rafmacalaba/datause-encoder-data huggingface dataset:
modern curriculum by altering age-old practice of educating predominantly male students and embracing girls.

**Table 3:** 2SLS estimates of determinants of female enrollment in secondary madrasas in 2003 <u>[Dependent variable: Total female enrollment in grades 6-10]</u>

||**model 1**||**model 2**||**model 3**||**model 4**||
|---|---|---|---|---|---|---|---|---|
||**2SLS**|**1stage**|**2SLS**|**1stage**|**2SLS**|**1stage**|**2SLS**|**1stage**|
|**# of BRAC schools in the union (in 2003)**|0.053||0.026||0.071||0.037||
||(2.45)*||(1.73)+||(2.34)*||(2.16)*||
|**Madrasa is co-ed (base cat: female madrasa)**|0.171|-0.462|0.145|-0.234|0.166|-0.244|0.142|-0.179|
||(5.57)**|(1.79)+|(5.32)**|-0.88|(5.32)**|-0.99|(5.16)**|-0.7|
|**Age of madrasa**|0.003|0.006|0.002|0.007|0.003|0.005|0.003|0.007|
||(5.07)**|-1.33|(5.04)**|-1.43|(5.09)**|-1.23|(5.23)**|-1.54|
|**Union has a govt. school**|-0.106|0.83|-0.06|0.681|-0.035|0.449|-0.017|0.57|
||(1.84)+|(1.67)+|-1.05|-1.23|-0.69|-1.13|-0.34|-1.24|
|**Urban**|0|0|0|0|0.349|-2.741|0.22|-1.811|
||(.)|(.)|(.)|(.)|(3.87)**|(8.73)**|(4.27)**|(4.76)**|
|**# of boys madrasas in the union in 1999**|||0.034|-0.853|||0.038|-0.894|
||||-0.9|(2.49)*|||-0.98|(2.69)**|
|**# of girls madrasas in the union in 1999**|||-0.06|0.921|||-0.07|0.937|
||||(3.32)**|(7.97)**|||(3.48)**|(8.29)**|
|**# of co-ed madrasas in the union in 1999**|||-0.01|0.238|||-0.014|0.294|
||||-1.36|(3.76)**|||(1.76)+|(4.79)**|
|**# of boys schools in the union in 1999**|||0.009|-0.585|||0.021|-0.826|
||||-0.28|(1.90)+|||-0.65|(3.06)**|
|**# of girls schools in the union in 1999**|||-0.02|-0.107|||-0.016|-0.116|
||||(2.08)*|-1.15|||-1.56|-1.28|
|**# of co-ed schools in the union in 1999**|||-0.01|0.141|||-0.012|0.163|
||||-1.42|(2.16)*|||(1.68)+|(2.60)**|
|**# of new madrasas in the union during 1999-2003**|||0.052|-0.424|||0.05|-0.304|
||||-1.32|-1.11|||-1.24|-0.82|
|**# of new schools in the union during 1999-2003**|||-0.045|1.243|||-0.054|1.12|
||||-1.54|(5.47)**|||(1.81)+|(5.13)**|
|**Average age of madrasas in the union**|||0|0.012|||0|0.013|
||||-0.25|(1.98)*|||-0.37|(2.28)*|
|**Average age of schools in the union**|||0|0.008|||0|0.007|
||||(1.89)+|(4.04)**|||-1.47|(3.72)**|
|**# of BRAC branches in the union (in 2005)**||0.305||0.388||0.229||0.35|
|||(5.22)**||(6.18)**||(4.01)**||(5.73)**|
|**# of BRAC female members in the union (in 2005)**||-3.003||6.027||-3.565||5.192|
|||-0.94||(1.73)+||-1.14||-1.54|
|**Constant**|-0.261|8.295|-0.061|-3.467|-0.371|8.919|-0.091|-2.691|
||(1.86)+|(2.69)**|-0.88|-1.02|(1.96)+|(2.97)**|-1.23|-0.82|
|**N**|6271|6271|5266|5266|6664|6664|5520|5520|
|**R-squared**||0.01||0.05||0.01||0.05|

**Note:** (a) All regressions control for district fixed-effects. (b) All-boys madrasas are excluded from the sample. (c) Models 1 and 2 exclude urban madrasas from the sample.

