rafmacalaba/datause-encoder-data huggingface dataset:
amount.<sup>11</sup> We calculate poverty measures in relation to the (2011) official poverty line and report weighted statistics.

We begin by constructing a baseline case that reflects observed incomes and dibao participation in the 2009 data. Baseline poverty levels are equal to those observed in the 2009 CHIP data, that is, they are the levels of poverty implied by ex post incomes in the data. The dibao budget for the baseline case is equal to the sum of local dibao transfer amounts for all dibao recipients (weighted) observed in the data.<sup>12</sup> We refer to this as the “observed” baseline. As shown in Table 17, for the “observed” baseline the poverty rate is 11.2% and the poverty gap index is 3.9%.

The simulations require a decision about how much to expand the program. For simplicity, we use a target budget equal to the amount of money that would be spent if the program were expanded to cover all eligible individuals in 2009 who were not yet dibao beneficiaries. In other words, we calculate the cost of providing local dibao transfers to all non‐ recipients whose per capita incomes were below the local dibao thresholds, and we add this cost to the baseline dibao budget. This yields a target budget equal to 2.54 times the baseline budget.

Simulations of the impact of expanding the dibao program are shown in Table 17. For the simulation with expanded coverage, we present two scenarios. Simulation (a) assumes perfect targeting: all added dibao recipients have income below the dibao eligibility threshold in their locations of residence, in other words, inclusionary targeting error among added recipients is zero. By construction, the target budget in the simulation is just sufficient to ensure that all eligible individuals receive dibao transfers, so exclusionary targeting error is also zero. Simulation (b) assumes no targeting: additional recipients are selected through random lottery from among all non‐recipients. Thus, the added recipients in simulation (b) include individuals whose incomes are above the dibao thresholds. These two simulations can be interpreted as optimistic and pessimistic targeting scenarios for expansion of coverage.

> 11 In cases where data for the village average dibao transfer are missing, we use the county average transfer. 12 Note that the dibao budget here does not equal the official number reported by MOCA, reflecting in part the lower dibao participation rate in the CHIP sample than in the official statistics (see Table 7 and related text).  Also, the dibao budget here differs a bit from the CHIP total dibao expenditures in Table 7, which is calculated using county average dibao expenditures.  Here we use village average dibao transfers.

22
Unfortunately, respondents struggled with these probabilistic responses, despite the visual aids. There was considerable heaping of responses at 50 percent, which Fischoff and Bruine de Bruin (1999) note often reflects individuals having no idea, as well as at 0% and 100%. Many of the responses about nested risks were not statistically consistent – for example, one quarter gave a higher probability of the risk of dying on the way to Europe than the probability of not making it to Europe for any reason (including death). There still appears to be some signal in these responses, in that they are correlated with other likely baseline predictors of migration, but this noise will make it hard to detect changes.

Finally, because of potential concerns about eliciting these subjective expectations from this population, we also implemented a behavioral task-based measure of interest in migration to Europe. During the interview respondents were given a flyer, which said if they want to find out more about wages for Gambians in Europe, they should send an SMS message in the next 7 days. We then measure whether treatment changed the likelihood of requesting this wage information, which we view as an action-based proxy of continuing to be interested in migrating to Europe.

We measure the short-term, immediate, changes in knowledge, beliefs and intentions through randomizing at the individual level the placement of these questions in our baseline survey: _Group A:_ received these questions _only before_ being shown the information video and receiving their intervention.

_Group B:_ received these questions _only after_ being shown the information video and receiving the intervention.

_Group C:_ received these questions _both before and after_ being shown the information video and receiving the intervention.

Our pre-analysis plan registered with the AEA registry then pre-specified that we could use both an _across-subject_ and a _within-subject_ design to estimate the short-term effects.

