rafmacalaba/data-use-mentions-extended huggingface dataset:
results are consistent with recent evidence on how some interventions in CBT can positively impact behavior (Blattman et al., 2017; Heller et al., 2017).

Although valid EEG recordings were approximately 52% of the total subsample, we showed in the attrition subsection that this rate was not different across T and C groups. Therefore, assuming that the process governing recordings validity follows a standard monotonicity property, we can compare outcomes across both groups, which will be valid estimates of the impact of treatment on outcomes for the full randomly selected subsample. However, as a check, we computed Heckman selection-corrected regression results for the same subsample outcomes (Heckman, 1979). These results are presented in Table A10 in the Appendix. We find these estimated ITT effects using the 598 observations. After correcting for the selection bias—using a dummy that indicates if the student was selected or not selected to participate in the neurophysiological experiments as selection variable—we notice they are very similar to the results obtained from the 308 valid EEG recordings. We also estimated Lee’s treatment effects bounds for nonrandom sample selection (Lee, 2009) and present these results in Table A11 in the Appendix. We find that zero belongs to the treatment effects interval for all variables except for valence, positive valence difference, and locus of control. Thus, attrition is unlikely to have biased the results reported above.

There is a lack of evidence of heterogeneous effects of ASPs on socioemotional skills and no evidence of differences in the impact on ER by participants’ initial propensity for violence. Therefore, we contribute to the literature testing if ASPs affect high- and low-violence children’s neurophysiological outcomes differently. In Table 6, we present the estimated effects using specification (2) for ER measures. Coefficients in row [i] show the ASP’s effects on low-violence treated students compared to low-violence children in the C group. Coefficients in row [ii] show the differences in effects between highly violent treated students and similar children in the control
Table C.6: Regional Technology Sophistication and Productivity

||(1)<br>|(2)<br>Avg. EXT|(3)|(4)<br>A|(5)<br>vg. MOS|(6)<br>T|(7)|(8)<br>Avg. GAP|(9)<br>|
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|ABF|GBF|SSBF|ABF|GBF|SSBF|ABF|GBF|SSBF|
|Regional Productivity|0.39***|0.41***|0.38***|0.25***|0.27***|0.21***|0.14***|0.15***|0.17***|
||(0.03)|(0.04)|(0.04)|(0.03)|(0.03)|(0.02)|(0.01)|(0.02)|(0.03)|
|Observations|16|16|16|16|16|16|16|16|16|
|R-squared|0.93|0.89|0.86|0.86|0.82|0.85|0.82|0.67|0.72|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Robust standard errors in parentheses. The observation is subregion within each country. The firm-level average of technology in each region is regressed on regional productivity
# **3. Improvements in Living Standards between 1995 and 2010**

The poverty reduction was accompanied by an improvement of living standards across all segments of the population. Figure 2 shows changes in real per capita consumption and consumption shares across 10 components between 1995 and 2010 for the bottom 40 percent, middle 40 percent (40<sup>th</sup> to 80<sup>th</sup> percentile) and top 20 percent of Nepal’s consumption distribution in 1995 and 2010. The top panel shows that in real terms, per capita consumption of the bottom 40 percent registered a cumulative growth of 62 percent, which translates to an average growth of 4.1 percent per year between the two survey rounds. The middle 40 percent saw a slightly higher cumulative growth of 66 percent than the top 20 percent group (50 percent).

With such growth in living standards over a 15-year period, one might expect notable changes in the way households allocate their resources among competing needs and wants. However, the share of food consumption remained almost unchanged between 1995 and 2010; households in the bottom 40 percent on average spent 70 percent of the total budget on food items in both years, and those in the middle 40 percent spent 61 percent in 1995 and 57 percent in 2010. The only exception was educational expenses. All three groups more than doubled their shares on educational expenses between these two years: 4 percent to 10 percent for the top 20 percent, 3 to 7 percent for the middle 40 percent and 1 to 5 percent for the bottom 40 percent. This increase is offset by a modest decline in the shares of general nonfood items and services.<sup>14</sup> F **igure 2: Real per capita consumption and consumption shares in 1995 and 2010**

||**NLSS-I**<br>**Real per capita consu**|**mption**|**(2010/11**|*
in the HSES.

