rafmacalaba/datause-encoder-data huggingface dataset:
_Table 5: Impact on Livestock_

|||Value of: (birr)<br>||HH has a<br>crossbre|t least one<br>ed: (yes=1)<br>|
|---|---|---|---|---|---|
||Large<br>livestock|Small<br>ruminant|Poultry|Cattle|Small<br>ruminant|
|round 3|5,684.438***|1,205.643***|68.052***|0.018|-0.010|
||(684.361)|(225.308)|(21.546)|(0.016)|(0.007)|
|FIF*round 2|64.113|-118.481|13.340|0.013|0.005|
||(659.644)|(215.374)|(21.148)|(0.018)|(0.009)|
|FIF*round 3|-605.558|49.192|-44.239**|-0.007|0.003|
||(853.432)|(297.188)|(17.822)|(0.022)|(0.008)|
|WIF*round2|868.754|237.752|-78.689***|0.074|0.040*|
||(1,275.121)|(412.703)|(26.310)|(0.047)|(0.023)|
|WIF*round3|-966.294|-892.280|-79.424***|0.043|-0.011**|
||(1,514.593)|(535.148)|(28.768)|(0.040)|(0.005)|
|WIF*FIF*round2|-1,470.739|-723.321|28.114|-0.002|-0.040|
||(1,728.361)|(626.306)|(55.762)|(0.055)|(0.027)|
|WIF*FIF*round3|2,870.331|2,978.463***|163.771***|0.030|0.010|
||(2,096.013)|(1,032.280)|(49.499)|(0.043)|(0.015)|
|Observations|2,428|2,428|2,428|2,428|2,428|
|R-squared|0.369|0.208|0.122|0.113|0.007|
|Estimates: FIF2 + WIF2 + FIF_WIF2|-537.900|-604|-37.230|0.084|0.005|
|P-value: FIF2 + WIF2 + FIF_WIF2=0|0.629|0.152|0.501|0.030**|0.854|
|Estimates: FIF3 + WIF3 + FIF_WIF3|1298|2135|40.110|0.066|0.002|
|P-value: FIF3 + WIF3 + FIF_WIF3=0|0.371|0.021**|0.347|0.150|0.909|
|Control Mean(BL)|7608|1026|91.95|0.084|0.012|

Note: Analysis of Covariance (ANCOVA) regression specification is estimated. Standard errors are clustered at the kebele level. The estimated equations include baseline values of outcome indicators as well as baseline sociodemographic characteristics such as sex of the household head, household size, age of the household head, number of students in the household, and region. The values of large livestock, small ruminant and poultry are winsorized at the top one percent. ***, **, and * indicate significance at the one, five and 10 percent critical level.

# **_Off-farm Income_**

For our final economic activity measure, we look at off-farm income (see Table 6). Although both interventions are designed to strengthen agricultural and livestock productivity in agricultural households, we found positive impacts on off-farm activities when either spouse is targeted. Targeting the female spouse in round 3 resulted in a significant positive effect on income from businesses that are managed or run by a woman. Income from these businesses increased by Br 974, which is 2.4 times the mean value of the same category of income for control households at baseline. Meanwhile, targeting either the male or the female spouse results in an increase in household wage income by the endline.

Targeting both spouses undoes both the business and wage impacts from targeting either spouse, and results in a significant negative interaction effect on income from businesses with female involvement. One reason for this could be that as the male spouse is also targeted, he may have 23
# **1 Introduction**

One of the biggest puzzles still to solve is how to address the persistently low labor force participation among women, especially among women with children. Globally, not even half of the women above 15 years old participate in the labor market (World Bank 2023). Compared to 20 years ago, the average female labor force participation rate has even declined globally and the gap relative to male labor force participation rates remains significant, standing at a female-to-male labor force participation ratio of 65 percent. A large body of literature explains the stagnating progress in female labor force participation via a child penalty (example studies are by Kleven, Landais, and Søgaard (2019); Lundborg, Plug, and Rasmussen (2017); Blundell et al. (2016); Ag ̈uero and Marks (2008)).

Several researchers have studied the impact of potential policies to address the gender gap in labor force participation, such as tax credits in the UK (Blundell et al. 2016) or the US (Bastian 2020), changes in parental leave policies (Tamm (2019); Bergemann and Riphahn (2010); Baker and Milligan (2010)), or the introduction of universal childcare (Baker, Gruber, and Milligan (2008); Lefebvre, Merrigan, and Verstraete (2009); Havnes and Mogstad (2011); Havnes and Mogstad (2015); Bauernschuster and Schlotter (2015); Herbst (2017)). However, to date, it is not clear which policy options are most effective in increasing women’s participation in the labor market. Furthermore, since results differ across countries and time periods studied, the contextual attributes that feed into policy impacts require further examination.