The _across-subject_ design uses groups A and B in all villages. Let _Group B_ be a dummy variable that takes value 0 if individual _i_ in village _s_ is assigned to group _A_ , and value 1 if they are assigned to group B. Then we estimate: 1I I I I 1G G G 1<sup>I</sup> I ∗G G G ii,ss ss ii,ss<sup>+ μμ</sup> ss ii,ss YY = aa+ ββ aaI + γγ<sup>II</sup> aaI 22
dHK*,Cc d, <0 <!-- Start of picture text --> HC£(a)<br>_"Engagement<br>ePi disengagementPartial |<br><!-- End of picture text -->

<!-- Start of picture text --> i i a aa aaa ma aa aaa a<br>i ¢ Se i<br>[7 H : el<br>{377 See:;<br>i¢ Human capital :<br>if : 2 H<br>i depletion<br>IEEESSS SSS SNES<br><!-- End of picture text --> <!-- Start of picture text --> sit a<br>~ % | | |<br>15 18 23 29<br><!-- End of picture text -->
- **Dean, Joshua T, and Seema Jayachandran.** 2020. “Attending kindergarten improves cognitive development in India, but all kindergartens are not equal.”

- **Delecourt, Sol`ene, and Anne Fitzpatrick.** 2021. “Childcare matters: Female business owners and the baby-profit gap.” _Management Science_ , 67(7): 4455–4474.

- **Devercelli, Amanda E, and Frances Beaton-Day.** 2020. “Better Jobs and Brighter Futures: Investing in Childcare to Build Human Capital.” _Washington, DC: World Bank_ .

- **Du, Fenglian, and Xiao-yuan Dong.** 2013. “Women’s Employment and Child Care Choices in Urban China during the Economic Transition.” _Economic Development and Cultural Change_ , 62(1): 131–155.

- **Evans, David K., Pamela Jakiela, and Heather A. Knauer.** 2021. “The impact of early childhood interventions on mothers.” _Science_ , 372(6544): 794–796.

- **Evans, David K., Pamela Jakiela, Heather A. Knauer, and Amina Mendez Acosta.** 2022. “Tools to measure the impact of early childhood development interventions on maternal mental health in low- and middle-income countries.” _SSM - Mental Health_ , 2: 100127.

- **Fitzpatrick, Maria Donovan.** 2010. “Preschoolers Enrolled and Mothers at Work? The Effects of Universal Prekindergarten.” _Journal of Labor Economics_ , 28(1): 51–85.

- **Fitzpatrick, Maria Donovan.** 2012. “Revising Our Thinking About the Relationship Between Maternal Labor Supply and Preschool.” _Journal of Human Resources_ , 47(3): 583–612.

- **Garces, Eliana, Duncan Thomas, and Janet Currie.** 2002. “Longer-Term Effects of Head Start.” _American Economic Review_ , 92(4): 999–1012.

- **Gelbach, Jonah B.** 2002. “Public Schooling for Young Children and Maternal Labor Supply.” _American Economic Review_ , 92(1): 307–322.

- **Gertler, Paul, James Heckman, Rodrigo Pinto, Arianna Zanolini, Christel Vermeersch, Susan Walker, Susan M. Chang, and Sally Grantham-McGregor.** 2014. “Labor market returns to an early childhood stimulation intervention in Jamaica.” _Science_ , 344(6187): 998–1001.

- **Greenstein, Theodore N.** 2000. “Economic dependence, gender, and the division of labor in the home: A replication and extension.” _Journal of Marriage and family_ , 62(2): 322–335.

- **Halim, Daniel, Hillary C. Johnson, and Elizaveta Perova.** forthcoming. “Preschool Availability and Women’s Employment: Evidence from Indonesia.” _Economic Development and Cultural Change_ .

- **Heckman, James J, and Ganesh Karapakula.** 2019. “Intergenerational and Intragenerational Externalities of the Perry Preschool Project.” National Bureau of Economic Research Working Paper 25889.

- **Hiller, Victor, and Nouhoum Tour ́e.** 2021. “Endogenous gender power: The two facets of empowerment.” _Journal of Development Economics_ , 149: 102596.

- **Hojman, Andr ́es, and Florencia L ́opez B ́oo.** 2019. “Cost-effective public daycare in a lowincome economy benefits children and mothers.” Inter-American Development Bank IDB Publications (Working Papers) 9786.

24
In columns (iii-iv) we use measures of market size, by 2-digit NACE industry and country. We use the Compustat data of U.S. listed firms to calculate SCALE as the log of median total assets and SIZE as the log of median total sales. Total assets and sales take values less than 1 (they are divided by 10 billion US dollars) so that the log is a negative number, and more negative values denote smaller sized industries. Since entry costs are more negative when low, the positive coefficient indicates smaller scale/size industries have relatively more entry in low entry cost countries.