Finally, if a household indicated they used public health services in the HSES, an in-kind health transfer is applied. The amount of the annual transfer per household is 1,655,041.8 MNT, based on total expenditures in public health divided by number of patients as per HSES.<sup>18</sup> In the case of public health and education the monetary value of transfers in kind has been scaleddown. This is because household surveys usually capture a level of consumption that is lower than household consumption in national accounts. In the case of Mongolia, for instance, public expenditures in education represent 8.2 percent of consumption expenditures as per aggregate fiscal and national accounts. But they would represent 13.5 percent if compared to consumption reported in the HSES survey. Without scaling down, transfers in-kind would have a disproportionately larger impact upon distribution of well-being as per survey data than they represent in aggregate data.<sup>19</sup>

> 17 Data on education expenditures by level of education and Aimag provided by ministry of Finance on December 2017. Data on students enrolled in each level of education and by Aimag, from 2016 Mongolia Statistical Yearbook. 18 HSES questions 3.10 and 3.11 indicate whether a household member consulted a health facility/provider, and whether it was a public or private service. Health expenditures by Aimag provided by Ministry of Finance on December 2017.

19 For more on the advantages and disadvantages of re-scaling in-kind transfers see (Lustig N. , 2017). MONGOLIA Distributional impact of taxes and transfers / May 2018 10
- The focus of long term strategies for cities must be first and foremost to create opportunities for all its inhabitants and to ensure the sustainability of the city’s economy.

- This will, almost certainly, make new investments or reinvestments necessary in many areas ranging from mobility to culture and to the green economy. Cities must be managed with flexibility so as to adapt to unforeseen new circumstances and today's investment decisions should not lock cities out of options tomorrow.

- In cities where public finances have deteriorated and where it is essential to restore their credit, urban decision-makers must make sure that their decisions are financially sustainable in the long term.

- While there has been a lot of talk about "smart cities" and new technologies among urban specialists and urban planners, it is ultimately the focus on basic economic principles, good governance and good institutions that will create sustainable, dynamic and livable cities.

# **Creating Opportunities for All**

Dark clouds gather on the horizon today for most cities in developing countries. The future is uncertain and risky. But the future is also full of opportunities. Cities, rich and poor, face risks but they also have opportunities to manage their city well, ensuring that growth is inclusive, greener and more sustainable when the crisis subsides and growth resumes.

Creating opportunities for the population means ensuring that the quality of life of its inhabitants—particularly the least fortunate—is improved; that the city invests in human capital formation; and that all inhabitants are participating and included in political and cultural life.<sup>12</sup> In order for cities to provide opportunities for their citizens it is helpful to understand the primary forces that dictate existing levels of opportunities experienced by urban populations. Bourguignon, Ferreira, and Menéndez (2007), using Brazilian household data, find that individuals’ place of residence, parents education, occupation, and race are the most significant variables linked to opportunity. Their analysis additionally reveals a sizable inequality of opportunities in Brazil and shows
care affordability (and quality) on FLFP rate are also positive but statistically significant only after excluding the year of enactment (and one period after). The magnitudes of the estimated effects of childcare availability and affordability are similar across samples, whereas the estimated impact of childcare quality is somewhat smaller.

The variations in the estimated effects could stem from both preference and supply factors. Firstly, it is possible that legislation aligns with existing evidence, with an emphasis on ensuring accessible and affordable childcare, while placing less emphasis on is more to mea- regulating quality. Secondly, quantifying quality challenging compared suring accessibility and affordability. As a result, the perceived quality of childcare may have a lesser impact on women’s decision to participate in the labor force compared to accessibility and affordability.