12
A detailed discussion of this procedure can be found in Appendix B. Depending on the data set (NFHS or TUS) these variables include individual and household demographics, education levels, 2-digit occupation and industry codes at the household and/or individual level, work status (school, domestic work, casual versus salary versus self-employment), labor-intensive “domestic” tasks typically carried out by females not in the labor force (e.g. collecting firewood or water, husking paddy and grinding grains), laborintensive agricultural tasks, land possessed, and total expenditures deflated using an all-India CPI (see Appendix Tables S.3 and S.4). When restricting estimation to the six states in the TUS, state fixed effects are also included. The _R_<sup>2</sup> from these predictive equations ranges from 0.14 to 0.35 for the NFHS REE estimates and from 0.37 to 0.63 for the TUS AL estimates, suggesting the variables in common with the NSS-E are rich enough to capture a lot of the sample variation in REE and AL from the other data sets. The estimation procedure here is adequate for estimation population or conditional means, which is the goal here, but it would need to be amended if one were interested in the variance or other moments of the distribution (e.g. fraction of households with TEE below some threshold).

# **2.4 Analysis of household characteristics**

While Table 1 makes it clear that demographic composition and rural/urban location will generate large differences in TEE (and its REE and AL components) across households, there is also considerable cross-sectional variation related to other variables. Before turning to estimates of population TEE over time, some interesting cross-sectional patterns are discussed to demonstrate the richness of these data. For this analysis, only the 1998-1999 Time-Use Survey and REE imputation procedure are used.

Figure 1 plots household TEE (averaged across individuals within the household) against mean years of schooling for members aged 18 and up, monthly per capita expenditures, and household size. Panel A shows that TEE declines in years of schooling, a net effect that combines a sharper decline in AL and a partly offsetting rise in REE. Panel B shows a similar pattern for per capita expenditures, except that in this AL rises with per capita expenditure for the poorest households, likely reflecting a combination of unemployment/underemployment for the poorest households and a higher proportion of non-earning children. TEE is falling in household size, particularly in the range of 2 to 6 members, which is mostly driven by lower 8
based on one or more predictor variables (determinants of high employment). This class of models estimates values parameters in a qualitative response model. It describes the possible outcomes as a function of the explanatory (predictor) variables, using a logistic function (Bishop 2006).

Logistic regression measures the relationship between a categorical dependent variable and one or more independent variables, which are usually (but not necessarily) continuous, by using probability scores as the predicted values of the dependent variable We follow Clarke and Barton (2000) and Kotsiantis and Kanellopoulos (2006) as they conclude that discretization refers to the process of converting or partitioning continuous attributes, features or variables to discretized or nominal attributes/features/variables/intervals. This can be useful when creating probability mass functions – formally, in density estimation (which is the logistic function for our case). Accordingly, we chose a threshold of 10% employment growth to define whether a firm is a high growth firm.

Logistic regression is adopted to predict whether a firm possess a given feature (more than 10% employment growth), based on observed characteristics of that firm (location, innovation, access to bank finance costs and performance of enterprises in terms of the economic and regulatory framework)<sup>24</sup> .

Finally, we undertake this exercise under an assumption that all explanatory variables are exogenous and the absence of simultaneity and omitted variables. Future research will build on the existing results, and augment it to ensure that there is no loop of causality between the independent and dependent variables and eliminate possible endogeneity<sup>25</sup> .