Columns (v-vi) use Compustat data on U.S. firms to calculate measures of concentration. In column (v) we calculate the share of total sales in the largest 4 firms (as measured by total sales) by 2-digit NACE code and country, and in column (vi) we calculate the HerfindahlHirschman measure of industry concentration. We test the null hypothesis that since entry should be higher in less concentrated industries, a positive coefficient suggests that if entry costs are high, entry should be lower in industries with less concentration. For both measures of concentration, we find positive but statistically insignificant coefficients. One explanation is that industries dominated by a few large firms may have substantial entry. For example, although the car manufacturing sector is dominated by a few international firms, there are many small and new firms that provide intermediate goods.

# **3.4  The Consequences of Preventing Free Entry**

Thus far, we have focused on how bureaucratic entry regulations differentially affect entry. This does indicate that these bureaucratic rules work as intended but it does not help us distinguish between the views that these entry barriers are socially harmful and that they are socially beneficial. If these entry barriers screen appropriately as in the view that they are framed in the public interest, we should find that incumbent older firms in naturally high entry industries should grow relatively faster (than similar industries in countries with low entry barriers) because efficient ex ante bureaucratic screening takes the place of growth-retarding wasteful competitive 22
14

# Investment Responds Slowly

Despite increased inflows of foreign savings, public investment has fallen in relation to GDP in all seven countries, recovering to pre-crisis levels only in Tanzania (see Chart XX). In the short run, the slowdown in public investment-in an attempt to reduce budget deficits and to cut uneconomic projects-without a compensating rise in private <!-- Start of picture text --> Chart IX: InvestmentIGDP<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>10<br>BunS Cm e daiw Gba aqap Nigm Scpl Toads<br>Saw=c Aima A4uaatmu Cm SmfaidI 03Bdm. nform. *A2RfkiniM.<br><!-- End of picture text --> investment, would result in depressing overall investment ratios. The conditions needed to encourage private investors have generally been lacldng so this slow response is understandable.

The relatively low investment rate is not a major obstacle to restoring growth in the short term as long as the efficiency of investment compensates for the low levels. After all, despite much higher pre-crisis investment rates, these countries did not grow fhster because of the poor quality of the investments. "White elephant" projects, infated contracts, flight capital, and other associated ills became rampant before-and eventually contributed to-the crisis in each case. A major aim of adjustment programs, therefore, has been to weed out these undesirable investments (particularly in the public sector) and to improve overall efficiency. Indeed, roughly similar investment ratios generated 1 percentage point of annual growth in the crisis period but close to 5 percentage points in the adjustment period.

A crucial issue is how long it will be before private investment will pick up the slack caused by this slowdown in public investment. The evidence so far is not reassuring. Domestic investors have been deterred in the short run by changes taking place as a result of restrictive monetary policies, high interest rates, devaluations-which increase the cost of imported inputsand trade liberalization. Foreign investors appear yet to be convinced that African econonues offer good investment prospects. The country studies confirm the vital importance of the stability, continuity, and credibility of policies for providing the appropriate signals to domestic and foreign investors. Of the seven countries, Ghana and-untl recently-Kenya came closest to
individual reported to have taken the medication in the preceding 24 hours, conditional on having such a prescription.

In the follow-up survey only, respondents were asked whether they use the services offered the RHC or HH serving the catchment area in which their village is located. Adults aged 40 and above in the PBF districts were significantly more likely to report using the service at the local primary level. With the propensity score weighting approach, we estimate an impact of 8 percentage points statistically significant at the 95% level. Without the weighting, the rate reporting using the local services is 85 percent versus 78 percent in the control districts. Among the sample of recently pregnant women, 92 of women in the PBF districts and 88 percent of women in control districts reported using the local primary services. With the propensity score weighting approach, the estimated impact is 6.4 percentage points (p = 0.133, presented in Panel C of Table 7).

Another indicator of interest is whether the PBF program affected bypassing of the local primary level facilities. In the baseline survey 28 percent of recently pregnant women reported receiving antenatal care at higher levels. The rate of women bypassing the local primary health facilities at the follow-up was 30 percent and as can be seen in Panel C of Table 7, the PBF intervention did not affect this bypassing behavior. It is important to keep in mind that while there were significant improvements in the quality of the RHCs, most of them still do not provide laboratory testing and even fewer have physicians or nurses with specialization in obstetrics.

