rafmacalaba/data-use-mentions-extended huggingface dataset:
the likelihood of systematic underreporting of incomes and assets by control households is considered low because (1) the data collection team did not reveal its connection with the CfW program, but, rather, identified itself as a national statistical office team that was conducting a socioeconomic survey; and (2) the endline survey took place in the days immediately after the control households had been registered for the CfW program; so, there was no incentive to underreport to influence the selection process. To the extent that control households had an incentive to underreport to participate in future rounds of the CfW or other programs, the treatment communities would have been subject to the same level of underreporting as the control communities.

Another possible concern relates to anticipation effects on the part of the control households. The control households could have made certain decisions (especially in relation to intrahousehold labor allocation) or delayed certain investments (for example, in new businesses) in anticipation of the CfW program, which would have led to the over‐ or underestimation of program impacts. The possibility of anticipation effects is considered low given that there was no systematic announcement or publicity related to the CfW program well in advance of the program launch among treatment or control communities. There is, in general, also considerable unpredictability associated with the delivery of government programs in Sierra Leone so that it is unlikely households would re‐optimize in anticipation of a government program.

# **IV. Descriptive Analysis**

## **A. Subproject Characteristics**

The community‐specific infrastructure projects organized within the CfW program are referred to as subprojects. The CfW subprojects had national coverage and were spread fairly evenly across urban and rural areas. Figure 2a shows the geographical distribution of all the subprojects covered under the evaluation; Figure 2b shows the precise location of the treatment sites based on Global Positioning System data.<sup>16</sup> The most common types of subprojects are feeder road rehabilitation projects (67 percent), inland valley swamp rice projects (9
# **1. Introduction**

In recent years, there has been renewed interest in understanding the nature of constraints that freight costs impose on trade, investment, and growth, especially in landlocked countries in Sub-Saharan Africa (SSA). There is a consensus about the need for African countries to have an efficient and low cost transport system in order to become competitive in the global market, even though this does not guarantee successful exportations. Longer transport time which leads to slow import processes, dramatically reduces trade (Hummels and Schaur, 2012). The delays and the unpredictability increase inventories and result in uncompetitive exports. In the case of ports, long cargo dwell times (CDT) are a critical issue. More than half of the time needed to transport cargo from port to hinterland cities in landlocked countries in SSA is spent in ports for land transport to landlocked countries (Arvis _et al._ , 2010).

Over the last decade, the international donor community has been investing in projects that facilitate trade and improve trade logistics in the developing world. These projects have assumed incorrectly that customs, terminal operators and other controlling agencies are solely responsible for the long delays in ports, with infrastructure coming in second. In reality, customs’ responsibility (especially for month-long delays) may not be as important as usually perceived and in-depth data collection and objective analysis are required to determine the actual drivers of long cargo delays. Such analysis has been lacking so far.

This study is part of a larger project conducted by the World Bank in 2011 which made possible the collection of data from firm surveys in several SSA countries. The paper analyzes cargo delays in ports based on data collected from the surveys conducted in Kenya, Nigeria<sup>2</sup> , South Africa, Uganda, and Zambia. The surveys were conducted from March to July 2011. Each of these surveys includes about 100 observations. Importers - manufacturers and retailers - from the most important sectors have been covered. The ports covered are (
among the wider population and school attendance among poor children. Females are more deprived in both instances. The employment indicator, meanwhile, sees no significant gender differences, while the gender gaps in legal identification are significant in all cases. The gender gaps are also larger among the displaced community, with the gap nearly five times as large among the displaced than non-displaced poor (24percentage point and 5-percentage point differences, respectively).

