rafmacalaba/datause-encoder-data huggingface dataset:
in the developing world, the potential of highly low-cost socio-emotional interventions stands as highly policy relevant. We aim to fill this gap.

We focus on Peru, a middle-income country that in recent years benefited from sustained economic growth along with substantial poverty reduction (monetary poverty reduced from 54% to 21% to between 2000 and 2015 according to official statistics), but where schooling achievement remains very low and highly unequal –by 2018, only 14% and 16% of students in the second grade of secondary level (equivalent to the eighth-grade) achieve a satisfactory performance in _Mathematics_ and _Reading Comprehension_ tests, respectively.<sup>11</sup> Within this context, we evaluate the impact of a growth-mindset intervention in Peru. The intervention, called ‘ _¡Expande tu Mente!_ ’, adapted existing tools (Yeager et al, 2016 and 2019; Paunesku et al., 2015) to a context in which online interventions are not feasible. The intervention, implemented with support from the Ministry of Education, consists of a 90-minute school session led by tutor teachers. To measure the impact of the sessions, 800 public schools were sampled in three regions in Peru. Two equally powered samples were created: a _metropolitan_ sample and a _regional_ sample. Within each sample, half of the institutions were randomly selected to participate in ‘ _¡Expande tu Mente!_ ’.

Packages were sent by courier and implementation monitored by phone. Tutor teachers from grades seven and eight (the first two years of the secondary level) were asked to deliver the sessions to all their students. Approximately 60% of all eligible schools delivered the sessions. We used information from the students’ census evaluation from 2015 and 2016 to test the academic impact of ‘ _¡Expande tu Mente!_ ’ sessions approximately 2 and 14 months after the intervention took place (respectively). Results after 2 months show that being assigned to the sessions improved _Mathematics_ test scores by 5% of a standard deviation (the point estimate for _Reading Comprehension_ is similar but statistically insignificant). We also uncover substantial geographical heterogeneity: the main result for _Mathematics_ is explained by an ITT impact of 13% in the _regional_ sample and virtually a zero impact in the _metropolitan_ sample. In the _regional_ sample, we also detect an ITT impact of 9% in _Reading Comprehension_ test students in the 12<sup>th</sup> grade (approximately 9,000 students). The duration of the Ganimian intervention is not reported, but it is based on the adapted 90-minute version reported in Outes et al. (2016). Interestingly, the author found no impact of the intervention on academic achievement, nor indeed on other dimensions.

> 11 <u>http://umc.minedu.gob.pe/wp-content/uploads/2019/04/presentacion-web-ECE2018-1.pdf</u>

4
primary activity of the head of household (Ravallion and Huppi, 1991), or in the very common consumption regression analyses, if the 7 day recall variable is used, measurement errors are introduced which will result in biased or inconsistent estimates on the role of household economic activities in poverty status.

**Table 8: Comparison of primary employment status in 2009/10: 7 day vs 12 months recall period, UNHS, individual**

||**Same primary activity**<br>|**in 12 month recall?**<br>|
|---|---|---|
|**_Primary activity- last 7 days_**|**_No_**|**_Yes_**|
|Private wage - non agriculture|8.6|91.36|
|Private wage – agriculture|10.3|89.71|
|Government wage|4.5|95.54|
|Own account - non agriculture|8.1|91.06|
|Own account – agriculture|5.8|94.23|
|Family worker- non agriculture|8.3|91.72|
|Family worker – agriculture|5.3|94.68|
|Total|100.0||

_Source: Authors calculations from the UNHS 2009/10. Note: Table based on a direct question asking the main 12 month activity which has 3 responses, i) the same as the 7 day main activity, 2) 7 day secondary activity and 3) other activity._

# **3.4 The recall period in the measurement of multiple economic activities**

The measurement of multiple activities of individuals and households is influenced by the recall period. Evidence from the various surveys shows that the difference between the short (7-day) and long (12 month) recall period in the number of economic activities measured is significant, with the shorter recall period consistently understating reports of secondary economic activities (see Table 9). A comparison of the share of the working age population reporting secondary activities by recall period a lower proportion captured with the 12 month period in all surveys, with the gap widest in 2005/6, where the proportion reporting a secondary activity in the 7 day recall question was _19 percentage points_ lower than the 12 month recall in 2005/06. This is despite the longer recall question being improperly framed in 2005/6 so the responses are not fully comparable (response options included inactivity ones). A similar result was found in the 2009/10 UNPS survey, when the questions were worded exactly the same. It is possible that the extent of secondary activities varied over time in Uganda, with a peak in 2005/6, but we can’t really be sure.

