rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Boys Soccer<br>|________________________________________|<br>Census Supply<br>________ _____________________________________ Census<br>| | | |<br>Baseline Endline Goal, Goal, 1st visit 2nd visit<br>|____________| |________|<br>Jul Aug Sep Oct Nov Dec Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan May Jun Jul Aug<br> 2016 2017 2018<br><!-- End of picture text -->

**Figure 3** Study Timeline

_Notes._ This figure presents the study timeline.

<!-- Start of picture text --> Boys Arm Treatment Effect<br>IPV often index p=.024 / b=-0.229<br>IPV in last year index p=.056 / b=-0.150<br>Sexual activity index p=.032 / b=-0.125<br>With same partner as baseline p=.331<br>Sex partner quality index p=.265<br>Violence attitudes index (Males) p=.016 / b=-0.290<br>IPV often index (Males) p=.837<br>IPV in last year index (Males) p=.278<br>Sexual activity index (Males) p=.213<br>SRH perceptions index (Males) p<.000 / b=0.293<br>-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5<br><!-- End of picture text -->

**Figure 4** ITT Effects of _Boys_ Treatment

_Notes._ This figure presents estimates of _β_ 1 from equation 1 for separate regressions with the outcome indices specified on the y-axis. Indices are centered around females (males) in ELA only communities who were not assigned (whose connected female was not assigned) to the _Goal_ treatment. Bolded markers are statistically significant at _p <_ 0 _._ 1 _. p_ -values and coefficient estimates are displayed beside each marker.

_Source._ Female and male baseline and endline data, balanced panel.

45
sub-Saharan Africa (excluding Nigeria). Table 4b shows the level of tariff ceiling bindings and other duties and charges for all agricultural products. As indicated by the tariff comniitments, many least-developed net-food importing countries were extremely cautious in their own liberalization commitments under the Round. Except Nigeria, which committed to reduce its rate of protection on wheat and coarse grains to 150 percent (from 190 and 452 percent in the 198292 period), very few commitments to reduce applied protection were offered. The data also indicate that these countries left themselves significant room to raise applied protection, given the very high levels of tariff bindings.

At the same time, the UR agreement is unlikely to burden least-developed and net-food importing countries with many new obligations. This is because many of these countries made no substantial liberalization commitments on border protection either in other sectors such as industry or services. Moreover, many of the general exemptions for development and balance-of-payments support remain available for them to legitimize trade restrictions.

The tariff data are derived from the TRAINS, WTO IDB database and from the details of the PSE and CSE database developed by the USDA. Where PSE and CSE data are not available for some commodities, they are set equal to zero.

# F. ESTIMATES OF WELFARE IMPACTS

The welfare effects of world price changes resulting from the URA for 14 countries based on the comprehensive measures (terns-of-trade and distortion effects) are shown in Table 5. In addition, the first-order terms-of-trade impacts estimated for the 54 least-developed countries are shown in Table 6. Estimates based on the baseline price changes (scenario II) and those based on the largest predicted price changes (scenario IV of Goldin et al) are presented.1 5 All estimates represent percent changes at the end of the implementation period.

The results are summarized as follows. First, as expected, the small world price changes applied to a small proportion of total expenditures result in relatively small (in proportion to total GDP) changes in welfare in many of the least-developed countries studied. Only in a few countries are the estimated welfare change constitute nearly one or over one percent of GDP. Among the countries considered, the largest welfare change as a result of agricultural price shocks appears to

> 15 Detailed estimates for all agricultural products are available upon request.

