rafmacalaba/data-use-mentions-extended huggingface dataset:
easily.<sup>5</sup> Instead, we used a linear probability model (LPM) because the coefficients are easy to interpret and marginal effects are easy to compute. In fact, Angrist and Pischke (2010) documented that OLS estimates of LPM produce coefficients that are mostly statistically indistinguishable from the marginal effects of the probit model. Moreover, because our measure of the individuals’ WTP is non-negative and only three respondents stated a zero willingness to pay, we use the OLS method to estimate the effect of information provision on WTP that was gathered through the BDM bidding method.

# **4. Descriptive Statistics**

This section discusses the descriptive statistics of the outcome variables, treatment variables and key socio-economic characteristics of the households in our study. Table 1 presents descriptive statistics of key socio-economic characteristics of the households considered in our study for the overall sample as well as for treatment and control groups.

As shown in Table 1, on average about 61% and 58% of the information treatment and control households, respectively, used kerosene as the main source of light in their house. This means the majority of households in both the treatment and control group use a dirty fuel source for lighting in their homes. In terms of use of clean lighting energy sources, about 11% and 12% of the treatment and control households, respectively, used solar, and 2.7% and 1.5% of the treatment and control households are connected to grid electricity. This is consistent with previous national level data on rural households’ access to off-grid electricity. For example, in the World Bank’s MTF survey for Ethiopia, about 11% of households in Ethiopia get electricity access through off-grid electricity sources mainly through solar technologies (MoWIE, 2019).

On average, treatment household heads were about 47 years old, and had about 3 years of education; about 92% were married and 92% were male. Likewise, the control household heads were about 48 years old, and
Following the baseline survey, a registry census was implemented in treatment villages to collect data to calculate a proxy-means score for each eligible household. The application of the proxy-means test sought to identify the poorest households to be selected as beneficiaries for the cash transfer program. A selection threshold was set for each commune, so that approximately the 40% poorest households would be selected to participate in the cash transfer program. Premand and Schnitzer (forthcoming) provide a detailed analysis of the implementation of proxy-means targeting in a subsequent phase of the program.

After the baseline survey was completed, targeting data collected, and the second public lottery implemented, the baseline survey data were merged with administrative data from the cash transfer program to identify which households from the baseline sample were selected as beneficiaries. In treatment villages, we can thus identify households that are actual program beneficiaries, and households that are actual non-beneficiaries.

The registry census to calculate PMT scores could only be collected in treatment villages. The collection of the registry census to calculate PMT scores did not take place in control villages. However, the baseline survey was designed so that it would also allow calculating PMT scores in both treatment and control villages. As such, we can “predict” potential beneficiary status of all households in the baseline sample. We do so by applying the PMT formula used to select beneficiaries to the baseline data collected in the treatment and control groups. Note that in practice the prediction of beneficiary status based on the baseline survey is imperfect. The groups of actual beneficiaries and “predicted beneficiaries” do not fully overlap in treatment villages. Still, we can identify these “predicted” (or potential) beneficiary households in the control and treatment groups. We use this “predicted beneficiary status” to estimate intent-totreat impacts of the cash transfers. We discuss this approach in more detail in Section 4, where we present the estimation strategy and the composition of the follow-up sample.

The interventions were implemented between February 2013 and April 2015. Program implementation was closely monitored. The
3.3. Model Specification To estimate the impact of the PSI-PMI program on student learning outcomes, we estimate the following model on the sample of the matched program and non-program students: j 0 1 j 2 j j (1) jj Where j is the performance outcome of student yy = ββ + ββ CC + ββ XX + εε in school on test . Our main outcome variables are standardized mathematics scores on the WASSCE and a dummy variable for having jj yy ii jj kk satisfactory scores<sup>9</sup> in mathematics and English on the WASSCE. The variable is the treatment variable, which has value 1 if the student is in the treatment group and 0 if otherwise. _X_ is a vector CC of covariates that includes the student- and school-level variables, and j is the idiosyncratic error term. In our estimations, we also control for any preexisting differences between the εε treatment and control groups. We estimated the program effect in equation 1 using OLS and directly through the propensity score matching estimator.

