rafmacalaba/data-use-mentions-extended huggingface dataset:
all entry and exit is random there should be no differences between entering, exiting and incumbent businesses.

Another related problem is that a business climate that encourages more market experimentation might have a larger long run contribution but a smaller short run contribution from the creative destruction process. That is, the greater market experimentation may be associated with more risk and uncertainty in the short run so that it is only after the trial and error process of the experimentation has worked its way out (through learning and selection effects) that the productivity payoff is realized. Thus, a business climate that encourages market experimentation might have a lower short run contribution from entry and exit but a higher long run contribution from entry and exit.

In short, the gap between the productivity of entering and exiting businesses is not by itself sufficient to gauge the contribution or efficiency of the creative destruction process. In addition, different types of distortions might be acting simultaneously in a country. It might be that different policies act to subsidize incumbents (preferential treatment for incumbents), other policies artificially increase the barriers to entry (poorly functioning financial markets and/or regulatory barriers), while other policies make exit more random for some types of businesses (e.g., poorly functioning financial markets for young and small businesses). As such, there might be too little churning on some dimensions and too much on others, the gap between entering and exiting businesses might be too large on some margins and too small on others.

All of these remarks suggest the need for both caution and creativity in using the firm demographic and productivity dynamic statistics that we analyze below. On the one hand, even this brief discussion makes clear that simple cross country comparisons on specific dimensions may be misleading or inadequate. On the other hand, this discussion suggests that creativity needs to be used to examine the connection between the churning and productivity dynamics along multiple dimensions. In like fashion, this discussion helps make clear why it is likely important to exploit variation beyond simple country variation but
27]|[-0.05]|[0.20]|[1.88]|[0.33]|
|2018-2019 Recession X Distance|-1.09**|0.89|-0.12|0.42***|0.37***|
||[-2.22]|[1.23]|[-0.27]|[3.13]|[2.91]|
|Constant|4.54***|13.08***|-6.04***|6.22***|3.66***|
||[9.75]|[16.54]|[-5.08]|[42.07]|[14.06]|
|Observations|1,134|1,134|1,134|1,134|1,134|
|R-squared|0.23|0.47|0.04|0.42|0.39|
|Number ofprovinces|81|81|81|81|81|

Notes: t-statistics based on robust standard errors (clustered by province) in parenthesis *** p<0.01,

** p<0.05, * p<0.1. All regressions use data on all 81 provinces and employ province fixed effects. Distance to major city is computed as the shortest distance between the province centroid and the centroids of the provinces with the two largest cities (Istanbul and Ankara) using the Vincenty method. This is measured in units of 100 km.

20
off quickly and there were fewer than 2,000 daily infections a day in May. The third wave had the highest increase in daily cases, hovering around 12,000 registered infections a day in July 2021.

The government introduced various containment measures. It closed schools and educational institutions on March 17, 2020.<sup>8</sup> A first lockdown was imposed from March 26 to May 30, 2020. The authorities declared a ban on passenger travel via water, rail, and domestic air routes from March 24, and all public transport on roads was suspended from March 26, 2020 (Kamruzzaman and Sakib 2020). In response to the second wave, a second lockdown was enacted from April 5 to 13, 2021. In contrast to the first lockdown, factories remained open, and banks operated on a limited scale. Shops and malls re-opened from April 25 amid protests from the shop owners. A third lockdown was imposed for a week, starting on July 1, 2021, and was later extended until July 13, 2021. The restrictions were relaxed for a week from July 14 to allow economic activities centering around Eid-ul-Azha. Finally, the fourth lockdown was imposed from July 23 to August 5.

Despite clear differences in the severity of the containment measures during the four lockdowns, the OxCGRT Stringency Index quantifying the stringency of the containment measures varied only marginally. The average Stringency Index during the first lockdown was 91, and it hovered around 84 during the second. During the third and fourth lockdowns, the average stringency of the containment measures stood at 86 and 92, respectively.

