rafmacalaba/data-use-mentions-extended huggingface dataset:
for further discussion). The most obvious explanation is a simple failure of memory, where respondents forget to report events that took place long ago. However, there is also evidence from cognitive research that the length of the recall period may have a bearing on how respondents interpret survey questions. Schwarz (2007), for example, shows that the longer the recall period, the more likely respondents are to interpret a given question as referring to major events only. This potential change in inferred meaning makes it difficult to distinguish between the effects of question interpretation and forgetting.

Several studies have found a positive association between the level of education – or direct measures of cognitive skills – and recall ability. Peters (1988), comparing lifecycle data from a retrospective marital history with panel reports for the same individuals in the United States, shows that more educated respondents report more consistently on marital events. McAuliffe, DiFranceisco and Reed (2010) find that arithmetic skills predict the accuracy of retrospective self-reports of sexual activity (also in the US). While evidence from the United States may not necessarily extend to developing countries, given potentially large differences in the quality of education systems, survey methods, etc., a few studies have found similar associations in Asia and Africa. Becket et al (1999, 2001) show that the reliability of reports in the Malaysian Family Life Surveys is higher for more educated respondents. Abebe (2013) finds that recall errors in sales revenues and output among Ethiopian shoemakers are negatively correlated with respondents’ years of schooling. Given this literature, we hypothesize that the accuracy of reports on farm labor is positively related to educational attainment, as a proxy for cognitive skills.

6
participate, and how to use the payments received. Non- _ejidatario_ households ( _avecindados_ ) were excluded due to budgetary constraints and past work suggesting minimal impacts on those households.<sup>11</sup>

## _Measures and Indices_

Outcome indicators were developed by drawing on instruments from previous evaluations of the Mexico PES program (Alix-Garcia et al., 2012, 2015) and CONAFOR’s Annual Participant Surveys, as well as field testing of new questions. The community leader provided information on land-cover management activities such as the construction of fire breaks, fences and boreholes, firefighting, pest control, nurseries, reforestation, pruning, post-harvest cleaning, patrolling for illegal logging and soil conservation. We created the index of land management by summing up the number of these activities practiced by each ejido. The household survey collected data on paid and unpaid forest management and community work by household members, household assets, consumption, educational attainment, credit constraints, attitudes and participation in community activities. Household wealth measures were constructed from questions related to housing characteristics, asset ownership and food consumption. We also collected other socio-economic indicators, such as livestock ownership and emigration patterns, as well as educational indicators (the fraction of children

> 11 This is of policy concern as the latter do not formally participate in key decisions related to the program, such as what parcels to submit and how to use the PES payments. Prior work in Mexico (Alix-Garcia et al. 2015), suggests that the program did not have negative impacts on households without full rights. Our community-leader survey indicates that non-ejidatario households represented approximately 30 percent of the total number of households.

11
) in USD, from the first reported price to the last new price, which is essentially a pooled cross-section regression. The models in Sections 2 and 4 suggest I should deflate by unit labor costs (ULC). However, they are not available (or are poorly measured) for many of the countries in the sample, so I deflate all import prices with the CPI instead to remove the effects of inflation. I calculate the change in the log of each independent variable (such as the bilateral exchange rate and competitors’ exchange rate) over the life of the good. I also restrict the sample to USD priced goods. Restricting the sample to USD is common in the literature (e.g. GI2011), it only reduces the sample size by 10% and does not have much effect on the results. My model also deals with transactions between parties and so I remove intra-firm transactions. As in Gopinath and Rigobon (2008) and GI2011, I add country × BLS lower primary stratum fixed effects, and cluster standard errors at this level for import prices. I remove the 1% tails. I estimate the empirical model for goods from 10 countries/regions. I select the largest trading partners of the US in 2010, which together account for about two-thirds of US imports.<sup>27</sup> As the top 10 countries often compete against firms from countries outside the top 10, I construct the competitors’ exchange rate variable using data from all countries.

