rafmacalaba/data-use-mentions-extended huggingface dataset:
work. However, employment details of other members of the household and more detailed gender‐disaggregated data on labor market participation (that includes the numbers of days/hours worked, daily or hourly earnings, sector of employment, and type of work) are needed to understand the difference between labor market opportunities for men and women. Other research points to the increased prevalence of GBV, especially faced by women and girls. Besides being a human rights violation, GBV is a manifestation of women’s lack of agency and can affect women and girl’s ability to access economic opportunities and services due to mobility constraints caused by actual and perceived GBV risks. GBV is thus a major cause of gender‐based vulnerability and therefore good data on GBV risk (that is, collected following UN Guidelines (UNDESA and UNWomen, 2013)) is needed to increase understanding of the impacts of exposure to GBV on refugee</mark> women and their families and allow us to explore the links between GBV and household poverty.

31
- 45 -

91. The scope for irrigation seems limited and cost - even when calculated on a conservative basis - rather prohibitive because irrigation is generally understood in terms of large scale, flow irrigation projects. If irrigation is understood in ite broader role of reducing moisture stress by managing water availability, a large number of options open up. These would include: (i) large-scale irrigation works involving construction of headworks and canals; (ii) small-scale irrigation works raised with local participation consisting of small bunds, excavation of tanks and earthen dams; (iii) lift. irrigation through either stationary or mobile water pumps; (iv) recessional water management where water in swamps and low-lying areas are channeled and regulated to meet irrigation needs; and (v) harnessing subsoil water.

92. It is important to take a comprehensive view of the water resources of an area on the basis of a properly organized hydrological survey. A World Bank/UNDP effort is under way to map out scientifically the potential surface as well as ground water resources in some countries of SSA.

# (e) Draft and Mechanical Power

93. One of the most serious shortages on African farms is that of draft energy. It will become a more serious constraint with the introduction of intensive agriculture. There is a mistaken and unfortunately widespread belief in Africa that solutions to the problem of energy shortage on farms lie in the increased use of tractors.
|-|43|

_Source:_ WBES and BEEPS 2005.

_Note_ : The CPI is measured from 0 to 100, and a higher number means a lower perception of corruption. BEEPS =The Business Environment and Enterprise Performance Survey; CPI = Corruption Perception Index; WBES = World Bank Enterprise Surveys.

An additional measure to consider is Transparency International’s Corruption Perception Index (CPI). This index ranks 176 countries on a scale from 0 (“highly corrupt”) to 100 (“very clean”) as determined by expert assessments and opinion surveys. In 2017, Denmark and New Zealand were perceived as the least corrupt countries in the world, while the country perceived as the most corrupt in the world was Somalia. The last column of table 1 displays the index values for the selected countries. We can see that Argentina ranks slightly below the Latin American and Caribbean average, and it ranks more than 40 points below developed economies like Sweden and Germany. Using this alternative measure, we can also conclude that corruption is an important problem for developing economies.

Figure 2 shows the correlation between the CPI and bribery incidence. As we can see, countries with a low CPI (high perceived corruption) tend to have a high percentage of firms facing bribery. The figure also shows that Argentina is below the linear predicted relationship between these two variables. This implies that the incidence of bribery in Argentina is lower than what a linear model 8
<mark>not lead to equal outcomes unless the factors behind the differential returns are rectified. Moreover, these studies have been largely limited to Africa. This is partly because it is more common for male and female farmers to manage different plots in Africa, facilitating gender differentiated analysis, and partly because high-quality nationally representative household surveys had recently become available in some African countries (Kilic, Winters and Carletto 2015). Research on this issue is surprisingly scarce in countries outside the Africa region, including South Asia.</mark><sup>2</sup> <mark>Sri Lanka provides an interesting case to study the productivity gender gap since the Household Income and Expenditure Survey (HIES) is representative at the national level and separately contains detailed information on agricultural production such as land, inputs, and crop mix at the level of the individual farmer. Initial analysis of the data reveals a stark contrast to the previous literature: it shows that productivity measured by yields (defined as output value per acre) is</mark> _<mark>higher</mark>_ <mark>among female farmers than male farmers.</mark> <mark>This paper explores the sources of this unconditional female productivity advantage using multivariate regression analysis and Oaxaca-Blinder decompositions. We find that the leading contributor to this productivity gap in favor of women is the smaller size of the plots that women cultivate, reflecting the inverse relationship between farm size and agricultural productivity commonly found in the literature. A</mark> nother important source is the gendered pattern of crop mix: women tend to cultivate more high-value, export-oriented crops than men, who are more likely to grow paddy, which suffers from low productivity. <mark>Male farmers, on the other hand, have greater access to agricultural resources, which helps offset partly their initial productivity disadvantage.</mark> <mark>The contribution of this paper to the literature is threefold. First, this is the first study to analyze the agricultural
Deliver? Evidence from the Weatherization Assistance Program. _Quarterly Journal of Economics_ , 2018, 133(3): 1597-1644.