# _7.2 Electricity Consumption in Dhaka during COVID-19_

Data on electricity consumption are available for all eight of the country’s divisions. To analyze the impact of COVID-19, we focus on Dhaka and aggregate the daily data to monthly frequency. To date, Dhaka has seen much higher numbers of infections than other divisions and accounts for nearly half of all deaths caused by COVID-19. Moreover, electricity consumption in Dhaka
Table 3 presents the number of jobs directly used in export production, the total number of jobs used in export production, and the (direct and total) job intensity of exports. The top panel includes only the subsample of countries that are present every year since 1997, which more accurately captures the evolution of the indicators than the bottom panel, which includes the whole sample (with varying number of countries over the years). Until 2007 both the direct and the total jobs contained in exports were increasing while between 2007 and 2011 both decreased. The job intensity of exports, on the other hand, has been steadily decreasing since 2001. This is different than what we saw for the labor intensity of exports, which has been decreasing since 1995 but experienced a slight increase between 2007 and 2011 (Figure 1). Notably, the change in the job intensity was much more pronounced than the change in the absolute number of jobs – meaning that the number of jobs fell relatively more than any shifts in export values. Again, this is consistent with a transition to labor-saving technologies in production.

**<u>Table 3. Total number of jobs in exports (JobX) and the job intensity of exports</u>**

|1997|2001|2004|2007|2011|
|---|---|---|---|---|
|_Balanced sample - 32 countries_|||||
|**JobX in thousands**|||||
|Direct<br>31,092|38,962|39,850|40,662|39,386|
|Total<br>66,187|87,825|88,311|89,265|83,553|
|**Jobs per $1 mil. of exports**|||||
|Direct<br>11|13|9|6|5|
|Total<br>24|29
cheaper, the proposed pricing structure would see significant savings, all coming from the richest 40 percent of households (JOD 95 million annually even assuming current levels of non-payment of bills, which, if addressed, could add another JOD 17 million to 60 million<sup>64</sup> ). The richest 20 percent would contribute 65 percent to the total savings. Overall poverty would decrease by 0.2 points while inequality would be reduced by 0.3 points compared to the baseline marginal impact of electricity residential tariffs.<sup>65</sup> 62 When the country removed the fuel subsidies in 2012 and food subsidies in 2018, it implemented cash schemes to compensate households for the increases in price (compensation for the fuel subsidy removal is not included in the current analysis since it is provided only when international oil prices exceed USD 100 per barrel, which they did not in 2018, and have not since 2014). To allocate the benefits, instead of targeting at the bottom, they used a combination of criteria to exclude clearly rich and thus non-eligible households. In the first case, the richest 30 percent of households were trimmed using a combination of self-reported income and ownership of cars, property and financial assets. In the case of the bread compensation scheme, about 20 percent of richest households were excluded using similar information. This was achieved by using a combination of self-reported information matched with information from administrative databases available at the time. New administrative data sources and the National Unified Registry (NUR) mean that the capacity to better implement such affluence testing is even greater now.

63 See current and reform scenario tariff structure in Appendix Table 9. We assume an inelastic demand, which means that the quantity of electricity purchased remains fixed as in the base scenario (no behavioural impacts). 64 Potential savings are estimated by applying the percentage change in the ‘survey’-budget, before and after the reform, to the administrative budget. Additional bill recovery from non-paying households in the top 40
limited number of dimensions of poverty, for example it does not provide information on health indicators. As such, two additional variables are derived from the accessibility analysis (Section 3.4), access to hospital and health facilities, and another measure, child stunting, is derived from the ‘Multiple Indicators Cluster Surveys’ (MICS) survey dataset using a small area estimation (SAE). SAE uses a regression formulation to distribute aggregated household information to a higher spatial resolution (e.g. from a national to a regional level), either within a survey data (using both aggregated and microdata) or across different surveys (Elbers et al., 2003). Here, we use a regression formulation using overlapping household information in the MICS data and the synthetic household data to construct one variable that is captured in the MICS data (child stunting) but not in the synthetic data (see Appendix B for details). We acknowledge that these variables covering household health status are an incomplete measure of health. We end up with nine variables to construct the MDP index as shown in Table 1.

In line with the OPHI MDP formulation, we also apply a weighting factor to the different variables, such that the three groups contribute equally to the MDP. In the end, the household’s MDP score is the weighted sum of the individual deprivations, which scales between 0 and 1.

