rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Gender and Agriculture: Inefficiencies, Segregation, and**

# **Low Productivity Traps**<sup>**1**</sup>

**Andre Croppenstedt, Markus Goldstein and Nina Rosas**<sup>**†**</sup>

1 This paper was prepared as a background paper for the World Development Report 2012. It has benefitted from numerous conversations with the core team, especially Carolina Sanchez-Paramo, Ana Revenga, and Sudhir Shetty, as well as numerous colleagues at the World Bank and the FAO. In addition, this paper draws on work performed for the FAO’s State of Food and Agriculture 2010–2011. We are grateful to Katia Covarrubias (FAO) for her support with the Rural Income Generating Activities data.

1
15 Given that the cost data presented in Chaitkin et al (2022) did not present country data following a request for anonymity from some countries sampled, this present study does not present country cost information.

16 This is excluding Angola, Kenya and the United Republic of Tanzania.

> 17 Inclusion of costs for middle-income countries may increase cost per capita.

> 18 Indeed, patient satisfaction is an outcome of the healthcare process itself, determined by the quality of the infrastructure and associated behaviors, including cleanliness, crowding and availability of WASH facilities. These factors also determine future health seeking behavior, not only of patients themselves but of others that hear of bad or good experiences with health care. A 2019 survey of over 1 million women and girls in 114 countries found that of the top demands for quality reproductive and maternal health care, respectful and dignified maternity care was the most cited need, followed by WASH services and facilities (White Ribbon Alliance, 2019).

46
or the pass-through of the subsidies is not complete (Busse et al., 2006; Chetty et al., 2009). National-level EV subsidies in our study are of considerable size and widely publicized. Recent research points to complete pass-through in consumer subsidies for alternative fuel vehicles (Sallee, 2011; Muehlegger and Rapson, 2018). Li et al. (2021) shows that consumers respond to EV prices and purchase subsidies similarly in China.<sup>7</sup> From a practical standpoint, while it is possible to separately estimate consumer responses to prices and subsidies as in Li et al. (2021), our data only include consumer subsidies at the national

> 6In India, all most all electric vehicle ownership constitutes two-wheelers and three-wheelers, which are not the focus of this paper (Punditz (2021)). In Brazil, EV sales of popular EV brands (e.g. Nissan Leaf and Tesla Model 3) were less than 50 units in 2019. In the recent decade, even the gasoline vehicle per capita ownership in major developing countries is below that of major developed countries (Li et al. (2020)).

> 7We drop 2019-2020 data for China in our analysis due to the heterogeneous impact of the pandemic across countries, especially between China and other countries.

5
_Quotak_ is a dummy which takes the value 1 if the product was eligible for quota preferences and zero otherwise. The dummy for quota is included in the regression because it is likely that the circumstances for products entering under a preferential quota are different than for products entering without quota preferences; for example traders using the quota preferences are likely to be better informed. Furthermore, we include a dummy (called _difference_ ) for agricultural products for which the difference between preferential and MFN tariffs exceeds 30%. These agricultural products benefit from seasonal preferential duty reduction. Since we have yearly data it was not possible to exactly calculate the tariff

> 13 For details on the index see: http://www.freedomhouse.org/research/freeworld/2003/methodology.htm. 14 Only four countries were not colonies in our sample.

> 15 From the 23,685 observations, 9,015 are not covered by any sectoral dummies. These observations belong to sectors such as metals, vehicles, optics, chemicals, plastics, stones, and glasses.

12
Table 9b: Impacts of PROSPERA on ENLACE tests All 6<sup>th</sup> Graders in Medium, High and Very Highly Marginalized Communities 2008 School Fixed Effects

