rafmacalaba/data-use-mentions-extended huggingface dataset:
is some such variation. Of the 8 villages that received such a new connection between January 2007 and June 2009, two enjoyed an operational connection throughout a full season for the first time in _kharif_ 2007. Another village followed starting in _rabi_ 2008, two more in _kharif_ 2008, and three in _rabi_ 2009. This clustering (on the _tj −_ 1) will make the standard error of the estimate of _βr_ correspondingly that much larger. More importantly, the variation in the date of first provision introduces a second problem: it could be that changes in the unobserved, time-varying components in (1) are correlated with such timing. By assumption, _wt_ is common to all villages; but a shock to a specific village might affect its chances of getting a road over the interval in question.

The ninth village’s road became fully operational only in October 2009, and the household data collected therein followed the pattern for those villages with no allweather road in that _kharif_ season. This village is, however, included in the ‘before and after’ comparisons arising from the focus group interviews, which were conducted in September 2010, by which time the respondents would have been able to form a more reliable – or at least settled – view of the changes wrought by their new road. A careful inspection of the household data also revealed that these villagers had been making unauthorised use of the road before its official completion, which led us to classify it as having an all-weather connection for the purposes of cross-section analysis in Sections 3 and 5.

To close this section, it should be remarked that the great majority of these villages are comprised of a collection of hamlets, scattered in varying degrees. Even within a village, therefore, the distance from the house or field to a passable road or track – or even the village primary school – can vary quite substantially. Such variation makes it unlikely that, for many of the variates that concern us here, the intra-cluster correlation coefficient is very high.

# **
growth or a different composition of growth.

# **Section 3b. Updating Key Well-Being Measures**

We first update estimates of monetary poverty using the 2016-2018 DHS. As discussed above, there is no direct way to estimate the poverty status of individuals in the survey. In order to estimate monetary poverty using the 2016-2018 DHS, we impute poverty following the Survey of Well-Being via Instant and Frequent Tracking (SWIFT) framework (e.g., Yoshida et al. 2022). In this approach, we first find all the variables that are

> 6The number of women interviewed in each household varied between one and 10.

> 7The number of men interviewed in each household varied between one and eight.

> 8These statistics are publicly available to be downloaded on the World Bank’s Poverty and Inequality Platform (”http:www.pip.worldbank.org.”).

8
.138 and 0.345 sd, consistent with moderate-to-large treatment effects. The effect on the socio-emotional index remain statistically insignificant.

Overall, these results confirm that low ITT impacts are consistent with moderateto-large effects on h-compliers.

**Conditional LATE** We next turn to the conditional LATE approach which allows us to estimate both _LATEhc_ and _LATEac_ . In a first step we reject that each set of instruments is weak (the last row of Table 11). This is reassuring as it confirms that the instruments selected do predict ASP enrollment well in the control group (and its substitution to CPS in the treatment group). For each set of instruments, we also run an over-identification test (except for the LASSO which is just-identified): since the instruments are all from the baseline survey and are interacted with the random assignment, they are exogenous and will fail the over-identification test only when treatment effects are not homogeneous. Failing the over-identification test would therefore be an indication that the constant treatment assumption is violated. In most cases, our instruments pass the over-identification test. For both aggregate indexes, the over-identification test is passed for all specifications.

28
Figures 11 a) to i) display a clear urban rural and north south divide, with lower shares of children of school going age being out of school in the urban and southern areas. Moreover, the figures demonstrate a difference in age group behavior. While in urban areas the share of children not in school doubled in wave 3 when moving from the 10-14 year olds to the 15-17year olds age group; in rural areas, children appear to start attending school in larger shares only at 10-14 years and results do not display the a similar decrease in school attendance among the older children. When comparing northern and southern areas this pattern is even more extreme. A potential reason might be that in urban and southern areas the opportunity costs of staying in school are higher with more job opportunities available than in rural and northern areas. The observed pattern is consistent among the GHS and MICS surveys and does not seem to have changed much over time until wave 4. Interestingly also school attendance patters do not seem to differ by gender until wave 3, not even in rural areas - at least not at the aggregate level, despite education projects oftentimes targeting rural girls. Wave 4 shows a jump in drop-out rates for children aged 15-17 in all areas and most extreme in urban and southern areas, whereby the surge of southern girls compared to a limited increase in southern boys dropping out of school is particularly noteworthy. More research is needed whether this is a result of higher opportunity costs for girls staying in school due to the availability of jobs, or girls being forced to work to provide for their families as monetary poverty rates started to rise for households with children in this age group in the South East and South South, as displayed in Figure 5.

