rafmacalaba/data-use-mentions-extended huggingface dataset:
13

**Figure 8. Observed Patterns of Structural Change across Country Income Groups**

<!-- Start of picture text --> Rural Share of Population, % Agriculture Share of GDP, %<br>70<br>100 60<br>90<br>80 50<br>70 40<br>60 30<br>50<br>40 20<br>30 10<br>20<br>10 0<br>0 Low income Lower Upper High income<br>Low income Lower middle Upper middle High income middle middle<br>income income income income<br>Agriculture Share of Employment, % The Case of the Republic of Korea<br>80 80<br>Agriculture Share of GDP<br>70 70<br>60 60 Rural Population Share<br>5040 50 Agricultural Share of Employment<br>40<br>30<br>20 30<br>10 20<br>0 10<br>incomeLow middle Lower middle Upper incomeHigh 0<br>income income 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008<br><!-- End of picture text --> Source: World Bank, World Development Indicators database. Note: Data for rural population and GDP shares are for 2008. Data for employment shares are for 2000 for low-income countries (because of limited data availability) and for 2006 for other country groups. Some small island states with very high rural population shares were removed as outliers from the high-income and upper-middle-income groups. Lines represent the range of values within each country group, and triangles the average.

Yet development strategies have often failed to deliver sustained growth and structural transformation in many developing countries, especially in Latin America and Africa. A recent assessment by McMillan and Rodrik (2011), based on a decomposition of productivity growth into two components (sectoral productivity and structural change), is illustrative.<sup>13</
34 Relationships among Them", World Bank Staff Working Paper No. 686, Washington, D.C., 1984.

13. Heller, Peter S., "Interactions of Childhood Mortality and Fertility in W. Malaysia: 1947-1970", Discussion Paper No. 57, University of Michigan, September 1976.

14. Hobcraft, John, John McDonald and Shea Rutstein (1983), "Child-Spacing Effects on Infant and Early Child Mortality " Paper presented at the Annual Meeting of the Population Association of America, Pitcsburgh, Pennsylvania.

15. Huffman, Sandra L., "Child Spacing: for Maternal and Child Health", Mothers and Children, Bulletin on Infant Feeding and Maternal **NL;U.L.LUvI,** VU-. **LI.** 1, ***, A1 L1L-A.J** _O ._ 16. Hull, Terence, and Valerie Hull. "The Relation of Economic Class and Fertility: An Analysis of Some Indonesian Data." Population Studies, Vol. 31 (March 1977), pp. 73-87.

17. Mcgreevey, William and Nancy Birdsall, The Policy Relavance of Recent Social Research on Ferility, Occasional Monograph Series, No. @ Washington, D. C. : Interdisciplinary Communications Program, Smithsonian Institution, 1974.

18. Mosley, W. Henry and Lincoln C. Chen, Child Survival. Strategies for Research, Supplement to Vol. 10, 1984.

19. Nerlove, M. and T. Paul Schultz. "Love and Life Between the Censuses: A Model of Family Decision Making in Puerto Rico, 1950-1960," Rand RM6322 AID, 1970.

20. Nortman, Dorothy and John Bongaarts, "Contraceptive Practice Required to Meet a Prescribed Crude Birth Rate Target: A Proposed Macro Model (TABRAP) and Hypothetical Illustrations", Demograpby, Vol. 12, No. 3, Aueust 1975.

21. Pebley, Anne R. and Sara Millman, "Birthspacing and Child Survival", International
0.21|-0.13|*|
|Optimism|0.62|0.51|0.88|***|0.47|0.56|**|
|Polychronicity|-0.72|-0.78|-0.58|***|-0.81|-0.76||
|Organization|1.39|1.41|1.32|**|1.43|1.40||
|Impulsiveness|-0.37|-0.40|-0.30|***|-0.40|-0.40||
|Number of Observations|1194|845|349||421|424||

_Source:_ Own computation from World Bank Kpong and Weta irrigation scheme survey. Asterisks denote significance of t-tests for equality of means between the preceding columns: *** p<0.01, ** p<0.05, * p<0.1.

19
(Eq 11) _Sct_<sup>[2]</sup> = α _Sct_ + (1‐α) _Sc,t‐1_<sup>[2]</sup> , where _Sct_<sup>[2]</sup> is the smoothed original series from _Sct_ = α _Xct_ + (1‐α) _Sc,t‐1_ , and α is the smoothing parameter estimated by minimizes the in‐sample sum‐of‐squared predicted errors; _Xct_ is the original series.