# **5 Robustness Checks**

We conduct additional exercises to probe the robustness of our estimates that include estimating modified versions of specification (2) to test the dynamic effects of the child care enactment and conducting placebo tests. First, the fully dynamic version of the TWFE model estimated OLS consistent estimates under the using yields only strong assumption of treatment effect homogeneity (Roth et al., 2023). To allow for heterogeneity in treatment effects across time and treated units, we estimate the event study estimates following Callaway and Sant’Anna (2021) and Sun and Abraham (2021), and stacked TWFE as in Cengiz et al. (2019). The Callaway and Sant’Anna (2021) approach provides sensible estimates even under arbitrary heterogeneity in treatment effects and it makes transpar20
for those with expenditures above 60 percent of the PL; this confirms that the poorest of the poor did not benefit from growth during the period. Many of those in this category are likely located in Central Mozambique (see Figure , bottom left graph) where for most households consumption reverted to its 1996/1997 levels. Finally, the positive performance of the South is less pronounced than in the previous period, but the poverty rate nevertheless fell to about 30 percent, and clear improvements can be observed across the distribution.

# **Conclusions**

Over the past decade there has been intense debate in Mozambique surrounding not only poverty rates and trends, but also data quality and analytical rigor. The aim of our analysis is to advance the discourse on poverty measurement by providing alternative methods to compute poverty rates. Our findings indicate that two interrelated factors have created significant weaknesses in the official reporting of poverty statistics, with the greatest degree of inaccuracy occurring between 2002/03 and 2008/09. We are presented with compelling evidence of the widespread underreporting of consumption expenditures in the survey data, and our assessment reveals serious methodological issues regarding the formulation and use of location-specific poverty lines based on those data. To address these issues we have proposed specific methods to reduce the occurrence and minimize the impact of underreporting and to construct less specific, yet more accurate, PLs based on a single national consumption basket. The national basket is developed using data from the first (and apparently most comprehensive) available survey (1996/97) combined with updated price data from each subsequent survey round. The revised figures produced by our methodology differ substantially from the official poverty lines, which are based on province-specific and time-flexible bundles with entropy correction (MPD-DNEAP, 2010; MPF/IFPRI/PU, 2004).

By examining the location-specific PLs we determined the likely cause of the excessively high regional differentials: observations affected by consumption underreporting, which are characterized by an unusually large share of non-food consumption, tend to skew local PLs upward in those areas
#|
|Sales-weighted demand elasticity|5.3|5.2|≈5|Gopinath & Itskhoki (2010)/Broda & Weinstein (2006)|
|Mean share of largest dom. frm in sector|0.27|0.27|0.12-0.45|Amiti et al (2016) - EMX (2015)|

# calc from median inverse Herf

country, since the average of the top 10 US trade partners’ GDP is approximately 3/20th that of the US.<sup>18</sup> The second calibration is a three-country symmetric model which can be simulated in general equilibrium, and is used to study the correlation of exchange rates. In the body of the paper the three-country model is simulated in PE (GE in the online appendix).

I set K = KUS = 15 in the three-country model to keep the number of firms per sector close to the number considered in AB (45 here, 40 in AB). The standard deviation (and distribution) of firm productivity is taken from AB (where log(zjk) ∼ N (0, θ<sup>2</sup> ) and θ = 0.385). I set the iceberg cost of exporting to be D = 1, 1.5 or 2 per sector (with equal probability) in order to match the aggregate import share in the US manufacturing data of 0.28, and also (approximately) the distribution of import shares, as measured by the average importweighted import share of around 0.5.<sup>19</sup> The fact that the import-weighted average import share is higher than the total import share suggests that imports are concentrated in particular sectors, which is important for the mechanism in the model.<sup>20</sup> I calibrate the process of simulated RERs to match those in the data (in the GE model in the online appendix RERs are driven by TFP shocks). For the US
to produce locally or to put up barriers to trade, even during economic crises. Intense competition, at first between American and Japanese producers, along with a series of boom and bust cycles driven by forces both internal and external to the industry (Brown and Linden 2009), are what pushed early fragmentation of electronics GVCs, rather than trade barriers and local content rules alone, as has been the case in the textile/apparel and automotive sectors.<sup>5</sup> Because trade barriers have been minimal in this industry worldwide, the main impact of the 2008-2009 economic crisis was to sharply reduce demand, driving the full absorption of operating inventories and accelerating existing trends toward industry consolidation and shifts to low-cost production geographies.

