rafmacalaba/data-use-mentions-extended huggingface dataset:
For instance, information regarding new technology may spur trading activity due to conflicting opinions on the future benefits of the innovation. The subsequent economic growth is a result of technological advancement rather than greater trading volumes (Levine 2005). This is one of the reasons why we apply the BMA, which is designed to address these issues.

Rajan and Zingales (1998) initiate the research on the finance-growth nexus using industry-level data. They show that more developed financial markets decrease firms’ cost of external capital. They also find evidence that industries that are relatively more dependent on external finance grow faster in countries with better developed financial intermediaries. Building on this methodology, Claessens and Laeven (2005) arrive at a similar conclusion using measures of bank competitiveness. They find that more competitive banking systems benefit financially dependent industries. Next, Beck et al. (2005) show that industries typically composed of small firms enjoy relatively superior growth rates in countries with developed financial sectors. This is consistent with theory positing that financial development is a crucial factor in alleviating financial constraints. Also, Hasan et al. (2009) examine the effect of financial development on regional growth in Europe and find that the efficiency of financial intermediaries (measured by bank efficiency) is substantially more important for growth than financial depth (measured by outstanding credit). Berger et al. (2004) also provide international evidence on the importance of bank efficiency for growth. Similarly, using German data, Koetter and Wedow (2010) find that bank efficiency is positively related to growth. Jayaratne and Strahan (1996) report that the relaxation of bank branch restrictions in the United States improves growth. Interestingly, they find that the relaxation of restrictions does not increase the volume of bank lending but improves loan quality. In addition, Cetorelli and Strahan (2006) extensively examine the mechanism how financial development affect growth and find that 7
-38 -

connection between smaller classes and achievement<sup>3 3</sup> , the finding has limited relevance in developing countries for several reasons. Most importantly, the experiment involves reducing class size from a much smaller initial level than is typical in the average low-income country. Gains in student learning also depends on changes in teaching and learning practices-from passive learning by students listening to a teacher talking in front of the class, to hands-on learning-that may be difficult to implement in systems without adequate support and resources for teacher training and classroom experimentation.

Evidence from the cross-country data used in the present study support the claim that lowering the pupil-teacher ratios has few payoffs (if any) in student learning in the typical context of developing countries. In table 9 we regressed achievement scores on international mathematics and science tests among 9-14 year-old children against selected regressors.<sup>34</sup> The results strongly suggest that variation in pupil-teacher ratios in the sample range has no impact on student achievement; in all three regression models, the coefficient on the indicator is statistically not different from zero. Instead, the educational attainment of adults in the country and the adult literacy rate show strong and positive impact. The estimates indicate that a oneyear rise in adults' average school attainment raises a country's performance on international mathematics and science tests by 0.84 points (or 0.17 of a standard deviation from the sample mean), and that a one percentage point advantage in the adult literacy rate raises test scores by 0.30 points (or 0.06 of a standard deviation from the sample mean).

**Table 9: Regression estimates of the correlates of scores on international mathematics and science tests, 1990s**

|.<br>Ln (per capita GNP, 1993)|~~1.11-<br>(1.5)||-|
|---|---|---|---|
|Adults' average
We find that even in this case (red “09” and “20”), the relative differences between reported and composite estimates are small. Thus, the NO2 composite estimate is completely reasonable.

# **6 Applications**

� We now turn to a number of applications of the NO2 composite estimate. The difference _y − z_ � between the composite estimate _y_ and reported GDP _z_ is the amount of _misstatement_ in GDP. When misstatement is positive, this indicates GDP understates true economic activity; when it is negative, this indicates overstatement. The standard deviation of misstatement is, where RMSE is the root mean square error from the regression to obtain the proxy. An ordering of all world countries based on this standard deviation provides a natural objective ranking of GDP accuracy. Such a world ranking is uniquely made possible by NO2, which both has data for all world countries, and explanatory power for fluctuations in each.

Table 7 presents estimates of this standard deviation over 2005-2020, by-data quality grade, ranked from highest variability to lowest. Thus, those at the top of the list have the most erroneous GDP data. At the bottom of the table, the overall means of standard deviations increase from grade A to E, indicating that our grades corroborate the grades on average, and are therefore not unreasonable. Specifically, the errors range from 0.25 to 5 percentage points on average, in line with Henderson et al. (2012)’s estimate of trend growth errors up to 3 percentage points. But at the same time, there are individual instances where our objective ordering represents a significant reordering of the existing subjective rankings. For example, Algeria, the most accurate of grade D countries, would be ranked as more accurate than Ireland, the most inaccurate of grade A countries. Overall, we judge that the Republic of Yemen had the most inaccurate GDP data over this period, and Australia the most accurate.