14
|||(0.052)|||(0.089)||
|---|---|---|---|---|---|---|
|Inc. Per Capita|||0.000|||1.000|
||||(0.000)|||(0.000)|
|Rural Location|||-0.044|||0.889|
||||(0.027)|||(0.146)|
|Household Size|||0.000|||0.998|
||||(0.009)|||(0.044)|
|Constant|0.370***|0.370***|0.378***|0.538***|0.538***|0.530|
||(0.045)|(0.045)|(0.067)|(0.089)|(0.089)|(0.163)|
|Observations|1,802|1,802|1,802|1,802|1,802|1,802|
|R-squared|0.052|0.055|0.055||||

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Logit model coefficients report odds ratios. Offers in each arm of the experiment include: (1) nothing – control group, (2) 2,500 tenge (T-Arm1), (3) 5,000 tenge (T-Arm2), (4) 10,000 tenge (T-Arm3), and (5) 20,000 tenge (T-Arm4) (1 USD=425 Kazakh Tenge).

The summary results from pooled regressions also maintain the same patterns at the national level for Uzbekistan. In Table 7, columns 1 and 4 show that assignment to any hypothetical incentive is associated with a 22 percent lower intention to vaccinate than the control group. Examining the results for each treatment arm (Table 7, columns 2 and 5) suggests that the effect remains uniformly negative across all offer amounts with modest differences between them. Including additional controls (Table 7, columns 3 and 6) does not meaningfully impact the estimated effect.

**Table 7: Main results table Uzbekistan**

||OLS: Ag<br>|ree to Vaccina<br>|te = 1<br>|Logit(odd<br>|s ratio): Agree t<br>|o Vaccinate = 1<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treat|-0.224***||-0.226***|0.373***||0.367***|
||(0.026)||(0.025)|(0.040)||(0.040)|
|T-Arm1||-0.226***|||0.370***||
|||(0.031)|||(0.052)||
|T-Arm2||-0.234***|||0.355***||
|||(0.031)|||(0.050)||
|T-Arm3||-0.235***|||0.354***||
|||(0.031)|||(0.050)||
|T-Arm4||-0.203***|||0.416***||
|||(0.031)|||(0.058)||
|Inc. Per Capita|||-0.000|||1.000|
||||(0.000)|||(0.000)|
|Rural Location|||0.104***|||1.719***|
||||(0.021)|||(0.205)|
|Household Size|||-0.002|||0.992|
||||(0.004)|||(0.020)|
|Constant|0.484***|0.484***|0.415***|0.938|0.938|0.651***|
||(0.023)|(0.023)|(0.034)|(0.088)|(0.088)|(0.107)|
|Observations|2,710|2,710|2,710|2,710|2,710|2,710|
|R-squared|0.037|0.038|0.046||||

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Logit model coefficients report odds ratios. Offers in each arm of the experiment include: (1) nothing – control group, (2) 50,000 soum (T-Arm1), (3) 100,000 soum (T-Arm2), (4) 200,000 soum (T-Arm3), and (5) 400,000 soum (T-Arm4).

12
This paper helps answer these questions through an evaluation of the impact of the Minas Fácil Expresso program in Minas Gerais, Brazil. This was a program designed to extend a business simplification program already present in the most populous municipalities in the state to its remaining municipalities. It was implemented first in 55 of the 822 unserved municipalities. We have 56 to 58 months of administrative data on firm registration rates and business tax receipts for each municipality, and show how difference-in-differences estimation can be used with such data to evaluate the impact of municipal reforms. Many of the non-treated municipalities are significantly smaller in population and had far fewer registrations pre-reform than the treated municipalities, which may make the differencein-differences assumption of common underlying trends less believable. We therefore use propensityscore matching and nearest neighbor matching to obtain a control group of more comparable enterprises, as well as using a group of municipalities in which the units were planned to be implemented but were not as yet implemented as an alternative control group.

Our preferred estimates show that municipalities opening a Minas Fácil Expresso office had a reduction of about 1.3 firms per month registering as a result of the reform, approximately a 10 percent reduction. This impact appears to be concentrated in the month of opening and month immediately after, with registration levels suffering less of a reduction in subsequent months. This result is robust to multiple different choices of a control group, to omitting one month with extraordinary registration patterns, and to the use of either difference-in-differences or fixed effects specifications. Consistent with no big changes in new registrations, we find municipalities do not receive any increase in businesses taxes as a result of the reform.

