rafmacalaba/datause-encoder-data huggingface dataset:
ratchet up government size ,it is remarkable that most of the expansion post-1945 has occurred in peacetime.

This growth in the industrial economies has been echoed in much of the developing world. At the start of the 1960s, government expenditure on average 12-I90thshdsitdt accounted for little over 15 % of GDP . At the peak in the mid-i980s this had shifted to over 30% of GDP, before declining to around 26% in the mid-1990s. Part of the growth post-1960 can be attributed to state-building in the aftermath of colonialism, but also to movements in international commodity markets.

This widespread growth in government has also been accompanied by major changes in the composition of expenditures. In the industrial economies between 19451970 much of the increase in government size was driven by increases in transfers and subsidies. After 1970 growth in interest payments has predominated. By the early 1990s the share of spending allocated to traditional public goods -- such as defence, law and order -- had shrunk to no more than 10 percent of general government outlays in the major industrial economies, while over half the resources diverted to government through taxation were transferred to specific beneficiaries. Despite efforts to reduce fiscal imbalances, including cuts in government consumption and transfer programmes, the persistence in government size in the industrial economies is striking.

Peacock and Wiseman (1961).

> **12** These are nominal ratios of total government expenditure to GDP. Government expenditure includes central govermment expenditure only. See World Bank (1997).

12
## Table 3

# Wage Premium for Formal Sector Jobs

|Study|Estimation<br>technique|Burkina<br>Faso|Cameroon|C6te<br>d'Ivoire|Mali|
|---|---|---|---|---|---|
|Berthelemy and<br>Bourguignon<br>(1992; Table<br>4.4, page 98)|OLS<br>with dummy<br>variable<br>for formal<br>sector jobs|||203.7 %<br>[CILSS, 1985;<br>N = 311]||
|Lachaud<br>(1993b; Table D,<br>page 54)|OLS<br>with dummies<br>for covered<br>and uncovered<br>wage jobs|57.1 %<br>[IIES, 1990-91; N<br>=219]|60.3 %<br>[IIES, 1990-<br>91; N=278]|40.9 %<br>[RIES, 1986-<br>87; N = 244]|9.6 %<br>[IIES, 1991;<br>N=202]|
|Miller and<br>Vallee<br>(1995; Table|OLS<br>with sample<br>split between<br>formal and in-||70.2 %<br>[RPED, 1994;|||
|6.11, page 176)|formal firms||N<br>517]|||

(Continued) 17
<!-- Start of picture text --> First child a girl<br>(a) 1972-1984 (N=1,183) (b) 1985–1994 (N=2,967) (c) 1995–2006 (N=2,740)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>First child a boy<br>(d) 1972-1984 (N=1,305) (e) 1985–1994 (N=3,213) (f) 1995–2006 (N=2,945)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 0 5 10 15 20<br>Quarter Quarter Quarter<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>35 35 35<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>35 35 35<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br><!-- End of picture text --> Figure 8: Predicted probability of having a boy and probability of no birth yet from nine months after first birth for rural women with 8 or more years of education by quarter. Predictions based on age 22 at first birth. Left column shows results prior to sex selection available, middle column before sex selection illegal and right column after sex selection illegal. N indicates the number of women in the relevant group in the underlying samples.

sex ratio of second born children.

For rural areas there is no sign of sex-selective abortion during the 1985–1994 period, but for the 1995–2006 period the percentage boys born to highly educated women who have a girl as their 32
these differences, the training provided is likely to be productivity enhancing for both firms and workers. This heterogeneity in the service provided illustrates well the complexity of comparing training incidence across countries. We assume that these differences in training content become less relevant, and eventually vanish, when comparing firms within the same country, city and sector of activity. This will be our main strategy throughout the empirical work.

The final sample covers more than 48,000 firms with non-missing training data across all the regions of the world. Table A1 in the appendix reports the country composition of the final sample. The final sample covers both manufacturing (78%) and non-manufacturing (22%) sectors. Within manufacturing the sample covers: Food and beverages (17%), Chemicals and Plastics (14%), Electronics (8%), Textiles (10%), Garments and Leather (20%), Metals and Machinery (19%), Paper, wood and furniture (10%) and Other (3%). Because the _Enterprise Surveys_ cover in a more consistent way the manufacturing sectors across all the countries, in the empirical work that follows we will test the robustness of our main results to the exclusion of this group. This is of particular concern when we compute the correlation between training and productivity due to the well known problems of measuring productivity in non-manufacturing sectors (e.g., Griliches, 1994).