|Variables|7th-6th score<br>9th-6th score|12th-6th score|
|---|---|---|
||_Panel A: Spanish_||
|PROSPERA beneficiary 2008|3.388**<br>4.619***|-0.014|
||(1.354)<br>(1.524)|(0.011)|
|Female beneficiary|0.848<br>1.69|0.016|
||(1.735)<br>(1.951)|(0.014)|
|Female|8.572***<br>-3.083*|-0.041***|
||(1.471)<br>(1.658)|(0.012)|
|Constant|-18.606**<br>44.358***|0.785***|
||(7.665)<br>(8.635)|(0.068)|
|Observations|43,245<br>42,914|21,372|
|R-squared|0.328<br>0.43|0.241|
||_Panel B: Mathematics_||
|PROSPERA beneficiary 2008|1.609<br>4.917***|-0.024**|
||(1.361)<br>(1.841)|(0.012)|
|Female beneficiary|1.491<br>4.964**|0.013|
||(1.744)<br>(2.358)|(0.015)|
|Female
cognitive tasks that are higher order in their complexity, and presumably are associated with larger costs of performing outside of a firm’s central location.

- B. Trends in Offshoring, Trade, Employment, and Wages

In this section we outline broad trends in the data for employment, wages, and the relationship between wages and measures of globalization. In Figure 1, we compare the trends in employment and wages in the manufacturing sector alongside the same trends in the service sector between 1979 and 2002. We present these trends separately for workers performing routine and non-routine tasks. Total manufacturing employment (using the CPS employment numbers) fell from 22 to 17 million from 1979 to 2002, with rapid declines at the beginning of the 1980s and in the late 1990s. Within manufacturing, the labor force has become increasingly high-skilled with a large decline of roughly 6 million in the number of workers in routine occupations, and a modest increase of roughly 1 million in the number of workers performing non-routine occupations.

In contrast, demand for both types of workers continued to grow in the service sector, and many of the displaced routine manufacturing workers may have found employment in the service sector. These trends have important implications for the US wage distribution. As shown in the bottom of Figure 1, where we report the real hourly wage among CPS workers, manufacturing workers enjoyed a large wage premium during the entire period among both routine and nonroutine workers. Insofar as manufacturing provided an opportunity to earn high relative wages – even for low-skill workers – its decline might also have played a role in increasing US income inequality during the period.<sup>10</sup> We turn now to an examination of how offshoring and trade may be related to these employment and wage trends within manufacturing and in the overall economy.

> and non-routine manual (Eye, Hand, Foot), non-routine analytic (General Educational Development, Mathematics), and non-routine interactive (Direction, Control and Planning) tasks. See also the data appendix. 10 See Autor
)|(0.0035)|
|15. Life Expectancy at age 65 (-)|-0.0115|-0.0205*|-0.0115|-0.0204*|
||(0.008)|(0.011)|(0.008)|(0.011)|
|16. Youth Population Share (-)|0.1195<br>(0.19)|-0.2076<br>(0.158)|||
|17. Working-Age Population Share (+)|||-0.0854|0.1816|
||||(0.202)|(0.172)|
|18. Elderly Population Share (-)|0.4932*|-0.9739|0.3857|-1.0107|
||(0.293)|(0.983)|(0.276)|(0.981)|
|19. constant|-1.1222***|0.3264|-1.01***|0.1662|
||(0.413)|(0.198)|(0.373)|(0.185)|
|R-squared|0.8724|0.8351|0.8723|0.8414|
|Adjusted R-squared|0.8408|0.7909|0.8407|0.7989|
|F-Statistic|47.92|36.07|47.11|55.09|
|F-Stat df 1|52|66|52|66|
|F-Stat df 2<br>Number of Countries|210<br>20|250<br>35|210<br>20|250<br>35|
|Number of Observations<br>Standard Errors are in parentheses.<br>*, **, *** denotes significance
Low contributory coverage affects more significantly older people in the lowest income quintiles, while those in the top income quintiles not only have a greater coverage but also have experienced an increase in the recent years. The implementation of the non-contributory pension allowed narrowing this gap (Figure 9).

Figure 9. Social Security Coverage per Income Quintile and Kind of Pension. Years 2006, 2010 and 2016 <!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>Both<br>30%<br>Social pension<br>20% Contributory<br>10%<br>0%<br>I II III IV V I II III IV V I II III IV V<br>2006 2010 2016<br><!-- End of picture text --> Source: author elaboration based on ENEMDU The results presented in Figure 9 allow identifying a significant and negative correlation between noncontributory pensions and income quintiles. In other words, the lower the income quintile, the higher the social pension coverage. Therefore, the social pension allowed narrowing the coverage gap between income quintiles.