One potential issue with the Penn World Tables A through E grades is that they
larger level of spatial aggregation increases the estimated impact of the shock—although not significantly—without affecting statistical significance (table 4.6). In future work it would be interesting to explore much larger areas of aggregation if this is possible (i.e., if enough variation in the hazard is left and GPS data are available) to see if this affects the results.

**Table 4.6. Results for different areas of spatial aggregation of hazard data**

||**Maize(GPS**<br>|**coordinate c**<br>|**ountries only)**<br>|**2**<br>|**0 km radius**<br>||<br>|**50 km radius**<br>||
|---|---|---|---|---|---|---|---|---|---|
||**10km**|**20km**|**50km**|**Maize**|**Highland**|**Roots**|**Maize**|**Highland**|**Roots**|
|**Landed h**|**ouseholds**|||||||||
|Hazard|0.066***|0.075***|0.086***|0.055***|0.045***|0.152***|0.056***|0.045***|0.154***|
||[4.252]|[4.608]|[4.765]|[3.852]|[4.495]|[8.
The commune questionnaires collect information on commune characteristics that affect the local living standards such as infrastructure, demography, and socioeconomic conditions. Commune data can be linked to household and individual data, but are only available in rural areas.

The 2006 VHLSS covers 9,189 households. The number of individuals in the survey is 39,071. This sample is representative for rural and urban areas, and all eight geographical regions.

# **3. Parental disability and child education in Vietnam**

One of the difficulties in analyzing disability is in definition and measurement. In this paper, we follow the approach of the UN Statistical Commission‟s Washington Group on Disability Statistics (Washington Group, 2009), which relies on the functional model of disability underlying the International Classification of Functioning, Disability and Health (WHO, 2009). Incorporated in this approach is the social model of disability, which conceives of disability as emerging from the interaction of functional limitations with barriers in the environment. That is a person is disabled – that is, not capable of fully participating in various aspects of society – if they have difficulties in functioning that are not accommodated for in the environment – where environment is interpreted broadly as including the physical, cultural, and policy environments.

Identifying disability in this way is quite complex. For the purposes of this paper – and following the Washington Group recommendations for assessing the impact of disability on equal opportunity – this paper uses the presence of difficulties in basic activities including seeing, hearing, walking, cognition, communication, and self-care as an operational proxy of functional limitation that puts him or her at risk of being disabled in the social model sense (Mont, 2007). Following Loeb, Eide, and Mont (2008), we will define a person to be disabled if she or he has a little difficulty in at least two of the 5
the results are robust. Our analysis shows that assessor characteristics and decision protocols do not significantly affect the classification of places. And the prediction approaches used to extend the classification beyond the original sample do not make a major difference either. Therefore, the proposed methodology may contribute to a growing literature in urban economics that applies innovative approaches to the identification of urban areas (de Bellefon et al. 2018, Diegel et al. 2019, Rozenfeld et al. 2011).

The paper is organized as follows. In Section 2 we present the stratified sampling strategy. In Section 3 we describe the images used for assessment and the collection of human judgments from different assessors. We also compare classifications based on different human judgment exercises. In Section 4 we introduce the additional sources of data and apply three approaches—Logit model, and LASSO and random forests methods—to predict the urban status of places in the sample. In Section 5 we show the results on the estimated urbanization rate for India in 2011, and also for fine administrative types and for states. Finally, we compare our results with urbanization rates from other studies.

# **2. A sample of places**

Important information about cities, towns and villages is captured by the administrative boundaries of localities. These boundaries are generally built on historical data and tend to reflect the best knowledge available on the spatial distribution of economic activities and people. They also define the constituency to which each local government provides services and is accountable. In this study, we used the administrative units adopted by the Census of India as our unit of analysis.

## _The universe of places_

To compile the universe of places we took advantage of newly digitized boundaries of administrative units in India, available down to the town or village level, to define the place of analysis. These boundaries are based on India’s _Administrative Atlas_  _2011_ (ORGI 2011b). They were generated as part of a broader research project, the Spatial Database for South Asia (Li et al.
transient, and most agencies are faced with fiscal constraints and shorter service terms. In a protracted crisis, IDPs may quickly fall back into extreme poverty with the cessation of aid, leaving a void which in many cases can only be filled by the government. More so, when the acute shocks subside, most returning IDPs come home to destroyed property, infrastructure, and lack of jobs, requiring substantial support from the government to set up their lives.

