rafmacalaba/data-use-mentions-extended huggingface dataset:
We provide suggestive evidence in support of the latter explanation, namely, that the commitment account helped shield funds from the social network. First of all, the actual amounts saved in the commitment accounts offered were very low (about an order of magnitude lower than the observed increase in inputs), with most savings actually occurring in ordinary accounts. This helps rule out that the impacts of the commitment treatment were due to literally “tying the hands” of treated farmers by mechanically preventing them from spending their profits earlier in the year. In addition, we find that the impact of commitment savings is higher for individuals who are wealthier at baseline, a sub-group of respondents that is likely to face higher pressure to share with others. The impact of the commitment treatment has no large or statistically significant relationship with hyperbolic preferences as expressed in the baseline survey. Thus, commitment savings accounts appear to be most useful to those who are likely to face “other control” problems, rather than those facing self-control problems.

Our results point to a potentially low-cost means for microfinance institutions to raise farm inputs and incomes for current loan customers. There are relatively straightforward opportunities to offer farmers innovative savings facilities in the context of the organized marketing process of many crops, in Malawi and elsewhere in sub-Saharan Africa. It is relatively common for lenders to have direct funds-transfer arrangements with cash crop buyers for loan recovery. When such arrangements exist already, current loan customers can simply be offered direct deposit of crop proceeds into commitment accounts.

Nonetheless, it is important to consider the external validity of these results, as they are likely to be most applicable to cash crop farmers where sale proceeds can be directly deposited into bank accounts. In the central Malawi region we study, however, tobacco farmers have similar poverty and income levels to those of non-tobacco-producing households.<sup>19</sup> As with all empirical research, future studies should test whether these findings hold in other contexts, such as in other countries and with other types of farmers who
tested heterogenous impact component and future machine learning capacity, will enable significant contextual factors identified in previous CBAs to be tested in future intervention CBAs.

The enhanced CBA APP confronts the human and material resource problem of repeated individual CBAs across different justice processes. Broader societal data (e.g. geographic, economic, demographic, climate, security) as well as data specific to civil, criminal, and informal justice sector capacity can be retained and employed by the APP across (where appropriate) justice processes. Identified intervention costs are disaggregated by cost bearer (e.g. criminal justice system, local government, business, and society) and 30
“North” and “South”, with relative abundance of skilled and unskilled labor, respectively. The North specializes in inputs that are relatively intensive in skilled labor while the reverse is true for the South. A fl ow of capital from North to South leads to a shift of production activities to the South. Those activities are relatively intensive in unskilled labor by Northern standards, but they use more skilled labor than other inputs previously produced in the South, so that the relative wage of skilled labor increases in both countries.

> 6 Gorg and Strobl (2001) use panel data on Ghanaian manufacturing plants to estimate the effect of exports and the use of imported machinery on the demand for skilled workers. They fi nd that the latter is unrelated to exports, but positively linked to the use of imported machinery that was purchased for the purpose of technological progress.

> 7 Note that Harrison and Hanson (1999) use a panel dataset of Mexican manufacturing plants but most of their relative wage and relative employment regressions are estimated by OLS in levels. The only exception is a speci fi cation including only trade policy variables (tariffs and import licenses without any other controls) that is also estimated in differences. The justi fi cation for their approach is "the very little time series variation in many of the other independent variables" (p. 146).

3
are particularly beneficial for struggling students who might have problems keeping pace with classroom instruction (Duflo, Dupas, and Kremer 2015; Banerjee et al. 2016), or even for more advanced students who might lose interest with a slower instructional pace.

The second mechanism operates through the activation of social norms of reciprocity induced by the receipt of an expensive device provided in a school setting (Akerloff 1982, Fehr et al 1998, Fehr and Gächter 2000).<sup>4</sup> In this context, getting access to an expensive gadget and the implicit signaling of the importance of learning may prolong student interest in and engagement with learning materials, especially for students who have mastered foundational reading skills.

