rafmacalaba/data-use-mentions-extended huggingface dataset:
a log-linear trend of _I,,_ adjusting for trend-breaks when appropriate. The depreciation rate is assumed to be 4 percent, and g is derived from the series of real GDP at market prices. Equation (1) is then applied to calculate the rest of the values of _K&._ To continue this procedure for the post1989 values, we used data on Gross Domestic Fixed Investment (GDFI), available for all countries except for Argentina. For this country, only the data on Gross Domestic Investment (GDI) was available. To solve this problem, we regressed the log of the ratio of GDFI to GDI on a linear and a quadratic trend, using the years for which both variables were available (from 1970 to 1990). The estimates were then used to extrapolate the figures of GDFI from 1990 through 1995.

2) Gross Domestic Product While comparing the WDI data for this series with the data from Nehru and Dareshwar (1993), we found considerable discrepancies in the levels but not in growth rates of the series. Thus, we performed the updating by multiplying the 1988 levels from the original source by the subsequent years' rates of growth, as derived from the WDI data base.

- 3) Labor Force

Nehru and Dareshwar (1993) use the population aged 15-64 years as a proxy for the labor force. Their data covers the period from 1960 to 1988. We updated this series with WDI data for the period 1989-1995. For the period 1950-1959, however, we only had access to data on total population. Thus, we regressed the log of the ratio of the workingage population to the total population on a linear and a quadratic trend, with data on the period 1960-1995. We then used the fitted values of this regression to extrapolate the figures for the working-age population from 1950 through 1959.
leading to a 2017 local demonstration against refugees in Nakivale. In our interviews in 2018, villagers living around the settlement expressed concerns about growing tension relating to land access sparked by the influx of refugees. The Ugandan vice chairperson of a church located adjacent to the settlement shared his feeling about the increasing tension over land: Refugees are good people. As long as refugees stay inside the settlement area, we have no problem. But when they come to our land, it is a problem. (Samson, Kazya church EA, Nakivale).

Since most Ugandan villagers living around Nakivale settlement are engaged in farming and animal husbandry that rely upon access to land, the increased occupation of land by refugees has posed a threat to their livelihoods and consequently made the relationship with refugees strained. Our survey data shows differences in opinions between the 70% of Ugandans around Nakivale who own land and the 30% who do not. Whilst across both groups about 8 out of 10 believe refugees are competitors, the landowners are significantly more likely to think of refugees also as economic contributors – 76% agree they are, as opposed to only 63% among hosts without land. This is perhaps explained by the fact that 24
Awareness of the NAIVS program among non-beneficiaries in non-lottery villages is high at 86 percent. In addition, there seems to be no difference in awareness between non-beneficiaries in lottery and nonlottery villages as the coefficient β _L_ on the dummy variable _Lottery_ is small and not statistically significant.

Column 2 of Table 4 reports whether the farmer was asked about his or her ability to afford the top-up when the VVC assessed his or her eligibility for the NAIVS program. This is important because the VVC reported to us anecdotally that they “knew” who could and could not pay and only offered the vouchers to those that could pay. Put differently, the ability to pay the top-up could have been used by the VVC as an excuse to target the program to specific individuals. Indeed, becoming a beneficiary increases the probability of being asked about the ability to pay for the top-up by roughly 8 percentage points (a 54 percent increase from a base of 15 percent among non-beneficiaries in non-lottery villages). This increase, however, is only weakly statistically significant at conventional levels (p-value is 0.093) and there is no difference among non-beneficiaries in lottery and non-lottery villages.

