rafmacalaba/data-use-mentions-extended huggingface dataset:
dependent variables in the empirical analysis.

# **5.2 Nonparametric Analysis**

Sex difference in the adequacy ratio of different nutrients at each age are nonparametrically (using locally weighted regression method, lowess with bandwidth 0.8) shown in Figures 1 and 2. These figures illustrate a number of points. First, sex disparity in adequacy ratio is least observed for calorie and among the micronutrients, for niacin. As discussed in the appendix, calorie requirement figures for children and adolescents are based on US National Child Health Survey (NCHS) standard and not based on actual energy expenditure because of lack of data on time allocation for these age groups. Only for these groups, the adequacy ratio seems to be less than 1. This might imply that perhaps these groups are having calories based on their actual energy expenditure (which is unobserved in the data), but still they are having calories less than NCHS standards to meet their full long-term growth potentials. On the other hand, for the adults for which the requirements are based on actual energy expenditure, the adequacy ratio for both males and females are at or above 1. Calorie intakes of all ages are also most stable (compared to other nutrients) across different rounds (as requirement figures are fixed, any movement in adequacy ratio across rounds implies fluctuation in intakes). All these are consistent with the view in nutrition literature that given the wide variety of sources to meet one’s calorie need, in normal circumstances (i.e., without famine) an individual can always meet his/her calorie need (World Health Organization, 1985).

Second, for most of the other nutrients, sex disparity is prominent, persistent, and often widens for adolescents and adults compared with children. For vitamin A and D in some rounds, at a relatively high age (above 70 or so), females’ adequacy ratio cross over males’. However, this cross-over should be interpreted with caution due to the limited number of observations at those
Two challenges must be addressed to construct a consistent time series based on the two nighttime lights data sources. First, DMSP-OLS data is captured over time using six different satellites; differences in the satellites, including the sensors and time of orbit, have resulted in significant differences in light values captured from different satellites across years (Wu et al., 2013; Zhang et al., 2016). Second, there are notable differences between DMSP-OLS and VIIRS data: VIIRS has a finer resolution, can capture nighttime lights at both lower and higher magnitudes, and suffers less from “blooming,” whereby lights in one pixel impact surrounding pixels (Elvidge et al., 2013). We therefore rely on a dataset from Li et al. (2020), who address both issues to develop a harmonized global nighttime lights dataset from 1992 to 2018; Li et al. (2020) use a stepwise calibration approach to create a consistent DMSP-OLS dataset.<sup>5</sup> One key difference between the calibrated DMSP-OLS data and the simulated DMSP-OLS-like data (i.e., VIIRS data simulated to be like DMSP-OLS) is that the DMSP-OLS-like data captures small values of

> 5The stepwise calibration approach refers to a four-step process, where DMSP-OLS satellites are individually calibrated to match earlier years; the process starts with earlier satellites and moves sequentially to later satellites. The method produces nighttime lights trends that more accurately track trends in electricity consumption compared to DMSP-OLS calibration methods developed by Zhang et al. (2016) and Elvidge et al. (2014). See Li and Zhou (2017) for details on how the dataset is created.

9
almost halved even in Rural Eastern Hill between the two survey years, a remarkable accomplishment in and of itself. The uneven progress in poverty reduction, however, can translate into widening inequality and, combined with the regional disparity in the depth of poverty, could possibly become the genesis of yet another social unrest that has plagued the country since the mid-1990s. This is an integral part of Nepal’s vigorously debated agenda on economic and social inclusion.<sup>23</sup> The second key challenge facing Nepal’s future is the extent to which the success in lifting people out of poverty translates into moving them into a secure middle class where the risk of falling back into poverty is minimal. How big is the size of this middle class in Nepal? Is it growing or shrinking? What are the characteristics of individuals that constitute this group? These are important questions that have started to emerge in Nepal. World Bank (2016) examines the dynamics of living standards movements across generations as well as within generations to examine long-run economic and social mobility as well as transitions in and out of poverty, vulnerability and the middle class.

