rafmacalaba/data-use-mentions-extended huggingface dataset:
(iii) During the last fiscal year, did this establishment spend on research and development activities, either in-house or contracted with other companies, excluding market research surveys?

The answers to these questions, coded as binary indicators, measure a firm’s engagement in innovation with respect to products, processes, and R&D, respectively. As defined in the questionnaire, these innovations can be more advanced inventions or production processes, or small improvements to existing processes and products.<sup>9</sup> To address the potential subjectivity of responses to these questions, WBES data are collected by interviewers guided by detailed instructions on what does or does not count as a “new or improved” product or process and as “R&D”.<sup>10</sup> Firms responding positively to questions (i) or (ii) were asked to describe the new or improved product or process in detail. (In case of multiple innovations by a firm, the respondent was asked about the product or process considered to be most significant as identified by the value of sales for product innovations and impact on operations for process innovations.) Further, firms responding positively to question (i) were asked whether the new or improved product (good or service) was also new for the establishment’s main market.

These questions should prompt respondents to exercise care in providing responses. In view of the above, as well as the general quality control measures adopted in collecting WBES data, the innovation information in WBES is deemed to be reliable. Indeed, it has been used in several peer-reviewed papers that study firm innovation in developing countries (Ayyagari, et al., 2011; Paunov, 2016; and Paunov and Rollo, 2016).<sup>11</sup> To examine how urban agglomeration affects firm propensity to undertake innovation-related activities, it is crucial to know the location of firms, ideally measured in a consistent way across countries. Publicly available WBES provide information on the
with high government debt than in those with low debt (World Bank 2015). In 2020, in part because of massive monetary policy accommodation by advanced-economy central banks, such a divergence was not evident: fiscal support packages were similar in countries with higher and lower debt (see discussion above).

Not only the size but the effectiveness of fiscal policy could be constrained when governments face high debt levels. Empirical evidence suggests that fiscal multipliers are smaller when government debt is higher or fiscal positions are weaker, whether defined in terms of debt or deficits (Huidrom et al. 2020; Ilzetzki, Mendoza, and Vegh 2013).

# 3.2.4. Slowing growth of output and investment

Higher debt typically comes with higher debt service. Since 2014, long-term interest rates for EMDEs have declined but their interest payments have risen (Figure 4). In commodityexporting EMDEs, interest payments, as well as interest rates, surged around 2015 as these economies rapidly accumulated debt to buffer the impact of a steep plunge in commodity prices (Supplementary Appendix Figure S2; Baffes et al. 2015). After the spike, a gradual decline in interest payments has been accompanied by an upward trend in long-term interest rates.

Spending on higher debt service needs to be financed through some combination of increased borrowing, increased taxes, and reduced government spending. Spending cuts may even include spending on critical government functions such as social safety nets or growthenhancing public investment (Debrun and Kinda 2016; Obstfeld 2013; Reinhart and Rogoff 2010).

High debt could also weigh on growth because it is a source of uncertainty about macroeconomic and policy prospects (Lo and Rogoff 2015). This includes risks that the government may need to resort to distortionary taxation to rein in debt and deficits. Higher interest rates and uncertainty would tend to crowd out productivity-enhancing private investment and weigh on output growth. Crowding-out of private investment due to high public debt is documented in Huang, Pagano, and Panizza (2020), and with data for China, and Panizza
to behave in accordance with fraudsters’ scripts, and become victims of specific fraud schemes. Furthermore, the need for social network expansion also increases the amount of social interactions of credit-constrained people, which would increase the probability of encountering fraudsters.

Based on the analysis above, we propose our hypothesis. Households subject to credit constraints from formal credit institutions are more likely to suffer from fraud victimization. Credit constraints make people less patient and more susceptible to fraud schemes disguised with high returns in short periods. Credit constraints increase the needs of creditconstrained people for social network expansion, which in turn may increase their amount of social interactions and therefore the probability of encountering fraud, in addition to inducing them to behave more cooperatively to disguised fraud schemes, both of which increase the chance of fraud victimization.