- 4) For each country _c_ , we estimated the following regression where β represents the yearly difference in OOPPC over the previous year, and ε _ct_ is a random error term:

(Eq 12) _OOPPCct_ = α + β _Year trendct_ + ε _ct_

- 5) For each country _c_ , we estimated the following regression that additionally accounts for non‐linearities in the time trend of OOP:

(Eq 13) _OOPPCct_ = α + β1 _Year trendct_ + β2( _Year trendct)_<sup>2</sup> + ε _ct_

- 6) For all countries _c_ , we estimated the following pooled regression that includes country fixed effects (γc) as well as a flexible time trend:

(Eq 14) _OOPPCct_ = αc + β1 _Year trendct_ + β2( _Year trendct_ )<sup>2</sup> + γc + ε _ct_

- 7) For all countries _c_ , we estimated the following pooled regression that includes three dummy indicators for World Bank (Wr) income group classifications as well as a flexible time trend:

- 8) The same regression for Equation 9, but correcting for AR1 serial correlation.

The estimated parameters were then applied to the testing data set to obtain predicted values for 2009 through 2013. These predicted values were then compared with actual data in 2009‐ 2013. To assess the fit of each model, we again calculate the mean square root of the squared error, displayed in Table C.1.

> � � (
_The Global Information and Communications Technology Industry: Where Vietnam Fits in GVCs_

**Figure 1. Top 13 Importers of Intermediate ICT Goods, 1988-2014, billions of current US$**

<!-- Start of picture text --> 300.00<br> 250.00 China<br>Hong Kong, China<br>United States<br> 200.00<br>Singapore<br>Korea, Rep.<br>Other Asia, nes<br> 150.00<br>Japan<br>Germany<br> 100.00 Malaysia<br>Mexico<br>Netherlands<br> 50.00 Vietnam<br>Thailand<br> -<br><!-- End of picture text --> Authors’ note: While Singapore does import intermediate ICT goods, a significant portion is re-exported. Hong Kong SAR, China, and the Netherlands also have extremely high re-exporters.

Source: World Bank MC-GVC Database using a consistent 175 country panel that accounts for 95-98% of world trade, see: http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator Complete data on imports and exports of intermediate and final goods for the world’s top 15 traders in 2014 clearly shows China position as the world’s leading production hub for ICT hardware more fully (see Table 2). China is the largest importer and exporter of intermediate electronic goods, and the largest exporter of final ICT goods with an astonishing 43% of world exports. Most of this activity is concentrated in the vicinity of the Yangtze and Pearl River Deltas (near Shanghai and Shenzhen respectively). China’s dominant share (34%) of intermediate ICT goods exports reflects the growth of local component manufacturing in the country to supply local assembly factories, mainly by foreign-invested enterprises headquartered in Chinese Taipei, China. China is also a large market for finished ICT goods, and ranks second in
18

# Figure 6: SPS and TBT notifications and specific trade concerns, 1995-2014

<!-- Start of picture text --> SPS ‐ Notifications SPS ‐ No. of concerns<br>1400 60 50 300<br>1200 50 40 250<br>200<br>1000 30<br>40 150<br>800 20<br>100<br>30<br>600 10 50<br>20<br>400 0 0<br>200 10<br>0 0<br>No. of new concerns<br>No. of resolved concerns<br>Notified measures Notifying countries Cumulative no. of concerns<br>TBT ‐ Notifications TBT ‐ No. of concerns<br>1800 90 80 120<br>1600 80 100<br>60<br>1400 70 80<br>1200 60 40 60<br>1000 50 40<br>20<br>800 40 20<br>600 30<br>0 0<br>400 20<br>200 10<br>0 0<br>No. of new concerns<br>No. of resolved concerns<br>Notified measures Notifying countries Cumulative no. of concerns<br>No. of new concerns<br>No. of new concerns<br><!-- End of picture text --> Source: Authors' calculations based on WTO I-TIP database Non-tariff measures are increasingly regulated by international trade agreements. As we will further discuss below, WTO rules cover NTMs in multiple agreements. The GATT contains general principles on non-tariff measures, while a number of other agreements cover specific issues, including subsidy rules in the Agreement on Subsidies and Countervailing Meaures (SCM), government procurement in the Agreement on Government Procurement (GPA), intellectual
able in Mexico. Table 2 provides estimates of the mobility coefficient over one quarter, one year, two year, and five year time periods. Since not all cohorts are aged between 25 and 49 in every quarter, less cohort-period observations are available for longer intervals. Table 2 presents results from the balanced pseudopanel, where the same cohort-quarter observations are used for estimation over different time lags.<sup>8</sup> Columns 1 through 4 provide the estimates of absolute mobility, while Columns 5 through 8 include cohort fixed effects and therefore give measures of conditional mobility. Absolute mobility increases slightly as one increases the time frame, but the estimate of β is still 0.933 over two year intervals and 0.950 over five year intervals.<sup>9</sup> Thus while poorer households experience slightly faster income growth than richer households, a household which has 10 percent higher income than another household today is estimated to still have 9.5 percent higher income five years later.