Though the impact of the PBF program on utilization was moderate, we find that the program improved the perception of the population regarding the quality of care in the local RHCs. Perceptions regarding the quality of the health facilities was collected in the follow-up survey only and therefore the results in Table 8 are based on a propensity score weighting analysis. Overall, reported satisfaction by the population is very high and there is little variation. Over 90% of respondents indicated positive agreement for all statements about the local RHC, with the exception of whether the RHC had equipment needed to provide high quality services. Despite this almost universally reported satisfaction, we find some statistically significant impacts of the PBF project. Women with recent pregnancies in the PBF districts are more likely to agree that the staff is competent (p = 0.070), that the facility is in good physical state (p = 0.042), that the staff works closely with the community (p= 0.040) and that the staff listens to the opinions of the community (p = 0.023). When asked about changes over the three years of project implementation, women in 15
Eaton and Kortum (1999, 2002), the use of imported intermediate goods implicitly involves the use of technology and knowledge embodied in them. However these studies analyze the gains from importing in an aggregate setting rather than the effects of importing on firm performance.

Recently, using micro level data from developed countries, some empirical studies have shown that importers show similar characteristics as exporters. In their review of firms in international trade, Bernard et al. (2007) compare the characteristics of exporting and importing firms in the US manufacturing census. They show that both types of firms show many similarities in their performance measures. Both exporters and importers are more productive, larger, capital and skill intensive than firms that do not have any trading relations with the rest of the world. Another study that shows a positive relationship between importing intermediate goods and productivity for Belgian firms is Muuls and Pisu (2007). However neither study discusses the relation between firms’ trade orientation and technological innovation.

In analyzing how firm evolution is related to foreign exposure, I introduce a simple theoretical model. Since the seminal works of Grossman and Helpman (1991), Aghion and Howitt (1992), and Romer (1990), many studies in the endogenous growth literature have found technological innovation to be the main determinant of growth. Following these studies, Klette and Kortum (2004) present a highly stylized model of firm and industry evolution. To their framework, I introduce exporting and importing decisions in a similar fashion to Melitz (2003)<sup>1</sup> and show how firms with different levels of global engagements differ in their evolution.

In determining how firms evolve, I look at growth rates of size measured as sales and employment and growth rate of labor productivity measured as sales per worker. In addition to these measures of growth, I analyze whether firms implement technological innovations. Enterprise Surveys collect information on several variables to measure innovation<sup>2</sup> . I look at the probabilities of introducing new products, improving any existing process, having any internationally recognized quality certificate, and using foreign-licensed technologies. The use of various measures of firm evolution provides robustness to the inferences derived on the relation between firm evolution and trade.

Only a few studies that use micro level data explore the links between technological innovation and trade in developing countries. This is a crucial question because the most significant source of technological progress in developing countries is related to their ability to absorb the technology created in developed countries. Almeida and Fernandes (2008) and Alvarez and Robertson (2004) provide two studies that analyze innovation in developing countries and only the

> 1 Melitz (2003) only considers exporting choice of firm.

> 2 See www.enterprisesurveys.org for the detailed description of the data and methodology.