# **3. Data**

We rely on data from the Chinese Household Finance Survey (CHFS), conducted by the Survey and Research Center for China Household Finance at the Southwestern University of Finance and Economics. This survey was started from 2011 with a sample of 8,438 households covering 25 provinces in both urban and rural China. The CHFS sample size is enlarged every other year with a new wave of the survey. The most recent wave was conducted in 2019 with 40,000 households. We use wave 3 (CHFS-III) in 2015 because this is the only wave with nationally representative information on fraud victimization.

CHFS adopts a three-level (county, community and residents) stratified random sampling method. In CHFS-III, firstly 350 counties were randomly selected nationwide, then four communities within each county were randomly chosen, and finally, 25-50 (20) households in each community in urban (rural) areas from each community were randomly selected. The final sample consists of 37,000 households in 1,394 communities in 350 counties, which are located in 29 provinces.

There are several major advantages of using CHFS-III for this research. First, apart from information on demographics, CHFS contains a wide range of information on income
- Time heterogeneity in bank tax adoption and bank size across US states. This policy is not simultaneously adopted by all states, with some states being early movers (Massachusetts and Maine in 1812) and others at late stage (New York and Connecticut in 1839 and 1850). We combine the size of the bank with the policy introduction at state level.

In both cases, we exploit time-series variation, exploiting monthly data for the case of Ethiopia and yearly data for the USA, combining this with the use of a difference-in-difference analysis. The results do not seem to reject the mechanism we proposed in the previous section.

# **2.1 Evidence from Ethiopia**

In this section, we present some empirical evidence on Ethiopia<sup>7</sup> and the behavior of its local private banks, exploiting the introduction of a new financial regulation measure introduced in April 2011.

> 7Ethiopia is the second-most populous country in sub-Saharan Africa with a population exceeding 90 million. It is also among the world’s poorest countries: per capita income lies at $500 (World Bank 2013), substantially lower than the regional average, yet growing steadily at more than 10% per year in the last decade. The government’s role in the economy is very active, and the financial sector is not an exception. The Ethiopian banking system presents a large state-owned bank, the CBE, which accounts for 50% of the market, and 14 private commercial banks, which account for the remaining part (in 2011). This contrasts with the trend observed in sub-Saharan Africa and across

16
3 Tracking Data**

We estimate models (6) and (7) for 1,799 functional urban areas (FUAs) whose data provide at least 30 degrees of freedom for estimation.<sup>11</sup> Table 4 enumerates the outlying observations that are removed prior to estimation. Observations with standardized z-values greater than 5.0 have been identified as outliers.<sup>12</sup> As the table shows, 1,761 of 1,799 FUAs have no outliers. Single outliers have been removed for 35 FUAs, 2 outliers for 2 FUAs and 3 for 1 FUA.

> 10 See particularly Weir et al. (2021) for a useful discussion of potential effects from year-to-year variability caused by differences in atmospheric circulation.  We address this issue in Section 5.5. 11 The sample is much larger than the sample used in Section 4 because models (6) and (7) do not absorb degrees of freedom with dummy variable controls for grid cells.

> 12 The z-value of an observation is its distance from the mean, measured in standard deviations.

12
,492|2,273|2,815|1,404|

NOTES: This table compares the socioeconomic characteristics of firstborn-boy (FB) and firstborn-girl (FG) families during the pre-ultrasound period and during the two post-ultrasound periods in the REDS sample. SC, ST, and OBC denote Scheduled Castes, Scheduled Tribes, and Other Backward Classes, respectively. The sample is restricted to first children who were alive at the time of survey.

