rafmacalaba/data-use-mentions-extended huggingface dataset:
, and where survey data may involve well in excess of 100,000 households. It is also complicated by the fact that researchers with expertise in CGE modeling are typically different from those with expertise in household modeling. As a result, it can be difficult to satisfactorily integrate the two frameworks into one grand model.

Therefore, we turn next to a series of studies that aim to capitalize on detailed household survey data, while retaining more manageable general equilibrium models. For lack of a better term, we label this category “micro-macro synthesis.” An alternative description might be “general

> 8  Of course, this is still just a sample of _all_ the households in a population.

16
> In wave 1, the assignment shares for the 6 treatment arms were equally 1/7; in wave 2, we used the assignment shares given by exploration sampling, keeping 1/7 of the sample as a control group in each wave. Due to indivisibilities, the total shares are close but not equal to the targeted shares, as shown in Table 2 in section 5.

**Estimating ORF effects.** In many applications, outcomes other than the targeted outcome are of interest to the experimenter. Here, we estimate the effects of the treatments on ORF with reading fluency exam scores obtained after the experiment was completed to learn whether the treatment arm with the highest engagement sees increases in children’s reading performance. We also briefly discuss the possibility that there are differences in how engagement with the calls translates into reading gains, which might imply that the call format with the highest call engagement may not be the format with the highest reading gains.

> 18The implementer has parental consent to use this phone number for school related communications. Based on enrollment data from the start of term 3, we randomly selected one student ID for measurement in the few cases where several student IDs were associated with the same parental phone number (likely siblings). Phone numbers and schools are de-identified by the implementer before sharing with the researchers.

> 19We also stratified assignment on whether the opt-in call or confirmation text message were answered. For example, in wave 1, a large proportion of the sample (796 student IDs) neither opted in nor explicitly opted out. However, the extensive-margin results (Appendix C.4) showed that most numbers answered the phone at least once during the experiment, and so we ignore this in the estimation.

19
It is especially noteworthy that output of the business sector was growing at an accelerated rate for the past three years reaching an exceptionally high rate (7Z) in 1987. This was accompanied by an unprecedented growth in total productivity (compared with the past fifteen years) which accounted for more than half of the growth of output (table 1). Host economists tend to attribute this remarkable growth performance to the redu:uion in inflation, to the contraction of the relative size of public sector and to the reforms undertaken in 1987. The drop in inflation reduced producers, uncertainty with regard to relative prices and shifted activities from the financial to the productive sphere. This process took place both within firms as well as on industry level through a change in the distribution of the labor-force in favor of the non-financial sector.

The government contributed to the process by freezing the number of employees in the public sector, so that practically the entire increase in the labor-force in 1986 and 1987 was absorbed by the business sector (see AR 87,5/ p. 73). In addition the goverament took the first steps in reformiug the structure of direct taxes which resulted in a reduction of personal income tax rates as well as corporate tax rates 61 (for details see AR 87, chapter on the public sector). These reforms may have contributed to the continued rise in productivity and to nutput growth. In addition, price stability and the reduced corporate tax rate stimulated the upsurge of investment in the business sector in 1987. The latter process was further encouraged by some initial, but important, steps in the direction of liberalizing capital markets.

There are some indications of a weakening of the growth momentum in 1988 as is shown by preliminary evidence from demand and production data as well as from the behavior of imports and commercial bank credit. It is
The information captured includes detailed socio-economic and labor market outcomes before departure, during migration and after return, full migration history and a detailed breakdown of migration costs and sources of financing, along with savings and wage expectations prior to departure. Despite their comprehensiveness on migration and employment history, a potential drawback of these retrospective surveys is that they may suffer from imperfect recall by the respondents.

# **2.2.2 Data from destination countries**

The second category of surveys are implemented in destination countries. In these surveys, migrants are typically identified through a question on their country of birth. Identifying

> In contrast, surveys asking whether the member has ever lived abroad allow to capture a representative sample of the entire pool of returning temporary migrants.