In addition to documenting the impact of this particular reform, we hope this paper can help serve two further related purposes. First, the administrative data used in the paper should be available for many other developing countries and the methods used here could thus be readily mimicked to evaluate other such municipal reforms. Such analysis is very cheap, and so we hope this paper might motivate other governments to carry out similar evaluations. Second, there is often a concern of impact evaluation publication bias, whereby results which show little impact or impacts that are counter to the hopes of the institution being evaluated are often not published. We hope that sharing the results of this study will be a contrast to this pattern, and be consistent with increasing interest in “learning from failure”.

The remainder of the paper is structured as follows: Section 2 explains the intervention which took place, Section 3 summarizes our main data sources, and Section 4 our estimation methods. The results are found in Section 5, while Section 6 concludes.

3
China and India were both highly dependent on coal as fuel for rail transport in 1980 (coal comprised well over 30 percent of fuel consumption for transportation in these two populous nations (see Table 3)). By 2005, coal had already been completely phased out as railway fuel in India, and its share in the fuel mix had declined to under 4 percent in China. Over the same period, considerable growth in the share of diesel in the fuel mix is observed in both countries, especially China. Some of this is the result of the direct substitution of coal with diesel in rail transport. However, since rail transportation itself has been significantly replaced with road transport, the substitution of coal with diesel only accounted for a small part of the gains in diesel consumption in China and India. Most of the increased diesel demand was due to the growth of road transportation.

The combustion of aviation fuels represented another notable source of emissions from transportation in 1980, but only in a few countries such as Sri Lanka and Vietnam. Reliable aviation fuel consumption data is not available for all countries; however, the share of aviation fuel in total transport sector fuel consumption as well as in emissions declined in the countries for which data is available, except for China and the Philippines. Utilization of electricity for transportation in 1980 was negligible in all countries except China, India, Korea and Mongolia, However, the share of electricity has increased slightly in China, India and Korea (along with the Philippines and Vietnam), whereas it has declined in Mongolia. Despite the small share of electricity in the energy mix in China and India, it is nonetheless an important source of CO2 emissions due to the high CO2 emission coefficient for electricity generation in these countries.

Liquefied petroleum gas (LPG) was not a major fuel for transportation in any of the countries, except Korea, where its share rose from 3.5 percent of the energy mix in 17
# **THE IMPACT OF CLIMATE CHANGE ON AFRICAN AGRICULTURE: A RICARDIAN APPROACH**<sup>**1**</sup>

**David Maddison, Marita Manley and Pradeep Kurukulasuriya**<sup>2</sup>

> 1 An earlier version of this Working Paper was published as CEEPA Discussion Paper number 15.

2 Respectively, the Department of Economics, University of Birmingham; the UK Department of Trade and Industry; and the UN Development Program. This paper is partly based on a dissertation of the same name written by Marita Manley and supervised by David Maddison. The empirical part of this paper uses data obtained from the project entitled _Climate, water and agriculture: Impacts on and adaptations of agroecological systems in Africa_ and funded by the Global Environmental Facility and the World Bank. The authors would like to thank James Benhin, Ariel Dinar, Rashid Hassan, Robert Mendelsohn and Matthew Cole. Any remaining errors are the responsibility of the authors. The views expressed in this paper do not necessarily reflect those of the World bank, UK Department of Trade and Industry or the UN Development Program. This paper was funded in part by the GEF and the World Bank. It is part of a larger study on the effect of climate change on agriculture in Africa, managed by the World Bank and coordinated by the Centre for Environmental Economics and Policy in Africa (CEEPA), University of Pretoria, South Africa.
productive (Table 23). Thus, southern firms have shown to not only pay less wages, but they are also less productive.