Table 3: Summary of Models’ Results:

||Model 1|Model 2|Model 3|Model 4|Model 5|
|---|---|---|---|---|---|
|Constant|-1.151|-1.035|-1.009|-0.907|-0.876|
|Manufacturing|– 0.312||– 0.402|– 0.345|– 0.325|
|Large|– 0.257|||||
|Medium||||0.272|0.268|
|Formal Training|0.223|0.217|0.312|0.269|0.299|
|Exporter|0.246|0.367||||
|Innovating|0.533||0.339|||
|UsingEmail||0.266||||
|High Regulatory<br>Compliance Cost|||0.254|0.278||
|Internationally<br>Recognized Certificate||||0.315||
|Have a Loan||||0.331|0.259|
|Gulf Country||0.778|||– 0.712|
|Yemen|||0.494|||

The logistic model is done using STATA package, Forward Log likelihood Ratio ratios where variable by variable is entered. This model will be tested with a significance level of 5%. Our response variable is "having high growth in employment (10% or more)" and the explanatory variables are " country, sector, size, firm offering formal training to employees, using email, number of power outages, export status, using technology licensed from a foreign owned company, developing a new product or upgrading an existing one or both, using bank financing for working capital, and using bank financing for new investments”.

> 24 See Hosmer 2000 for details on the motivation and adoption of logistic regressions.

> 25 See Green 2007.

25
# **Figure 3: Second-order dominance test for aggregate influence**

<!-- Start of picture text --> 0 2000 4000 6000<br>200000<br>150000<br>100000<br>50000<br>0<br>cumul. area under inverted citation curve<br><!-- End of picture text -->

# citations

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|Lagged share of wage income|||0.188<br>(0.117)|||0.083<br>(0.123)|
|---|---|---|---|---|---|---|
|Lagged share of non-farm income|||-0.060|||-0.212|
||||(0.134)|||(0.165)|
|Constant|1.809***|5.143***|4.536***|1.721***|1.471**|4.306***|
||(0.272)|(0.432)|(0.489)|(0.348)|(0.746)|(0.789)|
|Year FE|No|Yes|Yes||||
|Province FE|Yes|Yes|Yes||||
|Observations|567|567|567|567|567|567|
|R2/ Chi2|0.137|0.485|0.515|2390|2632|2023|
|Number of provinces|63|63|63|63|63|63|
|Arellano-Bond test for AR(1)||||-5.118|-4.641|-5.017|
|Arellano-Bond test for AR(2)||||2.257|1.338|0.710|
|Sargan test||||144.9|82.10|74.92|

Note: i) Authors’ estimation from VHLSSs using province FE model (columns 1 to 3) and GMM model (columns 4 to 6), ii) Robust standard errors in parentheses clustered at the province level, iii)*** p<0.01, ** p<0.05, * p<0.1.

45
**Table 7. Tobit regression of investment in non-agricultural business assets and**<sup>**ConsumerDurables (log)**</sup>

||Smallshock<br>Tobit<br>Tobit|**Big**<br>Tobit|shock<br>Tobit|
|---|---|---|---|
|Education(years)|0.362***<br>0354***|0.325***|0.318***|
||(3.76)<br>(3.67)|(3.32)|(324)|
|Headmigrationdummy|0.918<br>0.954|1.320|1.303|
||(1.12)<br>(1.17)|(1.59)|(I56)|
|Child migrationdummy|0.790<br>0.689|0.632|0.615|
||(1.26)<br>(1.09)|(099)|(096)|
|Ageofhouseholdhead|-0._115***_<br>-0.116***|-0.115***|-0117***|
||(3.86)<br>(3.90)|(3.81)|(3.85)|
|No.ofhhmembers>60|1.445+**<br>1.395***|1.110**|1113**|
||(2.97)<br>(2.87)|(2.25)|(2.25)|
|No.ofhhmembers<14|0.849***<br>0.801**|0886e**|0885***|
||(2.65)<br>(2.50)|(2.71)|(2.71)|
|No.ofhhmembers b/w14&60|0.754***<br>0.751'**|0.690***|0694***|
||(3.01)<br>(3.00)|(2.70)|(2.72)|
|Per capita paddyland|0631<br>0.712|0.593|0.609|
||(1.05)<br>(I19)|(0.97)|(1.00)|
|Per capitaupland|-0.286<br>-0.402|-0.506|-0.513|
||(0.59)<br>(0.82)|(1.01)|(I03)|
|Percapitaotherland|0.145<br>0.156|0.126|0.125|
||(0.59)<br>(0.64)|(0.49)|(0.48)|
|Householdexperiencedshock (yi)|-4.792***<br>-7.787***|-6.168***|-7.673***|
||(8.53)<br>(6.41)|(5.88)|(417)|
|Yunnandummy*****Shock(Y2)|4.078**||2.581|
||(2 52)||(092)|
|Guizhoudummy*****Shock(y3)|3.702***||2106|
||(2 64)||(0.88)|
|Yunnandummy|2.627***<br>1.196|2.392***|2139**|
||(3.07)<br>(1.17)|(2.74)|(2.35)|
|Guizhoudummy|1.354*<br>0.074|1.239*|1.032|
||(1.84)<br>(0.09)|(1.66)|(I33)|
|Constant|-1.162<br>0.175|-1.565|-1.270|
||(0.66)<br>(0.10)|(0.86).|(0.69)|
|Observations|1001<br>1001|1001|1001|
|Log-likelihood|-1907.61<br>-1903.43|-192691|-192636|
|**Testforneteffectofshockbyprovince:a**||||
|YI+Y2=<sup>0</sup>|0.88||0.90|
|yf+Y3=O|6.18***||0.00|
|Y2-Y3=<br>0|0.40||0.02|
|*significantat10%;**significantat**_5%/_o;**|significantat1%,|||