Columns 3 and 5 of Table 4 use as dependent variable whether beneficiaries report receiving the pack of vouchers in the seasons 2010/11 and 2011/12, respectively. Since we use administrative data to determine the beneficiary status of individuals, there are differences between the administrative data and the self-reported voucher allocation. Indeed, according to columns 3 and 5, around 22 percent in 2010/11 and 24 percent in 2011/12 of individuals classified as non-beneficiaries, respectively, claimed to receive vouchers in non-lottery villages. As expected, becoming a beneficiary increases significantly the probability of reporting having received the pack of three vouchers. This increase corresponds to around 34 percentage points in 2010/11 and 17 percentage points in 2011/12. While treatment compliance is far from perfect, beneficiaries in lottery villages are equally likely to receive the vouchers compared to beneficiaries in non-lottery villages. This suggests
passed since the child‘s birth. This means that, for example, an analysis of child survival based on individual data from the 2008 DHS could only use data on births that took place in 2003 or earlier. However, such births would have taken place in circumstances far removed in terms of time from the characteristics of the household (ITN ownership, water and sanitation access, etc.) at the time of the survey in 2008, and the survival of such children would be much less relevant to evaluating post-2003 changes in policies and programs.

Infant mortality, which refers to deaths in the period up to 12 months of age, is comprised of neonatal and postneonatal mortality. Neonatal mortality refers to deaths taking place from birth
each case.

On the other hand, the interpretation of these results regarding the inspection data needs to consider two things: first, the ability of the regulator to inspect water regulations relative to air emissions might be different and this is probably is reflected in the results. And second, it is not possible to conclude that the regulators should focus more on one category versus the other, in air emission for instance. The standards might be different in both cases and what one can learn from these results is that given the level of standards with which firms must comply, they seem to comply more with air emissions standards.

The annex tables extend this analysis to the industry level, that is, these regressions are run for the inspection data at the industry level. The conclusion, in this case, is that the relative air/water compliance depends on the industry. Although overall facilities seem to comply more with air pollution standards, it is the case that industries are more compliant with water in sectors like in mines, paper, steel, and thermal. It seems that it is the opposite only in the case of the aluminum sector. In the rest of the industries, there is no significant difference in the relative compliance with the two categories. This provides more detail and suggests different strategies for regulators when inspecting different industries, always keeping in mind the standards in place and how they are applied to each industry.

15
where ∆indicates deviation of observations from household mean. However, eliminating household fixed effects also eliminate time-invariant observable household characteristics, in my case which include spouses’ bargaining measures. Therefore, to assess intrahousehold bargaining, I can now only test the restrictions in equation 13. Moreover, the data is collected over four rounds within a year, so there will be very limited variation of household size, demographic composition, and per capita landholding (as landholding data is only from first round) across rounds. So the effects of these variables could be imprecisely estimated. The HFE estimates will be also based on a restricted sample of households that have at least one member of each of the age-sex group under consideration. Also, the noise to signal ratio is likely to increase due to differencing.

Finally, as I analyze intrahousehold bargaining for a number of nutrients, the likelihood that no gender differences are found along any margin is very low. So the results might be biased towards finding discrimination. Moreover, individuals’ consumption of one nutrient may affect the consumption of others as typically they consume a food-bundle in which different food items contain different nutrients in different proportions. To address these issues, adequacy ratios for all nutrients under level specification 7 and HFE specification 14 are estimated simultaneously in a seemingly unrelated regression (SUR) framework (henceforth, referred to as _SURLS_ and _SURHFE_ , respectively).

# **5 Data and Descriptives**

I use an innovative household survey data from the International Food Policy Research Institute (IFPRI). The data come from four rounds of surveys at four month intervals during 1996-97 (Round 1: June-September, 1996; Round 2: October-December, 1996; Round 3: February-May, 1997; and Round 4: June-September, 1997) in 47 villages from three sites in four districts of Bangladesh<sup>11</sup> . The survey objective was to evaluate the impact of commercial vegetable production in Saturia (site 1), polyculture fish production in household-owned ponds in Mymensingh (site 2)
of its area. Real GDP is measured in constant 2005 US dollars at PPP. I follow Dollar and Kraay (2003) and Alcala and Ciccone (2004) to use the PPP for converting GDP values into US dollars. Presenting GDP in PPP is more appropriate than deflating with market exchange rates for cross-country analysis. As for the area of the country, studies like Rodrik (1998) and Frankel and Rose (2000) shows that area has a significantly negative impact on openness. Large countries are less likely to trade because of relatively higher domestic demand and higher transport costs of exporting abroad. Moreover small size limits the country’s possibilities to diversify production. Another explanatory variable is the remoteness of the country from the rest of the world. Anderson and Van Wincoop (2003) show that a country’s trade with any partner country depends on its average distance from the rest of the world. Following the method introduced in Head (2003) I define a remoteness index.<sup>6</sup> Finally, I control for the past export growth performance of the country. This variable can control for the positive steps taken in the past to spur export performance such as implementation of trade liberalization policies, expansion of trade into new markets, exports of new products, or establishing trade agreements.