In contrast, Table 2 shows a high degree of conditional mobility. A ten percent difference in income between two households with the same fixed effect is reduced to a 8.3 percent difference after one quarter, a 5.5 percent difference after one year, and only a 0.5 percent difference after two years. By five years, the households have reversed rankings.

# 5.1 Mobility and Attrition

Measurement error will result in both OLS and IV methods giving inconsistent measures of mobility. However, a second source of potential bias in mobility estimates based on genuine panel data is that of non-random attrition. This is particularly likely to be a concern in many developing country contexts in which panel surveys track dwelling units, rather than households, over time. Thomas, Frankenberg and Smith (2001) note that this is the standard protocol for follow-up surveys conducted as part of the World Bank’s Living Standards Measurement Study, with second round follow-up rates of 87 percent in Cote

> 8 The point estimates
the Pacific, 27 in Europe and Central Asia, 20 in Latin America and the Caribbean, 14 in the Middle East and North Africa, 6 in South Asia, 40 in Sub- Saharan Africa, and 23 in the high-income OECD countries. Among them, 37 OIC countries are covered. For practical purposes, most small islands and economies at war were not included. The sample covers more than 94 percent of the world’s population and nearly 98 percent of global GDP.

Selected questions are presented in the first and second rows of the table. The row for OIC countries means the percentage of OIC countries that replied yes to the questions (for Yes/No questions), or means the average value for OIC countries. The same calculation applied to the groups of developing countries, OIC-GCC, and low incoming countries. OIC-GCC means OIC countries excluding the GCC countries covered by the CGAP database.

51
estimate the effects of closures declared by the regulator, PRODUCE. The scientific agency, Instituto del Mar del Per ́u (IMARPE), can declare closures of up to 10 days. These closures can apply to all fishing grounds (i.e., Peru’s entire Exclusive Economic Zone).

> 12The regulator uses real-time vessel movement data to monitor fishing. The regulator defines fishing as moving slower than two knots at a non-constant heading for more than one hour and penalizes vessels that move in this way inside an active closure (PRODUCE, 2016 _a_ ). This movement indicates fishing because all fishing in the Peruvian anchoveta fishery is “purse seine fishing”, which involves dragging a net in a circle around a group of anchoveta. Vessels’ GPS transponders can be physically disabled but not manipulated; the transponder is inside a closed, metal box and it transmits data to the regulator automatically every 10 minutes. If the vessel’s transponder does not transmit data to the regulator for more than two hours, for any reason, the regulator penalizes the vessel owner (e.g., PRODUCE, 2017 _b_ ).

10
0|

# 3. Empirical results

The CDM model allows us to explore the relationships between innovation efforts, innovation outputs, and productivity while dealing with issues related to selectivity bias (only a few firms report positive investment in R&D at any given time) and endogeneity (due to measurement error or unobservable factors that affect both productivity and the choice of inputs/innovation inputs). This approach has been widely used in the literature (Lööf et al., 2017) as it allows to unpack the relationship between innovation input and productivity by considering the innovation output.

Our work is closely related to Crespi et al. (2016). Both studies rely on data from World Bank Enterprise Survey: ours focuses on East Asia, while theirs on Latin America.