The student-level variables used as controls include the following: (i) students' performance in mathematics, English, and science three years earlier in grade 9; (ii) whether the student has repeated a grade; (iii) socioeconomic status, as measured by household asset ownership (TV, fridge, and car); and (iv) gender. The school-level variable is administrator capacity, as measured by the principal's level of education and experience. The standard errors are clustered at the school level.

Grade 12 students can take up to nine subjects in the WASSCE. However, only mathematics and English are compulsory for all students. This students’ choice of subjects taken could introduce a potential bias, since the choice of subjects will not be random. Hence, we focus on mathematics and English scores for the impact of the PSI-PMI program, although the program is designed for mathematics and science subjects (biology, chemistry, and physics).

> 9 A satisfactory score is
13 economy for FSU countries and a circa 1989 baseline for each CEE country in the sample. On the basis of the Berkeley-Duke research project on the USSR Second Economy conducted during the late 1980s, as well as the work of J. Braithwaite, estimates ranging roughly between 10 and 15 percent of total economic activity were arrived at.<sup>23</sup> Thus, for purposes of our calculations below, we use the 1989 midpoint estimate share of unofficial activities of 12 percent.

The next step is to calculate the figures for the overall GDP proxy, based on overall electricity consumption. We also compute the indices for the official GDP. First, we perform the computations for the unitary elasticity scenario.

Table 3: Ukraine

# Electricity Consumption (as proxy of Overall GDP) and Official GDP--1989-94

||1982|1990|1921|1922|1993|1994|
|---|---|---|---|---|---|---|
|Growth rate in<br>Electricity Consumption||0.0%|-2.2%|-6.2%|-7.8%|-11.7%|
|Estimated growth rate<br>in overall GDP (unitary<br>elasticity assumption)||0.0%|-2.2%|-6.2%|-7.8%|-11.7%|
|Overall GDP Index<br>(1989=100)|100.0|100.0|97.8|91.7|84.6|74.7|
|Official GDP Index<br>(Index 1989=88)|88.0|84.5|73.2|60.4|51.4|38.8|

The figures in Table 3 are our proxy variables for the calculations on the overall and official GDP, respectively.
# **Table 1.9. Fields’ Mobility Index and** **<u>Short-Term and Long-Term Equality, RLMS 1994-2015</u>**

|||**Gini Index **<br>|||**Theil index **<br>|||
|---|---|---|---|---|---|---|---|
|**Period**|**_M_**<sup>**_E_**</sup>|**Short-term**<br>**inequality**|**Long-term**<br>**inequality**|**_M_**<sup>**_E_**</sup>|**Short-term**<br>**inequality**|**Long-term**<br>**inequality**|**No. of individuals**|
|1994-1998|0.12|0.46|0.41|0.27|0.41|0.30|6 303|
|1998-2004|0.19|0.45|0.37|0.37|0.37|0.24|5 527|
|2004-2009|0.21|0.41|0.32|0.40|0.30|0.18|5 200|
|2009-2015|0.14|0.34|0.29|0.28|0.19|0.14|5 001|
|1994-2004|0.20|0.45|0.36|0.40|0.38|0.23|4 942|
|2004-2015|0.27|0.41|0.30|0.49|0.29|0.15|3719|
|1994-2015|0
2002) conducted their study in selected blocks in rural Karnataka, India. They rely on information gathered from a 400-household survey, and in-depth interviews with women and key informants for their analysis. They find that daughters do not actively assert their property rights as they continue to believe that dowry is their share of family wealth. Many daughters are also unaware of the stipulations that protect them from disinheritance.

> 16 The likelihood of female child mortality increased by a slightly larger amount (0.32 percentage points) when the analysis sample was restricted to reform states.