3
Table 6. Main features of global recoveries

||Glo|bal reco|veries (in|itial yea|rs)|Glob|al recove|ries (first t|hree ye|ars)|All|
|---|---|---|---|---|---|---|---|---|---|---|---|
||1976|1983|1992|2010|Average|1976-78|1983-85|1992-94 2|010-12|Average|years|
|**Activity**||||||||||||
|Output|5.2|2.7|1.7|4.4|3.5|4.4|3.6|2.1|3.4|3.4|3.7|
|Output per capita|3.4|0.9|0.2|3.1|1.9|2.6|1.7|0.6|2.1|1.8|2.0|
|Industrial production|7.9|1.7|-0.1|8.6|4.5|5.5|3.9|1.6|4.8|3.9|3.5|
|Trade|8.5|1.5|3.8|12.4|6.6|6.4|4.4|5.4|7.6|6.0|5.8|
|Unemployment rate|0.0|-0.2|0.3|-0.1|0.0|0.1|-0.2|0.3|-0.1|0.0|0.1|
|Oil consumption|6.2|-0.2|1.6|3.4|2.8|5.0|0.9|1.3|1.9|2.2|2.3|
|Investment|5.6|1.8|-1.8|5.3|2.7|5.0|2.8|0.3|5.1|3.3|4.4|
|Consumption|4.7|3.3|2.7|3.0|3.4|4.3|3.5|2.7|2.7|3.3|3.6|
|Output (PPP)|5.6|2.8|1.8|5.3|3.9|4.6|3.5|2.3|4.2|3.7|4.0|
|Output per capita (PPP)|3.8|1.0|0.3|4.1|2.3|2.8|1.7|0.8|3.0|2.1|2.3|
|**Financial markets**||||||||||||
|Capital flows|0.5|-1.5|2.0|8.0|2.3|-0.2|-0.1|0.3|1.0|0.2|0.2|
|Credit|3.7|4.3|2.4|1.1|2.9|4.4|5.7|2.5|1.8|3.6|5.2|
|Equity prices|2.6|23.2|10.4|17.4|13.4|-3.9|16.2|12.5|4.1|7.2|5.3|
|House prices|-0.4|-0.1|-1.7|1.3|-0.2|1.8|-0.1|-1.1|-0.2|0.1|1.8|
|Financial conditions|...|0.2|-0.1|-0.8|-0.2|...|0.3|0.2|-0.3|0.1|0.1|
|Inflation|-2.8|-2.3|-1.5|1.5|-1.3|-1.4|-1.0|-0.6|0.6|-0.6|0.0|
|**Interest rates, confiden**|**ce, and**|**uncertai**|**nty**|||||||||
|Nominal interest rate|0.3|-1.8|-1.0|-0.1|-0.6|0.2|-0.9|-0.9|0.0|-0.4|0.0|
|Real interest rate|1.8|-0.1|-0.1|-1.6|0.0|1.0|0.0|-0.5|-0.4|0.0|0.0|
|Business confidence|1.7|1.5|-0.1|1.7|1.2|0.6|0.5|0.2|0.4|0.4|0.0|
|Policy uncertainty|...|...|1.7|-6.6|-2.4|...|...|-6.1|7.5|0.7|3.8|
|**Policies**||||||||||||
|Government expenditure|4.1|1.7|5.0|2.3|3.3|4.1|2.9|3.8|2.3|3.3|4.8|
|Policy rate|0.1|-1.9|-1.0|0.0|-0.7|0.2|-0.9|-0.8|0.1|-0.3|0.0|

Note: All variables show percent changes, except in capital flows, unemployment rates, inflation, nominal and real interest rates, and policy rate, in which percentage-point changes of these variables are reported. “All years” refers to averages during 1950-2019. “...” indicates that data are either unavailable or aggregates are not computed due to small sample size. For details, see Appendix 2.

54
followed by BOM, which conditioned FX interventions to exchange rate values in the FX market, comes conceptually close to the theory of schedule intervention explored below.

Significant accumulation of FX reserves prompted the BOM to introduce, in May 2003, a reverse quantity-based rule aimed at slowing the pace of reserve accumulation. According to this rule, a preannounced amount of FX reserves was to be sold every quarter, through evenly distributed daily auctions, equivalent to 50 percent of the reserves accumulated in the preceding quarter. In addition to the rules-based intervention, if necessary, due to market conditions, the BOM reserves the right to organize unscheduled FX auctions, which it has not done since January 2009.

There is no significant correlation between changes in BOM FX reserves and the peso nominal effective exchange rate (Figure 3.1). This may partly reflect the focus of FX interventions on reserve management, as opposed to mitigating exchange rate volatility. It may also partly reflect the accumulation of FX reserves through excess Pemex export earnings, which were diverted from the FX market and thus did not affect the exchange rate.

<!-- Start of picture text --> Figure 3.1: Mexico -- Changes in FX Reserves and nominal effective<br>0.4% exchange rate (2000-12) 130<br>0.3%<br>120<br>0.2%<br>110<br>0.1%<br>100<br>0.0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br>90<br>-0.1%<br>80<br>-0.2%<br>ρ= -0.13<br>-0.3% 70<br>6-month moving average of monthly reserve changes in % of GDP Nominal Effective Exchange Rate of Mexican Peso (right axis)<br>Sources: IMF World Economic Outlook and World Bank Global Economic Monitor.<br><!-- End of picture text --> 10