Table A.5: Actual and ideal fertility and fraction of sons; pooling all families

||(1)<br>Actual|(2)<br>Ideal<br>Ac|(3)<br>tual - Ideal|
|---|---|---|---|
|**1. Pre-ultrasound: 1973-1984**||||
|Fertility|2.98|2.54|0.44|
|Fraction of Sons|0.58|0.57|0.01|
|N||23,414||
|**2. Post-ultrasound: 1985-1994**||||
|Fertility|2.17|2.47|-0.30|
|Fraction of Sons|0.55|0.56|-0.01|
|N||45,920||
|**3. Post-ultrasound: 1995-2005**||||
|Fertility|1.90|2.31|-0.41|
|Fraction of Sons|0.53|0.55|-0.02|
|N||44,813||

NOTES: This table reports the actual and ideal fertility and fraction of sons as reported by the mother, and the gaps between the two, over the three time-periods in our sample. Ideal fraction of sons = _idealboys_ + _ideal_ <u>(0</u> _._ 5 _kids∗idealeither_ <u>)<
hygiene practices, stimulating cognitive and language development, and providing warm and responsive social-emotional support in the early years (Cuartas, 2022). Studies demonstrate that increased years of schooling among mothers positively relate to increased maternal engagement with young children, through more stimulating activities, improved ECE attendance, and reduced use of harsh discipline (Cuartas, 2022; Hasan et. al, 2020). Maternal education has been shown to serve as a mediation pathway through which greater cognitive and verbal stimulation from mother to child occurs, which relates to children’s language skills, social-emotional responsiveness, and later social competence (Huang et al., 2022; Hasan et. al 2020).

Without sufficient knowledge, skills, and mental health supports in place, caregivers in Pakistan are not providing their young children with the early psychosocial stimulation they need to learn and thrive. Everyday activities that support learning in young children include telling stories, singing songs, going outside together, playing, reading books, or naming or counting objects. Rates of adequate early stimulation in Pakistan are lower than the average rate for least developed countries (48 percent) and considerably lower than the average rate for low- and middle-income countries (69 percent) for children aged 2-5 years (Lu et al., 2020). Based on data from the Multiple Indicator Cluster Survey (MICS), among Pakistani children aged 3-5, stimulation rates for the prior three days are just 35 percent in Punjab (2017-18), 45 percent in Sindh (201819), 35 percent in Khyber Pakhtunkhwa (2019), and 23 percent in Balochistan (2010) (Tomlinson et al., forthcoming). Children younger than 3 years old, whose brain development is at its most rapid and sensitive, have equally or even lower odds of experiencing adequate stimulation through responsive interactions, ranging from 26 percent in Punjab, to 29 percent in Khyber Pakhtunkhwa to 45 percent in Sindh (Tomlinson et al., forthcoming).<sup>2</sup> However, more
_Figure 13: Vulnerability threshold and the official and moderate poverty lines, current prices BYN_ <!-- Start of picture text --> 350<br>300<br>250<br>Vulnerability threshold<br>200 Official poverty line<br>Moderate poverty line<br>150<br>100<br>2014q1 2014q2 2014q3 2014q4 2015q1 2015q2 2015q3 2015q4 2016q1 2016q2 2016q3 2016q4 2017q1 2017q2 2017q3 2017q4<br><!-- End of picture text --> Note. Vulnerability threshold is estimated as an average predicted income for the probability of being poor in the interval between 0.09 and 0.11. Official poverty line varies with the individual’s age, the figure represents the average for the HBS quarterly samples.

Source: Authors’ calculations based on quarterly HBS 2014q1‐2017q4 There is some, but not complete overlap between vulnerable and moderately poor groups. We find that 6.7 percent of the population are both vulnerable and moderately poor; these people reside predominantly in rural areas; have low education and typically live in households with children. At the same time, 8 percent of the population are vulnerable but not moderately poor; these are mostly the people with low socio‐economic standing (rural area residents; low‐educated; partially employed or non‐employed) and relatively high incomes (most of them come from the 4<sup>th</sup> ‐7<sup>th</sup> disposable income deciles). Also, 12.6 percent of the population are moderately poor, but not vulnerable; these are mostly employed, working‐age people from large cities, who are not predicted to be vulnerable based on their socio‐economic characteristics, but fall into moderate poverty due to low wages.