In this paper, we explore the introduction of pre-primary, compulsory, preparatory classes in Romania for children aged six years old, to study the impact of childcare services provided by the government on maternal labor supply. We argue that the policy, which was introduced in 2012, resulted in a natural subsidy for childcare by the government, potentially liberating the time constraints of primary caregivers. Based on this rationale, the reform allows us to test one of the principles of the economic theory on maternal labor supply, which centers around households allocating time between childcare and labor (Fong and Lokshin (1999); Ho 1
las relaciones laborales y la innovación. Serie estudios 9. Ministerio de Trabajo, Empleo y Seguridad Social. BsAs, Argentina.

Novick, M. and Rotondo, S. (Eds) (2013). El desafío de las TIC en Argentina: Crear capacidades para la generación de empleo, UN‐ECLAC, November, Santiago, Chile.

Novick, M., Rojo, S., Castillo, V., and Breard, G. (2013). Empleo y organización del trabajo en el marco de la difusión de TIC en el tejido productivo de la Argentina: industria manufacturera, comercio y servicios, in Novick, M. and Rotondo, S. (Eds) El desafío de las TIC en Argentina: Crear capacidades para la generación de empleo, UN‐ECLAC, November, Santiago, Chile.

Stiroh, K. J. 2005. "Reassessing the Impact of IT in the Production Function: A Meta‐Analysis and Sensitivity Tests", _Annales D'Économie et de Statistique_ 79/80, 529561.

Stock J., and D. Staiger. 1997. Instrumental Variables Regression with Weak Instruments. _Econometrica_ , 65 (3): 557‐586.

Syverson, C. 2011. "What Determines Productivity?," _Journal of Economic Literature, American Economic Association_ , vol. 49(2), pages 326‐65, June.

24
# **3. Data and Estimation**

_Data_ . In order to conduct the econometric analysis discussed, we constructed a database of all East Asian countries drawing on multiple data sources including, World Development Indicators (WDI), PPI database and the database of Governance Indicators maintained by the World Bank. The database allowed us to cover 16 countries in East Asia: Australia, Cambodia, China, Fiji, Indonesia, South Korea, Laos, Malaysia, Mongolia, Papua New Guinea, Philippines, Singapore, Thailand, Tonga, Vanuatu, and Vietnam. Owing to limitations of data availability, we had to drop Kiribati, Marshall Islands, Micronesia, Myanmar, Palau, Solomon Islands, and Timor-Leste from the analysis.

_Variables_ . The time horizon considered in the paper covers 20 years from 1985 to 2004<sup>11</sup> . The _dependent variable_ is growth in the East Asian countries studied, as measured by the evolution of GDP per capita in constant 2000 prices. We consider a set of infrastructure stocks as _explanatory variables_ . Specifically, our analysis takes into account the following measures of infrastructure in 5 key sectors:

- (i) Telecommunications – telephone mainlines per 1000 people; mobile subscribers per 1000 people;

- (ii) Electricity – electric power consumption per capita (kwh);

- (iii) Roads – kilometres of paved roads per capita; percentage of road network paved;

- (iv) Water – percentage of population with access to an improved water source;

- (v) Sanitation – percentage of population with access to improved sanitation facilities.

As discussed in the econometric model developed above, we _control_ for the level of investment and of human capital, proxied by Gross Fixed Capital Formation (% of GDP) and secondary school enrolment (% gross)<sup>12</sup> , respectively. Natural logarithms are taken for both infrastructure and control variables. Additionally, we consider the five variables mentioned above (i.e. PPI; governance; inequality in infrastructure access; country income levels; and Pacific Island<sup>13</sup> vs.

> 11 Although data were available for the year 2005 for a few variables, our time series spanned the years 1985-2004 in the interest of consistency across all variables.

> 12 Gross (secondary) enrollment ratio is the ratio of total (secondary) enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown.

> 13 The 9 Pacific Island Countries (PICs) include Fiji, Federated States of Micronesia (FSM), Kiribati, Marshall Islands, Palau, Samoa, Solomon Islands, Tonga, and Vanuatu.