> 9The Mexican Migration Project data set was used by Del ́echat (2001), Rendon and Cuecuecha (2010), Angelucci (2012), Reinhold and Thom (2013), Gorlach (2020), Massey, Durand, and Malone (2002) and Massey and Gentsch (2014), among others.

> 10For studies using the BRMS data set, see Ahmed and Bossavie (2022), Bossavie, Gorlach, Ozden, and Wang (2021), and Bossavie, Gorlach, Ozden, and Wang (2022).

15
multiple waves. As discussed in Section 3, our estimation strategy relies on firms for which geo-localized data are available and that belong to this panel component. Restricting further to firms with non-missing sales data, our main estimating sample consists of 36,087 firm-year observations across 89 countries.<sup>7</sup> Table A1 provides a detailed list

> 4The WBES dataset is the most comprehensive resource for conducting firm-level studies worldwide, as discussed in Besley and Mueller (2018), Fisman et al. (2024) and Armangu ́e-Jubert et al. (2024).

> 5Firms are selected using random sampling techniques with three stratification levels to ensure national representativeness across firm size (5–19 employees; 20–99 employees; and 100+ employees) and sector (manufacturing, retail, and other services). For details on the WBES sampling methodology, see `https://www.worldbank.org/content/dam/enterprisesurveys/documents/methodology/ Enterprise%20Surveys_Manual%20and%20Guide.pdf` .

> 6The WBES collected information on firm geo-localization in 120 countries. This information is missing for 5% of firm-year observations. For firms with geo-localization available for at least one survey wave, we impute the same location across other waves to increase the sample size. Results are virtually identical when excluding firms with imputed geo-localization.

> 7Of the 120 countries for which there is information on firm geo-localization, 89 have at least two survey

4
3|0_._279|0_._0647|0_._0602|0_._282|2_._381|
|Test batch 4|0_._368|0_._00516|0_._0647|0_._936|2_._413|
|Test batch 5|0_._174|_−_0_._0247|0_._0504|0_._625|2_._165|

Data for households in survey sample. _N_ fail( _s<_ 0) = 12 _,_ 577, _N_ pass( _s_ ⩾0) = 10 _,_ 871. _μs↑_ 0 is the mean of the local regression using data for test-failers only, evaluated at _s_ = 0. Optimal bandwidth selected by the method of Imbens and Kalyanaraman (2012). Triangular kernel.

33
However, after closely examining the data, we find that this is not the case for many countries. Furthermore, changes in livestock used for slaughter are included as “increases in stocks,” not as fixed capital. Thus we construct a separate data series on livestock capital, recognizing that there may be some overlap, but also that livestock accounts for a considerable share of agricultural capital and should not be ignored. We return to this issue in Appendix A.1.

Conceptually, the calculation of the livestock is fairly straightforward. The Food and Agricultural Organization (FAO) reports the quantities of all farm animals -- cattle, sheep, pigs, poultry, and so on. We aggregate the value of these individual components to obtain the livestock. Ideally, we would use market prices of live animals to value local herds, but these data were not consistently available. In their place, we use regional export unit values, based on FAO trade data, to value domestic herds. We calculate separate prices for each region by dividing regional dollar export values by regional export quantities. These unit prices are then applied to national herd statistics for each category of livestock. We convert the aggregate values to constant (1990) dollars by using the U.S. gross domestic product (GDP) deflator for the agricultural sector.

# **Treestock**

Standing orchards, plantations, and smallholder trees represent another important category of investment in agriculture. According to United Nations accounting practices, the value of investments in treestock should be included along with other land improvements in national accounting systems. However, a close examination of country data suggests that, in practice, such stocks may go unaccounted (see Appendix A.1). We therefore construct a direct estimate of the value of treestock.