Looking beyond aggregates into the potential heterogeneities across the six regions, Figure 12 shows in addition to the rural-urban divide a north-south divide at a regional level. The highest share of children not in school, hence, the lowest school enrollment rates are observed for rural children of any age
|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: “Older” workers are those workers within fifteen years of retirement age (men over 45, and women over 40), and who cannot expect to receive the employer contribution as part of their pension. “Younger” workers (men and women who are under 45 and 40, respectively) can expect to receive full benefits at retirement. The additional variables included in Model 1 are a vector of city fixed effects; in Model 2 we also add individual level controls (age, age squared, gender, education, marital status, number of children), while Model 3 adds firm level controls (pre-treatment firm size and ownership indicator variables ) as well. Robust standard errors clustered at workplace of household head level are in parentheses. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

31
Table A3: Balance on observables along exposure to death in Myanmar

||(1)<br>No Violence|(2)<br>Violence|(3)<br>No Vio. vs.<br>Vio.|(4)<br>No Vio. vs.<br>Vio., Town FE|(5)<br>No Vio. vs.<br>Vio, Grid FE|
|---|---|---|---|---|---|
|Married|0.82|0.78|0.69|0.70|0.61|
|Age|27.87|28.39|0.30|0.36|0.30|
|Household size|5.11|5.13|0.67|0.89|0.78|
|Formal education|0.43|0.50|0.31|0.20|0.15|
|Math ability (index)|-0.02|0.03|0.30|0.30|0.28|
|Past Ag. Work|0.58|0.66|0.22|0.17|0.15|
|Observations|91|654||||

Columns (1) and (2) show the average value of the variable for respondents who did and did experience the death of a family or community member in Myanmar. All difference in means test control for gender because violence was targeted differently between men and women. Column (3) shows the p-value of the difference in means with no additional controls. Column (4) reports p-values while controlling for township fixed effects, while column (5) includes fixed effects using 55 by 55 kilometer grid cells for respondent location of origin in Myanmar.

48
6 income across different goods, household fixed effects and time varying village fixed effect panel regression techniques are applied to a three-round panel of about 1,800 rural households from two geographically and ethnically distinct regions in Tanzania— Kilimanjaro and Ruvuma, and a five-year household panel of 1500 rural households from two geographically and ethnically different provinces in western China—Gansu and Inner Mongolia. Estimates thus reflect revealed preferences in the market place derived from standard household budget surveys, as opposed to stated preferences, or revealed preferences derived from observed behavior in experimental settings.<sup>12</sup> Very few studies have examined the existence of mental accounting in developing countries or outside experimental settings.<sup>13</sup> To our knowledge, none have explored mental accounting by earned/unearned income as such in such settings.

Households are found to have a higher marginal propensity to spend unearned income on alcohol and tobacco, clothing, transportation and communication, while they are more likely to spend earned income on education. In most cases, unearned income is at the margin also more likely spent on non-staple food, while earned income is at the margin more likely used for buying staples. The results further confirm that men and women display clear differences in spending preferences, as has been widely documented in the literature. However, both men and women are also more likely to spend their unearned income on nonbasic adult and prestige goods such as clothing, transport and communication, and also alcohol and tobacco for men. Overall, the findings lend some credence to the age-old saying “Easy come, easy go”. The spending patterns from earned and unearned income sources differ, contrary to what standard economic theory holds, and in a manner that cannot be fully explained by intra-household dynamics or a series of other competing hypotheses, which are also examined.

The paper proceeds by reviewing the different income sources and consumption patterns across the rural settings studied here (section 2). The argument of mental accounting by effort is then presented more formally
<mark> The Estimation Methodology: Cost of Basic Needs (CBN) method</mark> The CBN method defines a poverty line as the cost of a basket of goods deemed to be sufficient for satisfying the basic food and non-food needs of the ‘reference’ population. It first determines the cost of basic food needs, aligns them to the normative minimum standard of well-being, and then adds a provision for basic non-food needs. This method is in most common use in a majority of the developing countries, and the application of CBN to EICV4 to arrive at the 2014 poverty line in Rwanda is detailed below.