**Figure 13 Average per capita consumption of the poor (Rs. 2010 Prices)**

<!-- Start of picture text --> 15,000<br>14,000<br>12,826<br>13,000<br>12,589<br>12,214 12,205<br>12,000<br>11,474 11,428 11,497<br>11,231<br>10,926<br>11,000 10,765<br>10,289<br>10,174<br>10,000 9,769<br>9,378<br>9,000<br>8,000<br>Kathmandu Oth urban R‐W Hill R‐E Hill R‐W Tera R‐E Tera
Accounting Research (CSMAR) database, the most comprehensive database of its type that contains all trading prices and financial statement data for listed firms trading on both the Shanghai and the Shenzhen exchanges. We also rely on land price in Shenzhen to estimate the value of titling when Allocated land was converted.<sup>20</sup>

> 20 These land prices are found on the website of the “Urban Planning Land and Resources Commission of Shenzhen Municipality”, the government land agency, and “Soufan.com”, the largest online source of land

19
> b0 = −0.78 which pins down a tight relationship of η = [ρ<sup>−1</sup> + 0.78(ρ − 1)/ρ]<sup>−1</sup> . I take a

> 18The average of nominal GDP of Canada, China, Mexico, Japan, Germany, United Kingdom, Republic of Korea, France, Hong Kong SAR China, and Brazil in 2010 USD at market exchange rates (from IMF, World Economic Outlook Database, April 2011). The average is 3.6/20 of that of the US, but I round down to 3 firms per country per sector to keep the aggregate number of firms as close to AB who have 40 firms per sector.

> 19These two statistics are calculated using 2005 import and export data from Feenstra’s trade database and 2005 manufacturing production from the NBER manufacturing database for 371 matched NAICS 6d sectors which had import, export and US production data.

> 20In contrast, AB set D = 1.45 to match the average of import and export shares over 1987-2003 of 16.5%. The discrepancy between my figure and that of AB can be explained by (i) manufactured imports greater than exports, and (ii) the share of manufactured imports trending up over time.

> 21For the US+10 country sample, I calculate changes in RERs over overlapping three year periods, as this is halfway between the average life-length of goods in the IPP and PPI samples. The resulting correlation in the US+10 country model between bilateral and competitors’ RERs is 0.3, which is slightly lower than the empirical sample (and results in a smaller bias of univariate bilateral PT). Depending on weights of different countries in the sample, this correlation is between 1/4-1/2. As the competitors’ RER averages across multiple RERs, it usually has a SD 1/2-2/3 as large as the typical bilateral RER SD.
using unit-level data for the years 19992004 from the Annual Survey of Industries (ASI), conducted by the Central Statistical Office of the Government of India. The ASI covers all factories registered under the Factories Act of 1948 that employ 20 or more workers, or that employ 10 or more workers and use electricity. Although the ASI data has a large sample size, certainly larger than Prowess, and it contains data on firm-level characteristics, it cannot be used to study firm-level export behavior. This is because even though the ASI provides information on whether a firm exports or not, the database does not follow firms over time. In other words, firms are sampled afresh every year and it is not possible to create a panel of firms over time. Data on the number of units, i.e. the plant or the factory, is used in the analysis since employment-level data is often scarce or missing. As ASI collects data for primarily the manufacturing sector, agglomeration measures do not account for the activities of services enterprises. This is a shortcoming of the analysis since service sector activity and clustering within a location might be strongly associated with the availability of essential inputs that might reduce entry costs into export markets. Data on market access is constructed using district-level population drawn by various surveys of the National Sample Survey Organisation (NSSO).

Data on measures of infrastructure, such as education, electricity and communications infrastructure, and on wages and wealth within the district are also drawn from the household surveys of the NSSO. These data are only available for 3 Rounds: Round 55.10 (July 1999 - June 2000), Round 60.10 (June 2004) and Round 61.10 (July 2004 - June 2005). The specifications with infrastructure variables thus refer to fewer years than those for firm-level and economic geography variables. And lastly, as mentioned

> 12 More recent data is also available, but the data extracted by the author stops at 2008. 13 Quoted in earlier version of NBER working paper.