**a** Test **statistic** reported is the t-value.

31
all developing countries experienced rapid mortality decline, a period of high population growth, and eventually fertility decline.

The size of surviving birth cohorts during the demographic transition was influenced by a complex interaction of fertility, mortality, and population momentum. Population momentum refers to the inertia that is inherent in population dynamics due to the fact that childbearing takes place two to three decades after birth. The rapid growth of birth cohorts in the 1950s and 1960s produced a rapid growth of the childbearing-age population in the 1970s, 1980s, and beyond. This growth of the childbearing-age population competed with the decline in fertility rates to determine the size of birth cohorts.

A good picture of trends in cohort size is provided by looking again at the case of Brazil, where excellent census data back to 1960 provides an unusually detailed record. Figure 9 shows the size of cohorts as they appear in the censuses from 1960 to 2000, using only the age groups below the age of 20 in each census. These are not the size of the birth cohorts at the time they were born, but rather are the size of the birth cohort as it had survived to the census. It is thus a fairly good measure of the size of surviving birth cohorts, a useful measure if we are interested in the size of youth cohorts. The figure shows that birth cohorts grew rapidly in the 1950s and 1960s, leveled off in the late 1960s and early 1970s, then grew rapidly again in the late 1970s, reaching a peak in 1982. These trends in cohort size provide a convenient summary of the population dynamics of the demographic transition. The rapid growth in the size of surviving birth cohorts during the 1950s and early 1960s reflected the rapid decline of infant and child mortality. The leveling off in the late 1960s reflected the rapid decline in fertility rates that began in the 1960s in Brazil. The rapid growth in the size of cohorts born in the late 1970s was due to population momentum, resulting from the increase in the size of the childbearing 11
# The Role of Financial (Mis)allocation on Real (Mis)allocation: Firm-level Evidence for European Countries

Ana P. Cusolito<sup>a</sup> , Roberto N. Fattal-Jaef<sup>a,</sup><sup>_∗_</sup> , Davide S. Mare<sup>a,b</sup> , Akshat V. Singh<sup>c</sup>

> _aWorld Bank, Washington, D.C., US_

> _bUniversity of Edinburgh, Edinburgh, UK_

> _cUniversity of Oxford, Oxford, UK_

_JEL classification_ : D24; D61; O47 _Keywords:_ total factor productivity, financial misallocation, economic growth, frictions

> _∗_ Corresponding Author

_Email addresses:_ `acusolito@worldbank.org` (Ana P. Cusolito), `rfattaljaef@worldbank.org` (Roberto N. Fattal-Jaef), `dmare@worldbank.org` (Davide S. Mare), `akshat.singh@economics.ox.ac.uk` (Akshat V. Singh)
# **Table of Contents**

I. Introduction ...................................................................................... 4 II. History of Japanese Emigration to Brazil................................................... 5

- (1) Overview

- (2) Prologue (before 1908)