To this day, how to provide a lasting solution to the plight of IDPs in general and during their return is still a big challenge to governments, policy makers, and humanitarian agencies. At the forefront of proposed reforms for welfare improvement of IDPs is aligning the short-term humanitarian assistance system with the long-term government social protection system (Gentilini et al., 2018; UNHCR, 2019). Although there

> 2 IDMC 2020 displacement data. See link

3
**Jamaica's Food Stamp Program: Impacts on Poverty and Welfare**

# **1 Introduction**

Over the last two decades, the proliferation of macro-economic adjustment and stabilization programs has given rise to a concem with the associated social costs, and an interest in the role of safety nets for the poor during the process of economic reform. However, few countries have been able to introduce safety nets _during_ economic reform; and among these countries, few have had available the datasets to facilitate an _evaluation_ of the role of the safety net in cushioning adverse short-run outcomes of adjustment on the poor. Jamaica is one of the countries which does have available nationally representative datasets (some in _panel_ form) that cover such programs as the Poor Relief and Public Assistance, the School Feeding Program and the Food Stamp Progran -all operating alongside the economic reform program. This paper will focus on the most important of them all: the Food Stamp Program (FSP).

There have been few impact evaluations of the Jamaican FSP. Studies to date have focused on access and targeting efficiency, but only rarely on the consumption effects or poverty impacts. For example, Grosh (1992) gives an overview of the FSP in terms of administration and operations, participation and targeting efficiency. Grosh (1992) finds that the FSP was better targeted than the previous general food subsidy program: 57 percent of FSP benefits accrued to the poorest 40 percent of the population in 1988, while 6 percent of FSP transfers accrued to the wealthiest quintile. This contrasts with the food subsidy program which resulted in 34 percent of the food price subsidy accruing to the
may diverge from their stated attitudes and intentions because they still lack the skills to enact change, despite wanting to.<sup>19</sup> One exception to the general pattern of improved attitudes concerns sexual autonomy: both treated men and women believe wives are justified in refusing sex in fewer situations than the control group. This unique domain where attitudes worsened across all groups aligns with qualitative evidence of strong public resistance to messages about women’s sexual rights.<sup>20</sup> While this consistent negative shift may not have increased friction within couples, it could have contributed to backlash through hearing messages on this topic in the training and community fora.

The program also increased treated women’s aspirations for agency, similar to spillover women. Treated women wanted more input in household expense and childbearing decisions (six and five percentage points respectively; table 4 and figure 5). While treated women’s husbands, unlike spillover men, did not seek more decision-making power, even this smaller increase in treated women’s aspirations for agency points to a potential shift in dynamics within the household power structures that may have been enough to trigger backlash.

We now turn to changes in treated men’s behaviors. Program effects on treated men differ markedly from spillover men, with some progressive changes. However, these new attitudes and behaviors adopted by treated men may have exposed them to community shaming, potentially pressuring them to reassert traditional identities through violence.

Table 3 reveals several progressive behavioral changes among treated men. Time-use diaries show both treated men and their wives increased time spent on domestic and care work, traditionally considered women’s responsibility. This was aligned with the program’s emphasis on positive parenting and active engagement in child development, and represented a behavior shift potentially visible to other community members. The program also enhanced treated women’s household decision-making power, as reported by both spouses (table 4).

Marriage formalization provides further evidence of publicly visible behavior change: among couples not formally
# **III. Data and quality measures**

# **_i. Robot data_**

Data on the use of robots across countries and industries are provided by the International Federation of Robotics (IFR), the same data used by Graetz and Michaels (2018), Acemoglu and Restrepo (2020). IFR provides information on annual shipments (sales) and a measure of industrial robot stock across roughly 100 countries and manufacturing industries.<sup>9</sup> Their definition of industrial robots comes from the International Organization for Standardization (ISO) 8373:2012. From this definition, a robot refers to a machine that embodies the following characteristics: can be reprogrammed, is multipurpose in function, allows for physical alteration, and is mounted on three or more axes.

IFR constructs this data set by consolidating information on industrial robot sales from almost every industrial robot supplier in the world. From the data we know the number of robots that are being sold to manufacturing sectors in countries over time. In addition, the IFR data provides information on the robot types (by application such as handling, processing, welding, assembling and so on), which is only available at the country level. One limitation of the data is that robot sales and stock are denominated in units, and thus information on the value of the assets is not included. Following Graetz and Michaels (2018), we construct robot stock at the country-industry level by taking the initial stock starting value from the IFR, then adding to this robot sales from subsequent years and assume annual depreciation of 10%.

One of the limitations of our study is that our robotics data is not comparable between developed and developing countries before 2000, which constrains our sample period to 2000-2015 (IFR 2018). Furthermore, robots are measured at the industry-country-year level therefore we cannot assess differences in the use of robots across establishments within an industry. However, one key

> 9 Note our final sample of countries is 59, after dropping jurisdictions with
2000, falling to 7.5 percent of the population by 2019. The share has also fallen if one includes the surrounding region of Tashkent together with the city: this figure totaled about 4.5 million in 2000 (18.3 percent of the total population) and 5.4 million in 2019 (16.3 percent).