In this sense, taking into account the last available year, while the compulsory contributory system provides coverage to 5% of older people from the first quintile and 14% of those in the second quintile, the non-contributory scheme reaches out to 49% and 40%, respectively. Conversely, in the fifth quintile, the contributory scheme scope is 58% while 11% receives a social pension.<sup>6</sup> In summary, the inclusion strategy developed during the recent years acknowledges two parallel ways: contributory system coverage expansion and extension of the non-contributory system scope. The first one is a process having begun in 2007 and having accelerated between 2009 and 2010. Likewise,

> 6 This result may suggest that there is an inclusion-related error that deserves revision.

15
32 To give a better idea of the path of relative price changes over time, Figure 4 presents cumulative relative price changes for main CPI categories as well as selected items from these categories.

The main messages from Eiga3 and Fig.4 can be summarized as follows:

# * **The biggest relative price shifts took place between 1989-1991.**

As Fig 3 and Fig. 4 suggest after 1991 no significant changes in the top and bottom relative price shifts occurred. Graphs confirm the notion that following the period of profound changes prior to 1992, most prices have stabilized and the process of adjustments that is still going on in some cases 3 is by far less abrupt than in the initial stages of transition.

# * **Increases in the prices of utilities have undoubtedly led[ the inflationary process.**

Out of _5_ biggest relative price increases, 3 took place in the sector of utilities. Prices of utilities rose by as much as 5 (gas) to 5.4 (central heating and water supply) times more than the CPI index. Pricing of most utilities are up till now controlled by the government which sets the pace of upward adjustments. (The issue of controlled prices will be taken up in more detail in the next section.)

# *** Relative prices of staple foods have risen significantly.**

Prices of bread and dairy products (see Table 7 below) have outpaced inflation more than twice. This substantial relative price increase may be partially due to extraordinarily low prices of these foods before 1989 reflecting high subsidies which were part of the government's social safety net at that time.

*** Commodities from the category** _electronics_ **experienced by far the largest and most permanent decline**<sup>**36**</sup> in its relative price.

> Slow gradual upward adjustments continue in the case of for example: central heating and hot water supply.

> 36 The relative price of the category _electronics_ continues to decline
research now lie elsewhere, as mentioned below.

# h) Approaches to the Problem of Double Deflation

The important unsolved problem in this study is that of double deflation. Virtually all analysts who have used the industry of origin approach have been unable to find separate PPPs for inputs. The double deflation approach is feasible for agriculture (Van Ooststroom and Maddison, 1985), but not for manufacturing in these three countries, because the Brazilian and Mexican censuses give only rather global value figures on inputs with no detailed quantitative information, and the US census gives detailed figures only for energy consumption, contract work, and inputs directly related to the production process.

In agriculture the difference between the gross output PPPs and the double deflated PPPs was rather small. For Brazil the 1975 PPP (Brazil quantity weights) was 7.35 cruzeiros to the US dollar, 6.63 for inputs and 7.57 for value added. For Mexico the 1975 PPP (Mexican weights) was 13.46 pesos to the US dollar, 13.68 for inputs and 13.36 for value added.

In manufacturing, inputs are much bigger in relation to gross output than in agriculture, but in the USA 60 per cent of these are from manufacturing itself and in Mexico 48 per cent (see input/output tables in chapter II). For manufaLturing as a whole therefore, it does not seem a priori likely that the PPPs resulting from "double deflation" would be very different from those in our study, but for particular branches they might vary a good deal more.
Furthermore, our estimating equation is less susceptible to reverse causality from exports to exchange rates for two reasons: our dependent variable, disaggregated exports, is less likely to affect a macroeconomic variable like the exchange rate; moreover, the latter is the exchange rate of another country. What about reverse causality from the exports to the index of competition? Our count-based index, while derived from theory under symmetry assumptions, has the empirical virtue of being based on the extensive margin and not being measured in value terms; hence being less related to the left hand side variable and less afflicted by reverse causality problem. Our value based index is potentially more vulnerable to the reverse causality problem because it is expressed in values, like the dependent variable. To minimize such endogeneity concerns we compute both indices for the initial period of the sample (i.e. for the year 2000).