18
||||(0.04813)||||(0.04809)||||(0.04816)||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Non-agricultural self-employment|||0.09238**||||0.09215**||||0.09237**||
||||(0.04205)||||(0.04208)||||(0.04206)||
|Remittances & Others|||-0.00285||||-0.00283||||-0.00285||
||||(0.09538)||||(0.09535)||||(0.09538)||
|Property income and investments|||0.19983**||||0.19978**||||0.19983**||
||||(0.08358)||||(0.08360)||||(0.08358)||
|Transfers and other benefits|||0.11145***||||0.11121***||||0.11145***||
||||(0.04162)||||(0.04159)||||(0.04167)||
|Own > median size of land (2.5 acres)|||0.19955***||||0.19939***||||0.19954***||
||||(0.03182)||||(0.03181)||||(0.03182)||
|Observations|5,896|5,893|4,128|4,125|5,896|5,893|4,128|4,125|5,896|5,893|4,128|4,125|
|Kleibergen-Paap rk Wald F|19.58|19.57|14.62|14.16|18.71|18.69|13.60|13.19|16.22|16.21|12.99|12.61|
|Root MSE|0.591|0.591|0.533|0.362|0.591|0.591|0.534|0.363|0.591|0.591|0.533|0.362|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status,|||||||||||||
|education, occupation, other income sources,|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|
|land ownership)|||||||||||||
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|

18
remains high and pressures on resources have been increasing. Much of the growth achieved by countries in this region is diluted by population growth. Through careful effort, Tanzania has achieved 6-7 percent real GDP growth over the past decade, but the effect of this on a per capita basis is almost halved by population growth (IMF 2010b:5).

Family planning programs are intrinsic to anti-poverty efforts by facilitating increases in living standards. They also form part of a package of measures addressing basic government failures that help sustain poverty and high fertility ― including efforts to improve health and schooling, and to expand incomeearning opportunities. Family planning programs help by increasing access to contraception, and by providing informational outreach to enhance perception of the benefits of shifting to a more secure equilibrium in which people have fewer children and are able to invest more in them.

The developing world offers sharply contrasting models. At one end of the spectrum is the East Asian region, where policies highly conducive to rapid economic growth were boosted by low dependency ratios resulting from rapid fertility decline. These countries are using these favorable conditions to build up human and physical assets, thus transforming their economies and locking in high growth potential even after the demographic ―window of opportunity‖ closes and the population starts aging. At the other end of the spectrum is Sub-Saharan Africa, characterized by high fertility and low growth in income per capita. The fact that many countries in this region have shown some fertility decline suggests a decline in desired family size, and family planning programs can build on this and accelerate fertility decline.

Rapid population growth exacerbates an even less tractable problem ─ environmental change and the management of global common property resources, whose depreciation will affect the sustainability of production possibilities. While changing consumption patterns with rising incomes is a strong driver of pressure on these resources, population growth is also key (Figure 1). Managing global common property resources has proven to be politically very challenging and ridden with difficulties. Family planning programs are far simpler to design and implement. The most feasible way to reduce mankind‘s ecological footprint may be to further reduce the number of feet being born.

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# **_3.1.Estimating the sectorial and size distribution of output, employment and productivity from UBI sample values_**

# 3.1.1. The distribution of UBI samples by industry and size and age groups of enterprises

In analyzing UBI data we need to take into account the fact that, unlike the data from the UBR based on which we are gauging the extent of structural change the Ugandan economy underwent in the 2000s, observations from the UBI are not census data and cover only a sample of business establishments. While the UBI surveys cover all enterprises employing 10 or more people, their coverage of smaller establishments is limited to stratified random samples drawn from across all sectors and locations.