13
**||||||
|Flood/drought|−0.0530***|−1.933***|−4.915**|0.276**|0.606*|
||(0.0140)|(0.701)|(2.111)|(0.129)|(0.349)|
|Crop/livestock loss|0.00696|−0.190|-3.048*|-0.0310|−0.427|

12 In constructing the FCS, CSI, and dietary diversity indicators, we relied on guidance provided by the World Food Programme (see https://www.humanitarianresponse.info/en/operations/afghanistan/document/guidance-note-calculation-household- <u>food-security-outcome-indicators)</u> 28
# **Estimating Local Agricultural GDP across the World**<sup>_†_</sup>

_Keywords_ : agriculture, regional economic activity, GDP, cross-entropy _JEL Classification Codes_ : Q10, R12

> _†_ The authors would like to thank the following people for discussions and prior reviews: Claudia Berg (World Bank), Gero Carletto (World Bank), Uwe Deichmann (World Bank), Rashmin Gunasekera (World Bank), Barbro Hexeberg (World Bank), Glenn-Marie Lange (World Bank), Michael Lokshin (World Bank), Eric Roland Metreau (World Bank), Jose Pablo Valdes Martinez (World Bank), Tim Robinson (FAO), Steven Rubinyi (World Bank), Juha Siikam ̈aki (IUCN), Ben Stewart (World Bank), Jeffrey R. Vincent (Duke University). We appreciate the use of non-timber value data from Juha Siikam ̈aki and ports data from Gilles Hosch. The authors would like to thank the participants of conferences including: American Association of Geographers Annual Meeting 2019 in Washington, DC, 3-7 April 2019; United Nations Economic Commission for Europe Workshop on Data Integration: Realising the Potential of Statistical and Geospatial Data in Belgrade, Serbia, 21-23 May 2019; International Institute for Applied Systems Analysis seminar in Laxenburg, Austria, 27 September 2019; IFPRI RISE Workshop in Washington, DC, 19 November 2019; and the Committee for the Coordination of Statistical Activities and United Nations Geospatial Network Joint virtual Workshop on the Integration between Geospatial and Statistical Information, 28 April 2021. We appreciate the support of the World Bank Strategic Research Program on Big Data. The maps displayed in this paper are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of the World Bank Group any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

> _‡_ World Bank. Corresponding author can be reached at: bblankespoor@worldbank
: Eurostat - <u>https://ec.europa.eu/eurostat/statistics explained/index.php?title=Glossary:At_risk_of_poverty_or_social_exclusion_(AROPE)</u>

> 17 The changes include: 1) Refining the criteria for severe material deprivation to establish a novel severe material and social deprivation rate, expressed as a percentage of the total population lacking a minimum of 7 items of 13 material and social deprivation components, and 2) Introducing the (quasi)-jobless household indicator, defined as "individuals aged 0-64 residing in households where adults (those aged 18-64, excluding students aged 18-24 and individuals self-identified as retired or recipients of any pension, except survivor's pension) worked for a duration equal to or less than 20% of their total potential work time during the preceding year.”

> 18 The indicator measures the percentage of the population that cannot afford at least four of the following nine items: 1) pay their rent, mortgage or utility bills; 2) keep their home adequately warm; 3) face unexpected expenses; 4) eat meat or proteins regularly; 5) go on holiday; 6) a television set; 7) a washing machine; 8) a car; 9) a telephone.

8
Organisation for Economic Co-operation and Development and World Trade Organization (2012d), _Trade in Value-Added: Concepts, Methodologies and Challenges_ , Paris and Geneva: OECD and WTO.

- Organisation for Economic Co-operation and Development and World Trade Organization (2013a). “OECD-WTO Database on Trade in Value-Added First Estimates: 16 January 2013”, Paris: OECD.

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- Organisation for Economic Co-operation and Development and World Trade Organization (2013c). _Aid for Trade at a Glance 2013: Linking to Value Chains_ , Paris: OECD.

- Oxfam “Behind the Brands” http://www.behindthebrands.org/en-us.

- Porter, M. (1985). _Competitive Advantage: Creating and Sustaining Superior Performance_ , New York: Free Press.