**Figure 6: Place of Origin and the Primary Reason for Migration**

<!-- Start of picture text --> Place of Origin Primary Reason for Migration<br>2% 1% 3%<br>4% 2%2%<br>19%<br>6% 4%<br>9%<br>21%<br>50%<br>77%<br>Work Related To Join Family<br>Uzbekistan Kyrgyzstan Moving with Family Study<br>Tajikistan Russian Federation Security Government moved<br>Kazakhstan Other CIS Other<br><!-- End of picture text --> _Notes: Derived from survey responses in the L2CU Baseline. Author’s Calculations. Migration is defined as moving from one district (or another country) to their present location in Uzbekistan. This information is presented for the set of people who report ever having moved in the nationally representative baseline survey (2018)._ Thus, according to official estimates, the city of Tashkent was, by far, the slowest growing “region” in the country between 2000 and 2019. Tashkent’s population only grew by 17 percent, while the Tashkent region (surrounding the city) was the next slowest growing, at 23 percent. The total population of Uzbekistan grew by much more over this time; it was about 36 percent larger in 2019 than in 2000, more than double the increase recorded in Tashkent. This trend is driven by higher fertility rates in urban areas paired with very low rates of migration.

But despite these low rates of migration and urban population growth, labor markets
Enterprise Surveys and World Value Surveys. Notes: The sample includes 61 countries, of which 28 are classified as more traditional and 33 as less-traditional. All models control for manager experience, indicators of firm age, legal status, size, whether the firm exports, whether it has financial statements certified by external auditors, the percentage of the firm that is privately owned, and industry, country, and year fixed effects. Standard errors are clustered at the country level. *** significant at 1%, ** at 5%, * at 10%.

Despite female-managed firms being comparably risky and productive, and in fact more profitable than their male counterparts, they operate with lower credit levels, indicating potential sub-optimal credit allocation. The reasons for this could stem from either demand-side constraints, where women-managed firms request lower credit amounts, or supply-side barriers, such as explicit or implicit discrimination by loan officers in allocating credit to these firms. While our data does not allow for precise identification of the source of sub-optimal credit allocation,<sup>21</sup> it suggests a potential misallocation of capital, particularly when considering the higher profitability and no significant differences in TFP for females compared to male-managed firms. Consequently, we now examine the relative efficiency of capital utilization in female-managed firms.

# **3.2 Capital Misallocation**

Drawing on extant literature, we present firm-level empirical indicators of capital misallocation. Notably, these indicators are at the firm-level, in contrast to widely known misallocation metrics (e.g. Restuccia and Rogerson, 2017; Hsieh and Klenow, 2009; Duranton et al., 2016, 2015b) that are aggregated following the same concept. Specifically, misallocation indicators at the level of a unit of analysis can be aggregated to construct meaningful misallocation metrics that allow for benchmarking allocative inefficiency relative, for instance, by gender, location, or sector. Our conceptual discussion of misallocation indicators at the firm-level is followed by a presentation of estimation results on gender-based differences in misallocation.

> 21The
# **6. References**

<mark>Djoumessi, Y. F., & Mbongo, L. D. B. E. (2022). An analysis of information Communication Technologies for natural disaster management in Africa.</mark> _<mark>International Journal of Disaster Risk Reduction</mark>_ <mark>,</mark> _<mark>68</mark>_ <mark>, 102722.</mark> <mark>Kianersi, S., Jules, R., Zhang, Y., Luetke, M., & Rosenberg, M. (2021). Associations between hurricane exposure, food insecurity, and microfinance; a cross-sectional study in Haiti.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>145</mark>_ <mark>, 105530.</mark> <mark>Kolbe, A. R., Hutson, R. A., Shannon, H., Trzcinski, E., Miles, B., Levitz, N., ... & Muggah, R. (2010). Mortality, crime and access to basic needs before and after the Haiti earthquake: a random survey of Portau-Prince households.</mark> _<mark>Medicine, conflict and survival</mark>_ <mark>,</mark> _<mark>26</mark>_ <mark>(4), 281-297.</mark> <mark>Llorente-Marrón, M., Díaz-Fernández, M., Méndez-Rodríguez, P., & Gonzalez Arias, R. (2020a). Social vulnerability, gender and disasters. The case of Haiti in 2010.</mark> _<mark>Sustainability</mark>_ <mark>,</mark> _<mark>12</mark>_ <mark>(9