Estimates of sectorial and spatial aggregates of employment, output and productivity based on UBI data need therefore be based on appropriate sampling weights in order to be consistent for true (population) values of those variables.

# ===Table 10 here==

It is quite significant in this context that the population distributions of employment across industries and business size groups in the UBR data sets shown in Tables 2 and 5 bear sharp contrast to the corresponding sample distributions from UBI data as portrayed in Tables 10 and 11 and the first panel of Table 12. The discrepancies reflect the fact that the UBI surveys were designed to oversample from some industries and from larger business size groups. They also underscore the critical importance of taking them fully into account by applying appropriate sampling weights (such as those shown in table 11 for single digit industries) for obtaining consistent estimates of sectorial productivity gaps from UBI sample data.

To illustrate, manufacturing plants constituted 23 percent of the sample of UBI 2009 even though the share of the traded sector as a whole in the population of enterprises was less than 10 percent (Table 11). And while a comparison of UBR 2001 and UBR 2010 shows that the employment share of the traded sector declined from about 24 percent to 17 percent over the decade, the employment share of enterprises sampled from the traded sector for UBI 2009 was lower than their share in the aggregate employment of the sample for UBI 2002 only be 2 percentage points. And within the traded sector, the UBI surveys oversampled manufacturing establishments relative to other industries and services. As a result the share of manufacturing enterprises in sample aggregate employment increased by 4 percentage points between UBI 2002 and UBI 2009 samples while that of the commercial farming declined by 7 percentage points. Each of these changes in sample composition was the opposite of what in fact happened to the actual share of manufacturing employment in aggregate employment in the economy between UBR 2001 and UBR 2010 (Table 12).

Similarly, the employment share of retail trading and hotels and restaurants in the sample of UBI 2009 was smaller than that sample of UBI 2002 by about a percentage point, while the true (population) employment share of retail trade and hotels and restaurants in fact increased sharply between UBR 25
# **Telecommunications Externality on Migration: Evidence from Chinese Villages**<sup>**1**</sup>

Yi Lu Huihua Xie Lixin Colin Xu NUS NUS World Bank

## **JEL codes: J2, O15, O50.**

**Keywords:** Landline phones; telecommunications; migration; network effect; psychological costs; China.

**Sector board** : FSE.

> 1 The views expressed here are the authors’ own and do not implicate the World Bank, the countries that it represents, and its executive directors.
|Cigarettes: Urban/rural household|0.06|0.00|14.74|0.00|0.05|0.06|
|---|---|---|---|---|---|---|
|Bidis: Urban/rural household|−0.04|0.00|−17.70|0.00|−0.04|−0.03|
|Betel leaf: Urban/rural household|−0.02|0.00|−12.74|0.00|−0.02|−0.02|
|Betel nut: Urban/rural household|−0.02|0.00|−14.38|0.00|−0.02|−0.02|
|Rolled betel leaf: Urban/rural household|0.02|0.00|14.78|0.00|0.02|0.03|
|**Rho**|||||||
|Household size|0.03|0.03|1.09|0.28|−0.02|0.08|
|Number of males 15+|0.12|0.06|1.84|0.07|−0.01|0.24|
|Education of household head|0.11|0.02|5.52|0.00|0.07|0.15|
|Gender of household head|0.34|0.13|2.61|0.01|0.08|0.59|
|Age of household head|0.01|0.00|2.68|0.01|0.00|0.01|
|Religion of household head|−0.21|0.06|−3.73|0.00|−0.32|−0.10|
|Urban/rural household|0.21|0.11|1.94|0.05|0.00|0.43|

_Source:_ Calculations using data from HIES 2016/17.