_Table 10: Proportion of deprived individuals across displacement status by gender, South Sudan_

|||IDPs<br>|||Hosts<br>||
|---|---|---|---|---|---|---|
||Male|Female|Difference|Male|Female|Difference|
|_All households_|||||||
|Years of schooling|0.43|0.48|-0.05|0.40|0.52|-0.12<sup>**</sup>|
|School attendance|0.20|0.25|-0.05|0.25|0.29|-0.03|
|Unemployment|0.05|0.00|0.05<sup>*</sup>|0.02|0.03|-0.01|
|Legal identification|0.48|0.74|-0.26<sup>***</sup>|0.60|0.74|-0.14<sup>***</sup>|
|Early marriage|0.08|0.75|-0.67<sup>***</sup>|0.10|0.53|-0.43<sup>***</sup>|
|_MPI poor households_|||||||
|Years of
>h< xSI<br>hwhenwh> ∆|
|**Per capita expenditure deciles**|**Average food bud**|**get shares, **wh **(%)**|
|1|40.8|**53.0**|
|2|33.0|**49.5**|
|3|28.9|**47.5**|
|4|27.8|**44.6**|
|5|25.3|**44.3**|
|6|23.3|**42.1**|
|7|**20.8**|**42.9**|
|8|**20.2**|40.9|
|9|**18.1**|42.7|
|10|**16.1**|41.6|

h h Note: Expenditure deciles where xMI < xSI are in bold type.

_Source_ : Authors calculation based on HEIS 2019 29
3 but consumption increases without bound. Wealth on this path is greater than under the standard Hartwick Rule, and maximum wealth is independent of the initial resource stock _S_ 0 .

_Proof:_ See Annex I.

Having established the properties of this specific sustainability rule for the Dasgupta-Heal economy, we now turn to the empirical application of the rule to historical data.

# **Hypothetical Estimates of Capital Stocks**

While the foregoing theory can be shown to apply to rules for _saving_ in an open economy, we will limit ourselves to _investment_ rules in the empirical application. All of the countries which are highlighted in the empirical results had significant net foreign debts in 2000. Rather than looking at the more complex question of whether resource rents could have been used to either pay down foreign debt or invest in domestic assets, we limit ourselves to comparing an estimate of the current stock of produced capital with a hypothetical estimate of how large this stock could be if resource rents had been invested in produced capital. We assume that all resource rents are invested in produced capital for simplicity, although the theory suggests more generally that resource rents could be invested in a range of assets, including human capital – if any of the countries highlighted below had in fact been investing their resource rents in human capital (quite unlikely given the observed levels of per capita income) then our methodology would produce a biased picture of their investment performance.

In order to examine a variety of counterfactuals, we derive four estimates of produced capital stock using empirical data covering 1970-2000: (i) a baseline capital stock derived from investment series and a Perpetual Inventory Model (PIM); (ii) a capital stock derived from strict application of the standard Hartwick Rule; (iii) a capital stock derived from the constant net or genuine investment rule; and (iv) a capital stock derived from the maximum of observed net investment and the investment required under the constant genuine investment rule. All investment and resource rent series are measured in constant 1995 US dollars at
2003; Singh and Pedersen 2003; Guidolin and La Ferrara 2007; Collier and Duponchel 2013; Klapper et al. 2013; Ksoll et al. 2015; Amodio and Di Maio 2018; Tandon forthcoming; etc.).<sup>9</sup> Thus, these previous results might not generalize to this unique setting among the mobile phone‐using population.

Second, these results have important implications for the targeting of food assistance across Yemen. The most recent official IPC classification of food emergencies in Yemen, which is the basis for much of the food aid distributed in the country, has identified more violent regions as being closer to famine. The announcement explained the classification stating that “armed conflict remains the main driver of food security (IPC 2018).” The previous official IPC classification in 2017 also explicitly included indicators that correlated with violence, which resulted in governorates being more affected by violence receiving much more food assistance than the rest of the country (e.g., Tandon and Vishwanath 2019). However, the results here contrast with this narrative, and suggest that reducing the emphasis on violence in the identification of regions with poor food security could lead to better targeting of assistance.