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on business. Beyond a handful of high-income economies, in the United States and Europe, it has not been possible to track the many dimensions of impact at the pace they have been unfolding. In low- and middle-income countries the sources of information are few. By the end of July 2020, the World Bank had released new enterprise surveys from 13 different countries measuring the virus’s impact on businesses - from Italy and the Russian Federation, to Albania and Zimbabwe (see Annex Table A1 for sample description). Despite their limitations,<sup>4</sup> the surveys, along with a variety of other sources used in this study, are telling as to how small businesses experience the shock differently than larger firms.

Without sales, half of businesses expect to shut down in less than a month according to the evidence. It is impossible to predict how many will make it through the pandemic as many close

> 2 Financial Times, 16 April 2020: _Covid-19 shuts down a quarter of UK businesses._

> 3 Washington Post, 12 May 2020: _Small business used to define America’s economy. The pandemic could change that forever._

> 4 The 13 countries for which follow-up surveys were released by July 2020 are located in two regions - Europe and Sub-Saharan Africa, with surveys in other regions scheduled for completion in fall 2020. Moreover, in all World Bank Enterprise Surveys, informal and micro-enterprises (with fewer than 5 employees) are not surveyed; neither are cooperative and 100% state-owned firms. The surveys encompass a broad range of industries: manufacturing, construction, wholesale and retail, hotels, transport and computer related services (ISIC Rev 3.1 codes 15-37, 45, 50-52, 55, 60-64, and 72).  However, this omits some sectors with a preponderance of SMEs, such as agriculture or mining, and some services sectors that are highly affected by the crisis, such as financial services, education or health services.

2
|**Table (B.5)**<br>Exploring Secondary Outcomes: Priming Effects on Information Misperceptions<br>Dep. Variable<br>(1)<br>(2)<br>**Perception about Size**<br>**Perception about Education**<br>Treatment (Priming=1)<br>6.225***<br>-0.391***<br>(0.885)<br>(0.142)<br>R-squared<br>0.015<br>0.003<br>Observations<br>3,273<br>2,901<br>Av. dep. var before treatment<br>29.18<br>6.84<br>_Notes:_ The dependent variable _Yi_ represents the outcome for individual _i_ as measured in the survey of their personal beliefs about the Venezuelans’ share of<br>Colombian population and the Venezuelans’ average years of education. The variable_Treatment (Priming=1)_is the assigned treatment status to COVID priming<br>of the individual. _∗∗∗_signifcant at the 1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%. Standard errors are reported in parentheses.|**Table (B.6)**<br>Heterogeneous Effects: Losing job during the pandemic<br>Dep. Variable<br>(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>**Altruism**<br>**Policy Altruism**<br>**Opinion on Effort**<br>**Opinion on Economy**<br>**Opinion on Taxes**<br>Priming = 1_×_Lose Job = 1<br>0.137<br
Figure 4: Treatment effects by malaria prevalence <!-- Start of picture text --> VC approval VEO approval Councillor approval<br><10 10-20 20-30 30-40 >40 <10 10-20 20-30 30-40 >40 <10 10-20 20-30 30-40 >40<br>Prevalence category Prevalence category Prevalence category (%)<br>WEO approval MP approval<br><10 10-20 20-30 30-40 >40 <10 10-20 20-30 30-40 >40<br>Prevalence category (%) Prevalence category (%)<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2<br>.2<br>0 0<br>0<br>-.2 -.2<br>-.2<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>-.2 -.2<br><!-- End of picture text --> _Notes_ : Plots represent treatment effects for each of the officials, by category of malaria prevalence (010%, 10-20%, 20-30%, 30-40%, and _>_ 40% prevalence), based on district level malaria prevalence in Chaki et al. (2013). Zonal fixed effects are included in all regressions.

33
- Torche, F. (2019). Educational mobility in developing countries. WIDER Working Paper Series 2019-88.

- Tsui, M. and Rich, L. (2002). The only child and educational opportunity for girls in urban china. _Gender and Society_ , 16(1):74–92.

- Xie, Y. and Hu, J. (2014). An Introduction to the China Family Panel Studies (CFPS). _Chinese Sociological Review_ , 47(1):3–29.