Figure 1 reports training incidence at the country level in our sample. Incidence is captured by the share of firms having provided on-the-job training. The dispersion in this figure is sticking. Developing world is very heterogeneous regarding the job training outcomes. Countries located in South Asia, Middle East and in Africa are among the ones supplying fewer job training. In particular, firms in Pakistan and Senegal report training incidence as low as 11% and 15%, respectively. Rather, firms in Eastern Europe, East Asia and in Latin America report higher training intensities. At least 70% of the firms in Slovakia, Chile and Thailand offer job training programs to their workforce. Nevertheless, one must be cautious when comparing these cross country incidences as they are likely to be affected by measurement error.

Table 1 reports the summary statistics for the main variables used in the paper. On average there are 30% of _micro_ firms (up to 10 permanent employees), 37% of _small_ firms (between 11 and 50 permanent employees), 22% of _medium_ firms (between 50 and 12
|P152398|2016|2016|6/22/2016|1 Active|Lesotho|
|---|---|---|---|---|---|
|P152398|2016|2017|12/13/2016|2 Active|Lesotho|
|P152398|2016|2017|6/16/2017|3 Active|Lesotho|
|P152398|2016|2018|12/15/2017|4 Active|Lesotho|
|P152398|2016|2018|6/22/2018|5 Active|Lesotho|
|P152398|2016|2019|11/30/2018|6 Active|Lesotho|
|P152398|2016|2019|5/31/2019|7 Active|Lesotho|
|P152398|2016|2020|12/27/2019|8 Active|Lesotho|
|P152398|2016|2020|6/21/2020|9 Active|Lesotho|
|P152406|2017|2017|11/17/2016|1 Active|Bosnia and Herzegovin|
|P152406|2017|2017|6/13/2017|2 Active|Bosnia and Herzegovin|
|P152406|2017|2018|12/8/2017|3 Active|Bosnia and Herzegovin|
|P152406|2017|2018|6/26/2018|4 Active|Bosnia and Herzegovin|
|P152406|2017|2019|12/21/2018|5 Active|Bosnia and Herzegovin|
|P152406|2017|2019|6/19/2019|6 Active|Bosnia and Herzegovin|
|P152406|2017|2020|12/19/2019|7 Active|Bosnia and Herzegovin|
|P152406|2017|2020|6/3/2020|8 Active|Bosnia and Herzegovin|
|P152440|2015|2020|7/29/2019|1 Closed|Kyrgyz Republic|
|P152441|2016|2016|6/28/2016|1 Active|Georgia|
|P152441|2016|2017|1/5/2017|2 Active|Georgia|
|P152441|2016|2017|6/30/2017|3 Active|Georgia|
|P152441|2016|2018|1/28/2018|4 Active|Georgia|
|P152441|2016|2019|9/19/2018|5 Active|Georgia|
|P152441|2016|2019|6/4/2019|6 Active|Georgia|
|P152441|2016|2020|2/20/2020|7 Active|Georgia|
|P152441|2016|2020|6/5/2020|8 Active|Georgia|
|P152449|2015|2015|6/23/2015|1 Closed|Gabon|
|P152449|2015|2016|4/24/2016|2 Closed|Gabon|
|P152449|2015|2017|10/31/2016|3 Closed|Gabon|
|P152449|2015|2018|8/9/2017|4 Closed|Gabon|
|P152449|2015|2018|6/25/2018<br>|5 Closed|Gabon|
|P152460|2016|2017|9/27/2016|1 Active|Vietnam|
|P152460|2016|2017|2/17/2017|2 Active|Vietnam|
|P152460|2016|2018|9/22/2017|3 Active|Vietnam|
|P152460|2016|2018|3/19/2018|4 Active|Vietnam|
|P152460|2016|2019|11/11/2018|5 Active|Vietnam|
|P152460|2016|2019|1/21/2019|6 Active|Vietnam|
|P152460|2016|2020|8/18/2019|7 Active|Vietnam|
|P152460|2016|2020|12/2/2019|8 Active|Vietnam|
|P152460|2016|2020|6/28/2020|9 Active|Vietnam|
|P152512|2016|2016|12/21/2015|1 Active|Central African Repub|
|P152512|2016|2016|6/16/2016|2 Active|Central African Repub|
|P152512|2016|2017|12/21/2016|3 Active|Central African Repub|
|P152512|2016|2017|6/19/2017|4 Active|Central African Repub|
|P152512|2016|2018|1/18/2018|5 Active|Central African Repub|
|P152512|2016|2018|4/18/2018|6 Active|Central African Repub|
|P152512|2016|2019|11/8/2018|7 Active|Central African Repub|
of expansion in sectors that we proved critical for expanding female employment like manufacturing and services. The second is cultural and related to the finding that women seem to exit the labor force around marriage age. This paper has not explored the other main pillar of the U-shape hypothesis, which is culture and gender norms. But, by exclusion and in view of the cohort analysis, this is one important lead for future research.