# **Background: The Current Conflict in Yemen and Available Data**

The conflict in Yemen has been ongoing since the mid‐2000s. The Houthis' movement began as a theological movement that protested the national government. However, following this protest, the government used the military to try to arrest their leader, which led to a number of wars during which the group made few gains, and was repeatedly beaten back by the military.<sup>10</sup>

> 8 This pattern was not present just prior to the conflict in the 2014 Household Budget Survey (World Bank 2017).

> 9 See also Blattman and Miguel (2010) for a thorough review of the literature.

> 10 See (accessed July 2017): <u>http://www.
concerns of pre-existing trends, we include lagged export growth.

The resulting specification is run separately to each destination, _n_ = _US_ , _CH_ , _RW_ : where _λ_<sup>_n_</sup> _iω_<sup>istheshareofvariety</sup><sup>_iω_tocountry</sup><sup>_n_intotalexportsofcountry</sup><sup>_i_.The</sup><sup>_λn_</sup> _iω_<sup>sharesare</sup> defined as the (pre-war) export values for continuing products divided by total country exports.

# **5.1 Heterogeneous Export Growth in Targeted Products**

The analysis reveals two key takeaways: i) substantial cross-country heterogeneity in export growth in targeted products compared to non-targeted products; and ii) a central role for the country-specific component of the tariff elasticities, rather than any other component, in explaining of this heterogeneity.

Figure 2 plots the export growth defined in (10) across countries.<sup>17</sup> By export growth, we specifically mean the growth of products taxed by US or China relative to other products within each exporter, importer, and sector. Hence, the figure indicates the countries that better exploited global export opportunities in products targeted by the trade-war tariffs.

On average, countries’ exports in targeted products increase by 6.7%, with a standard deviation just as large at 6.3%. For example, the increases in exports of targeted relative to untargeted products in Thailand and Mexico are 14.5% (se 4.9%) and 10.9% (se 6.4%), respectively, while Ukraine’s exports fall 12.1% (se 8.7%) and Canada’s export growth is just 2.1% (se 5.4%).<sup>18</sup> Through the lens of our model, the heterogeneity is expected. By construction of (10), it may be
we get this information directly from the census material (see table A.1). For the USA we used information from Automotive News, 1975 Almanac Issue, which is the most important trade journal of the automobile industry in the USA. It shows US production classified by model, together with technical specifications of each model. The figures show that<sup>4 cylinder</sup> cars accounted for only 9.7 per cent of total car output in the USA in 1975 (table A.2). For Brazil some indirect information on quantities<sup>is also</sup>
variable|(0.416)|(0.416)|(0.238)|(0.238)|
|Mean of women’s right to bequeath|0.454|0.454|0.454|0.454|
|<br>Sd. of women’s right to bequeath|(0.498)|(0.498)|(0.498)|(0.498)|
|<br>Mean of women’s right to sell|<br>0.402|<br>0.402|<br>0.402|<br>0.402|
|<br>Sd. of women’s right to sell|(0.490)|(0.490)|(0.490)|(0.490)|
|<br>Mean of men’s right to bequeath|<br>0.334|<br>0.334|<br>0.334|<br>0.334|
|<br>Sd. of men’s right to bequeath|(0.472)|(0.472)|(0.472)|(0.472)|
|<br>Mean of men’s right to sell|<br>0.299|<br>0.299|<br>0.299|<br>0.299|
|Sd. of men’s right to sell|(0.458)|(0.458)|(0.458)|(0.458)|

_Notes_ : IHPS data are used and the sample includes owned parcels only. Indicator variables for women’ ownership or men’s ownership are controlled for. Household fixed effects and parcel size are controlled for throughout. Parcel characteristics include soil type (sandy, between sandy and clay, or clay), land slope (
be a protective factor against violence (Levinson 1989, Counts et al. 1999, Counts et al. 1992, Perova et al. 2013). However, a dearth of rigorous evidence exists on the effectiveness of legislative and justice sector responses (including specialized police stations for women) and onestop centers – comprehensive care centers for survivors of VAW – to reduce VAW or mitigate negative consequences for survivors (Ellsberg et al. 2015).

