rafmacalaba/data-use-mentions-extended huggingface dataset:
0.014|-0.006|0.045*|0.023|
||(0.014)|(0.015)|(0.024)|(0.025)|
|Plots that Use Pesticide (% of total)|-0.180***|-0.150***|-0.250***|-0.201***|
||(0.054)|(0.050)|(0.068)|(0.072)|
|Export crops dummy (1 if Household produced any||-0.106|||
|<br>export crop)||(0.093)|||
|Constant|1.410**|<br>1.017*|-0.628|1.165*|
||(0.603)|(0.591)|(0.644)|(0.670)|
|Observations|3936|3936|2823|2749|

_Note: ***/**/* indicate statistical significance at the 1/5/10 percent level respectively._ In Table 10, we present results from the decomposition of each decile of the productivity distribution, using the RIF method. We see that the simple overall average productivity is lower for female-headed households in the bottom six deciles (10<sup>th</sup> to 60<sup>th</sup> ) but is not significantly lower than male-headed households in the top three productivity deciles. In our 2016 data, there is a significant correlation (at the 5 percent level) between the number of assets that households own and their agricultural productivity, suggesting a close relationship between poverty and low productivity as in 2008. This implies that the productivity gender gap is present among the poorest households in 2016.

Similar to the finding from the mean decomposition, the
<!-- Start of picture text --> Barge arrival<br>Gate in same 'Actual barge departure' next Barge arrival<br>Discharge from vessel<br>Rail arrival<br>Barge Departure previous 'Actual barge arrival' next Gate out<br>Loaded on vessel<br>Rail departure<br><!-- End of picture text -->

# Potential biases with the data

Despite the data being massive and provided by reputable industry sources, the data used in this analysis may imperfectly reflect the reality on the ground. Potential issues include:

- Although there are rigorous procedures to input the time stamps, the process is not fully automatized in some countries and may depend on practices by local operators, more so for aviation and postal data than for shipping. The very few countries for which there is a strong suspicion this is the case have not been included, as per the recommendation of the data partners (see appendixes by mode on the cleaning process). The published KPI only use data that the data partners consider reliable.

- The shipping data by TradeLens is a very large sample of movement of containers (in the double digit millions). There may be a selection bias in that more efficient operators tend to use advance digital tracking solutions. There is a also a possibility of bias due to some shipping line being more present in the sample than others. However, expert opinion is that it is very unlikely. Furthermore, expert reading of the dwell time data across ports suggests that the dwell time data is consistent with local knowledge when available.

- In some cases, the end of the import process registered in the data may not be the actual delivery to the importer. For instance, small developing economies including islands states may not move containers inland but rather unstuff them and store the goods in bonded facilities. The current system does not detect these old-fashioned logistics practices, which are progressively disappearing.

- Representativeness of transport corridor data for landlocked countries:
at baseline (as in equation 46). We set _σ_ ˆ _K_ = 2 _._ 377 as our estimate for the elasticity of substitution across origins of the same crop.

# **D.B Trade Costs**

In Section VI.A.2, we estimate the parameters ( _α_ , _{βk}k_ ) that govern trade costs according to equation (34). To estimate semi-elasticity _α_ , we use two key pieces of information: first, the tea transportation cost _pT,_ 100 is 1,496 LKR/MT for a distance of 100 km (according to the Ceylon Petroleum Corporation); second, the price of tea _ptea_ is 455,473 LKR/MT (according to the DCS). The tea transportation cost is calculated based on the average distance from farms to tea brokers’ warehouses in the city of Wattala, assuming that the cargo is transported by a lorry with a capacity of 7,500 kg charging typical prices.<sup>114</sup> To compute domestic “factory-gate” tea prices ( _ptea_ ), the price of green leaf needed to produce one MT of made tea is added to processing costs (labour, fuel, electricity, machinery upkeep, packing materials, and factory sundries). The corresponding iceberg trade cost can be written as _τni,tea_<sup>_A_=1 +</sup><sup>_<u>p</u>_</sup> _p_<sup>_T_</sup> _tea_<sup>_<u>,</u>_100, which we substitute in equation (34) to obtain the</sup> following relationship: where _α_ ˆ is our estimate for the semi-elasticity _α_ . We solve this equation to obtain: _α_ ˆ = 3 _._ 3 _×_ 10<sup>_−_5</sup> . To estimate crop-specific international trade costs ( _{βk}k_ ), we use FAO
frequent use causes consumers to learn how to use the financial technology in a more efficient and cost-saving manner.