# **References**

Akerlof, G.A. and R.E Kranton (2000) Economics and Identity, _Quarterly Journal of Economics_ , 115, 715−753, 2000.

Angrist, J. D. and Evans, W. N., (1998), Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size, _American Economic Review_ , 88 (3), 450-77.

Arellano, M. and Bond, S. (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. _Review of Economic Studies_ 58: 277–97 Arellano, M. and Bover, O. (1995) Another look at the instrumental variables estimation of errorcomponents models. _Journal of Econometrics_ 68: 29–51 Blundell, R. and Bond, S. (1998) Initial conditions and moment restrictions in dynamic panel data models. _Journal of Econometrics_ 87: 11–143 Bloom, D.E., Canning, D., Fink G., Finlay, J.E. (2009) Fertility, Female Labor Force Participation, and the demographic dividend, _Journal of Economic Growth_ 14 (2), 79-101 Boserup, E. (1970) Woman’s role in economic development. St. Martin, New York Deininger, K. and Squire, L. (1998) New Ways of Looking at Old Issues: Inequality and Growth. _Journal of Development Economics_ 57 (2), 259–287.

Diop, N., Marotta, D. and de Melo, J. (Eds. 2012) _Natural Resource Abundance, Growth, and Diversification in the Middle East and North Africa. The Effects of Natural Resources and the Role of Policies._ World Bank Directions in Development, Washington DC Durand, J. (1975) The Labor Force in Economic Development: A Comparison of lnternational Census Data 1946-66. Princeton, NJ: Princeton University Press.

Fields, G. (2001). _Distribution and Development, A New Look at the Developing World_ . Russel Sage Foundation, New York, and The MIT Press, Cambridge, Massachusetts, and London.

Gaddis, I and Klasen, S. (2013) Economic Development, Structural Change, and Women’s labor force participation. _Journal of Population Economics_ , DOI 10.1007/s00148-013-0488-2 Goldin, C. (1990), _Understanding the Gender Gap: An Economic History of American Women_ , Oxford University Press, New York, 1990.

Goldin C. (1995) The U-Shaped Female Labor Force Function in Economic Development and Economic History. In: Schultz TP Investment in Women’s Human Capital and Economic Development. University of Chicago Press; 1995. pp. 61-90 20
independent and causal role in promoting long-run economic growth. More recent evidence also points to the role of the sector in facilitating disproportionately rapid growth in the incomes of the poor, suggesting that financial development helps the poor catch up with the rest of the economy as it grows. These research findings have been instrumental in persuading developing countries to sharpen their policy focus on the financial sector. If finance is important for development, why do some countries have growth-promoting financial systems while others do not? How do we define financial development? And what can governments do to develop their financial systems?

This chapter addresses these questions. The next section provides a brief review of the extensive empirical literature on finance and economic development and summarizes the main findings. Section II illustrates the differences in financial systems around the world and discusses the role of legal, cultural, political and geographical factors in influencing financial development. Section III discusses the governments’ role in building effective financial systems. Section IV provides areas of particular emphasis for lower-income countries. Section V concludes.

# **I.  Finance and Economic Development: Evidence**

By now there is an ever-expanding body of evidence that suggests countries with better developed financial systems – mostly captured by depth and efficiency measuresexperience faster economic growth.<sup>3</sup> Cross-country studies show that better developed banks and markets are associated with faster growth and that this relationship is robust to controlling for reverse causality or potential omitted variables (King and Levine, 1993; Levine and Zervos, 1998). These findings are also confirmed by panel and time-series

> 3 See Levine (1997 and 2005) for a comprehensive review of this literature.

2
Boskin and Sheshinski (1983), and the microeconometric analysis of tax reform, a single household utility function is postulated which is said to contain three goods: an aggregate consumption good and male and female “leisure” goods. Each adult is assumed to divide his or her total time between market labor supply and “leisure”, where, in empirical work, the latter is measured as the non-market time of each adult member.