# 4. Main Reasons for Well‐Being Deterioration and Coping Strategies Reported by Households

## 4.1. Change in the well‐being of households and reasons for its deterioration

Households are asked on an annual
selfreported losses, which suggest that 100 hours of blackout would decrease the annual utilization rate by about 0.29 percentage points in the model without interaction (Table 4 model I). This makes us confident that our identification strategy in the previous section is valid and thus also provides reliable estimates for water and transportation.

A similar result is obtained for water outages, measured by the annual total outage duration. In this case, 100 additional hours of water outages are estimated to decrease utilization by up to 0.19 percentage points on average.<sup>5</sup> From among the three infrastructure variables considered here, water has the weakest explanatory power according to standardized beta coefficients (not shown).

For transport, there is also a significant impact of transport problems on the utilization rate, but numerical results are difficult to interpret since the metric is qualitative. In fact, it does not allow to investigate marginal effects, and we can only compare the _proportion_ of direct to indirect effects.

For assessing the impact of marginal increases in transport reliability, we use an alternative measure of transport reliability, the Logistic Performance Index’ (LPI) “timeliness” sub-indicator (Jaramillo et al., 2018). LPI can be used to proxy reliability, resilience and steadiness of transport infrastructure, i.e. the absence

> 3 Note that for transport, various country-level characteristics correlate with transport quality, making it hard to pin down indirect effects.

> 4 The average firm in the sample experiences 253.5 hours of blackout annually, while 10% of all firms face 720 hours or more.

> 5 The majority of firms does not face any water supply disruptions while a few firms have to cope with long and frequent interruptions. The top 5% of most disrupted firms face at least 120 hours, the top 1% faces 2700 hours or more.

21
What do these results mean? The magnitude of the coefficient seems to be large if we take into consideration the elasticity of growth per capita with respect to changes in the share of the working age population. However, change in age structure is a low frequency process. For example, between 1950 and 2010 the average per capita growth in Brazil was about 2.77, while the share of working age population increased by 0.2 percentage point. Assuming a coefficient of 1.5, changes in the working-age population share would have contributed to about 0.3 percentage point, which is about 11 percent of the average growth observed over this period.

Another issue regarding the share of working age population is that it might be driven by changes in the share of children or elderly in the total population. The descriptive statistics suggest that the rising share of the working age population is driven by a decrease in the share of children in most of countries. So, using a similar specification with share of children instead of working-age population, negative and significant coefficients should be expected, as presented in Table 7.

**Table 7 Effects of changes on the share of children (0-14) on the population on real GDP per capita growth**

||Fist-Differ<br>|ence<br>||Panel Fixe<br>|d-effects<br>||Generalize<br>|d Method o<br>|f Moments<br>|
|---|---|---|---|---|---|---|---|---|---|
|Variables|S1|S2|S3|S1|S2|S3|S1|S2|S3|
|Δ share of CHD|-0.384***|-0.351***|-0.387***|-0.304***|-0.253
these issues, a novel urban computable equilibrium model is developed. There are several key features of the model. First, it is founded in micro-economics; the behavior of households and firms, including their location choices, and their interaction through markets for goods and services, labor and land is directly modeled. Second, the model is highly spatially disaggregate, containing 266 distinct areas, the ‘unions’ of Dhaka, together with detailed knowledge of economic and residential activity in each and the transport network linking them. Third, the model covers three distinct types of productive activity, two laborskill levels, and explicitly models housing choice. These distinctions enable us to capture developing country features of a large ‘non-tradable-services’ sector that corresponds approximately to informal employment. Fourth, we capture the distinction between modern, potentially multi-storey, housing and traditional low-quality housing with two different building technologies, ‘modern’ increasing capacity by building tall, and ‘traditional’ where urban density can be increased by ‘crowding’. The model is fitted to data on Dhaka and then used to simulate the effect on Dhaka as a whole of alternative development strategies for land in East Dhaka.

In containing multiple sectors and skill levels, the model used draws on an older computable general equilibrium (CGE) tradition. It is important to have these features, particularly in a developing country city, precisely because of the wide range of skills contained in the labor force and the extreme heterogeneity of productive activities, including the high share of the 2
(Fajgelbaum et al., 2020; 2021 update cited in Fajgelbaum and Khandelwal, 2022) shows that the latter actions essentially fully undid the former.