The available information for constructing the treestock consists of FAO data on the area harvested by crops, production, and output prices. We begin by using the condition for long-run equilibrium, in which the cost of investment in the orchard equals
‐0.070|0.031|
|**Risk**||(.020)|(.430)|(.009)|(.881)|(.005)|(.107)|(.226)|(.577)|
||CoV ‐ HH Incidence of off‐farm employment|0.004|‐0.015|*<br>0.006|‐0.018|*<br>‐0.017|0.000|0.058|**<br>0.018|
|||(.447)|(.095)|(.466)|(.078)|(.487)|(.999)|(.017)|(.337)|
|<br>**es)**|HH Wealth + Education Index|‐0.031|1.085|***<br>‐0.143|***<br>0.918|***<br>‐0.243|1.646|***<br>‐0.006|1.359 ***|
|**lth**<br>**alu**||(.418)|(.000)|(.003)|(.000)|(.140)|(.000)|(.970)|(.000)|
|**Wea**<br>**10 v**|HH Ag Assets + Land Holdings Index|‐0.100|*<br>0.097|‐0.089|0.116|*<br>‐0.899|***<br>0.440|***<br>‐0.386|*<br>1.055 ***|
|
events require sufficiently long panels to track the effects of such gradual changes.

Lastly, considering the key role played by agriculture as the main income-generating activity for households living in rural areas, linking detailed information on agricultural output with high-resolution, crop- and areaspecific, growing-season climatic time series is required. In sum, when combined all together, the existing data gaps and needs represent one of the main obstacles that currently prevent further advancements in applied research.

# **3. Shedding light on the climate-migration nexus: An opportunity for innovation**

# **3.1 An empirically-oriented conceptual framework on the climate migration nexus**

An empirically-grounded conceptual framework on the climate-migration nexus, depicted in Figure 1, highlights data gaps that can be addressed through multi-topic household surveys. This framework draws inspiration from theoretical insights provided by the extensive literature on asset-based and shock-driven poverty traps (Barrett and Carter, 2013; Carter and Barrett, 2006; Carter et al., 2007 and 2008; Jalan and Ravallion, 2002; Letta et al., 2018) and incorporates the analysis of mobility patterns into the broader context of resilience and adaptation.

The framework starts by distinguishing migratory responses depending on the nature of the climatic event: slow-onset changes _vs._ fast-onset shocks. We include as slow-onset changes events such as desertification, progressive warming, droughts, and permanent changes in rainfall patterns.<sup>18</sup> Fast-onset events refers to devastating floods, torrential rains, hurricanes, cyclones, and storms. As discussed in subsection 2.2, this is a standard distinction in the literature (Cattaneo et al., 2019), which is rooted in the different nature of the climatic events: in the face of slow-onset changes and cumulative, repeated shocks, the outcome is typically either long-distance, (semi-)permanent migration or immobility, depending on how the shock interacts with the mediating channels and contextual factors. In the case of fast-onset shocks, which typically involve forced physical relocation from the place of living,
rather than the means from the surveys used to measure poverty, as appears to be standard practice in measuring relative poverty. Anchoring to the survey means also has the advantage that one can set separate relative poverty lines for urban and rural areas or sub-national regions.

If one sets the constant of proportionality in a strongly relative line based on the regression on survey means it would be 0.560 (s.e.=0.044).<sup>17</sup> Recall that the corresponding number using national accounts’ consumption was 0.382. The higher value using survey means reflects the fact that national accounts consumption includes things that are not typically included in survey-based measures. Under reporting/non response in surveys or over-estimation of consumption in national accounts, could also be playing a role.<sup>18</sup> Following the same procedure but using survey means we are led to use _k_ =1/2, which is consistent with past practice in setting strongly relative poverty lines. As before, we set the absolute line at $1.25 a day. The minimum cost of social inclusion must be in the interval (0, * $1.25). We set it half way, i.e., _Z_  $1.25 / 2 . Thus we use the following schedule of poverty lines for country _i_ at date _t_ (in $’s per day at the 2005 PPP for household consumption): _Z_ ( _M it_ )  max[$ 1.25 , ($ 1.25  _M it_ ) / 2]  $1.25 / 2  max[$ 1.25 / 2, _M it_ / 2] (3) These choices conform well with the empirical relationship between national poverty lines and survey mean consumption or income. Using the aforementioned constrained threshold estimator with surveys means instead of national accounts gave _Z_ ˆ * =$1.17 (s.e.=0.17) and
a residual in the national accounts process. Second, this allows for comparison with literature that has compared survey means with GDP (Pinkovskiy and Sala-i-Martin 2014, 2016; Bourguignon and Morrison, 2002).