We find compelling evidence of consumer learning as a result of active engagement with the financial technology. Our payroll account treatments increase active engagement with the account and cause consumers to learn how to use the features of the technology to their advantage. While direct deposits lead, mechanically, to more withdrawal and cash-out transactions, participants in the treatment group also make more deposits, send-money transactions, and accumulate higher account balances. We document consumer learning using two main outcomes. Our first outcome, _outside transactions_ , is an indicator for the likelihood of a worker making a transaction outside the workplace, where help with accessing the account is not readily available. Our second learning outcome _direct transactions_ focuses on mobile accounts, and measures whether participants learn to transact through their account without the help of a mobile money agent, thereby sidestepping illicit extra charges which are widespread in our setting and many other markets for consumer finance.<sup>4</sup>

> 4Annan (2019) shows that in Ghana consumers are overcharged in 20% of all mobile money transactions, with female customers being more than 90% more likely to be the victim of financial agent misconduct. See also “Financial Services Agent Survey” (EFINA, 2020), which shows that 40% of financial services agents in Nigeria overcharged

2
293|
|Stanford U|B. Douglas Bernheim; Antonio<br>Rangel;|Journal Article|2004|American Economic Review|Addiction and Cue-Triggered Decision Processes|275|
|Stanford U|<br>Victor R. Fuchs;|Journal Article|1996|American Economic Review|Economics, Values, and Health Care Reform|274|
|Stanford U|Kenneth J. Arrow;|Journal Article|1997|Environment and Development<br>Economics|Is There a Role for Benefit-Cost Analysis in Environmental, Health,<br>and Safety Regulation|249|
|Stanford U|Robert E. Hall; Charles I. Jones;|Journal Article|2007|Quarterly Journal of Economics|<br>The Value of Life and the Rise in Health Spending|192|
|Stanford U|David M. Cutler; Mark B. McClellan;<br>Joseph P. Newhouse;|Journal Article|2000|RAND Journal of Economics|How Does Managed Care Do It?|160|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed institution was given full credit for a publication in the case of a co-authored publication, but only the first institution was credited where an author listed multiple institutions. Institutional affiliations below the institution level were aggregated up to the level of the institution, so that for example publications originating from the Harvard School of Public Health were allocated to Harvard University along with publications originating from the Department of Economics at Harvard University. As explained in the text, only addresses with five or more articles to their name were retained for cleaning and aggregating.
|Source/Target<br>Note<br>share of transmission due to consumption-related activities<br>consumption-related activity transmission<br>share of infection due to work-related activities<br>work-related transmission<br>proportion of infected population<br>community-based infection<br>Lei et al. (2020)<br>within-household infection<br>post-lockdown _R_0 in<br>impact of lockdown on<br>Flaxman et al. (2020), 7-country sample<br>community transmission<br>population infection rate of 2.6%<br>start date of the reference lockdown<br>lockdown length from<br>length of the reference lockdown<br>Flaxman et al. (2020), 7-country sample<br>GDP decline in 2020Q1-Q2 in<br>strength of the reference lockdown<br>the 7 countries from Flaxman et al. (2020)<br>semi-elasticity of child mortality w.r.t. income<br>Authors’ estimation using DHS data<br>low-income countries<br>Authors’ estimation using DHS data<br>lower-middle income countries<br>Authors’ estimation using DHS data<br>upper-middle income countries<br>Assumed based on literature<br>high-income countries<br>Walker et al. (2020)<br>COVID-19 mortality, type-_m_<br>Walker et al. (2020)<br>probability of hospitalization, type-_m_<br>World Development Indicators<br>number of hospital beds per 1000 people<br>WHO Global Health Repository<br>non-COVID mortality, type-_m_<br>World Population Prospects, 2020<br>mass of type-_m_<br>annual risk-free rate of 4 percent<br>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
*67%**|**77%**|
|_Permitting New Entrants_|_100%_|_75%_|_75%_|_75%_|_50%_|_100%_|90%|
|_PPA Approvals_|_50%_|_67%_|_67%_|_67%_|_67%_|_33%_|57%|

Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform The new law governing the sector was incorporated in 2015 and all institutions are adjusting to the changes made under the law. At the time, when data was collected for the study, the new regulations and framework were being implemented and not enough time had passed to judge whether the new framework was actually being implemented in their true spirit. However, the perceived index gives us an opportunity to understand how regulation is being practiced in the sector as of 2015.

A very interesting picture emerges from the comparison of the de jure and perceived indices. The overall score on regulatory framework falls from 60 percent to 28 percent (table 9). On every indicatoraccountability, autonomy, tariffs, quality and market entry the regulator is seen to be not implementing all the practices that are part of the 2015 framework. The differences are extremely egregious in quality regulation and transparency practices. The regulator is unable to exercise its authority on tariff levels and

> 21For more detail about the material in this table, see the Annex.

27
* Pr( _w_  1 _w_  _w_ (0))  1 . That is, targeting the wealth poor when there is no intra-household inequality assures that one reaches all those households with undernourished individuals. By contrast, given that

* _w_ is an increasing function of  , when  approaches its maximum value, a high level of household wealth will be needed to assure that enough of the household’s resources “trickle down” to avoid undernutrition in women and children. (This is clear if one considers again the example when  represents intra-household inequality.) Specifically, _w_ *)1(  _w_ max and Pr( _w_  1 _w_  _w_ max )  Pr( _w_  1) .

By invoking continuity, it is clear that Pr( _w_  1 _n_  )1 must be a non-increasing function of  over (0, 1) and strictly decreasing for some sub-intervals. As  approaches its upper limit, the probability of reaching undernourished individuals by targeting poor households is no higher than the overall poverty rate.

This model formalizes the intuition that heterogeneity, such as due to intra-household inequality or the local health environment, diminishes the scope for reaching poor individuals by targeting poor households. But how much does this matter empirically? Is the wealth effect on individual nutritional status strong enough to allow satisfactory targeting of vulnerable women and children? The rest of this paper addresses these questions.

# **4. Data**

Our data are drawn from the Demographic Health Surveys (DHS) and the LSMS. We use the most recent DHSs available.<sup>18</sup> Table 1 lists the countries included in our analyses and the year of each survey.

**_Individual nutritional outcomes:_** We study the nutritional outcomes of women and children. For women, the two variables we employ are the body mass index (BMI) (also known as the <mark>Quetelet index)</mark> , defined as a woman’s weight
match with more aggregate sector‐level data of I/O tables. For each I/O sector, the tool then calculates an upstream tariff ( UT ) reflecting the total incidence of all intermediate input tariffs on sector i ’s production costs as follows: The variable w�� stands for the weight of upstream sector j in sector i ’s total production output and t� stands for the simple average of all ad‐valorem tariffs on products that belong to input sector j . The parameter β� measures the level of pass‐through of a tariff in sector j on output prices of sector j and can take values between 0 and 1. On the basis of equation (1), UTAS then establishes the effective protection rate ( EPR ) in each sector i . Effective protection rates measure how a sector’s output protection weighs up against the tariff costs it faces due to protection accorded to upstream sectors relative to a sector’s value added. They are calculated as follows: Equation (2) deducts from a sector i ’s nominal protection ( β�t� ) the additional production cost that producers face on account of upstream tariffs ( UT� ), where this difference is compared with the sector’s value added share in total output ( va� ). It therefore allows policy makers to gauge the net impact of an entire tariff schedule given the sector’s backward linkages.

> 6 This is done by calculating Leontief’s inverse.