- (3) Period 1 (1908-1924): Immigration by Private Initiatives

- (4) Period 2 (1925-1936): Government-sponsored Immigration

- (5) Period 3 (1937-1951): Immigration Stricken by the War and its Aftermath

- (6) Period 4 (1952-1987): Post-war Immigration

III. Government Policy toward Migrant Workers in Japan................................. 11

1. Recent Trend in Number of Migrant Workers in Japan

2. Basic Principles of the Japanese Immigration Policy 3. Three Categories of Migrant Workers in Japan

- (1) Legal Skilled Workers

- (2) Illegal Unskilled Workers

- (3) Legal Unskilled Workers—Migrant Workers of Japanese Origin ( _Nikkeijin_ )

4. Reasons for the Sharp Increase—Push and Pull IV. _Nikkeijin_ Workers in Japan and the Japanese Government Policy towards Them... 15

1. Dramatic Increase Since the Beginning of the 1990s

2. _Nikkeijin_ Workers in the Early 1990s—Single Males in Car Parts Factory 3. _Nikkeijin_ Workers in Recent Years—Settlers with Family

- (1) From Temporary Guest Workers to Settlers

- (2) Clustered Cities

- (3) Problems with Education, and Crime

- (4) Policies of the Japanese Government towards _Nikkeijin_ in Japan

- (5) Remittances

V. Limitation of the Existing Literature and the Brazil Nikkei Household Survey....... 22

|VI. Concluding Remarks........................................................................ 23|
|---|
|Bibliography....................................................................................... 25|
|Tables and Figures................................................................................ 26|
|Appendix I: Chronology of the Japanese Emigration....................................... 48|
|Appendix II: Methodology of the Surveys Cited in the Paper.............................. 51|

3
- National Research Council, 1986, _Population Growth and Economic Development: Policy Questions_ , Washington, DC: National Academy Press

- Paes de Barros, R. & Sergio Firpo & Roberta Guedes & Phillippe Leite, 2015. "Demographic Changes and Poverty in Brazil," Discussion Papers 0096, Instituto de Pesquisa Econômica Aplicada - IPEA.-

- <mark>Rajan, R. G., and A. Subramanian. 2008. “Aid and Growth: What Does the Cross-Country Evidence Really Show?”</mark> _<mark>Review of Economics and Statistics 90</mark>_ <mark>(4): 643–65.</mark>

- Emmanuel Saez & Gabriel Zucman, 2016. "Wealth Inequality in the United States since 1913: Evidence from Capitalized Income Tax Data," The Quarterly Journal of Economics, doi:10.1093/qje/qjw004.

- Szreter, S. 1993. “The Idea of Demographic Transition and the Study of Fertility Change: A Critical Intellectual History”. _Population and Development Review_ 19(4), pp. 659-701.

- Treisman, D. 2007. _The architecture of government: Rethinking political decentralization_ . Cambridge University Press.

- UN (United Nations). 2013a. _National Transfer Accounts Manual: Measuring and Analysing the Generational Economy_ . New York, United Nations.

- UN (United Nations).  2015. _World Population Prospects: The 2015 Revision,_ New York: United Nations, Department of Economic and Social Affairs, Population Division.

- World Bank. 2013. Poverty Assessment: Assessing a Decade of Progress in Reducing Poverty 2000–2010. Washington, DC: World Bank.

- World Bank. 2015a. _<mark>Global Monitoring Report 2015/16: Development Goals in an Era of Demographic Change.</mark>_ <mark>Washington DC: World Bank.</mark>

- ______. <mark>2015b.</mark> _Aging in East Asia and Pacific:  Capitalizing on the Demographic Transition_ . Washington, DC: World Bank.

- ______. <mark>2015c.</mark> _<mark>Golden Aging: Prospects for Healthy, Active, and Prosperous Aging in Europe and Central Asia</mark>_ <mark>.</mark> Washington, DC: World Bank.

- ______. World Development Indicators Database. <mark>Washington, DC</mark> : World Bank.

27
for first period consumption so savings fall. These three key effects yield an ambiguous net result on savings, but our simulations show that the two positive effects outweigh the single negative effect.