We extract national accounts data from the World Development Indicators (WDI) database, for both HFCE and GDP, using the series expressed both in current local currency units and in constant dollars. WDI’s data is a compilation of World Bank and OECD national accounts data sets, obtained from official national sources. The per capita estimates are derived using the mid-year population estimates from the World Bank population series data.<sup>6</sup>

# **_Household surveys_**

To assess the gap between surveys and national accounts, we compile a data set of 2,095 national household survey means for 166 countries from 1967 until 2019, together covering countries that account for 97 percent of the world population in 2017. The distribution of surveys by type and over time is illustrated in Figure 1. The vast majority of the surveys come from PovcalNet, the World Bank’s database for monitoring of global poverty (see Ferreira et al., 2016 for a description of data sources and methods used). The database contains income or consumption distributions from nationally representative household surveys typically carried out or supervised by national statistical offices or international agencies, used for national and international poverty monitoring.

For most high-income countries, the survey data available in PovcalNet are for income (rather than consumption), originating from the Luxembourg Income Study and the European Union Statistics on Income and Living Conditions (EU-SILC). To ensure better coverage of consumption surveys from high-income countries in our sample, we supplement with data from other sources. For European countries, we derive consumption means from Eurostat’s 6 From World Development Indicators (WDI), we use the following series for national accounts data: Household final consumption expenditure (current LCU) [NE.CON.PRVT.CN]; Household final consumption expenditure (constant LCU)
# **VI. Discussion of the results**

## **Analysis section**

The primary theory that we are testing in this paper is whether mass media has the ability to influence political actions. We use the _Doing Business_ data as a proxy to test the set hypothesis. Specifically, we analyze whether media coverage of _Doing Business_ serves as an impetus for the implementation of business regulatory reforms. An innocuous argument could be that countries that are active reformers on _Doing Business_ indicators naturally tend to have higher media coverage. But can media coverage actually spur reforms and turn regulations into actionable outcomes? To answer the posed question, we collected primary data on media coverage for 190 countries worldwide. Using Factiva and Google databases we were able to compile a comprehensive panel data set, perfectly matching _Doing Business_ historical time series on the number of implemented reforms as well as the Doing Business scores and rankings ( _see_ section on methodology outlining the data collection process and limitations).

To explore strengths and directions of relevant correlations, we first run a number of simple OLS regressions with the latest data at hand ( _Doing Business_ 2018). In line with the set hypothesis, the primary dependent variable - “Reforms” - comprises absolute numbers of implemented _Doing Business_ reforms by each of the sampled 190 economies over a given period of time. The set parameters for our applied OLS regression model are the following:

## **<mark>Model 1:</mark>** <mark>Reforms</mark> j <mark>� α</mark> 0 <mark>� α</mark> 1 <mark>local_media</mark> j <mark>�</mark> _<mark>β</mark>_ <mark>X</mark> j <mark>� ε</mark> j