# **ICT Hardware**

The ICT hardware industry is the world’s most important goods-producing sector. Each year, the industry generates a mushrooming array of products and services. Now deeply entwined in our social fabric, ICT products and systems support critical aspects of communication, education, finance, recreation, and government. While statistics on global manufacturing employment are not available, consider the case of the United States, where innovation in ICT hardware, which employed 1,054,700 in 2014, has helped spawn a host of upstream and downstream ICT service industries, including computer systems design services, telecommunications, as well as data processing, hosting, and related information services, which together employed 3,205,300.<sup>6</sup> Maintenance and repair, and wholesale and retail trade of electronic goods accounted for another 329,800 US jobs in 2014.

Worldwide, thousands of companies from dozens of countries contribute to the industry on a daily basis. Even a single product can contain work carried out by dozens of firms in multiple countries. Because there is less need for co-location of engineers than in other technology-intensive sectors, it is relatively easy for ICT firms to engage in the twin strategies of outsourcing and offshoring. As a result, international sourcing
# List of templates

|Template 1|Jurisdictional responsibilities in infrastructure service delivery|12|
|---|---|---|
|Template 2|Special funds financing infrastructure service delivery|15|
|Template 3|Private participation in infrastructure|16|
|Template 4|ODA in infrastructure service delivery|18|
|Template 5|Basic budgetary institutions, national level|21|
|Template 6|Budgetary cycle diagram, national level|23|
|Template 7|Governance of public operators|26|
|Template 8|Macroeconomic parameters for normalization|30|
|Template 9|Macroeconomic parameters for budgetary context of infrastructure spending|35|
|Template 10|Functional and economic classification of government expenses (central and local)|40|
|Template 11|Public operators’ financial data|42|

# List of tables

|Overview table 1 Data collection matrix|3|
|---|---|
|Table 1.1 Rwanda 2006, jurisdictional responsibilities in infrastructure service delivery|14|
|Table 1.2 Uganda 2006, special funds in infrastructure|16|
|Table 1.3 Kenya 2006, private participation in infrastructure|17|
|Table 1.4 Sierra Leone 2007, ODA in Infrastructure|18|
|Table 1.5 Indicative checklist of documents and sources|19|
|Table 3.1 Indicative checklist of data sources and documents|29|
|Table 3.2 COFOG codes capturing infrastructure cost elements|32|
|Table 3.3 Economic classification of government expenses—current expenditures|34|
|Table 3.4 Economic classification of government expenses—capital expenses|35|
|Table 3.5 Kenya functional code mapping|37|
|Table 3.6 Rwanda economic code mapping|38|

# List of figures

Figure 2.1 Turkey 2004, budgetary cycle diagram 24

# List of boxes

Box 1.1 On-budget versus off-budget entities 11 Box 3.1 Why use the GFSM 2001’s economic
agile. On the other hand, the smaller scale (in terms employee size) of informal firms does not leave much scope for adjustments on either the extensive or the intensive margins. The overall effect depends on the net of the two effects and is an empirical question.

Our data suggest that the scale effect dominates such that informal firms have about an 8 and 14 percentage-point lower probability of adjusting on the extensive and intensive margins, respectively (Table 4, panel A column 1 and 5). The elasticity of adjustment is lowest among informal firms driven by necessity and highest among the parasite type, suggesting that the available scale of adjustment could be driving these results.<sup>14</sup> Shapley’s decomposition of the probability of employment adjustment at extensive and intensive margins suggests that informality explains 60% and 72%, respectively, of these adjustments (Figure 5, panels a and b). The size of the shock (change in sales) is much more important at the extensive margin, indicating that larger negative effects of the crisis on sales induce more adjustments on the extensive margin. Firm-level constraints (mainly uncertainty) explain 11% of the adjustments on the extensive margin, while a much smaller share on the intensive margin. The overall characteristics of informality explain a larger share of adjustments on the intensive margin, and so do the external attributes such as age, size, and sector.