> 1 For a discussion of issues in education service delivery in developing countries, see World Bank (2017), Pritchett (2013), and World Bank (2003).

> 2 While digital technologies can be used to support teacher preparation and instruction, in this paper we focus on technologies targeted directly at students.

> 3 A survey covering 22 SSA countries found that, in 2010, the “median country” had 1.4 students per textbook in both reading and math in primary education, ranging from about less than one pupil per book to 11 for reading and 13 for math. A 2008 study for secondary education found that in 18 of 19 SSA countries surveyed there was severe book shortage for most students (Fredriksen, Brar, and Trucano 2015).

> 4 See Sobel (2005) for a discussion of theoretical models of reciprocity and Gneezy and List (2006) for a test of reciprocity in a set of field experiments.

2
.

The evidence comes from 40 Local Government Areas of Tanzania over the period 2016-2018, of which 18 LGAs had been selected in 2013 to receive the World Bank capacity building project. The project team indicated at the outset that the selection of these 18 LGAs was purposive, based on negotiation between the World Bank and the Government of Tanzania (World Bank, 2012). Thus, we knew at the outset that these 18 LGAs were likely to be systematically different from other LGAs in the country on both observable and unobservable variables. To minimize these differences, we worked closely with the government and the World Bank project team to identify comparable LGAs (22 non-project LGAs were identified to serve as comparators) which would not be receiving the project, complementing this identification process with propensity score matching. The difference-in-difference strategy we adopted was supported at the outset by evidence that in the years preceding the project, project and non-project LGAs had similar growth in local, "own source" revenues, a key measure of state capacity. This similar growth in the absence of the intervention is the fundamental assumption behind the difference-in-differences estimation strategy.

We then implemented two rounds of surveys across these 40 LGAs (18 receiving the project and 22 comparators) of 474 government officials and 2,998 citizens, two years apart, during a

> 3Total revenue includes own source revenues and transfers from the central government. 2011 is the most recent year for which we have revenue data disaggregated at the LGA level.

> 4We discuss the infeasibility of randomization in the section describing the institutional context.

2
companies. As a result, ROIC measures the return that an investment generates for the providers of capital and reflects management’s ability to turn capital into profits.<sup>12</sup> In calculating ROIC, we also subtract cash stocks in excess of those estimated required for transactions purposes. Following Koller et al. [2017], we treat cash above 2% of sales as excess cash and subtract it from the firm’s invested capital. In section 4.1 we undertake robustness tests allowing for varying percentages. Our estimates are not affected by firms’ decisions on whether to stockpile cash in low-tax jurisdictions in order to manage their tax liabilities, as is the case of many large U.S. multinationals.

We define _ROIC_<sup>_unadj_</sup> _Star_ as a dummy variable that takes the value 1 if the firm’s ROIC is above the 90<sup>_th_</sup> percentile of ROIC across all firms in the US economy in a particular year and 0 otherwise. To replicate the figure in previous studies such as Furman and Orszag [2015] and Koller et al. [2017], we restrict our sample to large firms (defined as firms with assets more than $200 Million in 2009 dollars, adjusted for inflation) and drop firms with negative invested capital. As noted before, Figure 1 shows that there is a large rise in capital returns over the past three decades where the ratio of the 90<sup>_th_</sup> percentile ROIC firm to the median ROIC firm has increased by over 69%. We also see that the divergence of the top decile of firms from the rest of the economy really takes off in the 1990s.<sup>13</sup> These results are qualitatively consistent with Furman and Orszag [2015], Koller et al. [2017] and the Council of Economic Advisors [2016], all of which were produced using a proprietary dataset of US firms from McKinsey &
. They show that inflation exceeds the official CPI inflation for the elderly, lowincome households, and households with children over 18 years old. In Ireland, inflation impacts more rural than urban households, followed by lower-income families and pensioners, because of differences in spending on heating and transportation (Lydon 2022). At the same time, the aggregate cost of living in cities larger than 100,000 inhabitants is higher than in rural areas of Spain (Lasarte-Navamuel et al. 2017).