## 3.1 What are the determinants of innovation investments?

The first stage of the CDM model seeks to estimate how firms decide whether and how much to invest in R&D. The firm’s innovative effort is assumed to be a latent variable that could be (imperfectly) observed when firms report positive R&D expenditures. We thus apply a generalized Tobit model estimated by maximum likelihood, considering the following potential determinants:

- **Internal capabilities.** Proxies include (i) the firm age which captures the tacit knowledge accumulated through experience and learning by doing; (ii) human capital which reflects the cognitive skills needed to absorb new knowledge and develop new technologies (Acemoglu et al., 2006); (iii) a group dummy which identifies whether the firm is part of a large group or a subsidiary of a multinational which in turn may imply easier access to more sophisticated knowledge (Girma and Gorg, 2007) and/or human capital (Kumar and Aggarwal, 2005); (iv) FDI; (v) sales diversification is an indicator of the scope of the firm’s productive capabilities and the extent to which the firm’s knowledge base is focused on narrow/specialized sectors or can
>weekly discount factor<br>Eichenbaum et al. (2021)<br>initial infected population<br>Eichenbaum et al. (2021)<br>efciency loss due to infection<br>steady-state labor supply = 1<br>disutility from labor supply<br>loss of type-_m_ reduces _u_(_·_) by _lm_<br>elasticity of substitution between household members<br>ASPIRE dataset<br>external transfer payment|the calibrated parameters discussed in the main text. Country-specifc parameters are presented in the appendix.|
|---|---|
|Value<br>country-specifc<br>country-specifc<br>country-specifc<br>0.27<br>2.32<br>country-specifc<br>7<br>38%<br>0.15<br>0.10<br>0.03<br>0.00<br>country-specifc<br>country-specifc<br>country-specifc<br>country-specifc<br>country-specifc<br>0_._961_/_52<br>0.1%<br>0.8<br>1.0<br>3.0<br>1.55% of GDP|te: This table lists|
|Name<br>_πI_1<br>_πI_2<br>_πI_3<br>_πI_4<br>_ξ_<br>-<br>-<br> ̄_μ_<br>_νj_(1)<br>_πd_(_m_)<br>_πb_(_m_)<br>_k_<br> ̄_πn_(_m_)<br>_l_(_m_)<br>_β_<br>_ε_<br>_φ_<br>_θ_<br>_σ_<br> ̄Γ_t_|No|

30
3 often associated with an increase in fertility. Only with education of completed primary school was fertility likely to fall with increased education. The reasons for these effects were primarily the sometimes positive effects that education had on fertility through reductions in breastfeeding and possibly improved maternal health and fecundity. The 1979 study also documented that female education was much more likely to have a negative effect on fertility than male education. Male education was less lik-ly to have negative effect on fertility than female education in part because it was more likely to have positive effects on the ability to afford children while having less of a negative effect through postponing marriage.

Since 1979 the WFS surveys have allowed these findings to be tested against a larger, more comparable set of data from a large number of developing countries. A number of studies have been done using the WFS data. (Jain, 1981, Cochrane, 1983 and U.N., 1987). The 1983 study using data from 20 countries confirmed the facts that (1) in the poorest, most rural areas fertility was likely to increase with small amounts of education before falling with higher amounts, (2) female education had stronger negative effects on fertility than male education, and (3) education's effects are more likely to be negative in urban than rural areas. A much more exhaustive study of data from 37 WFS studies confirmed these facts as well. (U.N., 1987). Table 1 and Figure 1 illustrate these relationships for individual countries and for aggregates of thes.e 37 countries respectively.

The 1987 U.N. study also tracked the channels through which education affected fertility and looked at regional, as well as development level differentials in the effect of education on fertility. Work by Cochrane and
# **2 Survey methods to measure sensitive topics**

There is robust evidence that people underreport the truth about sensitive topics when they are directly asked in a survey. Such research compares self-reported survey answers to observed or independently verifiable outcomes such as tax records, receipt of welfare benefits, drug prescriptions for mental illness, and voting in an abortion law referendum (Bharadwaj, Pai and Suziedelyte, 2017; Meyer and Mittag, 2019; Gottschalk and Huynh, 2010; Murray-Close and Heggeness, 2018; Rosenfeld, Imai and Shapiro, 2016).

However, reliable, unbiased observational data are rare for many sensitive topics, including IPV.<sup>5</sup> This is particularly true in developing countries, where there is limited available administrative data from the medical or legal services that IPV survivors may use. Further, where these services are available, the people able or choosing to access them are likely not representative of the broader population of those experiencing IPV.<sup>6</sup> As a result of these administrative data limitations and concerns about underreporting on face-toface surveys, researchers are considering alternative survey methods to minimize misreporting bias. Two popular alternative methods, ACASI and the list experiment, address slightly different motivations for misreporting. The ACASI method is primarily designed to overcome misreporting driven by shame when responding face-to-face to an enumerator, while the list experiment addresses misreporting driven by a preference for anonymity or fear of repercussions if one’s answer becomes known, as well as shame. Table 1 shows these methods by their approach to questioning and administration.