- <mark>Ganelli, G.</mark> & <mark>Tervala, J. (2016). The Welfare Multiplier of Public Infrastructure Investment,</mark> _<mark>IMF Working Paper</mark>_ <mark>WP/16/40</mark>

- <mark>Garrett-Peltier, H. (2017). Green versus brown: Comparing the employment impacts of energy efficiency, renewable energy, and fossil fuels using an input-output model.</mark> _<mark>Economic Modelling</mark>_ <mark>, 61, 439-447.</mark>

- Gechert, Sebastian & Rannenberg, Ansgar. (2018). Which Fiscal Multipliers Are Regime-Dependent? A Meta-regression Analysis. _Journal of Economic Surveys_ , September 2018, v. 32, n. 4, pp. 1160-82

31
For the cohort of exporter entrants in 2003 that survive until 2011 the patterns are qualitatively similar but quantitatively different. Specifically, Panel A of Table 9 shows that the destination intensive margin accounts for 80% of the short-run export growth of the cohort of exporter entrants in 2003 but for just 50% of their long-run export growth between 2003 and 2011. The product intensive margin accounts for 88% of short-run export growth of the cohort of exporter entrants in their continuous destinations but for less just 46% of their long-run export growth in those destinations between 2003 and 2011, as seen in Panel B of Table 9. So over the long-run churning in destination markets and churning in HS 6-digit products (for continuing destinations) is critical in explaining export growth of the cohort of new exporters but less so in explaining export growth of the 2002-2011 continuing exporters.

# **5. Conclusion**

This paper examines the micro dynamics behind the dramatic export boom experienced by Turkey from 2002 to 2008 as well as the underlying forces for the strong contraction ensuing from the 2008-2009 global financial crisis and for the recovery thereafter using a novel and rich exporter-level customs dataset. Growth in exports of continuing exporters accounts for the majority of short-run export growth during the entire period. For these continuing exporters, short-run growth is dominated by exports to their continuing destinations, and for these continuing destinations, short-run growth is dominated by exports of continuing products. However, there is a high degree of dynamism across firms, destinations, and products from year to year. More importantly, churning across exporters, destination markets and products account for an important part of Turkey’s long-run export growth in the whole 2002-2008 period, even if the intensive margins still dominate. The patterns of micro-dynamics of export growth are verified across all sectors, a finding which is novel to the literature. These patterns are also mostly verified across groups of destination markets, a noteworthy exception being the greater prominence of net exporter entry in accounting for long-run growth in exports to the Rest of the 23
- Bernard, T., S. Dercon, and A. S. Taffesse. 2011. “Beyond Fatalism – An Empirical Exploration of Self-Efficacy and Aspirations Failure in Ethiopia.” _CSAE Working Paper_ 2011–3. Oxford: Centre for the Study of African Economies, University of Oxford.

- Bernard, T., and A. S. Taffesse. 2014. “Aspirations: An Approach to Measurement with Validation Using Ethiopian Data.” _Journal of African Economies_ 23(2): 189–224.

- Bicchieri, C. (2006). _The grammar of society: The nature and dynamics of social norms_ . Cambridge University Press.

- Binzel, C., and J. P. Carvalho. 2017. “Education, Social Mobility and Religious Movements: The Islamic Revival in Egypt.” _Economic Journal_ 127(607): 2553–2580.

- Bloem, J. R. 2021. “Aspirations and Real Estate Investments in Rural Myanmar.” _Journal of Economic Inequality (forthcoming)_ . January 21 version, available at: https://bit.ly/3fD9QVk

- Bloem, J. R., D. Boughton, K. Htoo, A. Hein, E. Payongayong. 2018. “Measuring Hope: A Quantitative Approach with Validation in Rural Myanmar.” _The Journal of Development Studies_ 54(11): 2078–2094.

- Bogliacino, F., and P. Ortoleva. 2015. “The behavior of others as a reference point.” _Documentos FCECID Escuela de Economía_ 67. Bogota: Universidad Nacional de Colombia, Facultad de Ciencias Económicas. Available at: https://bit.ly/2zh53of

- Boswell Dean, E., F. Schilbach, and H. Schofield. 2018. “Poverty and Cognitive Function.” In C. B. Barrett, M. Carter, and J.-P. Chavas (Eds.). _The Economics of Asset Accumulation and Poverty Traps_ . Chicago, IL: University of Chicago
||(t-stat)||
|Inflation (CPI growth %)|27.7|21.8|+19.9|+18.5|-1.4|(0.2)|Yes|
|Liqudity ratio (M2/GDP %)|35.3|37.0|-23.9|-27.2|-3.4|_(0.5)_|No|
|GNP growth (%)|2.3|2.6|+0.6|+0.2|-0.4|(0.6)|No|

Note: Source: _International Financial Statistics._ Annual data l975-97. The liquidity ratio takes the ratio of end-year M2 to the mean GDP of same and following year. 19 liberalizing coimtries including Portugal as in Table 1. Control group: US, UK, Germany, Italy, Sweden, Switzerland and Canada. Date of liberalization is that method B above.