<u>Hagemann (1982)</u> reports geographical differences in the rates of inflation in the US. He also shows that households with the head 45 to 64 years of age and those headed by retiaries face different inflation rates compared to CPI inflation. <u>Antón et al. (2016)</u> analyze data from 31 European countries, and Cullinan et al. (2011) data from Ireland to study the implications of high inflation on the well-being of people with disabilities. Both studies conclude that people with disabilities are more vulnerable to high inflation than the general population.

5
sector’s minimum wage was raised, but only to a compromise of BDT 5,300 (USD 68) a month for low-skilled workers. This represents an increase by 77% compared to the previous minimum wage.<sup>3</sup> Figure 3 provides a histogram of the monthly earnings of workers in the RMG sector in 2013 quarter 1 (before the Rana Plaza collapse) and 2015 (after the minimum increase had been in effect for over a year) using the Bangladesh Labor Force Survey data described in section 3.1. Note that while our data on earnings includes all take home pay (inclusive of payments like bonuses and overtime pay) the minimum wage applies only to base pay, so that more workers earn below the new minimum wage than would be expected if they reported base pay separately. Overtime pay is likely to be important: it is legally required for every extra daily hour beyond 8 hours for each of the six working days (Menzel and Woodruff, 2021), and 62% of workers in 2013 quarter 1 worked strictly more than 48 hours per week. Indeed, an estimated 20% of workers’ take-home pay is overtime (Reuters, 2018). Even so, 1.3% of workers earned total take-home pay below the new minimum wage in 2013, while none did by 2015.

# **3 Empirical strategy**

## **3.1 Data and outcome variables**

We use the Labor Force Surveys (LFS) conducted in 2003, 2005, 2010, 2013, 2015, and 2016 by the Bangladesh Bureau of Statistics. The Bangladesh LFS was designed to be representative at – – the national level for both rural and urban areas as well as for each of Bangladesh’s eight divisions. All working-age (age 15 to 64) individuals from the selected households are interviewed for detailed labor market outcomes including labor market status, type of employment, monthly wages for those that are paid employees, hours worked, detailed occupation and
., 2014, 2017; Samir & Lutz, 2017). Within each country, representative weights of the households are adjusted so that the population matches SSP projections in terms of population size and composition (including for instance the number of children and education level of household members).

Following previous work (Rozenberg & Hallegatte, 2015; Hallegatte & Rozenberg, 2017), we consider two socioeconomic scenarios: SSP4 and SSP5. The SSP5 scenario is optimistic regarding economic growth and poverty reduction, with rapid growth, declining inequality, universal access to basic infrastructure services by 2030, and relatively slow population growth. Due to these shifts, this scenario forecasts a rapid decrease in the number of people living below the extreme poverty line by 2030. In the SSP4 scenario, by contrast, the population is growing faster with slow improvement in education levels, economic growth is also slower and more unequal. As a result, extreme poverty decreases much less and can even be stagnating (or increasing in number of people, since population is growing fast).

The SSP scenarios do not prescribe every dimension of future development, and our approach probes these unconstrained dimensions to understand the drivers of extreme poverty within countries and regions. Most importantly, the SSPs do not specify the relative sizes of the agriculture, services, and manufacturing sectors within each country’s economy. We allow these parameters to fluctuate randomly within a wide range of possible outcomes.

Then, we adjust the income of each household based on assumption on future productivity gains in each of sectors, and for skilled and unskilled labor. Similarly, we adjust income for changes in social protection and pension systems, again allowing them to vary within bounds calibrated on historical values.