Based on the index created, we define two levels of MDP:

- **_Moderate MDP:_** _a household has an MDP of 1/3 or higher_

- **_Extreme MDP:_** _a household has an MDP of 1/2 or higher_

12
Mode 4 trade.

At the same time, there is considerable heterogeneity in the types of documents that are required to be submitted in support of a business visa or work permit application. For instance, Attström et al. (2013) note that even in the case of the common Schengen Visa, some EU Member State Consulates require official translations of supporting documents, imposing additional costs in terms of resources and time on the applicants. Harmonizing these requirements and declaring a common set of documents across visa jurisdictions would go a long way in reducing costs imposed by regulatory heterogeneity.

Likewise, the visa processing time (in number of days) in 2017 varied from as high as 32 for Canada to as low as 1 for Japan (see Figure 4). The average for the sample is 13, with the OECD average higher at 15 and the non-OECD average much lower at 9. Interestingly, 21 of the 45 countries (all OECD) needed 15 days on average to process a business visa, which could be a reasonable target, especially for the five countries at the top of this distribution (Canada, Turkey, Israel, Costa Rica and New Zealand). Again, this reduction in visa processing time is likely to facilitate Mode 4 trade, also by making the process more certain and predictable.

Another interesting measure covered in the STRI data is the duration of the visa for crew members (in number of months). While the sample average in 2017 was only five months, the Indian visa allows crew members almost two years, while the visas of four other countries (Australia, Turkey, Russia and Malaysia) allow at least 12 months (see Figure 5). Longer visa durations will certainly facilitate the movement of aircrew, seamen and truck drivers in delivering services by reducing recurrent costs in re-applying for visas.

Longer visa durations for services suppliers should thus be a significant aspect of any TFS agenda on the MNP. Ideally, the duration of a visa should match the time needed by the service supplier to deliver
# 1 Introduction

A large body of research shows that there are high potential returns to early childhood programs, especially when they are targeted to disadvantaged families (Heckman 2006; Heckman and Masterov 2007). However, not all early childhood interventions are equally successful. Early childhood interventions have a larger chance of producing long lasting impacts when they also lead to permanent changes to parental behaviors, which outlast the duration of the program. When this occurs, and the quality of the environments in which children grow up is changed, we can see as a result sustained changes in child health, cognitive and socio-emotional skills. Examples of interventions which have produced long-term impacts on child development and where home environments played a key role in these changes are Abecedarian (Campbell and Ramey 1994) or the Jamaica Study (Gertler et al. 2014; Grantham-McGregor et al. 1991).

This paper documents the medium-term experimental impacts on home environments and child development of a low-cost and short duration group parenting program, which was delivered at scale in Chile. This program aims to affect child development by promoting sustained changes in parenting behaviors. The program is the Chilean adaptation of the Canadian program “Nobody is Perfect”, which has operated in the Canadian public health system for more than 30 years. The adapted version of the program is called “Nadie es Perfecto” (NEP hereafter).

NEP aims to improve the quantity and quality of parental investments in children by providing parents with the information, motivation and self-confidence to implement positive parenting behaviors. This program is delivered by trained local public health workers who organize weekly group sessions with caregivers of children of 0-5 years old, using an experiential learning model through group discussions. We show that, despite its short duration and intensity, the program produces impacts on home environments and children’s language development, lasting well beyond the conclusion of the program.

Our data come from a large-scale field experiment with two treatment arms and a control group. Parents in the control group have access to regular health preventive services offered
_“I wanted my own house after my marriage and shouldn’t be dependent on anyone.”_ Female (Ahmedabad) _“We took in joint name because my mother also paid some money.”_ Male (Bengaluru) _“I had invested in this property from my salary and savings. That is why it is in my name also.”_ Female (Mumbai) _“Things were not very good when I got married. I didn’t get any property from my in-laws. I worked very hard and got this property here.”_ Female (Ahmedabad) _“Both of us contributed to buy land. I felt that there should be an equal right for both of us.”_ Joint owner, male (Bhubaneswar) Reflecting the composition of India’s labor force,<sup>25</sup> only one of the females in the FGDs was the primary earner for her family; many were not earning at all, though some had finances to invest in the property. Many females voiced their husbands’ perspectives about the necessity of home ownership, rather than state their own views. Often, this viewpoint was couched in terms of the husband’s concerns about inheritance of the property, should there be an accident or illness, or for the family’s general future security. Males, too, voiced this concern.