B.2. **Producer price data.** The construction of the producer price variable is similar to that for import prices, though more prices and firms are sampled as part of the PPI and so the sample size is larger. The PPI sample period is also slightly shorter, running from 1994-2006, and the life of goods is longer, with a mean life of around 4 years. As with the import data, non-market transactions are excluded. I drop all prices specifically for export. There are
**Table 4: Distribution of answers for the political questions**

|||_Question: your opennes_<br>|_s to vote for a strong_<br>|_candidate from t_<br>|_his party_<br>||
|---|---|---|---|---|---|---|
||Not open|Somewhat not open|Somewhat open|Very open|Missing|Total|
|DP|4163|279|374|1478|601|6895|
||60%|4%|6%|21%|9%|100%|
|FDC|3963|325|487|1629|491|6895|
||57%|5%|7%|24%|7%|100|
|NRM|264|72|342|6075|142|6895|
||4%|1%|5%|88%|2%|100|
|UPC|4104|330|411|1544|506|6895|
||60%|5%|6%|22%|7%|100|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

_Notes_ : Response distribution for the political opinion questions. Column 1 indicates the four largest Ugandan parties, namely the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC).

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- 27 -

consumption. In case the Nordin’s difference is negative, it is assumed to take a small but positive number to avoid logarithms of zero.<sup>17</sup> Two weather-related variables are selected: monthly average of maximum temperature and monthly precipitation. These data are available and presumably considered to be most relevant to the electricity demand in Albania. Besides two weather-related variables, monthly household income and the number of household members are included in _X_ 1 and _X_ 2. To control the extent to which individual households rely on electricity and gas for their basic home appliances, a number of dummy variables representing electricity use and the number of home appliances held by each household are also included.<sup>18</sup> A dummy variable for urban areas is introduced to control for a systematic heterogeneity between urban and rural areas. Urban residents seem to use more electricity; their income is higher, and they own more electric appliances.

The model includes a variable that represents frequency of power outages, which is measured by the average duration of power outages per day. Households experienced about 3 hours and half of power outages during the sample period. For obvious reasons, electricity cannot be consumed if power outages occur. This captures some undervaluation of the demand in the observed data.

As instrumental variables, two block rates, a threshold between the two blocks and the normative amount of consumption applied to those who have no meter. Recall that the block rates and threshold are common to the whole nation but vary from year to year. In addition, the normative amounts can be different among districts and between urban and rural classifications. It also differs between the summer and winter season. The analysis uses the only summer tariff schedules to make them consistent with the sample period.

> 17 An alternative is to use the Nordin’s difference variable as it is. The main estimation results have not change much even if the logarithm is not taken.

> 18 The home appliance ownership is represented as
proportion of refugees – is expected to depend on the characteristics of the EA. In refugee camps and settlements, the initial placement of refugees is determined by camp authorities. The allocation process first and foremost depends on space availability, arrival date, and administrative decisions that are often quasi-random (MacPherson and Sterck 2021; Siu et al. 2021). Movements within settlements and camps are possible but not frequent. For example, our panel data for Kenya shows that in any given year around 1.7% of refugees in Kakuma relocate within the camp (Betts et al. 2021). In cities, the process is completely different. Refugees decide where to live and usually co-locate with fellow nationals. For these reasons, our presumption was that the exclusion restriction would be more likely to be satisfied in refugee camps.

We draw two conclusions from this DAG. First, controlling for kin and neighbours’ perception of refugees ( _PHH_ and _PEA_ ) is essential to limit omitted variable bias in OLS

> 13A fourth possible factor influencing respondents’ interactions with refugees is the perceptions that other hosts living around have about refugees ( _PEA ⇒ Ii_ ). For example, if refugees have a bad reputation, respondents may avoid contact with them to protect their own reputation. While such a link is certainly possible, it would not affect the validity of the IV specification. However, a strong link going the other way around ( _Ii ⇒ PEA_ ) would be more problematic. Implicitly, our empirical strategy assumes that the effect of one particular individual (here the respondent) on group behaviour (here the perceptions of the respondent’s contacts) is negligible (assumption of “atomicity”).