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- Rugman A and Verbeke A, 2004, A perspective on regional and global strategies of multinational enterprises, _Journal of International Business Studies_ , 35, 3-18

- Rugman Alan M., Li Jing, Oh Chang Hoon, (2009). "Are supply chains global or regional?", _International Marketing Review_ , 26(4-5), 384–395.

- Saez, S. and A. G. Goswami (2010). “Uncovering Developing Countries’ Performance in Trade”, _Economic Premise_ , 39, Washington, DC: World Bank.

- Sanyal, K. and R. Jones (1982), “The theory of trade in middle products”, _American Economic Review_ 72(1), 16-31.

- Staritz, C., G. Gereffi and O. Cattaneo (2011), “Shifting End Markets and Upgrading Prospects in Global Value Chains”, _International Journal of Technological Learning, Innovation and Development_ 4(1/2
(upward mobility) or _d_ (downward mobility), and _o_ = _n_ (unconditional mobility) or _c_ (conditional mobility).

We define the unconditional (probability of) upward mobility for the whole population as follows Note that this higher income category _k+1_ is not just the next higher income category, but can generally include any higher income category. The corresponding probabilities of unconditional downward mobility can be obtained by reversing the inequality signs in Equation (7) for individuals’ income level in the second year.

Focusing on the income category _k_ in year _1_ , we define the measure of conditional upward mobility for the whole population as follows<sup>12</sup>

# **4. Data**

We analyze the Russian Longitudinal Monitoring Survey (RLMS), which is an annual and nationally representative panel household survey. Our analysis covers 24 years (22 survey waves) from 1994 to 2017. We restrict the estimation sample to working-age adults, who are 16 years old or older, and exclude households where all members are younger. We also exclude households with an unusually large number of members (e.g., having more than five adults and three children).<sup>13</sup>

> 12 See Dang _et al._ (2019) for more discussion on these measures of mobility.

> 13 Such households represent less than 3% of the data. See Appendix A, Table A.3 for the distribution of household types. But we offer estimates using the whole unrestricted sample in Table 5. The results suggest that the scale parameters for children are lower when using a pooled model and even negative (but insignificant) when using fixed effect ordered logit. At the same time, the adult scale parameter is robust to using an unrestricted sample.

11
, H. (2003). Solid Waste Management in Africa: A WHO / AFRO Perspective. Paper 1, presented in Dar Es Salaam at the CWG Workshop, March 2003. Retrieved electronically: http://www.skat.ch/sfweb/activities/ws/cwg/pdf/cwg-01.pdf

- Sterner T. and H. Bartelings (1999). Household Waste Management in a Swedish Municipality: Determinants of Waste Disposal, Recycling and Composting, Environmental and Resource Economics, 13(1999):473-491.

- Thomas-Hope E. (1998). _Solid waste management: critical issues for developing countries_ . Kingston: Canoe Press.

- Tiller, K. H., P. M. Jakus and W. M. Park (1997). Household Willingness to Pay for Dropoff Recycling, Journal of Agricultural and Resource Economics, 22(2): 310-320.

Wang, H. and J. He (2010). Estimating Individual Valuation Distributions with Multiple Bounded Discrete Choice Data. **_Applied Economics._** 2010.

- Wang, H., J. Xie and H. Li (2009). Water Pricing with Household Surveys: A Study of Acceptability and Willingness to Pay in Chongqing, China. **_China Economic Review_** , 2009.

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picture text --> _Source:_ AICD water supply and sanitation database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Access figures calculated by the AICD using data from the 2005 Demographic and Health Survey.

Water utility performance is plagued by inefficiencies and lack of investment. In the late 1990s, legislation established the Zimbabwe National Water Authority (ZINWA) to take responsibility for water supply and sanitation nationwide. But water supply and sanitation systems significantly deteriorated under the ZINWA, affecting utility performance and service quality. The lack of investment in the sector for almost a decade has led the ZINWA to have distributional losses as high as 45 percent of production (see table 7), more than double the level of a well-performing utility and one of the highest among southern African utilities (figure 10a). Water production is estimated to be 30 percent below requirements (World Bank 2008a), while bill collection is very low at around 20 percent as of April 2009. Since dollarization in 2009, collections ratios started to pick up, but are still at very low levels.