11
case), reflecting in part geographic and sectoral “imbalances” in the growth process, which have dulled the impact of growth on poverty.

# **_China_**

China since around 1980 is often cited as an example in which rising inequality allowed rapid growth and (hence) rapid poverty reduction. There can be no doubt that absolute poverty in China has fallen greatly since around 1980. While China’s poverty rate today is probably slightly lower than the average for the world as a whole,<sup>14</sup> it was a very different story around 1980, when the incidence of extreme poverty in China was one of the highest in the world.<sup>15</sup> That is huge progress. However, there were some significant setbacks for China’s poor. Poverty reduction stalled in the late 1980s and early 1990s, recovered pace in the mid-1990s, but stagnated again in the late 1990s. About half of the decline in poverty came in the first half of the 1980s.

Income inequality has also been rising, though not continuously and more in some periods and provinces. Figure 5 gives the estimates of the Gini index, which rose from 28% in 1981 to 39% in 2001.<sup>16</sup> The Gini index is only one possible measure of inequality, and may not reflect well how we would weight gains at different level of living (Atkinson, 1970). A more flexible way of representing the distributional impacts of China’s growth is the growth incidence curve (GIC) given in Figure 6 (following Ravallion and Chen, 2003). This gives the rate of

> 14 See Chen and Ravallion (2004) who estimate that in 2001, 17% of China’s population live below $1 a day at 1993 Purchasing Power Parity; the corresponding figure for the world as a whole is 18% (21% for developing countries alone). 15 The proportion of China’s population living below $1 a day in 1981 is estimated to have been

> 64%.  Based on the “$1 a day” poverty rates for 1981 from http://iresearch.worldbank.org/povcalnet, only four countries (Cambodia, Burkina Faso, Mali and Uganda) had a higher poverty rate than this in 1981.

> 16 Note that the latter figure is somewhat lower than past estimates for China; this is because corrections have been made for urban-rural cost-of-living differences, which have tended to rise over time because of higher inflation in urban areas.   Without these corrections the Gini index for 2001 rises to 45%.

17
scenario (a reduction of 4.1 percentage points from the reshaping leading economies scenario). A small number of countries see their real income decrease less in this scenario. For example, Thailand, where real income was expected to decline by 4.4 percent in the _Reshoring leading economies_ scenario, now in the _Reshoring All_ scenario sees its real income decrease by 2.4 percent (a positive difference of 2 percentage points – the highest increase between _Reshoring leading economies_ and _Reshoring All_ from all countries and regions). This relative gain in income is driven by terms of trade effects as real GDP decline is still higher under _Reshoring All_ scenario.

Implementing GVC-friendly policies reverses the losses due to reshoring by leading economies in all regions. Instead of reshoring their production, developing countries have the option to counteract reshoring policies, by generating a more GVC-friendly environment, which leads to faster growth of trade and income and deeper integration into GVCs in line with their comparative advantages. Countries that were previously already deeply integrated are the ones expected to see the highest gains, with Thailand (10.7 percent), Malaysia (7.2 percent), Turkey (7 percent), and Vietnam (6.8 percent) benefiting the most from this approach (Figure 10). On the other hand, the only countries showing losses in terms of real income are the countries reshoring their production, i.e. China (-1.9 percent), followed by Europe (-0.4 percent), and the United States (-0.2 percent). However, it is noteworthy that each of these losses are smaller than in each of the previous scenarios. In fact, developed countries and China are better off in a scenario where developing countries implement GVC-friendly measures.

Figure 10. Real income compared with COVID L-shape recovery under reshoring by leading economies, by all countries and in the GVC-friendly scenario in 2030 (deviations from the L-shape COVID recovery, percent) <!-- Start of picture text --> 15.00<br>10.00<br>5.00<br>0.00<br>-5.00<br>-10.00<br>-15.00<br>Reshoring leading economies Reshoring all GVC friendly liberalization + TF imports<br>%<br><!-- End of picture text --> Source: Envisage simulations.

The reshoring of production by the leading economies will inevitably lead to a decrease in global trade by 17 percent. Highest losses are concentrated in developed countries. The countries suffering the highest reduction in exports are the United States (-24.9 percent), rest of high-income countries (-24.2 percent), Europe (-22.2 percent) and China (-20.8 percent) (Figure 11). Developing countries also suffer a reduction in exports, albeit to a lesser degree. The Philippines, the developing country that has the highest reduction 21
- Romer, Paul M. 1986.  "Increasing Returns and Long-Run Growth," _Journal of Political Economy_ 94(5), 1002-1037.