24
**Table 6. Effect of preschool availability on female’s work status**

||(1)<br>Has a<br>second job|(2)<br>Self-<br>employed|(3)<br>Government<br>worker|(4)<br>Private<br>worker|(5)<br>Unpaid<br>family<br>worker|
|---|---|---|---|---|---|
|**Panel A: Public**||||||
|Public * Eligible|0.008|0.017|0.006*|0.011|0.041***|
||(0.010)|(0.010)|(0.004)|(0.011)|(0.011)|
|Public Preschools|-0.015*|0.001|-0.004|-0.020*|0.006|
||(0.009)|(0.007)|(0.005)|(0.010)|(0.010)|
|Eligible Child|0.001|-0.001|0.000|-0.040***|0.007**|
||(0.003)|(0.003)|(0.001)|(0.004)|(0.003)|
|FDR q-value|0.435|0.299|0.299|0.435|0.002|
|**Panel B: Private**||||||
|Private * Eligible|0.001*|0.002**|-0.000|-0.000|0.000|
||(0.001)|(0.001)|(0.000)|(0.001)|(0.001)|
|Private Preschools|-0.000|-0.003**|-0.000|-0.000|0.002|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|
|Eligible Child|-0.004|-0.007|0.002|-0.038***|0.012*|
||(0.004)|(0.005)|(0.002)|(0.006)|(0.006)|
|FDR q-value|0.204|0.204|0.992|0.992|0.992|
|Observations|225,985|226,400|226,400|226,400|226,400|
|Mean|0.077|0.172|0.037|0.185|0.125|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dependent variable indicated in column heading on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, a dummy for having a preschool-aged eligible child, and their interaction. Comparison group includes all mothers of children of the wrong ages and non-mothers. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. FDR q-values for the interaction coefficient of preschool density and eligible child are computed over all 5 outcomes and are shown in the last row of each panel. FDR q-values indicate the probability of false positives among _significant_ tests. Number of female-year observations and means of dependent variables are indicated in the last two rows.

51
However, when we include quality, we find that this share goes up to 20–44 percent. These results suggest that measuring human capital with quality substantially increases the role of human capital in explaining cross-country output per worker differences.

#$(&$%)(#$ (&"%) In Table III, we show results using the following decomposition: . We compare #$("$%)(#$ (""%) results to the recent literature, which varies from nearly all (Jones 2014) to potentially none (Caselli and Ciccone 2018). We find that when including our measure of quality, the share of human capital varies between 46 to 58 percent. Together with Table II, our results suggest human capital accounts for between a fifth to around half of cross-country income differences – a middle ground in a literature which ranges from zero to nearly all. These results are consistent with models of human capital capturing the role of educated entrepreneurs and more comprehensive measures of human capital including schooling, learning, and health (Gennaioli, La Porta, Lopez-de-Silanes, and Shleifer 2013; Campbell and Üngör 2020). In this development accounting exercise, our central contribution is not to provide a conclusive result, but rather to motivate the use of a direct measure of schooling quality and thus a better measure of human capital.

In Table IV we further find the average relationship between learning and income masks significant heterogeneity across countries. First, we find human capital explains between a tenth and a fifth of cross-country income differences among low-income countries but up to two-thirds among high-income countries. This suggests human capital plays a more central role as economies develop. Second, we find the income gradient is often as steep or steeper as the quantity to quality gradient, more than tripling the contribution of human capital. We find even steeper gradients by regions. For example, when measured by schooling, human capital accounts for 31 percent of cross-country income differences in Europe and just 5 percent in sub-Saharan Africa. When we include learning, this gap widens to 67 percent in Europe but just 8 percent in sub-Saharan Africa. This substantial heterogeneity reveals the importance of the inclusion a global distribution of countries covering multiple stages of economic development to account for the role of human capital.

Finally, we compare our measure of human capital to alternatives in Table V. We find that our measure of human capital has a stronger and more statistically significant association with growth than human capital measures in prominent global databases such as the Penn World Tables (PWT) and the Human Development Index (HDI). This is the case in when comparing measures on their own in columns (1)-(4). Each variable is transformed to a log scale to compare percent changes or 13
Men were slightly<sup>7</sup> more affected by underemployment than women (5.0 percent of female workers were underemployed, versus 5.8 percent of male workers).