With less than 90 connections per employee, the ZINWA‘s labor productivity is one of the lowest in southern Africa (figure 10b). The recent transfer of responsibility for ensuring provision of water and 27
I use data on firm observations from 1989 to 2005. For these years, there is information on all the firm variables used in the production function estimation as well as quantities and sales values of products of firms. There are data on 5 _,_ 850 manufacturing firms with 45 _,_ 835 firm year observations. The firms belong to 13 major industry groups.<sup>19</sup> Industries are classified according to the National Industrial Classification (NIC 2008). There is a one-to-one correspondence between the NIC and ISIC (The United Nations International Standard Industrial Classification).

The data set is appropriate for this study because it tracks firm performance over time for a variety of variables. In addition to the usual variables on revenue and inputs, which are relevant for productivity estimation, it also contains data on variables relating to firm activity in international markets (exports and imports) as well as on investment in knowledge (R & D spending). It also has product-level data on all products produced by the firm which allows for the construction of firm specific price deflators.

All input variables have been deflated to 1989 _−_ 90 levels using deflators from the 1993 _−_ 94 price series. Energy, Wages and Capital Stock were deflated by the power and fuel, wholesale price index and machinery deflators respectively. I created input price deflators for each industry by passing output deflators through the input-output matrix for each industry. Summary statistics for the variables used in the production function are reported in Table 3.

The measure of quantity I use is the firm sales revenue deflated by the firm specific price deflator. The import intensity term is the total expenditure on intermediate inputs divided by the expenditure on domestic inputs. All firm output and input variables (quantity, labor, capital, energy, material inputs and import intensity) are expressed as natural logarithms while the export and R & D indicator are discrete variables. There are 544 export entrants where an export entrant is as defined in subsection 4.2.
(enrolling in a bachelor’s program, enrolling in a SC program, or not enrolling in higher education), which might bias OLS estimates of labor market impacts. Second, while a standard Instrumental Variables (IV) approach could address self-selection issues in homogeneous treatment effect settings, under general conditions it would not identify the average effect of SC programs (Heckman and Urzúa, 2010). More precisely, in our essential heterogeneity setting, a standard IV strategy would only identify the effect of SC programs on compliers relative to the next-best option that the student would have otherwise chosen–enrolling in a bachelor’s program or not enrolling at all in higher education–without identifying the separate margins of choice. In other words, a standard IV strategy would identify the effect of SC programs relative to a mixture of the other two options. Only under additional assumptions could one decompose this effect into its sub-components and provide the underlying pairwise comparisons (as in Kline and Walters, 2016; Hull, 2018). Rather than impose additional assumptions, we exploit rich student longitudinal data and local exogenous variation in higher education supply to estimate the share of students who respond along each margin of choice–expansion and diversion–while also estimating SC programs’ labor market effects relative to the next-best option.

Evidence on these margins is novel for developing countries. While other studies have explored higher education returns in developing countries, they have either compared returns across countries (Ferreyra et al., 2017, 2021) or, within a country, have estimated returns for new _versus_ existing programs (Camacho et al., 2017), by program and field (Ferreyra et al., 2020), or for selective institutions (Barrera-Osorio and Bayona-Rodríguez, 2019). Unlike previous studies, we quantify the margins of choice and argue that these are critical to who benefits from SC programs, and why. Consider, for instance, a policymaker who builds a new higher education institution (HEI) specialized in SC programs. This might lead some
|Year Efects|No|Yes|Yes|
|Country Efects|No|Yes|Yes|
|Sector Efects|No|Yes|Yes|
|Pseudo-_R_<sup>2</sup>|0.101|0.239|0.203|
|Number|8,475,013|6,375,804|790,361|

_Notes_ : Authors’ calculations using SEDLAC data. Robust standard errors in parentheses. Significance: *** 1%, ** 5%, * 10%.

33