**Table 23: Region**

||||||**Center**|||||
|---|---|---|---|---|---|---|---|---|---|
|**Variable**||**19**|**93**||||**19**|**99**||
|**reg2**|**Wage**|**P>Izl**<br>|**Prod**|**uctivity**|**P>Izl**|**Wage**|**P>Izl**|**Productivity**|**P>Izl**|
|Pooled|0.01|0.69||-0.03|0.30|-0.12|0.00|<br>0.13|0.00|
|Micro|-0.10|0.25||-0.36|0.03|-0.29|0.00|<br>-0.13|0.28|
|Small|0.06|0.16||0.35|0.00|-0.14|0.00|<br>0.00|0.98|
|Medium|-0.04|0.17||-0.30|0.00|-0.10|0.00|-0.05|0.40|
|Large|0.04|0.10||0.15|0.00|-0.03|0.30|0.27|0.00|

||||||**South**|||||
|---|---|---|---|---|---|---|---|---|---|
|**Variable**||**1**|**993**||||**19**|**99**||
|**reg2**|**Wage**|**P>Izl**|**Prod**|**uctivity**|**P>IzI**|**Wage**|**P>fzl**<br>|**Productivity**|**P>fzj**|
|Pooled|-0.21|0.00||0.01|0.90|-0.32|0.00|<br>-0.25|0.00|
|Micro|0.10|0.42||-0.20|0.40|-0.43|0.00|<br>-0.50|0.02|
|Small|-0.30|0.00||0.18|0.17|-0.46|0.00|<br>0.06|0.77|
|Medium|-0.30|0.00||-0.50|0.00|-0.18|0.00|-0.61|0.00|
|Large|-0.18|0.00||0.43|0.00|-0.22|0.00|-0.14|0.28|

|||||**M**|**exicoCity**|||||
|---|---|---|---|---|---|---|---|---|---|
|**Variable**||**1**|**993**||||**19**|**99**||
|**reg2**|**Wage**|**P>Izl**|**Prod**|**uctivity**|**P>Izl**|**Wage**|**P>Izl**<br>|**Productivity**|**P>Izl**|
|Pooled|0.06|0.00||-0.06|0.06|-0.10|0.00|-0.01|0.86|
|Micro|-0.09|0.34||0.02|0.92|-0.24|0.00|0.36|0.06|
|Small|0.02|0.68||-0.01|0.86|0.01|0.86|<br>-0.46|0.00|
|Medium|0.05|0.10||-0.24|0.00|-0.12|0.00|<br>-0.12|0.13|
|Large|0.09|0.01||0.07|0.17|-0.11|0.00|0.01|0.92|

_Source_ Author's calculations based on ENTRAM-ENESTYC.

In 1999, micro firms -in the central, southern, and Mexico City regions paid lower wages than northern firms by 29; 43; and 24 percent, respectively. Small firms located in the center and in the south paid 14 and 46 percent, respectively, lower wages than northern firms. Medium-size firms paid 10, 18, and 12 percent (center, south, and Mexico City, respectively) lower wages than those firms located in the north. Finally, large southern firms paid 22 percent lower wages than large northern firrns, and large 24
Table 4. Regressions for nte of change in TFR in dute quinquenia folowing sta of fertlity transition