The focus of this study is on the help-seeking behavior of women experiencing VAW in Rio de Janeiro, Brazil. The severe difficulties facing women living in situations of violence in seeking support have been illustrated in the multi-country study on violence against women conducted by WHO (2005). The data collected showed that across countries, over half of physically abused women (between 55 percent and 95 percent) reported that they had never sought help from formal services (health, justice or shelter) or from people in positions of authority (police, NGOs, local leaders, and religious leaders). The study shows further that even in those countries where services are available to survivors of violence, barriers such as fear, stigma and the threat of losing their children impeded women from seeking help. On the other hand, the top facilitators for supportseeking were the severity of the violence, the expected impact on children, and support to reach out to services from friends and family. Those often serve as the first contact for women in violent relationships – something that had also been shown in previous research for the United States (Rose et al 2000, Counts et al. 1999) – though, as will be seen in this paper, such contact can bear both impeding or facilitating functions.

Brazil has increased efforts to address violence against women since the early 2000s. Since the creation of the Federal Secretariat for Women’s Policies in 2003, anti-VAW policies and legislation have been strengthened. Efforts include the enactment of the Maria da Penha Law (MdP) in 2006, which has been internationally lauded as
For 2011, we use the tariff rate data in the ―Unified Customs Tariffs of the Customs Union of Belarus, Kazakhstan, and Russia‖ as of January 1, 2012.<sup>11</sup> The reason that we use the tariff data of January 1, 2012 for 2011 is that we want to have an assessment of the impact of the Customs Union on the tariffs of Russia. Since for 2010, we use pre-Customs Union tariffs, and for 2012, we use tariff rates that are impacted by the WTO commitments, we need to take a date for the tariff rates that is representative of the Customs Union tariff rates. The changes in the tariff code implemented on January 1, 2012 were potentially significant and are the most representative of the tariffs of the Customs Union. We should interpret ―2011‖ in our calculations as Customs Union tariffs, and we can interpret the difference in tariffs between 2010 and 2011 as the impact of the Customs Union schedule on the tariffs of Russia.

The source of data on tariff rates of Russia after the WTO accession is a combination of the Customs Union tariff schedule mentioned above and the Schedule of Concessions and Commitments on Goods of the Russian Federation related to the WTO accession. Commitments at the WTO are known as ―bound‖ tariffs; this means they are the maximum tariff that can apply at the tariff line level. The actual applied MFN tariff may be less than the bound tariff, a situation referred to as ―water in the tariff schedule.‖ For the years 2012 and later, we take the applied MFN tariff as the minimum of the bound tariff in any year and the applied tariff of the Customs Union as of January 1, 2012.

The WTO commitments schedule became publicly available from the WTO after the Russian Federation was formally invited to join the WTO on December 16, 2011. The Russian Federation is scheduled to implement its commitments to the WTO in stages, beginning with the summer of 2012, with the longest transition period eight years after
Policy Research Working Paper 6336

# **Abstract**

This paper studies the effect of political regime transitions on public policy using a new data set on global agricultural and food policies over a 50-year period (including data from 74 developing and developed countries over the 1955–2005 period). The authors find evidence that democratization leads to a reduction of agricultural taxation, an increase in agricultural subsidization, or both. The empirical findings are consistent with the predictions of the median voter model because political transitions occurred primarily in countries with a majority of farmers. The results are robust to different specifications, estimation approaches, and variable definitions.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. 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 alessandro.olper@unimi.it, jfalkowski@wne.uw.edu.pl, and Jo.Swinnen@econ.kuleuven.be.

_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
depicts countries with which Pakistan under-performs above the 45-degree line below. In South Asia, Pakistan’s exports to Sri Lanka have grown in response to a bilateral trade agreement in place. Pakistan trades heavily with members of the Gulf Cooperation Council (GCC) which is explained by proximity and trade complementarities as well as from the fact that these serve as re-export bases for Pakistan’s trade with a number of third countries.