In practice, we apply a numerical sampling approach where we pseudo-randomly generate the value of parameters within a range based on historical data. For instance, we look at the value and change in the share of employment in agriculture in the past 20 years to determine a range of possible change in agriculture employment in a
Panama, Tanzania, and Uganda host the most foreign EMDE groups. Panama is host to banks from many different home countries <!-- Start of picture text --> 20 14<br>18 No. banks, 2013<br>12<br>16 No. home countries, 2013 (rhs)<br>14 10<br>12<br>8<br>10<br>6<br>8<br>6 4<br>4<br>2<br>2<br>0 0<br>No. of home countries<br>No. of home-country banks in host country<br>PAN TZA UGA IDN CHN GHA ZMB UKR CRI SLV KHM GTM ARM DZA EGY<br><!-- End of picture text --> EMDE groups have grown quickly in importance relative to various EMDE hosts’ local banking systems, including Panama, Uganda, El Salvador, Ghana, and, Zambia <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>PAN TZA UGA IDN<br>GHA ZMB CRI SLV<br>country's financial sector)<br>Total Assets home-banks (% Total Assets host<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Source: Claessens and van Horen (2015); Bankscope; team calculations.

Although limited in scope, cross-border claims data from the Bank for International Settlements (BIS) suggest that banking ties between EMDEs have grown faster than elsewhere and that intraoffice exposures are sizeable (Table 2, Panel C).<sup>4</sup> The latter indicates that shocks can be more easily transmitted to other countries via the group, as was witnessed during the global crisis in Eastern Europe.
# **Annex II – Cambodia Transactions Profile**

Cambodia Expenditure Transactions Profile, Central Government, 2017 ‐ Amounts Ranges in Riels.

<!-- Start of picture text --> 95.3 97.6 99.9 100.0<br>100<br>88.4<br>90 80.9<br>80 72.2<br>70<br>59.0<br>60<br>50 40.7 39.4<br>40<br>29.6<br>27.2<br>30<br>15.9<br>20<br>7.8 5.6 9.3<br>10 0.0 0.1 0.4 0.9 2.7<br>0<br>< 4m 4 ‐ 10m 10 ‐ 20m 20 ‐ 40m 40 ‐ 100m 100 ‐ 200 ‐ 400 ‐ 2,000 ‐ 4,000 ‐ >=<br>200m 400m 2,000m 4,000m 100,000m 100,000m<br>Riels<br>Cumulative Percent Transactions Cumulative Percent Amount<br>Percentage<br><!-- End of picture text --> Cambodia Expenditure Transaction Profile, Provincial Treasuries, 2017‐ Amount Ranges in Riels.

<!-- Start of picture text --> 96.5 98.1 98.8 99.7 99.8 100.0 100.0<br>100 91.6<br>84.5<br>90<br>80 71.6<br>66.0<br>70<br>56.9<br>60<br>46.8<br>50<br>35.5<br>40 30.0<br>30 24.0<br>16.1<br>20 10.7<br>6.1<
the EM-DAT database, defined as the values of total losses “directly or indirectly related to the disaster”, in 2022 USD values. Using LSMSISA microdata, we aggregate the estimated value of crop losses for each household. In this case, the value of losses is calculated by multiplying the potential value of total output using _current_ prices by the estimated percentage of output lost at the plot level. Contrary to the rest of our analysis, we do not apply a common set of prices to value output and losses, but rather median crop prices faced by farmers at the time of their crop production. The loss values are then converted to 2022 USD values, in order to allow comparison with EM-DAT data. As above, we use population weights as expansion factors, that we multiply with our loss value estimates.<sup>57,58</sup>