_“My husband feels that even I must be part of it. He thinks about me. He feels that if our children don’t take care of us then what will happen in future. I see the main motive and he takes care of me. So, he registered in my name also.”_ Joint owner, female (Mumbai) _“My husband has three brothers. We have bought this house after marriage. If my husband is the owner, his brothers can claim it and that will be a family problem. So, it is in my name claiming that it is ours and
agencies. About half of the time spent by firms in inspections and visits is spent with officials from the revenue department, and the second most time with officials from the industrial works department. One possible area of concern is contract enforcement. According to the 2004 Doing Business database, solving a payment dispute in Thailand takes on average 390 days, compared to 300 days in Malaysia and 241 days in China. However, the Thailand Investment Climate Survey (WB 2005a) suggests that about one-fifth of payment disputes are successfully settled in court, a similar number to Malaysia, compared to only 5 percent in China.

Overall, regulation remains an important longer-term issue, as Thailand has to keep up with the improvements in the investment climate of its competitors. Governance appears to have worsened recently, as the influence of old institutions has weakened and new institutions have failed to be empowered, leading to a greater politicization of the allocation of public resources and regulation of economic activity.

> 7 This section draws on Chapter 1 of the Thailand Investment Climate Assessment, drafted by Ana Fernandes. The cross-country indicators of governance compiled by Kaufmann, Kraay and Mastruzzi (2005) are composite indexes based on a large number of data sources reflecting the perceptions of governance across and within countries: surveys of firms and individuals, assessments of commercial risk rating agencies, non-governmental organizations, and multilateral aid agencies.

> 8 Most of Thailand's six million Muslims live in the south of predominantly Buddhist Thailand. In September 2005, the death toll from clashes between Thai authorities and Muslim students and separatists surpassed 1,000 with more than 1,500 wounded since January last year.

28
**Table 5.5. European airports best and worst practice values**

|**fCost**<br>**indicators:f;;**<br>;;;t0f<br>;<br>0;tXX0g S<br>'Total cost per WLU|0;f000000tX<br>Mso|0000**0**<br>i,j 00<br>;30<br>e<br>143|0<br>iL<br>LX<br>o|<br>**2**ulhous<br>94|
|---|---|---|---|---|
|'Operating cotper WLV3|Vieana|10W<br>58|Oso|1,94|
|' ital<br>cost per WVLU||_6_|Oslo|0.99|
|*Nt rMnet<br>ves<br>a e<br>n U d|||||
|alt<br>_<br>L<br>as.sbetn<br>S, ,.;<br>S* ,<br>po<br>_|;_5_<br>Vigo;|g;<br>;.12|GlpWkk>|<br>65|

Source: Prepared with data from Doganis and Graharn (1995)

*Note: Monetary values are expressed in US dollars.

54
# **Figures 1(a)-(h): Argentine macroeconomic performance in comparative perspective**

<!-- Start of picture text --> (a) Growth in GDP per capita, 1990 to (b) Consumer Price Inflation, 2001-2013<br>2013<br>Argentina Average EM-G20<br>Argentina Average EM-G20<br>30.0<br>15.0<br>25.0<br>10.0<br>20.0<br>5.0 15.0 Disputed<br>Data<br>0.0 10.0<br>-5.0 5.0<br>0.0<br>-10.0<br>-5.0<br>-15.0<br>(c) General Government Balance, % of (d) General Government Gross Debt, % of<br>GDP, 1995-2013 GDP, 1993-2013<br>Argentina Average EM-G20 Argentina Average EM-G20<br>4.0 150.0 Devaluation (even<br>much of domestic<br>2.0<br>100.0 debt in dollars)<br>0.0<br>1995199719992001200320052007200920112013 50.0<br>-2.0<br>0.0<br>-4.0<br>-6.0<br>(e) External Debt, % of GDP, 1990 to (f) Total Reserves, % of GDP, 1990 to<br>2013 2013<br>Argentina Average EM-G20 Argentina Average EM-G20<br>150.0 35.0<br>Note: relatively<br>closed economy => 30.0<br>100.0 relatively high as % 25.0<br>of exports 20.0<br>50.0 15.0<br>10.0<br>5.0<br>0.
-- Start of picture text --> Percent 1980-2006 2007-2020<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Primary balance<br>revenue<br>Government expenditure Government consumption Government<br><!-- End of picture text -->