A larger steady state capital stock generates knowledge spillovers and increases labor productivity in Figure 3, in accordance with (22). There is an offsetting effect, however, because the size of the labor force also increases when more children survive into the working age. This reduces, but does not dominate, the effect on wages and the productivity growth rate. Since it is costly to rear children for the purpose of providing for old age consumption, the need for children as a savings device falls when savings, and thus second period income, increase. This exerts downward pressure on fertility, (1+ _n_ ) (Figure 4). For the same reason the altruistic gift rate also decreases (Figure 5).

<!-- Start of picture text --> Figure 4. Child Survival & Fertility Figure 5. Child Survival & Transfers<br>n z<br>μ1 μ1<br>Source: Author Source: Author<br><!-- End of picture text --> There are consequently feedback mechanisms through savings and fertility: the more _μ1_ increases, the more the savings rate and productivity growth will increase, and the more fertility and altruistic gifts decrease. When fertility falls, more resources are released for consumption and savings, so the capital stock increases and further enhances productivity growth.

An additional mechanism through which second period income increases is the PAYG system. In accordance with (4), the more _μ1_ increases, the higher will be the number of workers who pay fixed pension contributions—leading to higher benefits per retiree. This increases second period income, and reduces the altruistic transfers through less need for costly children as a savings mechanism. In this case where pension contributions are fixed, current workers know that the more children they have, the higher will be the number of future workers to pay fixed contributions to them—generating more retirement benefits. This effect is an additional element that causes high fertility, but even though a system with fixed contributions is included in Wigger (2002), Wigger does not analyze this link, and so leaves out the importance of the incentives for high fertility caused by the fixed contributions—and the potential gains of switching regime to a fixed benefits PAYG system.<sup>16</sup>

> 16 The latter argument implies that, no matter how many children workers have, the old age pension benefits will remain the same. Incorporating such a system will lead to two important effects: first, the fertility incentive of pension benefits is removed so fertility (and therefore also altruistic gifts) can be expected to fall and productivity growth can be expected to increase. Second, if workers need fewer children to finance old age consumption, they will finance this through the other available channel: capital savings. More savings lead to a higher capital stock

12
some of these investments will have to be channeled toward reliable guaranteeing supply. The lack of reliable supply is a significant problem confronting many economies in SSA. Data from the World Bank’s Enterprise Surveys indicate that the frequency of power outages in a typical month in the region averages 8.5 times, with each episode lasting an average of 4.2 hours. The chronic unreliability of supply negatively impacts both firm-level productivity and overall economic performance Mensah (2016).

Overall, there is a general country-level positive correlation between rates of electricity consumption and economic performance (Ouedraogo, 2013). Firm-level evidence corroborates the same trend. For instance, Neelsen and Peters (2011) find that electrification in southern Uganda had the effect of crowding in market demand, thereby positively impacting the bottom lines of micro-enterprises in the region. In the same vein, Peters et al. (2011) find that grid extension in rural Benin had a direct positive effect on the creation of new firms. In related work, Dinkelman (2011) finds that rural electrification had a net positive effect on employment in South Africa. These studies suggest potential positive economic impacts of increasing electricity access in SSA because of its effects on firm-level productivity. Increased productivity and employment, in turn, are likely to result in higher tax revenues for African governments. Figure 2 gives some credence to our assumption of the positive correlation between electricity access and tax compliance. As a general matter, rates of tax mobilization across Africa are positively correlated with rates of access to electricity. In the next section, we delve into the empirical evaluation of the claims stated above.

In this paper, we go beyond the firm-level analyses present in extant works and focus on the potential aggregate effect of increasing electricity access across different African economies. Notice that our estimates are inherently conservative, in that the figures on potential revenue increases are based only on assumptions about firm-level improvement in productivity and the associated increase in tax payments. Taxation from the household sector is unaccounted for, mainly due to data unavailability. Thus, the actual gains in tax revenues are likely to exceed our simulated estimates.

# **3 Empirical Strategy and Data**

This section presents the empirical strategy of the paper as well as a description of the data and construction of critical variables for the analysis.

8