Underemployment is more prevalent in rural areas where 6.2 percent of workers are in underemployment against 3.3 percent of workers in urban areas. The difference between men and women is slightly more marked in urban areas: 2.8 percent of working women are in underemployment versus 3.8 percent of working men, while in rural areas the differential between men and women is smaller: 5.8 percent of working women are in underemployment against 6.6 percent of working men.

Comparing 2007 and 2009 shows that all the previous indicators worsened over the recent period: underemployment has become more of an issue in the context of the crisis. The underemployment rate was up by 2 percentage points from 3.5 percent in 2007 to 5.5 percent in 2009 (restricting sample to workers who hold 1 or 2 jobs<sup>8</sup> ).

Significant proportions of workers do not have sufficient hours in their main job. About 25 percent of workers held more than one job in 2009.<sup>9</sup> The results show that 33 percent of workers spent less than 35 hours on their job in the previous week. Moreover, 85 percent worked fewer hours than “usual”. Of course, those who work fewer than 35 hours in their main job are much more likely to hold several jobs than those who work fulltime. While 19 percent of those who worked full-time in their main job had a second job or more, about 39 percent of those who worked less than 35 hours a week in their main job had a second job or more in 2009. The proportion of workers who held more than one job was up from 18.2 percent in 2007 to about 25 percent in 2009; this share increased significantly for both full-time workers and persons working less than 35 hours.<sup>10</sup> What are the reasons underlying short hours? Looking at the reasons stated by workers who report working fewer hours than their usual number of hours in their main job in the previous week, we see that most (about 61 percent) do so because of reasons that are

> 7 This result is not statistically significant.

> 8 The share of persons who are in underemployment is not readily comparable since the 2007 LFS requested information about the first two jobs only. We therefore restrict the 2009 sample to those with 1 or 2 jobs. This leaves out less than 2 percent of workers, and the share of workers who are in underemployment is virtually unchanged.

> 9 This proportion was slightly higher for women (27.1) than for men (23.6).

> 10 About 17 percent of those who worked full-time had a second or more, while 37 percent of those who worked less than 35 hours a week in their main job had a second job or more in 2007 (LFS, 2007).

22
**Figure 6 Monopoly Equilibrium with a Kinked Demand Curve (Inelastic Demand between Two Price Boundaries** **_p_ 1–** **_p_ 2)**

<!-- Start of picture text --> p<br>p1<br>AC<br>MC<br>D<br>p2<br>MR<br>Te Q<br>Source: Authors.<br><!-- End of picture text --> Demand for product _Y_ is normally equal to _Te_ in this scenario, which would be, for example, the total number of people affected by a given disease every year who absolutely need to purchase medication. However, if the price reaches a superior boundary _p_ 1, some of these patients will not be able to afford this medication and will not purchase it. If the price is as cheap as _p_ 2, some healthy people will rush to purchase the drug at this competitive price, either to use it or to resell it later. In between these two boundaries, all normal users will be willing to purchase the drug, regardless of the price. The monopolistic traders choose to apply a constant markup to keep their profits unaffected, even in the case of higher total logistic costs.

In Figure 7, the darker π2 section is equal to initial profits, π1, despite the net increase in average cost. In addition, in this case the cost-plus pricing strategy is not the profit-maximizing strategy (a price of _p_ 1 would optimize profits in both cases). But it might be a better strategy in the long term, because charging the maximum price, _p_ 1, to all customers willing to pay a price between _p_ 2 and _p_ 1 might lead to a significant amount of lost sales if market demand evolves toward a continuous demand curve between the two price segments observed. Said differently, the inelastic demand function observed here is very likely to be elastic in the long term, because customers would find substitutes. The monopolistic trader therefore prefers to raise his prices to reflect higher logistics costs but to lower the price when logistics costs fall again. However, it is socially impossible to charge very high prices for necessity goods, and a monopolist would therefore face social unrest and public regulation if he were to raise his prices to the profit-maximizing price in all situations.

14