The increase in interest rates and their volatility have not been the only factor influencing economic development over the past two decades. Fuwthermore, financial liberalization has normally been a complex, long-drawn out, and sometimes reversed process. It would be surprising if a before and after comparison based on our single date (method JR) were to show a significant impact.<sup>33</sup> 28
Policy Research Working Paper 7845

# **Abstract**

This paper uses household surveys from 89 countries to estimate the rate of extreme poverty among children in the developing world. The estimates are based on the same surveys and welfare measures as official World Bank poverty estimates. Of children under age 18 years, 19.5 percent are estimated to live on less than $1.90 per day, as opposed to 9.2 percent of adults ages 18 and above. Poverty rates are high for children ages 0 to 4 years, slightly higher among ages 5 to 9 years, and steadily decline for successively older age groups. The analysis also examines the sensitivity of age-based poverty estimates to the use of alternative household equivalence scales when adjusting the international poverty line accordingly. Child poverty rates remain above 17 percent, and are greater than adult poverty rates, for all reasonable two-parameter equivalence scales.

This paper is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at dnewhouse@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
STEVEN PENNINGS 4 The findings in this paper are also related to GI2011, who provide a range of evidence on the extent of real rigidities in pricing. One of their findings is a large response of import prices to the TWI (orthogonal to the bilateral RER), and another is sizable pass-through of import competitors’ prices, both of which are consistent with evidence presented here. This paper provides a more systematic analysis by deriving a measure of competitors’ RERs from theory, comparing magnitudes in theory vs data, considering implications for bilateral pass-through, examining pass-through to US producer prices (as predicted by theory) and testing the mechanism which works through foreign competitors’ prices (rather than all competitors’ prices).<sup>6</sup>

# 2. Simple Model

As a guide to the empirical work, I begin with a simple reduced-form model based on Burstein and Gopinath (2014) and GI2011 where the firm’s markup depends on a sectoral/aggregate price, and then extended it to a three-country setting. The model produces four key qualitative predictions which are outlined in Testable Predictions 1-4, for which I find supporting evidence in BLS microdata in Section 3 (results summarized in Box 1). In Section 4, I show how a similar specification comes from a multi-country version of AB, which I use to produce quantitative estimates from regressions run on simulated data.

2.1. **Model set-up.** There are three countries: the US, country i and some other third country c (this could be the “competitor” country). Let pijk be the log of the price of an imported good k produced in country i, in sector j, at the docks in the US (I suppress the firm/good index k and the sector j), relative to some numeraire. Then:

# pi = μi + mci

where μi ≈ μ ̄i − Γ [pi − p] is the log markup, which I assume is related to the relative price
select into certain choices based on their initial internal expectations of likelihood of achievement of target.

In particular, we expect firms that are less motivated to undertake serious sustainability efforts (and hence more likely to fail to achieve pre-set targets) to be more likely to resort to measures such as setting later target dates or including penalty-minimizing call options. Such firms face a higher probability of penalty payouts, and hence have greater incentives to seek insurance against these payouts via structuring loopholes to minimize penalties. The existence of SLB premiums tends to attract more of these low-motivation firms to issue SLBs, which makes such decisions more relevant.

While it is not possible to gauge internal motivation of issuers to undertake sustainability efforts, we do have information on their past performance track on this front. Given that the majority 50.62 percent (Figure 3) of sustainability targets are related to reduction in CO2 emissions intensity, the paper exploits past CO2 emissions by the issuer as a proxy of their sustainability performance. Specifically, it examines if issuers with higher past CO2 emissions are more likely to exploit structuring loopholes to minimize penalty payouts relative to issuers with lower past CO2 emissions. We test formally for this relationship by conducting a bondlevel regression for the following specifications: TargetPeriodi = α+ β. CO2Emissionsi,t−1 + Controlsi + εi [5] Callablei = α+ β. CO2Emissionsi,t−1 + Controlsi + εi [6] Where TargetPeriodi refers to the time to target date from issue divided by the time to maturity from issue for bond _i_ ; CO2Emissionsi,t−1 refer to CO2 emissions by the issuer in the year preceding the issue; Callablei is a dummy variable that equals 1 if the SLB has a penaltyminimizing call option and 0 otherwise; Controlsi as the same as in equation [1]. Specification [5] is an OLS regression run only for SLBs with step-up penalties following specification [2]. Specification [6] is a logistic regression run for all SLBs for which CO2 emission data is available.