The household income budget constraint in the model initially sets expenditure on the market good equal to the sum of individual net-of-tax labor and non-labor incomes. The _full income_ budget constraint is then derived by combining the time constraints with the initial budget constraint and setting expenditure on the market good and individual nonmarket time uses equal to the sum of full labor and non-labor incomes, that is, total household full income. In this sense income can be said to be _pooled_ . Solution of the model yields consumption and “leisure” (non-market time) demands/labor supplies as functions of the market wage rates (again the price of the consumption good is usually normalized at unity) and household full income.

In what follows, this model will be referred to as the _standard household model_ . It is a highly simplified demand/labor supply model of a two-adult household. Empirical work on this model in the developed economy literature is based on household survey datasets with missing information on the allocation of time to non-market activities. The “leisure” variables of the model are each calculated by subtracting time spent in market work from total time available. To interpret the resulting number pure leisure is clearly a mistake, since it includes time spent in household production for other family members. Nevertheless, this has been a common practice. For example, in the survey by Blundell and MaCurdy (1999), the “standard family labor supply model” for the two-adult household is said to treat the family as a single decision-making unit that maximizes joint utility over consumption and the two leisures, where the former is market consumption and the latter refer to pure leisures as assigned goods.<sup>15</sup>

> 15See also Deaton’s (1997, p. 229) discussion of leisure as an assigned good.  The same interpretation is also evident in Bergstrom’s (1997) survey of theories of the family.  For example, in his discussion of the Schultz (1990) model, Bergstrom states that “Schultz is able to peek inside the family black box and observe consumptions of leisure by husbands and by wives”.  Schultz notes explicitly that the “leisure”

9
# **(panel OLS, reproduce Table 5)**

**_Dependent variable: Growth rate of GDP per capita_**

||Fast growing<br>countries<br>**(1)**|Comparison<br>group<br>**(2)**|Whole sample<br>pooled<br>**(3)**|Whole sample<br>with dummies<br>**(4)**|
|---|---|---|---|---|
|Private investment<br>(% of GDP)|-0.035<br>(-1.2)|-0.023<br>(-0.361)|0.004<br>(0.143)|-0.040<br>(-1.437)|
|Initial human capital|0.005<br>(1.536)|-0.003<br>(-0.614)|-0.004<br>(-1.361)|0.001<br>(0.259)|
|Tax revenue|-0.182|-0.025|0.004|-0.042|
|(% of GDP)|(-2.981)***|(-0.372)|(0.093)|(-0.913)|
|Other revenue<br>(% of GDP)|-0.312<br>(-4.164)***|-0.206<br>(-1.934)*|-0.073<br>(-1.819)*|-0.129<br>(-3.286)***|
|Productive expenditure (-1)<br>(% of GDP)|0.429<br>(4.807)***|0.004<br>(0.096)|0.059<br>(1.671)*|...|
|DFAST*Productive expenditure (-1)<br>(% of GDP)|...|...|...|0.136<br>(3.643)***|
|DCOMP*Productive expenditure (-1) <br>(% of GDP)|...|...|...|0.010<br>(0.165)|
|Non-productive expenditure (-1)<br>(% of GDP)|0.267<br>(2.827)***|0.008<br>(0.141)|-0.013<br>(-0.301)|0.007<br>(0.153)|
|Budget surplus|0.260|0.161|0.072|0.126|
|(% of GDP)|(4.26)***|(2.393)**|(1.928)*|(3.146)***|
|Inflation - consumer price index|-0.095<br>(-4.122)***|0.006<br>~~(~~0.936)|-0.002<br>(-0.378)|0.004<br>~~(~~0.72)|
|Growth rate of GDP per capita (-1)|0.833<br>(12.681)***|0.750<br>(12.883)***|0.811<br>(18.484)***|0.784<br>(17.996)***|
|No. of observations|132|176|308|308|
|Adjusted R2|0.739|0.566|0.718|0.73|

Note: The estimation method is the ordinary least squares for panel data. 3-year moving average data are used. (-1) indicates variables lagged one period. D-FAST is the dummy variable which is 1 for fast growing countries and 0 otherwise. DCOMP is the dummy variable which is 1 for the comparison group and 0 otherwise. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients.

31
immediate aftermath of crisis. The post-decentralization period saw a further decline in the typical district growth rate, with the median district growth in 2001-05 falling to only 3 percent.