17
resource rents on the business environment can have a major impact on the ability of domestic entrepreneurs to open a business and for small and medium-sized firms, in particular, to effectively compete in markets. This will likely have a broader effect on entrepreneurship rates in a country. Although cross-national data with wide coverage are hard to come by, a World Bank study using data for up to 90 countries finds that a lower quality business environment will result in lower rates of entrepreneurship, as measured by ―entry density‖ rates (see Klapper and Love 2010). While rents may over significant periods of time help sustain a stable dual system of restricted access for domestic private sector actors, coupled with significant and largely unproductive public distribution mechanisms, this development trajectory poses significant challenges for the pursuit of a more dynamic and inclusive growth strategy.

**Figure 15. World Bank “Doing Business” Rankings, Regional Averages in 2010 (By Region, 2005-2011)**

<!-- Start of picture text --> 0 20 40 60 80 100 120 140 160<br>OECD high income<br>Eastern-Europe/Central Asia<br>East Asia/Pacific<br>Latin America/Caribbean<br>Middle East/North Africa<br>South Asia<br>Sub-Saharan Africa<br>Avg Country Ranking, Overall Ease of Doing Business<br>Avg Country Ranking, Overall Ease of Starting a Business<br><!-- End of picture text --> _Source:_ <u>www.doingbusiness.org. Lower rankings indicate greater ease of doing business</u> and starting a business. Ranking for Libya is missing and therefore not included.

71. Policy measures that might reverse this trend are greater reform of the regulatory environment that individuals and local businesses face. A few oil-dependent countries in the MENA region, such as Saudi Arabia and Bahrain, have made notable progress in recent years in reforming the regulatory barriers to domestic business activities. Others, such as
Czubala, Witold, Ben Shepherd and John Wilson. 2009. “Help or Hidrance? The Impact of Harmonized Standards on African Exports”, Journal of African Economies doi:10.1093/jae/ejp003 Eaton, Jonathan and Samuel Kortum. 2002. “Technology, Geography, and Trade”, Econometrica, 70(5): 174179.

Hanson, David. 2005, ”CE Marking, Product Standards and World Trade”, Edward Elgar, Northampton,USA.

Helpman, Elhanan, Marc Melitz and Yona Rubinstein. 2008, “Estimating Trade Flows: Trading Partners and Trading Volumes”, The Quarterly Journal of Economics, Vol. 123, No. 2, pp 441-487, May.

Jarmin, Ron S. and Javier Miranda. 2002, “The Longitudinal Business Database”, Working Papers, U.S. Census Bureau, Center for Economic Studies Moenius, Johannes. 2000. “The BISTAN Data Retrieval Reference”, mimeo. University of California at San Diego.

Moenius, Johannes, 2004, “Information Versus Product Adaptation: The Role of Standards in Trade”, International Business and Markets Research Center Working Paper, Northwestern University.

Moenius, Johannes, 2006a, “The Good, the Bad, and the Ambiguous: Standards and Trade in Agricultural Products”, Paper presented at the IATRC Summer Symposium, Bonn, May 2006, http://www.agp.uni-bonn.de/iatrc/iatrc _program/Session_ %203 _/Moenius.pdf_ Moenius, Johannes, 2006b, ”Do National Standards Hinder or Promote Trade in Electrical Products?” IEC Centenary Challenge 2006, http://www.iec.ch/academia/papers/moenius.pdf Pierce, Justin R. and Peter Schott. 2009. “A Concordance Between Ten-Digit U.S. Harmonized System Codes and SIC/NAICS Product Classes and Industries”, NBER Working Paper No. 15548. Portugal-Perez, Alberto, Jos ́e-Daniel Reyes and John S. Wilson. 2010. “Beyond the
.12|0.10|0.03|0.14|0.31|0.47|1650|

Source: Based on EU Data from UN COMTRADE Statistics.

Exports of household type refrigerators provides an interesting illustration of sectoral “hick-ups’ usually triggered by restructuring. They fell dramatically in 2000 and rebounded massively the following year exceeding the earlier peak level in 1997 by a factor of 2.2.