4.1.3 The Tension between Trade Policy and Fiscal Policy Space Classical trade policy reforms typically do not involve large real resource costs: lowering tariffs or liberalizing services policies does not require financial investments of comparable scale to infrastructure upgrades or fiscal transfers. But many of the policies examined in this paper, or subsumed under the heading of reducing trade costs, do not fall into that framework. Implementing active labor market policies or improving infrastructure and connectivity, as well as many other people-, sector-, and place-oriented policies, involve significant real resource costs. As such, they require the government to have sufficient fiscal space to contemplate responsibly financing the chosen policy measures. This issue is all the more salient at the present time, when necessary fiscal support measures, as well as activity-based reductions in tax takes, due to the COVID-19 pandemic, have reduced fiscal space for countries at all income levels.

In high income countries, and some middle-income countries, the question of financing public spending has little if any connection to trade policy, because trade taxes are not a significant source of government revenue.<sup>6</sup> In terms of averages, the proportion is 6.4% in lower-middle income countries, 3% in upper middle-income countries, and 0% for high income countries (unavailable as an average for low-income countries). There are many countries, however, where the connection is much tighter. Although data availability is only partial, the World Development Indicators record eight low and middle-income countries where the proportion of trade taxes in government revenue is 20% or higher, and 35 where it is 10% or higher. The largest proportion is 37%, in Somalia. So for a significant number of countries, there can be a tension between liberalizing trade by lowering effective tax rates— which tends in turn to lower tax revenues—
Third, the education of adults in the household has a significant relationship with the enrollment of children in practically all the countries studied, even after controlling for household wealth. The results do not however confirm the notion that the education of adult females is _always_ more strongly related to the education of children that that of adult males. While this is true in some countries, the story is complicated and varies across countries. The findings do however confirm that in a subset of countries with a large female disadvantage in enrollment, the education of adult females has a larger impact on the enrollment of girls than that of boys. This outcome is consistently found in India, Nepal, and Pakistan.

Fourth, the presence of a primary and a secondary school in the community has a significant relationship to enrollment in some countries only (notably the Western and Central African countries). Moreover, the presence of a school does not appear to be differentially related to the education of boys and girls in a systematic way across countries, even those with a high female disadvantage in enrollment.

# II) Data and methodological approac

The data used in this paper are those collected as a part of the Demographic and Health Surveys (DHS). These are large, nationally representative household surveys, and the data from 57 surveys (from 41 countries) are analyzed here.<sup>2</sup> Basic information on the number of households in each sample, as well as the number of individuals in the sample of 6 to 14 years olds, and 15 to 19 year olds, are in Table 1. The DHS were not designed to collect information on education. Rather, they were a systematic data collection effort whose main purpose was to obtain nationally representative and cross-nationally comparable household-level data related to 2 There are three main designs of the survey instrument. DHS I surveys were carried out between 1985 and 1989 and do not contain the requisite education data. DHS II were collected between 1990 and 1993, and _5_
59|0.10|0.44|0.69|0.20|0.51|0.63|0.52|
|Employed in Industry|0.13|0.18|0.10|0.20|0.14|0.06|0.16|0.14|0.07|0.11|
|Employed in Services|0.28|0.51|0.17|0.49|0.27|0.13|0.47|0.21|0.17|0.21|
|**Access to basic services**|||||||||||
|Improved water|0.80|0.90|0.76|0.94|0.89|0.63|0.87|0.76|0.68|0.82|
|Improved sanitation facility|0.40|0.53|0.34|0.57|0.47|0.21|0.47|0.38|0.26|0.39|
|Access to electricity|0.64|0.88|0.53|0.90|0.69|0.40|0.77|0.59|0.47|0.62|

Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods Table C3: Estimation results of regressions on log expenditures with DOU classifications

||||Log of per|capita nominal|consumption ex|penditures|||
|---|---|---|---|---|---|---|---|---|
||(1)<br>All|(2)<br>All|(3)<br