Export performance of countries is measured by log of export sales in constant 2005 US dollars which is obtained from World Bank Development Indicators (WDI). By measuring exports in constant US dollars, I assume that exported goods’ prices are roughly equalized across countries. Endogeneity is a major concern in the estimation, especially for the relationship between GDP and export. Gravity models assume that the volume of trade between two trading countries is positively related to the GDPs

> 6

> Remoteness is defined as _remotei_  1/  _jGDPj_ / _Distij_  where Distij is the distance between two countries _i_ and _j_ . Djankov et al. (2010) use this index in their estimation of a modified gravity equation. They
banks, which allows us to control for any unobserved time-invariant effect by introducing country dummies. This smaller sample includes 486 banks across 20 countries, out of which 89 are Islamic banks.

In Table 1, we present data on 22 countries with both conventional and Islamic banks.<sup>4</sup> Specifically, we present the number of Islamic and total banks as well as the share of Islamic banks’ assets in total banking assets, all for 2007, the latest year in our pre-crisis sample. Further, we report the number of listed banks, for both Islamic and conventional banks. On average,

> 3 We use unconsolidated data when available and consolidated if unconsolidated is not available, in order to not double count subsidiaries of international banks.

> 4 Two countries are part of the crisis sample and are used in the analysis in Section 5.

9
Peters (1998) explicitly consider this issue in their model of immigration and public pensions). In particular, if individuals are forward looking, we would expect that as they approach retirement they would gradually weight more heavily the positive effects of immigration on capital returns than its negative effects on the labor market even if they are in the labor force. As a robustness check, we also estimate a specification to test if the age patterns change when controlling for pensioner status, but our main findings were not affected.<sup>5</sup> The data for this paper come from the European Social Survey (ESS) for the survey years 2002, 2004, 2006, 2008, 2010 and 2012. The survey contains about 1,500 to 2,500 individual observations per country/year. The ESS covers 36 countries, 24 of which are included in our analysis since only those were surveyed in at least four years and thereby allow to observe at least two cohorts with the same age, a requirement to disentangle cohort and age effects. We exclude non‐citizens from the sample, since we want to capture the preferences of potential voters.

Card et al (2012) investigates the drivers of attitudes towards immigrants in Europe using, among others, the following variables of the ESS:

1. To what extent do you think [this country] should allow people of the _same race or ethnic group_ as most people to come and live here?

> 5 The results are available from the authors upon request.

12
dataset|
|_harvest_value_LCU_|Value of plot<br>harvest, in<br>LCU|<br>Harvest valued using median<br>prices per in a defined<br>geographical area. The area in<br>question is the EA (or admin<br>1 in some surveys) under the<br>condition that there are more<br>than 10 observed sales in the<br>area. If there are less than 10<br>sales, prices are calculated at<br>a higher geographical level.<br>Prices are calculated<br>independently for each crop<br>type.<br>UGA: sale prices multiplied<br>by 100 in wave 5 and divided<br>by 100 in wave 1 season1.|Summed up to the plot level in the<br>plot dataset|
|_harvest_value_USD_|Value of plot<br>harvest, in<br>USD|,<br>Harvest values<br>(_harvest_value_LCU)_are<br>converted to current USD and<br>deflated to 2020 dollar values,<br>using exchange rates and a<br>deflator extracted from World|Summed up to the plot level in the<br>plot dataset|

26
between countries, women tended to be somewhat less willing to be vaccinated than men, and younger respondents marginally more willing (Solís Arce et al., 2021, p. 1387).