Another indication of the ongoing change in Bulgaria’s export basket triggered by industrial restructuring is the sheer growth of electro-engineering products. The value of their aggregate exports in 2003 was 27-times higher than in 2000, with their share in EU external imports rising 117-times in 2003 over the 2000 level.

# **E.** _Conclusions_

The answers to three questions raised in the introduction to this section can be summarized as follows. First, changes in import demand suggest that the process of industrial restructuring has at last begun. The share of manufactures in Bulgaria’s imports has not only significantly increased but, within manufactures, there has been the shift towards products with higher technology content and capital goods. Bulgaria’s growing integration into pan-European markets for manufactures has accompanied this shift.

Second, Bulgaria’s export offer has become more processed indicating progress in modernization of industrial capacities. The fall in the share of traditional inputs, i.e., products used for further processing, in Bulgaria’s exports has been accompanied by a marked increase in the share of machinery. This, combined with the increase of more technologically advanced manufactures in Bulgaria’s exports, suggests a gradual shift towards more processed exports.
when the cash payments took place), effects (ii) and (iii) may have gained traction, causing conflict to _increase_ . The scope for mechanism (i) seems more limited in the context of this paper, given that much of the observed violence stemmed from foreign groups. (Indeed, throughout the sample, the role played by home-grown violence was limited relative to violence perpetuated by foreign groups.) Also—if anything—we would expect this effect to occur with a lag (as it may take some time for cash transfers to be invested and raise productivity).

After the end of the program, both mechanisms (ii) and (iii) may have become less salient. Given that the government-led cash transfer program only lasted for two years, the scope for appropriation activities and the incentives to undermine the expansion of government presence may have receded after program completion in a given location. Further, the Niger cash transfer program has had well-documented medium-term impacts on economic welfare, income-generating activities, and assets (such as livestock) (Bossuroy et al. 2022; Stoeffler et al. 2020). To the extent that these effects raise the marginal productivity of labor, they are consistent with a higher (local) opportunity cost of conflict, which tends to reduce incentives to engage in conflict for domestic actors. While this effect may well have contributed to the demise of the conflict spike after the end of the program, its role may be moderate, given the dominant importance of foreign actors.

Our findings are related to the literature on the natural resource curse and on higher rents fueling conflict (see, e.g., the survey by Rohner 2018 and recent papers by Caselli et al. 2015; Berman et al. 2017; McGuirk and Burke 2020), as well as the literature on how higher or lower productivity and opportunity costs of conflict attenuate or fuel fighting, respectively (Miguel et al. 2004; Dell et al. 2014; K ̈onig et al. 2017;
Figure 4: Share of overall transport expenditure and GDP per capita <!-- Start of picture text --> 50<br>Honduras<br>Panama<br>40<br>Turkey<br>Mongolia South Africa Finland<br>30 Burkina Faso Lao PDR FijiJamaicaIraq Namibia Mauritius Cyprus FranceUnited Kingdom Luxembourg<br>20 MadagascarMalawiNigerLiberiaAfghanistanMozambiqueTogoHaitiMaliNepal G Rwandauineaambia, TheTanzaniaUgandaTimor-LesteTajikistanKyrgyz RepublicCambodiaPakistanKenyaBeninLesothoCameroonCote d'IvoireGhanaGhanaZambiaVieIndiaNicaraguatnam BoliviaNigeriaPhilippinesBhutanMoldovaCongo, Rep.GuatemalaMoroccoCabo Verde S KosovoIndonesrArmenia i Lanka GeorgiaParaguayiaBosnia and HerzegovinaThailandAzerbaijanPeruSerbiaBelarusMontenegroMaldivesColombia Gabon RomaniaMexicoKazakhstanCroatiaLatviaPolandHungaryLithuania Eston Slovak Republici a GreeceSloveniaSpainItaly BelgiumSwedenDenmarkIrelandSwitzerland<br>10 Burundi Sierra Leone Sao Tome and Principe Senega Mauritanial Ukraine North MacedoniaBulgaria<br>Congo, Dem. Rep. Bangladesh<br>Ethiopia South Sudan El SalvadorAlbania<br>0<br>4 6 8 10 12<br>Log of 2015 GDP per capita<br>Share of overall transport expenditure<br><!-- End of picture text --> Note: Authors’ calculations using the updated Word Bank Global Consumption Database.