# **_The food component_**

The food component of the poverty line is almost universally anchored to nutritional requirements for good health, as established by the Food and Agriculture Organization (FAO). This does not necessarily generate a unique monetary poverty line, since many bundles of food goods can potentially yield the same nutrition. In practice, a diet is chosen which accords with ‘prevailing’ consumption patterns of a certain reference group considered to be appropriate in defining poverty. Ravallion (1998) asserts that the idea of respecting consumer choice in the definition of a poverty line is extremely important to arrive at a more objective and relatable poverty measure. Early attempts to determine the minimum cost of achieving the basic-needs vector at given prices ignored preferences. In that case, the resulting poverty lines can sometimes be so alien to consumer behavior that their relevance as a basis for policy becomes doubtful. Instead, current practices aim to anchor the choice more firmly to existing demand behavior. Among the (infinite number of) consumption vectors that could yield any given set of basic needs, a vector is chosen which is consistent with choices **_actually made_** by some relevant reference group. Poverty is then measured by comparing actual expenditures to the CBN. A person is not deemed poor who consumes less food (say) than the stipulated basic needs but could consume it on rearranging her budget allocation. This
0.183|0.141|0.098|0.099|0.076|

Notes: See Table 1a. This table shows the number of employees in the unorganized sector to be greater than the combined number of employees in female-owned and maleowned establishments, because the unorganized sector total includes establishments for which the gender of the owner is unknown. The table also shows Total employment and Organized employment to be greater than combined male and female employment in Manufacturing, reflecting discrepancies in the raw ASI data. Employee gender is known for only ~55% of the employees in the organized manufacturing sector, so the employment shares in Panel B are calculated over only these known figures.
International Finance Statistics databases. Choi and Hashimoto (2018) proxy for data transparency policy reforms using subscriptions to the IMF’s Data Standards Initiatives and find that such reforms lead to falls in sovereign bond spreads. The study finds that the larger quantity of data produced, as measured by the index, is positively correlated to the quality of bureaucracy, investment and financial sector development. More recently, Hodelin (2022) find the that the quantity of data produced in the World Bank’s World Development Indicators is positively related to economic growth, especially for middle income countries.

We build on these studies by using a comprehensive and rigorous measure of data transparency, known as the Statistical Capacity Indicator (SCI). The SCI measures both the production of micro and macro data as well as the frequency and quality of such data through adherence to international standards. The SCI has been used to assess the statistical capacity of economies and regions (Devarajan, 2013; Angrist et al., 2021) Kubota and Zeufack (2020) use the SCI to explore how data transparency can reduce the external cost of borrowing, as proxied by sovereign bond spreads, in Sub-Saharan Africa. Furthermore, we explore the effect of data transparency on overall economic growth over a period of considerable uncertainty – during and after the global financial crisis. Transparency is a good antidote for uncertainty. Shambaugh and Shen 2018) find that government transparency reduces the duration of inflation and currency crisis by reducing information asymmetry, improving effectiveness of monetary policy and bolstering public confidence. As far as we are aware at the time of writing, our study is the first to empirically explore the effect of data transparency, using a comprehensive measure, on overall economic growth for developing economies during and after the global financial crisis.

# **III. Estimation Strategy**

The identification strategy involves two strategies. The first is dynamic panel estimation methods to account for endogeneity concerns. The second is cross-sectional data where we lag the predictors to the initial time period of the data set (
.37|2%|10,545|0.22|0.41|1%|10,503|
||_University_|0.18|0.38|2%|10,545|0.11|0.31|1%|10,503|
|Books at home|_11-100_|0.58|0.49|10%|9,639|0.69|0.46|4%|10,241|
||_101-500_|0.08|0.27|10%|9,639|0.10|0.30|4%|10,241|
|Home computer (|1 or more)|0.16|0.37|0%|10,743|0.15|0.35|4%|10,245|
|Home language s|ame as test|0.32|0.47|4%|10,364|0.34|0.47|1%|10,517|
|PISA score||360|74.9|100%|10,761|391|75.3|100%|10,647|