Between-country variation in attitudes to COVID-19 vaccines is a similar standout point in another large study focused on 15 countries in Africa (Africa CDC, 2021). Stated willingness to take a COVID-19 vaccine varied from 94 percent in Ethiopia to 59 percent in the Democratic Republic of Congo (Africa CDC, 2021, p. 10). The types of people most likely to state they would be vaccinated also varied between the surveyed countries. However, older people were more likely to state they were willing to be vaccinated in most countries. In the plurality of countries differences were not clear between genders. Where differences were present it was usually the case that men were more favorably disposed to the COVID19 vaccine than women (Africa CDC, 2021, pp. 22-65).

With respect to rationales for people’s beliefs, the most common explanation for wanting the vaccine in the 13-country international study was protecting oneself from the illness. The most common reason for not wanting the vaccine was concern about side effects (Solís Arce et al., 2021, p. 1387). Concerns about vaccine safety also stood out as an issue in the Africa-focused study (Africa CDC, 2021). In addition, the Africa-focused study found that participants’ beliefs about vaccines more broadly, and whether vaccines in general were safe, were clearly correlated with their stated willingness to take a COVID-19 vaccine (Africa CDC, 2021, p. 11). This finding fits with a larger, pre-COVID global literature which found that people’s contemporary willingness to be vaccinated is strongly related to past vaccine behavior (Brewer et al., 2017, p. 165).

> 2 In addition to the work reviewed here, some ongoing COVID-19-related work is taking place at present in developing country contexts. For example see details at: <u>https://
Policy Research Working Paper 10810

# **Abstract**

Whether and the extent to which social norms matter for women’s labor force participation has been shown to vary by context. This paper presents rigorous evidence on how these relationships hold in the case of Nepal, where female labor force participation remains among the lowest in the world. Using a representative survey covering four provinces in Nepal, data were collected from 2,000 married Nepali women and men on their own beliefs about norms-related behaviors, their expectations of how common it is for others in their social group to engage in those behaviors, and the expected social consequences surrounding those behaviors. Overall, the study finds that personal beliefs and social expectations are generally not very restrictive among respondents, and that there are limited linkages between social norms and women’s work outcomes. However, the study also shows that norms matter for selected subgroups and under certain circumstances that are related to the woman’s role as a mother and in the household as well as to her job characteristics. The findings indicate that relaxing norms in those specific circumstances can help to promote women’s labor force participation in Nepal.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jalaref@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br
5 Border measures traditionally have been the main means by which governments distort prices in their domestic markets for products, with the price of tradables relative to nontradables affected by interventions in the market for foreign exchange, and the relative prices of the various tradables affected by trade taxes-cum-subsidies or quantitative trade restrictions. Product-specific domestic producer or consumer subsidies have played a more limited role (because of their much greater cost to the treasury), with a few exceptions most notably in agriculture in high-income countries (Legg 2003; OECD 2005a). With the freeing up of most foreign exchange markets over the past two decades (Hinkle and Montiel 1999), the phasing out of most export taxes (Piermartini 2004; Theile 2004), and the conversion of many non-tariff trade barriers into tariffs including for farm products (Ingco 1996), the task of measuring the extent of distortions to goods markets is made much easier in that attention can focus on import tariffs and OECD agricultural subsidies. Services regulations also could distort incentives in the agricultural and industrial sectors, so it is worth exploring their effects on farm income too -- although much controversy still surrounds their measurement and how they should be modeled.