The explanatory variable “local_media” captures country-specific _Doing Business_ media coverage in terms of numbers of local media articles addressing _Doing Business_ in a given year
>Domestic Product (GDP), or<br>unemployment and population<br>statistics. These statistics can be<br>published as aggregates for the entire<br>country.|As Open Data Watch states<br>"Official statistics provide an<br>indispensable element in the<br>information system of a<br>democratic society, serving<br>the Government, the<br>economy and the public with<br>data about the economic,<br>demographic, social and<br>environmental situation."|Following data must be online to qualify for<br>assessment:<br>•<br>Country Population (Required:<br>census data, updated every year,<br>Optional: vital statistics of birth<br>and death)<br>•<br>Gross Domestic Product<br>(measured in current or constant<br>prices, updated quarterly, last<br>update must not be more than 3<br>months ago)<br>•<br>National unemployment (absolute<br>numbers, or expressed as<br>percentage of entire population,<br>updated quarterly, last update<br>must not be more than 3 months<br>ago)|
|Draft<br>legislation|Data about the bills discussed within<br>national parliament as well as votes on<br>bills (not to be confused with passed<br>national law). Data on bills must be<br>available for the current legislation<br>period. This data category draws on<br>work by the National Democratic<br>Institute (NDI) and theDeclaration of<br>Parliamentary Openness.|Open data on the law-making<br>process is crucial for<br>parliamentary transparency:<br>What does a bill text say and<br>how does it change over<br>time
!-- End of picture text --> Source: Own calculation using archived data from Bureau van Dijk’s Bankscope and BankFocus.

Note: **EU** stands for Western Europe, Southern Europe, Northern Europe; **CE & BC** for Central Europe & Baltic Countries; **WB** for Western Balkans; **SC** for South Caucasus; **CA** for Central Asia; **EE** for Eastern Europe.

Significant changes in risk-weights and declines in the stringency of what constitutes Tier 1 capital call into question how informative these indicators are for bank risk. To test the importance of these variables, we next examine how bank risk is related to the quality of bank capital and risk-weights using bank-level information collected through Bankscope and Bank Orbis. Our measure of bank risk is the z-score, which is calculated as the sum of average bank returns on assets (net income divided by total assets) and the bank equity to assets ratio, scaled by the standard deviation of return on assets over a four-year rolling window. A higher z-score indicates lower bank risk (Mare et al., 2017).

In the first analyses we examine the relationship between bank risk (z-score) and regulatory capital (RC/RWA) and simple leverage (Equity/TA). The sample includes only developing countries in the ECA region and excludes high-income countries. In the analyses we control for a number of banklevel variables. These controls are: bank size (log(TA)), which is the natural logarithm of total assets; bank liquidity, which is liquid assets divided by total assets (LiquidA/TA); bank profitability measured as return-on-assets (ROA); reliance on short-term funding measured as short-term funding divided by total assets (ShortFund/TA); and loan ratio which is net loans divided by total assets (Loans/TA). All capital ratios and controls are lagged by one year. We also include year and region fixed effects
23.1**|15.8|
|Domestic water consumption|liter/capita/day<br>72.4|||NA|
|Urban water assets in need of rehabilitation|%<br>35.5|**42.0**||25.0|
|Revenue collection|% sales<br>96.0|**75.0**|**95.0**|99.2|
|Distribution losses|% production<br>33.0|**53.0**|**50.8**|23.1|
|Cost recovery|% total costs<br>56.0|**48.4**|**61.8**|80.6|
|Total hidden costs as % of revenue|%<br>130.0|**183.7**|**128.9**|84.9|
|U.S. cents per m<sup>3</sup>|**Ghana**<br>**Mid 2000s**<br>**Late 2000s**|Scarce<br>resour|water<br>ces<br>Ot|her developing<br>regions|
|Residential tariff|**41.7**<br>**46.2**||60.26||
|Nonresidential tariff|**219.8**<br>**142.0**||120.74|3.0 – 60.0|

Source: Banerjee and others 2009; Morella and others 2009. Derived from AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data The performance of Ghana Water Company (GWC) has improved somewhat in recent years following institutional reforms and the contracting of