|Period and predictors|(1<br> <br>B|)<br>(t)|(2<br>8|)<br>(t)|
|---|---|---|---|---|
|First quinquennium|||||
|Constant|-1.45E-01|-8.57|||
|Female enrollment|5.26E-04|1.81|||
|Urban|9.90E-05|0.61|||
|IMR|1.36E-04|1.05|||
|IMR change|-1.85E-04|-0.94|||
|Asia region|-9.25E-04|-0.10|||
|<br>LAC region|-5.60E-03|-0.79|||
|Start year|-8.51E-04|-1.12|||
|TFR at start<br>|1.36E-02|3.27|||
|R<sup>2</sup>|0.37||||
|Second quinquennium|||||
|Constant|-3.34E-01|-5.56|-3.39E-01|-5.55|
|Female enrollment|-8.68E-05|-0.08|-6.77E-05|-0.06|
|Urban|-8.67E-04|-1.52|-8.633-04|-1.48|
|IMR|5.23E-04|1.15|5.28E-04|1.11|
|IMR change|-1.04E-03|-1.51|-1.05E-03|-1.47|
|Asia region|-1.60E-02|-0.48|-1.60E-02|-0.47|
|LAC region|-3.04E-02|-1.22|-3.06E-02|-1.19|
|Start year|2.75E-03|1.02|2.72E-03|0.97|
|TFR at start|4.87E-03|0.33|5.36E-03|0.30|
|Previous change|--|--|3.24E-01|9.68|
|R2|0.30||0.55||
|Third quinquennium|||||
|Constant|-2.99E-01|-4.25|-6.59E-02|-1.15|
|Female enrollment|3.05E-04|0.25|3.65E-04|0.37|
|Urban|-1.02E-03|-1.53|-4.162-04|-0.73|
|IMR|5.11E-04|0.95|1.45E-04|0.32|
|IMR change|-8.94E-04|-1.10|-1.64E-04|-0.24|
|<br>Asia region|-8.31E-04|-0.02|1.03E-02|0.32|
|LAC region|-2.87E-02|-0.98|-7.39E-03|-0.30|
|Start Year|2.52E-03|0.80|5.93E-04|0.23|
|TFR at start|4.23E-03|0.25|8.22E-04|0.06|
|Previous change|--|--|6.99E-01|3.86|
|R<sup>2</sup>|0.22||0.56||

14
The COVID-19 pandemic hit the two main European destinations for Gambian migrants, Italy and Spain, hard and early. As of the time of our survey, Italy had over 317,000 confirmed cases and almost 36,000 confirmed deaths from COVID-19, and Spain over 778,000 confirmed cases and almost 32,000 confirmed deaths.<sup>16</sup> In contrast, Senegal has been lauded for its policy response to the pandemic (e.g. Chakamba, 2020) and had fewer than 15,000 confirmed cases and only 311 confirmed deaths. On a per capita basis, The Gambia had 0.05 deaths per 1,000, Senegal 0.02 deaths per 1,000, Italy 0.6 deaths per 1,000, and Spain 0.7 deaths per 1,000. While testing rates differ across countries, death rates are less likely to be underreported, and the fatality rates in the main European destinations were at least ten times that of The Gambia and Senegal. In addition, the responses to containing the virus were also stricter in Europe compared to The Gambia and Senegal, where total lockdown was never implemented.

The COVID-19 migration policy responses of some EU countries have implications on irregular entry, and its associated risks (Sanchez and Achilli, 2020). The closure of ports, quarantine at sea<sup>17</sup> , and reduced presence of search and rescue<sup>18</sup> , have increased irregular migration risks and also alter migration routes, with a rising number of African migrants trying to reach Europe through the Western Mediterranean route.<sup>19</sup>

# **3.3 A COVID-19 Priming or Salience experiment**

A priming experiment exposes individuals to some stimuli designed to activate a mental concept, and then measures the impact of this primed concept on subsequent responses or behaviors. This approach was first used in psychology and social psychology, with Bargh and Chartrand (2000) providing a guide. Priming can involve prompting individuals to think about specific concepts directly, or implicitly through having them unscramble sentences and/or correct errors in a

> 16 Data for October 1, 2020 from Our World in Data (2020). “Data on COVID-19 (Coronavirus)”. <u>https://github.com/owid/covid-19-data/blob/master/public/data/owid-covid-data.xlsx</u> [accessed 25 January, 2021]. 17 European Council on Refugees and Exiles (2020). “Med: 150 Stranded at Sea as Malta and Italy Declare Ports “unsafe” “. <u>https://www.ecre.org/med-150-stranded-at-sea-as-malta-and-italy-declare-ports-unsafe/</u> [accessed 29 January, 2021].