*
|**Dimensions**<br>**of Food**<br>**Security**|**Food Security Indicator**|**Description**|
|---|---|---|
||Household Food Insecurity Access<br>Scale (HFIAS)|Subjective indicator based on perceptions and experience. The measure<br>is based on statements such as: (1) You were worried you would run<br>out of food because of lack of money or other resources; (2) You were<br>unable to eat healthy and nutritious food because of lack of money or<br>other resources; (3) You ate only a few kinds of food because of lack of<br>money or other resources; (4) You had to skip a meal because there<br>was not enough money or other resources to get food; (5) You ate less<br>that you thought you should because of lack of money or other<br>resources; (6) Your household ran out of food because of lack of money<br>or other resources; (7) You were hungry but did not eat because of lack<br>of money or other resources; (8) You went without eating for a whole<br>day because of lack of money or other resources. Later modified into<br>the Household Hunger Scale (HHS)|
||Child Dietary Diversity Score (CDDS)|fl h f fd d b h h d hld d|
||Mother’s Dietary Diversity<br>Score(MDDS)|Reects te variety o oo consume y te moter an ci an is<br>based on individual level intake information|

# **2.1.2 Available Indicators for Food**

Table 3 lists the various indicators of food security each capturing different dimensions of the ideal food security measure and
woman.<sup>9</sup> Data on current and previous reservation status allows us to test for persistence of such effects, i.e., if -in line with the notion that gender attitudes change slowly with individuals altering their attitude only after having been exposed to female leadership for some time (Beaman et al. 2012)- past reservation of a village for female leadership affects current outcomes. Synergies between political and economic empowerment (Deininger _et al._ 2019) would yield the same result. Letting _v_ denote villages, _i_ individuals, and _t_ time, we assess the impacts of female reservation on outcome variables relating to individual _i_ ’s labor force participation as well as other outcome variables by estimating the following equation.

force participation as well as other outcome variables by estimating the following equation. i 0 1 2 3 i 4 i (1) ii<sup>1</sup> ii<sup>2</sup> vv dd where _Yiv_ is the outcome variable of interest for individual YY = ββ + ββ RR + ββ RR + ββ XX + ββ _i_ in village VV + uu _v_ , + εε is an indicator variable that equals one if council leadership in village _v_ was reserved for women in the most recent election (i.e., the ii<sup>1</sup> RR _pradhan_ at the time of the survey was a woman who assumed her position as a result of reservation) and zero otherwise; is an indicator variable that equals one if council leadership in village _v_ had been reserved for a woman in the previous election and zero otherwise;ii<sup>210</sup> **_X_** is a vector of household and RR individual controls; **_V_** is a vector of village and _pradhan_ characteristics; a district fixed effect; and _εiv_ an error term. Our main interest is in _β1_ and _β2_ , the parameter estimates of current or past reservation on dd uu individual outcomes relative
of a broader effort to operationalize the international best practices in collecting individualdisaggregated survey data on asset ownership and work and employment. The individual interviews were capped at four per household and it was ensured that the head of household and his/her spouse (if one exists) were among the interviewed individuals.<sup>7</sup> Within-household interviews were always administered in private and were attempted to be administered simultaneously and with a gender match-up between the enumerator and respondent. Appendix I includes the protocol for administering the IHPS individual questionnaire.<sup>8</sup> In comparing the two survey approaches, our prior is that self-reporting with one-on-one interviews would more accurately reflect labor outcomes among respondents. Our analysis focuses on individuals aged 18-64, for whom the information was elicited during the IHS4-IHPS overlapping fieldwork period of April 2016-January 2017.<sup>9</sup> To compare the IHS4 to the IHPS, we pool the data on 17,433 IHS4 respondents together with 4,377 IHPS respondents that self-reported and were interviewed in private. The combined sample includes 21,810 observations. The IHS4 sample is a mix of self-reporting individuals as well as individuals for whom the information was provided through a proxy. The latter set constitutes 39.4 percent of the IHS4 sample.<sup>10</sup> We do not discard any IHS4 observations on the basis of self- versus proxy reporting since the official statistics would be based on the totality of these survey data, irrespective of the deviations from the international best practices.

Conversely, there are 969 IHPS respondents that were eligible but were unavailable for the individual interview, constituting a non-response rate of 16.8 percent (of a total of 5,776). To use the IHPS dataset in a way that can gauge the accuracy of the IHS4 data, we focus only on the IHPS individuals that have been subject to individual interviews and correct for non-response. To do the latter, we calculate weights by first running a logistic regression of individual