**Fact 3: Within countries, richer households spend more on transport than poorer households** Figure 5 uses data from the Word Bank Global Consumption Database to plot the share of overall transport expenditures in total expenditures per quintile of total expenditures within country. Richer households spend relatively more on transport and the difference between the richest and the poorest quintiles is the highest in poorer countries. On average, households in the richest quintile spend 15% of their expenditures on transport, while households in the poorest quintile spend 5% of their expenditures on transport. These shares vary across income groups. The share of transport expenditures for households in the richest quintile compared to households in the poorest quintile is twice larger in high-income countries, almost 3 times larger in upper-middle income countries, and more than 3 times larger for lower-middle and low-income countries.

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contributions from the villages to the project. Villages often made available the land on which project playgroups were housed. In contrast, other early childhood programs range in cost from US$37 per child in India to US$52 per child in Mexico to US$66 per child in Brazil—suggesting that this package was slightly less costly (Barnett 1997; Evans, Myers and Ilfeld, 2000).

Teachers who worked in the project playgroups were predominantly women from the village who often had children of their own. Some had prior work experience in health and education. Others had no such prior experience (Hasan, Hyson and Chang, 2013). Taken together, the package of interventions under the Indonesia ECED Project helped finance the demand for and supply of playgroups in a sample of villages that are broadly representative of rural Indonesia (Hasan, Hasan and Chang, 2013, Appendix 9).

# **III. Data**

This paper uses data on project playgroups located in villages that received the block grant and were part of an impact evaluation of the Indonesia ECED Project (N=432).<sup>9</sup> All of these playgroups were surveyed at baseline in 2010. In 2013, one project playgroup per village was sampled for follow up (N=245). The rationale for the selecting these 245 for follow up was that several of the children we had been following for the impact evaluation were enrolled in them.<sup>10</sup> This was the last survey conducted while the project was still active. In 2016, three years after the project had closed, we conducted phone interviews with administrators of all 432 project playgroups to confirm whether they were still open or had closed. If the playgroup was closed, we visited the village and interviewed former center administrators and teachers to collect data on the date of closure, key reasons for closure, as well as what happened to the building, toys, and teachers after the project playgroup closed. If the
loans in treatment areas in February 2015 (x-axis). The Figure shows a clear upward trend in sanitation loan disbursement.

By the time of the endline survey, about 20 percent of client households in treatment areas had taken a sanitation loan. A small number of loans - 21 in total - were also provided in the control areas, mainly as a result of clients asking for these loans; rather than them being (mistakenly) advertised in the control areas. These were given in two branches, primarily in February and March 2016.<sup>37</sup> The vertical lines in the Figure indicate the points at which the interest rate on sanitation loans was reduced from 22 to 20 percent (left vertical line) and from 20 to 18 percent (right vertical line). Similar interest rate reductions were made on other loan products at the same time. Though this is not causal, the graph suggests that there was almost no demand response from clients to these interest rate reductions.

> 37Through regular analysis of administrative loan data during the course of the experiment, this provision of sanitation loans was quickly detected and field staff were provided with refreshers on the experimental design.

23
the activity tracking data, each participant also responded to a 24-hour recall time use module at the second and third visit (Days 9 and 17, respectively), which was modeled after the time use module of the Women’s Empowerment in Agriculture Index (WEIA). The module intended to capture each respondent’s main and secondary activities in 15-minute intervals during the reference day, relying on a list of 26 activities (Appendix A). One of the activities ‘Plan the household’s finances or bills’ was never reported. Only 25 activities are thus used during the analyses. The time use activities broadly describe how Malawian people allocate time in their daily lives, such as ‘Farming’ and ‘Cleaning house’. The time use activities can therefore be considered as broad group activities. **Figure 1** shows the relative subdivision of the 25 activities that is derived from the number of reported minutes in the time use survey.

4