- ------. 1990. “Endogenous Technological Change.” Journal of Political Economy 98(5), S71-102.

Rosenbaum, Paul R. 2010. _Observational Studies_ . New York: Springer.

- Rosenthal, S. S., and W. C. Strange. 2004. “Evidence on the Nature and Sources of Agglomeration Economies.” In _Handbook of Regional and Urban Economics, Volume 4: Cities and Geography_ , edited by J. V. Henderson and J.-F. Thisse, 2119–71. Amsterdam: Elsevier.

- Stern, Nicholas. 2002. _A Strategy for Development_ . Washington, DC: The World Bank.

- World Bank. 2005. _A Better Investment Climate for Everyone_ . World Development Report, World Bank.

- ------  The World Bank Enterprise Survey Database. www.enterprisesurvey.org.  Washington, D.C. United States.

- ------   2010. Doing Business 2010.  www.doingbusiness.org.

- ------ 1994. World Development Report: Infrastructure for Development. Washington, D.C., United States.

- Xu, L. C. (2011). The effects of business environments on development: Surveying new firm-level evidence. _World Bank Research Observer, 26_ , 310-340.

33
# 2. Data

The Programme for International Student Assessment (PISA) is the Organization for Economic Cooperation and Development’s (OECD) international student assessment targeted to 15 year olds in grade 7 or higher, and it has been conducted every three years since 2000. In addition to testing students’ ability in mathematics, science, and reading, PISA also collects background data about each student, his or her family, and his or her school. Schools are the primary sampling unit in PISA, and they are selected by one or more sampling frames or explicit strata. In Korea, there are several strata which divide the sample into general and vocational schools and by metropolitan, urban, and rural areas. For this paper, only observations from the general school strata and for grade 10 students were included.

Both the 2003 and 2006 PISA school questionnaires include questions about tracking within school; however, in 2003, the question specifies tracking among mathematics classes while in 2006, the questions specifies any subject. In order to clearly estimate the association between tracking and learning achievement, this paper uses the 2003 dataset and focuses on mathematics achievement. The school tracking question for 2003 is drawn from the principal’s questionnaires: Schools sometimes organize instruction differently for students with different abilities and interests in Mathematics. Which of the following options describe what your school does for 15-year-old students in Mathematics classes? (question 16) The options included (a) Mathematics classes study similar content, but at different levels of difficulty; (b) Different classes study different content or sets of Mathematics topics that have different levels of difficulty; (c) Students are grouped by ability within their Mathematics classes; (d) In Mathematics classes, teachers use a pedagogy suitable for students with heterogeneous abilities (i.e. students are not grouped by ability). For each option, a principal could choose either “for all classes,” “for some classes” or “not for any classes.” The three different types of tracking captured by parts (a) through (c) of question 16 all occur in Korea. The Ministry of Education encourages schools to divide and instruct students in three cohorts based on ability, although some schools divide students into two cohorts. Additionally, many schools will divide students by ability only for a portion of the weekly instruction and maintain mixed classes for the remainder in response to parents and students wanting homogeneous assessment of students. Table 1 outlines how school principals responded to the question: 4
Determinants and Dynamics of Business Aspirations: Evidence from Small-Scale Entrepreneurs in an Emerging Market<sup>_∗_</sup> Patricio S. Dalton<sup>_†_</sup> Julius R ̈uschenp ̈ohler<sup>_†_</sup> Bilal Zia<sup>_‡_</sup>

**Keywords:** Aspirations, adaptive learning, micro-enterprises, innovation, small business growth, firm performance.

**JEL Codes:** O12; L26; M20; O17; M50

> _∗_ We thank the Abdul Latif Jameel Poverty Action Lab (JPAL) for hosting our study, in particular Ni Luh Putu Satyaning Pradnya Paramita, Dwitri Amalia, Raisa Annisa, and Lukman Edwindra for excellent research assistance. We also thank participants at the 8th Wageningen-Lucerne-Tilburg Development Economics workshop for valuable comments. This paper was produced under the framework of the “Enabling Innovation and Productivity Growth in Low Income Countries (EIP-LIC/PO5639)” project, funded by the Department for International Development (DFID) of the United Kingdom and implemented by Tilburg University. Additional funding was provided by The World Bank Strategic Research Program.