7
<!-- Start of picture text --> Lowess smoother<br>Central Eastern<br>Northern Western<br>0 2 4 6 0 2 4 6<br>Distance to markets index<br>bandwidth = .8<br>1<br>.5<br>0<br>1<br>.5<br>Use of hybrid, improved seed<br>0<br><!-- End of picture text --> Source: Staff calculations using UNPS 2005/06‐2013/14 Other factors that are likely to affect the decision to adopt agricultural inputs are weather conditions and agricultural prices.<sup>18</sup> Given that the decision to adopt agricultural inputs is taken early on in the cropping season, we analyze whether lagged values of weather index and agricultural output prices affect a farmer’s decision to use inputs. Past weather conditions may affect the present decision to use inputs through short‐term bias. Individuals tend to weigh recent information more heavily when making a decision, and thus, if the previous season was a good one (bad one) as a result of good weather conditions (bad weather conditions), it is likely that the farmer will decide to (not) use inputs. We measure weather conditions through the Water Requirement Satisfaction Index (WRSI), which is estimated based on a maize crop model using satellite rainfall data at the sub‐county level.<sup>19</sup> The index ranges from 0 to 100, where 100 means there was no deficit in the water needed, and each household was assigned the average between the main and short seasons for the previous cropping season. As shown in Figure 4.2, weather conditions vary considerably across regions and over time.

Similarly, lagged output prices may affect input use as they are usually a good predictor of current prices, as evidenced by the fact that commodity prices (particularly maize and banana) are
that nearly one-third of the firms report zero score, while the average for all firms is 0.28 (standard deviation of 0.29).

_Energy Intensity_ . Energy intensity is the amount of energy used (expenditure or physical quantity) normalized by output, measured using last fiscal-year sales. Studies suggest large heterogeneity in energy intensity within narrowly defined sectors, and these variations are higher than that in other inputs (Lyubich et al., 2018). This is larger than the productivity spread for relatively homogeneous industries, such as cement (Syverson, 2004).

Energy intensity in expenditure (and physical quantity) is calculated by summing energy expenditure (quantity) for firms that report electricity use and at least one out of three possible primary fuel types. More than half of the firms in the ( _>_ 20,000 firm) sample report non-missing values for all fuel types. Summary statistics for both energy intensity and management measures are reported in Appendix Table A3. For a description of data preparation and cleaning, as well as the components of the generic and energy-centric management practices, see Appendix Section A1.

# **3.2 Stylized Facts about Management Practices and Energy Use**

After cleaning data from the World Bank Enterprise Surveys for 31 countries, our sample includes 14,713 firms reporting energy consumption in value terms and 8,272 firms reporting energy consumption in physical quantity terms. For 8,476 firms, the generic management score can be computed, contingent on firms responding to at least six of the eight aggregated questions from the management practices module. The energy-centric management score can be computed for 18,621 firms that answer at least eight of the nine energy management practice questions from the Green Economy module. Appendix Table A3 presents descriptive statistics for key variables included in our regression analysis. In this section, we highlight the main stylized facts on management practices and energy use that emerge in our data.<sup>6</sup>

> 6For our regression, we use data
br>- Bribery incidence|To what extent do you believe that the government manages public resources in a transparent<br>and trustworthy manner?<br>To what extent are you satisfed with the government’s e↵orts to fght corruption?<br>(not at all, not very much, quite, very)?<br>From your point of view, is corruption a practice that is very frequent, quite frequent,<br>not very frequent or not frequent at all in Morocco?<br>How often is it necessary to pay a bribe, make a favor or give a gift to get better<br>services from the public administration?<br>(very often, quite often, rarely, never)|1-4 (not at all-very)<br>1-4 (not at all-very)<br>1-4 (very - not all frequent)<br>1-4 (often-never)|
|**Policy credibility**|Do you think that public policy announcements are never, sometimes, often,<br>or always followed by actions?|1-4 (never-always)|
||In the last three years, at what frequency have you had the feeling of being treated||
|**Equity of treatment by the public administration**|inequitably by the public administration<br>(never, sometimes, often, always)|1-4 (never-always)|