Table 1: Method comparisons by <u>questioning</u> method and administration

||Question & answer through enumerator|Question & answer through tablet|
|---|---|---|
|Direct questioning method|Face-to-face (status quo)|ACASI|
|Indirect questioning method|List experiment||

## **2.1 More Privacy: Audio Computer-Assisted Self-Interview**

ACASI is a method
# **Table 5: Displacement and elite capture**

||Gives inputs to<br>others|Used inorganic<br>fertilizers in<br>2009/10|<br>Used improved<br>seeds in<br>2009/10|Related to<br>government<br>officials and<br>influentials|Socializes with<br>government<br>officials and<br>influentials|Paid to become<br>beneficiary in<br>2010/11|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Beneficiary|-0.024|0.129*|0.031|-0.040|-0.019|0.004|
||_0.605_|_0.077_|_0.634_|_0.465_|_0.735_|_0.838_|
|Lottery|-0.086**|0.135|0.119|0.006|0.008|0.012|
||_0.014_|_0.139_|_0.129_|_0.915_|_0.886_|_0.269_|
|Lottery x Beneficiary|0.112**|-0.265***|-0.042|0.079|0.052|0.002|
||_0.041_|_0.005_|_0.663_|_0.309_|_0.518_|_0.910_|
|Number of contacts|523||||||
|Number of households|294|427|427|427|427|920|
|R-squared|0.017|0.019|0.010|0.007|0.004|0.
, we calculate the median across all months and years. Finally, we use the maximum monthly average level of particulates for each grid-cell for the month-of-year that had the highest average pollution from 2015 through 2020. For example, Delhi is assigned the average December PM2 _._ 5 concentration because that is the month-of-year in which average particulate values are highest. In the analysis of the correlation between wealth and particulate pollution from non-dust and sea salt sources we use the dust and sea salt removed estimates produced by the Van Donkelaar group at the annual level and match to RWI points in the same way.

As a robustness check we also re-estimate our models with available ground monitor measurements of PM2 _._ 5. All ground measurements come from the OpenAQ network ( `https: //openaq.org` ).

**Elevation data–** We use data from NASA’s ASTER project that provides a global digital elevation model (DEM) that provides elevation at 30m resolution comprehensively around the world.

**Pollution source data–** We use data from the EU EDGAR database that provides information on contribution of different economic sectors to particulate pollution for each point on a grid covering the whole planet. We aggregate these sectoral figures into broad categories (e.g. power generation) and calculate the total contribution of each broad category within each coun-

13
translation of approaches to low-income regions through use of underlying global data sets. This paper’s analysis focuses on past and current weather patterns and does not include an estimation of future changes due to global warming.

# 3. Data on Hazards and Welfare

## 3.1. Hazard data

For the analysis described in this paper, we constructed a database of historical hazard data for all SSA using remotely sensed satellite data and indices that were reputable and publicly available and that had high levels of spatial and historical coverage. The considered hazard variables include rainfall, vegetation cover, evapotranspiration, soil moisture, and the WRSI. Given there is no single definition of drought, these hazard data were used to develop different measures of climatic conditions as a proxy for drought. These measures range from simple ones based on the amount and timing of precipitation during the growing season in a defined area, to more complex composite measures combining precipitation with other information capturing the impact of increased temperatures on water availability and agricultural stress at a location. Appendix B provides more details on all the drought measures considered and the sources of the data.<sup>10</sup> Table 3.1 provides a brief overview of the measures used in the analysis. These measures by and large focus on the first three months of the growing season.

**Table 3.1. Summary of range of hazard measures derived from remotely sensed data**

|**Data type **|**Source**|**Hazard measures**|
|---|---|---|
|Rainfall|CHIRPS global rainfall data set from US<br>Geological Survey (NASA)|Standardized anomaly of cumulative<br>rainfall (based on the first three months of<br>thegrowingseason)|
|Vegetation<br>(greenness)|NDVI product of MODIS vegetation<br>indices|NDVI standardized anomaly (based on the<br>average over the first three months of the