> _†_ Tilburg University, Department of Economics and CentER. Email: p.s.dalton@uvt.nl, j.ruschenpohler@uvt.nl

> _‡_ The World Bank, Development Economics Research Group. Email: bzia@worldbank.org
C8_howfind2 How did you find this previous MAIN job?

# **[DO NOT READ CHOICES BELOW. LISTEN TO SPONTANEOUS RESPONSE AND SELECT APPROPRIATE CODE FROM LIST.]**

1. By starting my own business

2. Contacted employer directly 3. Through a public employment agency 4. Through a private employment agency 5. Through friends or relatives 6. Through school/university employment center 7. By responding to a vacancy 8. Through a union/professional register 9. Through job training programs/courses 10. Other 888. Don’t know 999. Refused C9_numemp2 About how many persons (including yourself) worked in the enterprise or household business where you were employed in this previous MAIN job?

1. 1 2. 2-5 3. 6-9 4. 10-25 5. 26-50 6. 51-100 7. 101-249 8. More than 250 888. Don’t know 999. Refused

C10_hours2 How many hours (on average) did you work during a normal WEEK in this previous MAIN job?

1. Fewer than 15 hours per week 2. 15-30 hours per week 3. 30-45 hours per week 4. 45-60 hours per week 5. 60-75 hours per week 6. More than 75 hours per week 888. Don’t know 999. Refused

C12a_earnings_cash2 How much did you usually earn PER MONTH in cash from this previous MAIN job (in Taka)?

**[FOR SELF-EMPLOYED, EARNINGS REFER TO PROFITS, NOT REVENUES. FOR FAMILY MEMBERS WORKING IN A HOUSEHOLD BUSINESS, RESPONDENT SHOULD SAY HOW MUCH HE OR SHE EARNED, NOT HOW MUCH THE BUSINESS EARNED.]**

**[If answered then go to C12c]**

**[If don’t know then put “Don’t Know” and go to C12b] [If refused then put “Refused” and go to C12b]**

2
then cash transfer programs or programs enhancing the delivery of basic services facilitating investments in physical and human capital are likely to be the most appropriate. The 4Ps program in the Philippines is a prime example of a cash transfer program aimed at addressing chronic or poverty-induced vulnerability. In contrast, if vulnerability is primarily “risk induced” (i.e., high uninsured income fluctuations) then an insurance type of program may be needed to increase resilience. The insurance provided would allow households to make production and investment choices based on their expected returns rather than the potential implications of these investments on welfare.

Extending some of the ideas of Hill and Porter (2017), the analytic framework adopted is operationally useful and complementary to the _Listahanan_ Proxy Means Test (PMT) as well as as the (PMT+) approach summarized by Mills, Del Ninno and Leite (2015) in their survey of effective targeting mechanisms in Africa (Del Ninno and Mills, 2015). In short, the approach adopted for identifying the vulnerable consists of the following three steps:

**Step 1:** The mean PCE of households is estimated by a linear regression model that summarizes household welfare at any given point in time as a function household observed characteristics and the recent typhoon shock experienced. Specifically, household welfare at any given time is specified by the equation below:

where ln hb denotes the log of real or cost-of-living-adjusted per capita expenditures (PCE) defined by the ratio the nominal ,.of household _h_ , in barangay or cluster _b,_ in year _t_ , in bb semester CC _s_ , adjusted by the Consumer Price Index in province in year _t_ , in semester hkkbb _s_ , hb denotes household observable characteristics, such as age and education level of the PPCCPP ppb CCPPCC pp household head, the total number, age and gender composition of household members, bb ownership of assets such as livestock, phone radio, TV etc., characteristics of the household XX residence including type and quality of water and sanitation facilities, main source of energy, type of floor and material of construction for walls and roof, and b is the typhoon-related shock experienced (proxied by the windspeed) by the barangay _b_ in which the household bb residence is located in year _t_ in semester _s_ . The term denotes binary variables identifying WW regions (region-specific fixed effects for the 17 regions in the Philippines), denotes binary rr variables for year effects and denotes semester fixed effects and a full set of interactions γγ bb among the three groups of fixed effects.<sup>4</sup> The error term hb summarizing the influence of δδ bb σσ bb ee

> 4Thus, instead of using specific barangay variables as determinants of household consumption as done in the _Listahanan_ (Velarde, 2018) the approach above uses barangay fixed effects that control for all time invariant observable and unobservable variables at the barangay-level.

6