> 18 IOM (2020). “Covid-19 Control Measures, Gap Measures in SaR Capacity Increases Concern About ‘Invisible Shipwrecks’ “. <u>https://www.iom.int/news/covid-19-control-measures-gap-sar-capacity-increases-concern-aboutinvisible-shipwrecks</u> [accessed 29 January, 2021].

> 19 IOM (2020), “Irregular Migration Towards Europe: Western African Route: Migration to the Canary Islands”. <u>https://reliefweb.int/report/world/western-african-route-migration-canary-islands-irregular-migration-towardseurope. [accessed 29 January, 2021].</u>

11
The size of the funded enterprises is comparatively larger than SBIRI, with almost all recipient companies having sustained revenue streams from other products. Out of the 61 projects funded, 19 companies have current annual revenues of over $100 million (on their own or combined with group companies), 16 between $25 million and $100 million, and the rest of them less than $25 million.<sup>24</sup> The skewed distribution towards larger firms reflects the ability of beneficiaries to undertake larger research challenges leveraging their presence in the market and their ability to enlarge their research effort with support from BIPP. The projects, other than those relating to infrastructure development (2) have a development or validation phase of 3 to 4 years, prior to their readiness to go to the market.

The composition of approved relative to proposed BIPP projects across biotechnology application areas is provided in Table 5. The pattern of applications is relatively similar to SBIRI projects, with healthcare again the most important application area. Most healthcare projects are related to vaccine development for infectious diseases, reflecting India‘s comparative advantage in providing preventive care solutions for infectious diseases not just for its own population but for other developing regions of the world as well. However, in terms of approved projects, there is a marked skew in favor of clinical and especially field trials, highlighting the program‘s intent to help bring projects to commercialization. While overall roughly one in eight projects is approved, the approval ratio rises to one in six projects for clinical trials and better than one in two projects for agricultural field trials (where 5 out of 7 received projects were approved).

**Table 5: BIPP approvals relative to applications by area**

|Category|% of applications received to<br>total applications|% of approvals secured to total<br>approvals|
|---|---|---|
|**Healthcare**|36|35|
|**Agriculture**|19|18|
|**Clinical trials**|14|18|
|**Industrial products &**<br>**processes**|9.5|5|
|**Bio-medical devices &**<br>**instruments**|8|5|
|**Infrastructure**|7|3|
|**Bio-energy &**<br>**environmental biotech**|5|8|
|**Field trials**|1.5|8|
|Total|100|100|

Source: DBT BIPP database.

> 24 The reported figures are based on published figures and information from companies on unpublished revenues. Unpublished figures do not factor group revenues and stand-alone revenues of companies.

31
46 provide a means of understanding these factors. One possibility is to consider an alternative model, less directed at parsimony and more at capturing critical moments when change occurs, for such moments reveal essential political dynamics at work in a society. Some elements of such a model are sketched out in brief below.

First, given the centrality of policy elites and the state to the policy process and economic development, an approach that encourages understanding what occurs within the state and at the intersection of state and society would be appropriate way to begin the task of explaining policy in developing countries. For instance, how do policy elites and policy managers perceive the issues, stakes, options, and constraints surrounding a particular policy problem? In response to such questions, concepts such as self-interest and power maximization explain little that is substantively interesting. In the context of developing countries, in which policy elites play critical roles in deciding policy outcomes, a model of political economy should provide a means of understanding what their preferences are, how they are formed, and how interaction among policy elites and between them and others influences the choices made about the content of public policy. A recent study of twelve reformist initiatives, for example, found that policy elites come into any particular decision making situation with general policy references formed by ideological predispositions, professional expertise and training, memories of similar policy situations, position and