# _Sampling weights_

In the survey data, household sampling weights are used to compute estimates that are representative of the national or subnational population. These reflect the inverse probability of selection into the sample, are adjusted to account for non-response and survey design choices, and are post-stratified to ensure that they sum to known household population totals.<sup>59</sup> Moreover, a further adjustment was made to ensure that the weights in the study sample sum to the total population of households, because only a subset of LSMS-ISA households report cultivating seasonal crops. More formally, <mark>we can define a set of households indexed by h (where h = 1, ... , N</mark> 1 <mark>) within a country-wave, where each household is associated with a sample weight W</mark> h<sup><mark>. After restricting the dataset</mark></sup> <mark>to households that cultivate crops, the total number of households in the country-wave drops from N</mark> 1 <mark>to N</mark> 2 <mark>, and
4|4.1|4.4|4.6|6.8|5.3|6.2|3.4|1.2|-1.1|
|Debt|28.4|25.2|31.0|39.6|43.0|54.0|56.5|65.4|70.6|68.6|60.1|

Sources: World Bank Staff estimates of the overall deficit and public and publicly guaranteed debt from the February 2019 database compiled for the Regular Economic Report for the Western Balkans.

Note: The assessments for Albania’s and Serbia’s deficit rules rely also on estimates of real rates of economic growth from the same database. Albania’s growth rate in 2017 was below the threshold that triggers the maximum-deficit rule, so that cell is not shaded.

# **_How well known are the rules?_**

**One factor that is likely to affect compliance is whether the rules are widely understood** . As noted earlier, national fiscal rules are more likely to work when noncompliance is politically embarrassing, and this in turn is more likely if the rules are well understood, at least among a group of politicians, journalists, academics, think-tank analysts, businesspeople, and others interested in public finances.

**To shed some light on the extent of public awareness of fiscal rules, the World Bank conducted an online survey on the subject in December 2018 (see Appendix)** . In all, there were 197 responses to the survey, which asked 19 questions. The respondents were from all the countries of the region, though representation varied from country to country, and were from many backgrounds, including the media, business, academia, the public sector, and elsewhere (Figure 2). The survey was not intended to be representative of the populations of the countries: those who responded were probably more likely than the average person to be
(see Figure 27, Annex III). A misplaced GPS point in this environment could easily return an extreme value.

Paradoxically, in Figure 7 the estimated Loess regression lines and a 45 ° line are close in _absolute_ terms for short distances (roughly 30‐45 minutes). This indicates that survey based data may be used confidently in this range (for example, for analysis of accessibility to schools) but researchers and policy makers should employ alternative data, such as our model, for analysis of larger distances. Importantly, this finding does not appear to hold for banks.

When survey‐specific shortcomings are considered alongside the household idiosyncrasies noted in the literature review, the reported coefficients of correlation for model data vs. the HRVS data appear reasonably strong. The clearest point of comparison is a study in Sri Lanka, where comparison between cost time models and surveyed travel times using a ground‐validated model and GPS points still yielded 30% variation in reported travel times around the model mean for the area of study (Ahlström _et al_ . 2011). Despite limited ability to validate model inputs and household survey points, and the above‐mentioned GPS inaccuracies, our reported R values do not greatly exceed this 30% band. Therefore, while it is impossible to guarantee the accuracy of the model at every point in Nepal, we are confident in its analytical validity.
walking distance to primary schools and 1 hour to secondary schools in its Consolidated Equity Strategy (MOE 2014).

The Department of Roads (inconsistently) embraces more sophisticated approaches to measuring accessibility when planning new infrastructure. This is largely to comply with the Government of Nepal’s 2007 goal to bring the entire population of the Terai and Hills within 2 and 4 hours walking distance of paved roads, respectively. Consultants working for the Department of Roads (DOR) accordingly constructed their own 90 meter resolution cost time model and gridded (raster) population data set for focus areas of the Strategic Road
club level. The list of tobacco clubs in central Malawi (all of which had existing production loans with OIBM) was provided by OIBM in cooperation with the two tobacco buyer companies. Prior to randomization, treatment clubs were stratified by location<sup>11</sup> , tobacco type (burley, flue-cured or dark-fire) and week of scheduled interview. The stratification of treatment assignment resulted in 19 distinct location/tobaccotype/week stratification cells.

> 11 “Locations” are the tobacco buying companies’ geographically-defined administrative units within which extension services and contract buying activities are coordinated.

9