> 7 An exception is a paper by Chevassus‐Lozza et al. (2013) who show that under imperfect competition and product differentiation the impact of lower input tariffs on the probability of firms entering export markets will differ between high and low productivity firms.

> 8 Notice that the partial equilibrium approach implies that UTAS considers sectors in isolation from each other. Substitution effects between sectors are not taken into account. UTAS therefore captures predominantly short‐term effects. Substitution effects that take longer to materialize and may kick in once firms start
Policy Research Working Paper 10349

# **Abstract**

This paper studies the effect of proposing a monetary incentive for vaccination intentions, with a survey-based randomized controlled experiment conducted separately in three countries, Tajikistan, Uzbekistan, and Kazakhstan. Respondents from nationally representative surveys were randomly assigned to a control group (for which no incentive was proposed) or to one of several treatment groups with varying levels of hypothetical compensation. Offering incentives markedly reduced overall vaccination intentions —all three counties. Country-level results ranged from no meaningful effect on vaccination intentions (Tajikistan) to a decline of up to 22 percent (Uzbekistan and Kazakhstan).

In follow-up questions, most respondents said they disapprove of offering financial incentives for vaccination, and especially in contexts with strong negative effects in the experiment. The results contrast with the well-established efficacy of monetary incentives to influence vaccination behavior in other settings, but they are consistent with findings from the behavioral literature in which incentive payments signal inferiority or disutility. The findings suggest that policy makers and practitioners should use caution when considering extrinsic incentives for vaccination and other health interventions where effects have not been tested.

This paper is a product of the Poverty and Equity 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 wseitz@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
time and across regions. Combining multiple data sources—including nation-wide private enterprise surveys, industrial firm surveys, the listed firm database, the Chinese patent database, and other related databases—we first study the evolution of the BE: how the provision of infrastructure has changed, and how firms’ dependence on formal market-supporting institutions (i.e., courts, contract, and external finance) has evolved over time. We also examine the set of institutions and government activities that are regarded as disruptive. These include inefficient regulations, barriers to entry, and expropriation in the form of taxation and extralegal fees. Second, we establish stylized facts about Chinese firms and entrepreneurs, which have likely changed alongside the business climate. We examine what changes have occurred over time and space for _firm attributes_ such as firm size, capital intensity, export, innovation, and diversification, as well as _entrepreneur attributes_ including their education level, family background, working experience, and participation in political and social organizations (or political connection). Finally, we discuss the business strategies adopted by Chinese entrepreneurs in an environment of imperfect property rights protections.

In our analysis, we highlight how non-elite firms (small and medium firms, non-state firms, non-politically-connected firms) fare relative to elite firms (large firms, state-owned enterprises, and private firms with political connections) in order to understand the nature of the Chinese economy, including its inclusiveness. Moreover, we examine how economically lagging regions compare with more advanced areas in China in terms of their BEs as well as their entrepreneur and firm characteristics. Regional disparity has been a key challenge for China. Indeed, while the ratio between GDP per capita of the richest region to that in the poorest region is 18 (Galor 2005), the corresponding ratio between the richest and the poorest prefecture in China is 27.<sup>6</sup> Such regional disparities within China have lasted for centuries (Pomeranz 2000). This study attempts to shed light on whether these disparities can be partly
lines. The last two rows present the mean and median of the underlying poverty lines used to estimate the currently-used $2.5 and $4 per person per day poverty lines updated to 2011 PPPs using national CPIs, both unweighted and weighted by the _national_ population of each country.

Regional extreme poverty lines based on the most up-to-date official extreme poverty lines are approximately $2.8 per person per day on average, while regional moderate poverty lines are approximately $5.9 per person per day on average. However, there is variation across different specifications in the table. Extreme and moderate regional poverty lines based on mean values of the official poverty lines tend to be higher than those based on median values (approximately ten percent on average), which provides evidence of the impact that outliers have on regional averages of poverty lines. Similarly, unweighted poverty lines tend to be higher than the population12
jobs. In addition, most Venezuelans work in the informal sector, which before the pandemic has no access to job protections and unemployment insurance. In line with Mazza (2020), Venezuelans in Brazil appear to be disproportionately working in tourism, retail and personal care, the informal sectors that are heavily impacted by the pandemic.