**Figure 5**

<!-- Start of picture text --> Pakistan's Export Partners, 2007-09<br>PREDICTED VS. ACTUAL (GRAVITY)<br>16<br>USA<br>IND<br>JPN CHN<br>GBR<br>FRA DEU<br>RUS ITA<br>BRA AUS KOR<br>THA BGD SAU<br>12 MEX TUR<br>VNM M YS CAN ARE<br>EGY ZAF<br>HKG<br>SGP KWT LKA<br>NPL<br>QAT OMN<br>8<br>8 12 16<br>Log of Actual Exports, 2007-09<br>Log of Predicted Exports, 2007-09<br><!-- End of picture text --> Source: Authors’ calculations Furthermore, how a country’s industry is faring in international competition can be gauged by its share in strategic markets, such as those that are expanding (and importing) rapidly. Generally, there is no correlation between Pakistan’s exports and their growth in the world market. Ideally, in the figure below showing world growth (on the y-axis) and the importance of such high-growing products in a country’s export portfolio (x-axis), we seek a positive, upwardsloping relationship. The top exports of Pakistan -- cotton, apparel, leather -- are not growing as rapidly as other products like fruits/grain/oil seeds, cereals, chemicals, ship/boats, cement, plastics, etc. The growth orientation of
**Figure 3 Fiscal procyclicality and macroeconomic factors in commodity-exporters**

**A. Procyclicality in commodity-exporters, by degree of capital controls**

**B. Procyclicality in commodity exporters, by exchange rate regime type**

<!-- Start of picture text --> Correlation<br>Correlation<br>1.0<br>1.0<br>0.8 *** 0.8<br>***<br>0.6<br>0.6<br>0.4<br>0.4<br>0.2<br>0.0 0.2<br>-0.2<br>Low High 0.0<br>Fixed Floating<br>Capital controls<br>Exchange rate<br><!-- End of picture text --> _Sources_ : Arroyo Marioli and Vegh (2023); Chinn and Ito (2006); Ilzetzki, Reinhart, and Rogoff (2022); World Bank. _Note_ : Bars show average correlation between the cyclical components of real GDP and real government spending within groups. The cyclical components are derived using the Hodrick-Prescott filter. Vertical lines show 25th and 75th percentiles.

A. Based on Chinn-Ito index of financial openness. A country is classified as having high (low) capital account restrictions if its Chinn-Ito index score is below (above) the median. Sample has 36 countries with low capital controls and 54 countries with high capital controls. *** indicates that the difference between the means for the two country groups is statistically significant at the 10 percent level or better.

B. Sample has 63 countries with fixed exchange rates and 29 with floating rates, based on the classification in Ilzetzki, Reinhart, and Rogoff (2022). The difference between the means for the two country groups is not statistically significant.

30
Appendix Chart 6 Contributions of Shocks to Output Growth (Percent of total variation) <!-- Start of picture text --> By global and domestic shocks<br>Global Domestic<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br>By structural shocks<br>Oil GS GD GMP DS DD DMP<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br>By types of shocks<br>Oil and supply Demand MP<br>100<br>80<br>60<br>40<br>20<br>0<br>70 70 99 70 85 99 08 20<br>-23 -98 -23 -84 -98 -07 -19 -23<br><!-- End of picture text --> Sources: Authors’ calculations based on data from January 1970 through December 2023 for the G5 economies (Canada, the euro area, Japan, the United Kingdom, and the United States). Notes: Forecast error variance decompositions of domestic output growth on FAVAR models (as explained in Section IV) that consist of four global variables (global output growth, inflation, monetary and oil prices) and three domestic variables (domestic output growth, inflation, and monetary policy rates). “OP” = oil price shock, “GS” = global supply shock, “GD” = global demand shock, “GMP” = global monetary policy shock, “DS” = domestic supply shock, “DD” = domestic demand shock,