The latest release of the GTAP dataset, Version 6, includes estimates of bilateral tariffs and of domestic and export subsidies as of 2001 for 87 countries and country groups spanning the world, and for 57 aggregated sectors of the economy. This is a substantial improvement over Version 5 of the GTAP dataset, which relates to policies in 1997. The new protection data come from a joint CEPII (Paris)/ITC (Geneva) project. The product of this joint effort, known as MAcMaps, provides HS6 tariff level
# All imports

Figure 1 OECD import concentration, 1963-2006 Extra-OECD imports only <!-- Start of picture text --> 105<br>120<br>100<br>110 Herfindahl 95<br>Theil<br>90<br>100<br>85<br>90 80<br>75<br>80 Theil<br>70 Herfindahl<br>70 65<br>60<br>60<br>1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 1963196619691972197519781981 198419871990 19931996 199920022005<br><!-- End of picture text --> Note: base 100, 1963; simple averages of indices over all products. Data from COMTRADE As for the extensive margin, Figure 2 shows the evolution of simple and importweighted averages, across SITC4 lines, of the number of exporters to the OECD over the sample period.

Table 2 Regression results, OECD import concentration on time trend

||All im<br>|ports<br>|Extra-OECD i<br>|mports only<br>|
|---|---|---|---|---|
|Regressors:|(1)<br>Herfindahl|(2)<br>Theil|(3)<br>Herfindahl|(4)<br>Theil|
|time|-0.002***|-0.013***|-0.007***|-0.023***|
||(-13.19)|(-34.03)|(-31.80)|(-43.53)|
|timesq|0.000|0.000***|0.000***|0.000***|
||(1.616)|(4.723)|(23.17)|(28.
<br>|s)<br>|||
|Exports of ICT Network products (in million of US dollars|20.6|19.8|14.2|**13.3**|16.7|21.4|27.1|45.5|
|Exports of Automotive Network|113.6|79.7|61.6|67.8|**40.4**|45.5|48.4|61.8|
|Total|134.2|99.5|75.8|81.1|57.1|66.9|75.5|107.3|
|_Memorandum:_||(|inperce|nt)|||||
|Share of IT network in total producer networks' exports|15.3|19.9|18.7|16.3|29.2|31.9|35.9|42.4|
|Share of the EU-25|51.0|44.6|57.4|65.0|58.3|56.8|46.4|66.4|
|_of which: CEEC-8_|_14.4_|_17.1_|_14.7_|_9.9_|_9.6_|_8.5_|_6.5_|_7.5_|
|Share of ROW|34.5|38.3|27.9|25.0|32.1|34.6|47.1|26.1|

Source: Computations based on UN COMTRADE Statistics.

Post-1999 developments in Bulgaria’s trade in producer network products flag its return to trends prevailing in other CEEC-10 economies and worldwide. First, in line with worldwide trends, Bulgaria’s trade in ICT network products has displayed much stronger dynamics than that in automotive network. While the value of automotive exports stood still in 2003 at 54
data (1981-82 to 2017-18) on employment (total persons engaged) and real wages (based on total emoluments paid) from Annual Survey of Industries, Ministry of Statistics and Program Implementation, Government of India,<sup>27</sup> to compute the average labor supply elasticity, which turned out to be around 0.4. We introduced this labor supply equation in the model.

# **_c. Links between Production, Consumption and International Trade:_**

The sub-modules explained above are linked with each other. The percentage change in sector-level domestic consumption, _qdmk_ **_k,s_** , with a corresponding price change _pmk_ **_k,s_** , substitutes for imported subsector goods, _qimk_ **_k,s_** , with a corresponding price change _pimk_ **_k,s_** . The CES elasticity between these two variables is **_σD,k_** , and this substitution takes place based on their respective price differentials from the sector-level domestic prices _pdk_ **_k,s_** , as illustrated by equations (2) and (3):

# **For all** **_k_ in** **_SECT_ and** **_s in_ REG:**

**_qimk(k,s) = qdk(k,s) – σD(k) * [pimk(k,s) - pdk(k,s)]_**

**_........................... (2)_**

**_qdmk(k,s) = qdk(k,s) - σD(k) * [pmk(k,s) - pdk(k,s)]                              ........................... (3)