Macro evidence suggests that the unemployment rate rises sharply during recessions, while the recovery is only slow and gradual during expansions. This mismatch of sharp spikes in unemployment during recessions and slow job gains during expansions is known as “cyclical asymmetry” (Andolfatto and Spewak, 2018; Andolfatto, 1997; Ferraro, 2018, 2023). Micro evidence in Cirera et al. (2021) confirms this pattern using BPS data. Our work suggests that the elasticity of change in employment with the drop in sales was comparable for both formal and informal firms in the first phase of the crisis. In the recovery
The presence of JA trade costs (in the case of the U.S. mainland), and tariffs (in the case of ROW) means that the data shares of purchases from those sources are larger than the distribution weights. The distribution weights can be uncovered by dividing the trade shares by the trade costs associated with an origin and product _θj,t_<sup>_l_=</sup> (1+ ̃ _τSj,t_<sup>_l_</sup> _<u>j,</u>_<sup>_l_)</sup> _t_<sup>1</sup><sup>_−σl_.The data required for this transformation - the values of</sup><sup>_τ_ ̃</sup><sup>_l_</sup> _j,t_<sup>and</sup> _σ_<sup>_l_</sup> - are also applied where necessary in _P_<sup>�</sup> _t_<sup>_l_,andthus</sup><sup>_Pt_.SincepurchasesofPR</sup> output face no trade costs, _θPR_<sup>_l_=</sup><sup>_S_</sup> _PR_<sup>_l_.The</sup><sup>_αl_parameters are the observed share of</sup> sectors _l_ in expenditure in the IO table, whereas the initial values of _E_ ( _P, U_ )<sup>0</sup> are the total expenditures observed in the table, and inflated by GDP growth to 2016.<sup>43</sup> Our calculation of CV is accomplished as follows. Let _U t_ be the numerical value of utility associated with the initial price index _Pt_<sup>0and observed expenditures</sup> _E_ ( _Pt_<sup>0</sup><sup>_,_</sup> _U t_ )<sup>0</sup
There is a wide variety of existing questions, tests or modules to measure such skills. Many instruments have been designed to assess skills in lab conditions, and some standardized instruments have been developed for inclusion in surveys in developed country settings. Increasingly, economists are also trying to include various measures of abilities and personality traits in household surveys conducted in developing countries. But little validation of such instruments has occurred for such contexts.<sup>3</sup> Many questions can be raised about the applicability of some of the existing scales for poor rural populations, given the high level of abstraction of many questions, low levels of education in the respondent population, difficulties of standardization for enumerator-administered tests conducted in the field, and translation challenges.<sup>4</sup> This study aims to test the reliability and validity of several commonly used scales and tests, and highlights both random and systematic measurement error that needs to be accounted for when measuring skills in developing-country field conditions. We do so by analyzing the test-retest reliability and the Cronbach’s alpha to estimate internal consistency of various existing scales. We subsequently use exploratory factor analysis, correction for acquiescence bias, and item response theory (IRT) to reduce some of the observed measurement error, and analyze validity and reliability of the improved constructs.<sup>5</sup> We then study the predictive validity of both the original scales and the improved constructs and analyze the extent to which the skills measured predict agricultural productivity and economic decisions related to the adoption of advanced agricultural practices.<sup>6</sup> This tests the potential role played by these skills in agricultural production, and shows that they might be important omitted variables when not included in the analysis of agricultural decision-making. More generally, understanding the role of skills in agricultural decisions can be key to inform policy interventions that directly aim to improve skills, or the success of which could be conditional on the skills of the target population.

Almund et al. (2011)
approximately one-third satisfy the Poverty 1 criterion, 70 percent satisfy the Poverty 2 criterion, and 25 percent satisfy both criteria. We run separate regression models for three summary outcomes of the intervention on both students and parents: the overall index of student achievement, the indicator for enrollment in seventh grade, and the overall index of parental engagement. Estimation

> 13For details on the Poverty 1 index, refer to `https://www.gob.mx/cms/uploads/attachment/file/ 685308/Nota_t_cnica_IML_2020.pdf` , accessed on August 2022.

22