# **IV. Data**

We focus on the period 2000-2008, during which concerns about China’s exchange rate policy have been most debated. For this period, we compile disaggregated data on bilateral exports from the UN Comtrade database. We collect data reported by the importing countries, which is generally regarded as more reliable than data on exports (i.e. exports from _i_ to j are measured by imports of _j_ from _i_ ). The data are for roughly 6000 non-oil HS 6-digit lines covering 900 4-digit products. We cover the 57 major importing countries (making sure that we include all countries that together accounted for over 95 percent of total exports of developing countries) and 124 developing country exporters which are potentially in competition with China (summary statistics are provided in Appendix Table A1 and the list of importing and exporting countries covered in Appendix Table A2).<sup>10</sup> The list of developing countries is based on World Bank country classification, and is comprised of all low and middle income countries (2010 GNI per capita of $12,275 or less).

> 10 In principle, we
Obviously, each employer has different perceptions and expectations toward engineering graduate skills. The respondents‘ perceptions and expectations may arbitrarily influence the ratings of the satisfaction and importance levels because of, for instance, wording and orderings of questions. Therefore, many economists are skeptical about the meaningfulness of the answers from so-called ―subjective questions‖. We acknowledge this subjective element of this analysis. However a growing literature within different strands of economics, such as happiness and competitiveness use subjective survey data for econometric and/or psychometric analysis. In addition, management and marketing professionals employ a battery of satisfaction surveys, e.g., employee satisfaction surveys, customer satisfaction surveys, etc. to inform key decisions. We therefore follow the advice of Bertrand and Mullainathan (2001) who argues that subjective measures may be helpful as explanatory variables with due diligence to the interpretation of the results.

# **4. Characteristics of Respondents**

This section shows descriptive statistics of the respondents (employers). The surveyed employers are those that hire engineering graduates. The summary of descriptive statistics of 157 employers is presented in Table A2-1 in Annex 2.

**_Size_** : Half of respondents are large companies with more than 500 employees, while the other half is equally divided into medium (with between 100 and 500 employees) and small employers (with less than 100 employees).  Given that a half of the employers in our sample are large companies with more than 500 employees, we may have oversampled large companies. As a result, the outcomes of the survey may reflect more views of the larger companies.

**_Location_** : More than 40% of the responding employers are from the North region where Delhi is located, and 27% and 19% are from West and South regions, respectively.  Statewise, Delhi dominates the sample (27%), and Maharashtra where Mumbai is located accounts for 19%.  Other major states have a share of 38% in total; Uttar Pradesh 8.9% (
discretion in meeting regulatory capital requirements and are often allowed to use the internal ratings-based (IRB) approach to compute capital requirements. As the failure of larger banks can have systemic consequences and pose public externalities, our results have important policy implications.

We further investigate whether countries are influenced by neighbors while adopting capital regulation. Several studies have found strong peer country effects in financial regulation. Demirgüç-Kunt, Kane and Laeven (2008), for instance, show that adoption of explicit deposit insurance increases with the proportion of neighboring countries that introduced explicit deposit insurance at the same time. We examine peer country effects in three new capital regulations that have been implemented in the aftermath of the global financial crisis. Specifically, we analyze whether countries have adopted Basel III, countercyclical capital buffers or capital conservation buffers. Using data from the latest round of the BRSS, we create three dummy variables corresponding to each of the three regulations and set them to one if a country has adopted these new capital rules. We use the regional adoption rate to capture peer effects.<sup>24</sup> This variable reflects the extent to which these new capital regulations are believed to be best practice by countries in the region. As more countries adopt a regulation in a given region, we expect other neighboring countries to follow suit and adopt the new regulations. We use the following model to test these conjectures: I(Regulation Adopted= 1)i In the logit regression, we use the same set of controls as described in Equation (1a) above. The dependent variable is an indicator variable set to one if a given country has adopted a new capital regulation. The Region Average is the proportion of countries in each region (excluding the given country) that have adopted these new capital regulations. The results from the logit regressions are reported in Table 5. We find strong evidence of a positive association between region peer effects and the adoption of the new Basel III capital regulations. These reforms have been adopted regardless of whether the countries
,581).<sup>6</sup> Each wave includes two visits to each household during the autumn months (post-planting) and the spring months (post-harvesting).<sup>7</sup> Some important factors such as labor market outcomes, food consumption, and expenditures were collected during both visits. Total consumption includes all food consumption and purchases (including meals outside home) as well as non-food expenditure (e.g., education, housing rents, clothing, fuel, utilities, transportation, communication, reaction and other services). Total consumption is estimated using the mean of two visits for each of the three waves of the GHS. Poverty status of households is calculated based on international poverty lines of US$1.90 and US$3.20 per day (2011 PPP) and after applying the Consumer Price Index (CPI) to adjust for both spatial and temporal inflation. Table 1 provides summary statistics of key indicators derived from the GHS survey.