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deposits (which includes both mandatory and voluntary banks’ reserves). For Argentina, Brazil, and Uruguay, the correlation coefficients between exogenous changes in ECRR and changes in total banks’ reserves are 0.31, 0.68, and 0.99, respectively (statistically significant at the one percent level in all cases). This suggests that exogenous ECRR changes are important determinants of changes in total banks’ reserves.

# **3 Evidence from traditional identification strategy**

Before turning to the new identifying strategy, this section relies on the traditional time identifying strategy used in the monetary policy (Leeper _et al._ , 1996; Bernanke _et al._ , 1997, 2005; _Christiano et al._ , 1999) and the macroprudential policy (Lim _et al._ , 2011; Tovar _et al._ , 2012; Glocker and Towbin, 2012) literatures, which assume that innovations in central bank policies have no contemporaneous effects on macroeconomic outcomes.<sup>11</sup> We first estimate the effects of monetary and macroprudential policies on economic growth using quarterly data and the following panel SVAR:<sup>12</sup> where subscripts _i_ and _t_ denote country and time, respectively, _A_ are matrices of parameters, _αi_ is the country fixed effect, ∆ _Y_ is a vector composed by real GDP growth rate, inflation, ∆ _ECRR_<sup>_all_</sup> (the percentage point change in effective constant legal reserve requirement), and ∆ _IR_<sup>_all_</sup> (the percentage point change in the central bank interest rate), in that order, and _μ_ is the error term.<sup>13</sup><sup>_,_14</sup> It is important to note that for now (i.e., when evaluating the evidence

> 11See Coibion (2012) for an excellent review and a discussion of the limitations of this approach for the case of the monetary policy in
vious measure, we compute an average at the firm level of this index keeping constant the HS6 product range exported by each firm in 1999.

After a liberalization event, it might also happen that the number of countries or firms exporting a certain HS6 variety to destination _j_ stays constant, while the volumes exported might change. This in turns would certainly have an impact on the export decisions of French firms. To capture this type of competition, we construct a measure of intensive margin competition faced by French firms in each Asian markets. To do so, we combine import data at HS6 product and country of destination level from BACI with the French customs dataset. Similarly to the other measures, we generate for each firm _f_ and country of destination _j_ the average of import volumes faced by each HS6 product _g_ over all products exported by that firm to that country in year _t_ . As usual, we keep constant the HS6 product range of each firm at year 1999.

# _Baseline Specification_

In this section we exploit the exogenous variation in tariffs across firm-country pairs to analyze the role of Chinese unilateral trade liberalization on French firms’ export behavior relative to tariff changes in other Asian destinations. To capture these effects, we will then estimate the following equation: where the dependent variable, _Xfjt_ , is the number of products exported by firm _f_ to country _j_ in year _t_ . In an alternative specification, we also explore how the intensive margin of trade is affected by Chinese tariff reductions using as a dependent variable firms’ export sales. _τfj,t−_ 1 is the average tariff faced by firm _f_ when exporting to country _j_ in year _t −_ 1.<sup>19</sup> _Chinaj_ is a dummy variable equal to one if the country of destination, _j_ , is China. _τfj,t−_ 1 _× Chinaj_ is an interaction term between the average lagged tariff faced by each firm and the dummy variable for China. _Zjt_ are controls at the country level that 14
benchmark poverty estimates, which is identical to the questionnaire for the base survey that permits the estimation of a wide range of competing imputation models. TA 2 households were administered a _light_ questionnaire variant that only included questions that permit the estimation of a data-modest subset of imputation models, which additionally includes the TA 1 food consumption module but with a reduced list of key food items. Finally, TA 3 households were administered an alternative _light_ questionnaire that shares the same core as the TA 2 questionnaire and that includes alternate, aggregated versions of TA 1 food and non-food consumption modules. These data are in turn complemented with the data from the nationally representative Tanzania National Panel Survey (TZNPS), and specifically the TZNPS 2020/21 and 2019/20 rounds that are used as base surveys for the estimation of the imputation models that are in turn applied to each treatment arm to obtain across-year predictions.