<!-- Start of picture text --> 48350<br>78281.2 76538.34 74498.1 77291<br>30517<br>26734.565 27408.134 28090.177 18347<br>16470<br>591 2774 12172 5712 4893<br>840 396 665 1335<br>Time<br>Time<br>Brazilian Families Brazilian Persons<br>Venezuelan Families Venezuelans Persons Venezuelan Families Venezuelans Persons<br>(a) Cadastro Unico Registration (b) Bolsa Familia Access<br>80000 80000 50000<br>40000<br>60000 60000<br>30000<br>40000 40000<br>Venezuela<br>Brazilians(000)<br>20000<br>PBF Beneficiancies<br>20000 20000<br>10000<br>0<br>0<br>2017m1 2017m2 2017m3 2017m4 2017m5 2017m6 2017m7 2017m8 2017m9 2017m10 2017m11 2017m12 2018m1 2018m2 2018m3 2018m4 2018m5 2018m6 2018m7 2018m8 2018m9 2018m10 2018m11 2018m12 2019m1 2019m2 2019m3 2019m4 2019m5 2019m6 2019m7 2019m8 2019m9 2019m10 2019m11 2019m12 2020m1 2020m2 2020m3 2020m4 2020m5 2020m6 2020m7 2020m8 2020m9<br>2017m1 2017m2 2017m3 2017m4 2017m5 2017m6 2017m7 2017m8 2017m9 2017m10 2017m11 2017m12 2018m1 2018m2 2018m3 2018m4 2018m5 2018m6 2018m7 2018m8 2018m9 2018m10 2018m11 2018m12 2019m1 2019m2 2019m3 2019m4 2019m5 2019m6 2019m7 2019m8 2019m9 2019m10 2019m11 2019m12 2020m1 2020m2 2020m3 2020m4 2020m5 2020m6 2020m7 2020m8 2020m9 2020m10 2020m11 2020m12 2021m1<br><!-- End of picture text --> Figure 10: E↵ect of Covid-19 pandemic on access to _Bolsa Familia_ In response to the Coronavirus pandemic, the federal government, initiated two main policies to alleviate the impact of the pandemic. They expanded the PBF to include eligible families
population remaining on January 1<sup>st</sup> , 1975, in columns 7 and 8, assuming migration away. The same exercise can then be repeated using the 1975-79 death probabilities reported in columns 9 and 8. The number of casualties during the 1975-79 period is then computed in columns 11 and 12 and added to the 1970-74 death tolls to give the 1970-79 figures in columns 13 and 14. These figures can then be compared with the excess deaths figures reported by Heuveline (2001 (b)) for the period from January 1<sup>st</sup> 1970 until January 1<sup>st</sup> 1980, reported in columns 15 and 16. The ratio of both figures is computed in columns 17 and 18.

Table A1 indicates that when the Heuveline’s reconstruction is used as a benchmark, the sample based method very substantially underestimates the extent of mortality. The ratios in columns 17 and 18 indicate that the sample based method would only account, depending on gender and birth cohorts, for between 28% and 65% of the mortality figures obtained using the reconstruction method. If one considers that the denominator in these ratios is only “excess deaths” while the numerator is supposed to represent all deaths, the underestimation is actually even worse. It also seems that the mortality of younger cohorts is more underestimated than for older cohorts, and also that female mortality estimates suffer from a somewhat larger bias than estimates for males.

Several factors can account for such large differences in the estimates of the extent of mortality. First, it should be emphasized that in computing the ratios in columns 17 and 18, both the numerator and the denominator were point estimates within substantial confidence intervals. The death probabilities reported in columns 3, 4, 9 and 10, are all estimated with standard errors reported in table 1. Heuveline (1998(a), 2001(b)) also report central estimates within a substantial range. However, the general pattern of underestimation by