**Table 3 : Regional GDP per capita** **<u>of Indonesia’s districts</u>**

||**(Median) Growth Rate**<br>|**in %**<br>||
|---|---|---|---|
||**1993-97 **|**1999-2001 **|**2001-05**|
|Sumatra|5.8|5.5|3.5|
|Kalimantan|5.7|6.2|2.7|
|Java&Bali|5.7|1.3|3.1|
|Sulawesi|5.3|5.5|3.0|
|Maluku +Nusa Tenggara|5.2|3.7|2.8|
|Papua|4.5|5.3|3.1|
|**Total**|**5.6**|**3.5**|**3.0**|
|Eastern Indonesia|5.2|4.8|2.9|
|Western Indonesia|5.7|2.9|3.1|
|Java|5.6|1.4|3.2|
|Off Java|5.5|5.0|3.0|

Source: Authors’ calculation based on GRDP data from BPS. Note: Growth rates use real GDP in 1993 prices for 1993-97 and 1999-2001 and in 2000 prices for 2001-05.

Districts outside Java bounced back faster than districts in western Indonesia after the crisis. Prior to the crisis, median growth rates of districts in Java and off-Java were about the same, at 5.5-5.6 percent. However, districts off-Java recovered from the crisis far faster than those in Java. Median growth between 1999 and 2001 was 5 percent off-Java, compared with a typical growth rate of only 1.4 percent on Java. The same was true of eastern Indonesia,<sup>20</sup> which recovered from the crisis far faster than districts in western Indonesia. For example, the median growth rate in real GRDP per capita for Sulawesi was 5.5 percent. This relatively high growth in eastern Indonesia was triggered by growth in the agricultural and mining sectors. This is consistent with Alisjahbana and Yusuf (2004), who state that during the crisis the government tried to increase foreign investment by expanding oil, gas and mining exploitation.

Other factors also appear to be associated with district level growth. For example, cities ( _kota_ ) have grown far faster than districts ( _kabupaten_ ) since decentralization. Table 4 shows the median growth rate of _kota_ and _kabupaten_ for the three time periods. Cities bounced back far faster from the economic crisis (in part because they were much harder hit), but this faster growth rate has continued after decentralization, averaging 3.6 percent in cities compared with 3.0 percent in districts.

> 20 Papua, Sulawesi and Nusa Tenggara.

13
electronic transfer to the clearing-house, which transfers the contributions and manages the accounts on their behalf.

For matching fund contributions to work, it is essential that employers be allowed to deduct the matching contributions from their corporate income tax. While this may seem trivial, the use of simplified taxation in some countries might be an important restriction on the use of this mechanism. For example, in the case of Brazil, more than 80 percent of the companies pay taxes according to a simplified taxation ( _lucro presumido_ ) that assumes a certain rate of return over the company revenues. In such context, there is no room for deduction of matching contributions from the income tax.

# **Default Contribution Rates**

Default contribution rates are an important feature of voluntary pension plans. The pension plans in the United Kingdom are rigid in terms of the contribution rates, as the amounts to be contributed are only those specified in the regulation. Contribution rates in the United Kingdom are relatively modest for the time being, but they are expected to increase gradually between now and 2019. The changes in contribution rates of employers and employees in the United Kingdom appear to have been established to soften any criticism of the introduction of the automatic enrollment into workplace pensions. The United Kingdom is planning to lift the contribution limit starting 2017.<sup>29</sup>

**Table 4. Default Contribution Rates in Selected Collective Pension Programs**

<u>(as a percentage of the wage)</u>

||**United Kingdom**|**New Zealand**|**Italy**|**USA**<br>**Can**|**ada/Quebec**|
|---|---|---|---|---|---|
|Contribution<br>Rate<br>Employer|1%|3%|6.91%|Safe<br>harbor<br>provisions: non<br>elective 3%, or<br>matching<br>contributions|0%|
|Automatic Escalation<br>Employer|2%<br>2017‐18;<br>3% 2018/10+|No|No|No|No|
|Contribution<br>Rate|1%|3%|‐|Safe<br>harbor|2%|
|Employee||||provisions: 3%+||
|Automatic Escalation<br>Employee|3%<br>2017‐18;<br>5% 2019|No|No|Safe<br>harbor<br>provisions:<br>1%<br>increase<br>per<br>year upto 6%<br>2%<br>3%<br>4%|2014‐2015;<br>2016‐2017;<br>2018+|

Source: national sources In the case of New Zealand, employers contribute at a rate of 3 percent of wages, and employees can choose between three contribution rates- 3 percent, 4 percent, or 8 percent. An employee can contribute

> 29 Department for Work and Pensions (2013).

21