables: the Linaburg-Maduell transparency index, the political structure of the SWF, religion, corruption, private credit, bank efficiency, and the proximity to the technology frontier.

To increase the quantity of SWF investments in Africa, policy makers should set up good governance and transparency policies by promoting a low level of corruption and high levels of property rights. African countries should reform their financial sectors by rendering their stock markets more attractive. Finally, strong governance and a higher level of coordination among public entities positively affect the efficiency and effectiveness of public investment programs.

20
upper-secondary on-time graduation and test scores. A one standard-deviation (SD) increase in grade 6 test scores is associated with an increase in the probability of on-time lower secondary graduation by 10 percentage points and (conditional on this) with an increase in learning outcomes by 0.6 SD. They also find a strong association between grade 6 test scores and grade 12 outcomes. Lastly, they find that grade 6 test scores and future outcomes have a strong relationship even when comparing individuals with identical family backgrounds, which is done using a subsample of twins.

# **IV. Data and Methodology**

We are fortunate to have access to a number of administrative data sets that we combine to create a data set with information on achievement tests, characteristics of students, PROSPERA receipt and school quality. We now describe the data sets used and our methodology.

From the school years 2006-2007 to 2013-2014, the Mexican Secretariat of Public Education (SEP) applied the Evaluación Nacional de Logro Académico en Centros Escolares, ENLACE. The test gathered information at the end of each academic year on student performance in math, Spanish and a rotating subject for all third to ninth graders in private and public schools. Beginning in 2007-2008, ENLACE was also given to students in their final year of high school (grade 12). The test was intended to be a low-stakes assessment that would be informative about learning outcomes to SEP and to parents. The proportion of students who take the test is close to 90% (Hoyos, Estrada and Vargas 2018). The last school year of application of the test was 20132014. ENLACE was eventually replaced by PLANEA (National Plan for Evaluating Learning “Plan Nacional para la Evaluación de los Aprendizajes”) during the Pena Nieto administration, this test however is applied only to a sample of students in each school.

An important issue related to using the ENLACE for this study is the possibility of cheating and, in particular, student copying on the ENLACE exams. When the government stopped applying the ENLACE tests