Annex 1 provides more detailed descriptive statistics for all key variables. Descriptive statistics show that the country’s reformist drive in the post-Arab Spring has not yet fully delivered on citizens’ expectations with regards to policy performance. Satisfaction with economic performance and the delivery of social services is relatively low: 27 percent of respondents are quite or very satisfied with the government’s economic performance with regards to job creation or poverty reduction; and 30 percent of the population is quite or very satisfied with the management of public health and 35 percent with that of public education. Perceptions of
_ESTIMATING FOOD PRICE INFLATION_ _27_

# Tables and Figures

Table A1—Summary of raw food price data.

|Country|Currency|Markets|Items|Data Coverage|Start date|
|---|---|---|---|---|---|
|Afghanistan|AFN|9/40|4|69.27%|Jan-07|
|Burkina Faso|XOF|63/65|3|47.79%|Jan-07|
|Burundi|BIF|61/68|7|35.93%|Jan-07|
|Cameroon|XAF|12/51|11|20.82%|Jan-07|
|Central Afr. Rep.|XAF|18/40|3|36.89%|Jan-08|
|Chad|XAF|35/56|3|39.17%|Jan-07|
|Congo, Rep.|XAF|5/11|7|44.47%|May-10|
|Congo, Dem. Rep.|CDF|26/83|10|35.43%|Nov-07|
|Gambia, The|GMD|15/28|11|41.91%|Jan-07|
|Guinea-Bissau|XOF|3/45|7|56.63%|Feb-16|
|Haiti|HTG|9/9|6|68.83%|Jan-07|
|Iraq|IQD|18/18|13|48.02%|May-11|
|Lao PDR|LAK|17/17|5|46.04%|Feb-12|
|Lebanon|LBP|26/26|15|61.49%|Mar-12|
|Liberia|LRD|18/24|3|49.44%|Mar-07|
|Mali|XOF|77/126|6|58.37%|Jan-07|
|Mozambique|MZN|25/52|7|63.13%|Jan-07|
|Myanmar|
<br>issues)|Self-assessment<br>using a seven-point<br>Likert response<br>format for four<br>dimensions (sense<br>of meaning-beliefs<br>and attitudes,<br>competence, self-<br>determination, and<br>impact or efficacy)<br>averaged into a<br>single measure of<br>intrapersonal<br>empowerment|Survey data from<br>a sample of 324<br>middle managers<br>from different<br>units of a<br>Fortune 50<br>organization|Intrapersonal<br>empowerment<br>in the<br>workplace|Intrapersonal<br>empowerment<br>mediates the<br>relationship<br>between<br>workplace social<br>structure and<br>innovation, but<br>not effectiveness.|Intrapersonal<br>empowerment as<br>a mediator<br>between the<br>social context<br>(support,<br>information,<br>resources and unit<br>culture) and<br>behavioral<br>outcomes|Individual level;<br>perceptions (also<br>informational resources<br>and culture included in<br>social structural<br>context). Market<br>Domain.|
|McMillan, et al.|Gaining|Psychological|Data from|Predictors of|Predictors of|Ongoing|Individual and|

38
Inequality, Economic Growth and Nonrenewable Resources Table 9: Estimated Results from the Non-Recursive Model Specification (income-group heterogeneity)

||**IV Fixe**<br>|**d Effect**<br>|**3S**<br>|**LS**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc**|**Gini index**|**GDPc**|
|Nonrenewable resource dependence|-0.185***|-0.008|-0.173***|-0.007|
|Lower income|dropped|dropped|5.637**|49.933***|
|Lower income* Nonrenewable resource<br>dependence|0.100**|-0.638***|0.070|-0.640***|
|High-low education ratio|-0.000|-|-0.440*|-|
|High-low education ratio square|0.006|-|0.014**|-|
|Consumer price index (2005)|0.004|-|0.012**|-|
|Services value added share|0.072|-|0.063|-|
|Manufacturing value added share|0.047|-|0.086|-|
|Agriculture value added share|-0.123**|-|-0.123**|-|
|GDPc|-0.046|-|-0.032|-|
|Log Labor Force|-|-15.487***|-|-15.901***|
|Log gross fixed capital formation|-|7.880***|-|7.164