Through our research, we make novel contributions to the literature by (a) providing experimental evidence regarding the effects of target survey design on poverty imputation, (b) sidestepping usual concerns regarding the “validation” of imputed estimates by offering a real-life setting with benchmark data, and (c) providing new evidence regarding the minimum-required base and target survey sample sizes. To our knowledge, we offer the first study that leverages a randomized and nationally representative survey experiment to rigorously study these interconnected, but little-explored, research questions that are at the heart of survey-to-survey imputation. In this sense, our work is also broadly related to a growing literature that relies on randomized survey experiments in low- and middle-income contexts to gauge the relative accuracy and cost-effectiveness of competing survey methods vis-à-vis gold-standard measurement approaches (Beegle _et al._ , 2012; Arthi _et al._ , 2018; Gourlay _et al._ , 2019; De Weerdt et al, 2020; Kilic _et al._ , 2021; Abate _et al._ , 2023).

5
here for comparison with more objective measures defined above. About 28% of our sample claim access to finance to be a major or severe obstacle to the operation of their business. It is also more likely to be claimed by small firms than large (30% vs. 24% for large). The access obstacle is slightly higher in 2007 9at 31%) than it was in the previous years (27% in 2003 and 25% in 2005).

Finally, as savings and investment are likely to be sensitive to firm perception of uncertainty, we consider two available subjective perceptions of uncertainty – **macroeconomic uncertainty** (such as inflation, exchange rate and others) and **regulatory policy uncertainty** . We define two indicator variables for firms that claim either of the two obstacles as a major or severe obstacle to their business. They are referred as Macro obstacle and Policy obstacle.

# **4.2 Construction of Variables for the Listed Firms Dataset**

The first key variable of interest is financial savings, which is defined as a year to year change in the stock of cash. This definition follows Riddick and Whited (2009). For example, if the cash stock of a company has increased by 1 million relative to the previous year, we consider this increase as a new financial savings. To compare financial savings across companies of different size we scale financial savings by total assets of the previous year.<sup>9</sup> The second key variable of interest is physical savings, i.e. investment in property, plant and equipment (PPE). Unfortunately we do not have a direct measure of investment in our balance sheet or income statements data because this is a supplemental data item often not required for companies to file with the exchange. To construct a proxy for investment we measured a change in gross value of PPE from year to year, scaled by the previous year value of net PPE.<sup>10</sup>
# _Factors Affecting the Credibility of the 1993-1995 Results_

There are a number of problems associated with the 1993 and 1995 survey data, including sampling problems in 1993, the small sample size in 1995, the estimation of housing expenditure, the limited time period of the year during which the survey was conducted, the problem in correctly coding the consumption of home production data in 1993, the method used to estimate purchased food expenditure, and the non-representativeness of the consumer price index. In many cases the biases appear to be in the direction of overestimating the incidence of poverty in 1995.

## Survey design **and implementation**

A major difference between the 1993 and 1995 priority surveys is the size of the sample. The 1993 survey is based on a sample of 9,600 households, while the 1995 sample of 1,000 households is a little more than a tenth the size. The size of the some of the cells is very small in 1995, thus restricting the analysis one can do. Second, the 1993 sampling procedure was based on a household listing done in 1991. Surveying of the households began in March 1992, almost exclusively in Abidjan, but it was interrupted a few months later due to lack of money. Surveying resumed in June 1993 and was completed in November 1993. On average 22 months passed between the household listing and survey. A problem arose that many of the households that were listed in 1991 were not relocated in 1993. In urban areas, about 50 percent of the households selected were not surveyed, mostly because they could not be located, while in rural areas about a third of the households were not surveyed. Thus, the more stable households are over-represented in the sample, while the least stable-- and perhaps the poorest-are underrepresented. However, because of the high replacement rate in Abidjan, INS decided to keep the 1680 households in Abidjan that had been surveyed in 1992 as part of the sample, and to reject those that had been surveyed in 1993. Presumably,