# **F. Expenditure volatility in commodityexporters, by regions**

<!-- Start of picture text --> Percent<br>16<br>12<br>8<br>4<br>0<br>SSA MNA EAP LAC SAR ECA AEs<br><!-- End of picture text --> _Sources_ : Arroyo Marioli, Fatás, and Vasishtha (2023); IMF WEO database; World Bank.

34
where . The designer is to offer a product that pays ₹100 if the next realization of the index is below some trigger and ₹0 otherwise. Suppose that the designer aims to offer as much cover as possible for a pure premium of ₹10. Now, if the designer chooses for some small , at this trigger the product would have paid out twice in the past. An Historical Burn Analysis would lead to an estimate for the pure premium of . However, for the uniform distribution, we know that which is strictly greater than ₹10 for small enough .

Both ratemaking methodologies presented in this section would be vulnerable to this form of data mining. Both methodologies rely on either a raw or a smoothed historic burn cost approach and if this is biased downwards due to data mining in product design then average pure premium rates will also be biased downwards.

Products cannot be rated on an actuarially sound basis with this form of data mining. One could reduce the bias in rates by calculating historical burn costs for a particular product using weather data series from all nearby weather stations, and then charging a premium based on the average of the average burn costs for this product. However, this would involve cumbersome calculations and would introduce significant inefficiency in estimation; that is to say that premiums would often be either too high or too low, even if they were fair on average.

A way to guard against this form of data mining is to require that all products for a particular crop in a particular season differ only by a limited number of parameters. A standardized product could offer full cover for modeled crop loss above a specified deductible. The deductible parameter could be chosen by the insurer for each product, based on statistical principles, while the shape of the product would be determined by an agronomic model.

As another example, all Kharif maize products sold across India might include four phases of deficit rainfall cover, one phase of consecutive dry day cover, and one phase of excess rainfall cover. All such products would
percent).<sup>23</sup> Put another way, migrants share about 2.7 percent of their very substantial growth by insuring family members at their original location.<sup>24</sup> This is a lower-bound estimate because we cannot exclude the possibility that we are only measuring a subset of relevant shocks: if shocks are self-reported then respondents may fail to mention those that were effectively insured. Fortunately, the survey provides an alternative shock measure, which is not self-reported. We have historical rainfall data from the Tanzanian Meteorological Agency for gauges in 8 weather stations across the Kagera Region. Each baseline village can be linked to its closest rainfall station as both databases record GPS location. The mean distance to a rainfall station is 19 km and the median is 15 km. For each village we can calculate average monthly z-score deviations of rainfall during the two rainy seasons, in relation to the 30 year average (1980-2010) for that village. Rainfall shocks are then constructed by truncating the positive yearly average rainfall deviations to zero. We calculate a non-migrant household’s own shock as the most negative shock in the 1994-2009 period. The first two columns in Table 10 show that rainfall shocks are important in determining consumption growth, with every standard deviation decrease in (negative) rainfall deviation causing consumption growth to decline by 20 to 32 percentage points, depending on the specification.

# _[Table 10 here]_

Knowing that rainfall shocks drive the incomes of the stayer households, we can use them as an alternative shock indicator. We replace the network shock variable with the baseline village rainfall shock variable in Equation (6). This rainfall shock is constructed as the most negative rainfall deviation in the baseline village after the migrant left. With baseline village fixed effects, the specification exploits the variation arising from the fact that migrants leave at different times.<sup>25</sup> Column 3 repeats the results from Table 8 for comparison purposes and column 4 replaces the network shocks in