> 4 Examples of such targets include: the UN Sustainable Development Goals (SDG 9 includes Target 9c to “ _Significantly increase access to information and communications technology and strive to provide universal and affordable access to the internet in least developed countries by 2020_ ”); the Broadband Commission for Sustainable Development 2025 targets for “Connecting the Other Half”, and; the World Bank’s Digital Economy for Africa (DE4A), which aims to ensure that all Africans have universal and affordable access to ICT by no later than 2030.

> 5 More information on the GHS including design, survey instruments, datasets and methodology can be found in this link: http://surveys.worldbank.org/lsms/programs/integrated-surveys-agriculture-ISA/nigeria

> 6 Given the panel nature of the survey, some households moved from one wave to the next and efforts were made to track these movers.

> 7 All households were visited twice regardless of whether they participated in agricultural activities.

4
2 and an unknown extent of (possibly illegal) plantings in Argentina, no other developing countries have widespread adoption yet of this new technology.<sup>2</sup> The other reason cotton is in the news is because four poor cotton-exporting West African countries (Benin, Burkina Faso, Chad and Mali) have demanded that cotton subsidy and import tariff removal be part of the World Trade Organization’s Doha Development Agenda. However, cotton subsidies are mostly provided by governments in high-income countries, and those governments have yet to be persuaded by other cottonexporting countries to abandon them – notwithstanding the fact that part of the US cotton subsidy program has been ruled illegal following a WTO dispute settlement case brought by Brazil.

What is at stake here in terms of economic welfare in various developing countries? Specifically, how much are developing countries foregoing by procrastinating in their approval of GM cotton production? How does that compare with the effects on developing country and global welfare of removing cotton subsidies and import tariffs? And how much greater would be the gains to cotton-producing developing countries from GM cotton adoption if global cotton markets were not distorted by subsidies and tariffs?

After presenting a brief background to the world’s cotton market in Section 2, this paper seeks to address these questions by using a well-received model of global economy known as GTAP (developed by Purdue University’s Global Trade Analysis Project) and the latest version of its related trade and protection database, described in Section 3. Empirical simulation results are presented in Section 4. These are followed by a

> 2 Experimental work has begun in numerous other developing countries though, including in countries as poor as Burkina Faso. For a thorough review of such developments, see FAO (2004, Ch. 4).
firms. The key innovation of the analysis is testing for the impact of energy price variations separately for electricity and fuels, the two main types of energy

> 1Coady et al. (2015) estimate that energy subsidies would account for between 13% and 18% of GDP in Developing and Emerging Asia, the Middle East, North Africa, and Pakistan (MENAP), and the Commonwealth of Independent States (CIS).

> 2Eventually a considerably less ambitious carbon tax was approved at the end of 2013, including the rule of capping the tax to a level which would not increase the fuels retail price by more than 3%.

> 3These figures are based on the audited Indonesian government expenditure reports.

> 4These types of concerns are illustrated for instance in a recent interview of a top bureaucrat in the Indonesian Ministry of Industry, who stated that it is important to maintain cheap fuel prices to ensure industrial development including downstream industries (Sulmaihati, 2019).

1