rafmacalaba/datause-encoder-data huggingface dataset:
Finally, initial country conditions, reflecting the level of country development and living standards (such as life expectancy and/or human capital indexes), influence the effects of different public spending categories on growth<sup>26</sup> . Therefore, omitting initial conditioning variables in the analysis could distort the final results<sup>27</sup> .

Other issues arise from methodological problems<sup>28</sup> associated with separating short-run and long-run effects of public spending on growth, as well as testing for the endogeneity<sup>29</sup> of public spending with respect to growth, and the existence of nonlinearities<sup>30</sup> .

The objective of this paper is to conduct a quantitative assessment of the impact of public spending on growth using an endogenous growth framework. The main issue is to test whether the historical evidence reflected in GDP per capita growth rates of around 3% or more for almost half a century (1960-2006) in the selected country sample supports the theoretical background underlying endogenous growth models with respect to the role of public spending. Knowing more about how the effects of public spending on growth have crystallized and evolved over time in those fast growing countries could be helpful for other parts of the world.

# **<u>III. EMPIRICAL METHODOLOGY</u>**

_Function Specification and Variable Definitions_ The quantitative analysis is conducted using panel data for the selected seven countries<sup>31</sup> during the period 1970-2006<sup>32</sup> , focusing mainly on the effect of public spending on growth (See Appendix I for data information)<sup>33</sup> .

Different function specification forms were first considered, following Barro and Sala-iMartin, Bleaney et al., 2001; and Bose et al. 2007. The model chosen for the study is similar to the ones presented in previous papers but introduces some specific characteristics and innovations to fit the country sample and the objectives of the broader analysis.

Initially, the direct sensitivity of GDP per capita growth to public spending is tested using a function specification in which the rate of growth of GDP per capita is the dependent

> 26 Intuitively, building basic physical and social infrastructure should trigger a much more significant growth effect in low income settings than in countries where a certain threshold has already been reached. 27 Levine and Renelt, 1992.

> 28 Bleany, Gemmel, and Kneller, 2001.

> 29 Public spending may affect growth positively but faster growth may also induce larger government spending.

> 30 See Barro, 1990.

> 31 These seven fast-growing countries were considered as “developing” countries at the beginning of the period of analysis.

> 32 Although the qualitative analysis starts in 1960, compatible data are not available before 1970 and therefore the econometric work focuses on the period 1970-2006.

> 33 Annual data are used in some of the regressions.  Three-year averages are also calculated and used for some others.

8
- 25 -

nonfarm earnings to differences in agricultural income. These data reproduced and plotted in Haggblade, Hazell and Brown (1987), show a clear positive relationship between per capita agricultural income and per capita rural nonfarm earnings, reinforcing results from our earlier cross-country analysis reported in Figures 1-3.

By regressing rural nonfarm income on agricultural earnings, we can use these data to generate crude estimates of the agricultural growth multipliers. Equations (1) and (2) display the results of two ordinary least square regressions of per capita nonfarm income (RNFY) on per capita farm income (AGY). The figures in parentheses are standard errors. Note that the Sierra Leone regression includes all regions except Scarcies, which is an atypical rural region, one with a high proportion of fishing income which is clearly unrelated to agriculture.

# (1) Togo

RNFY - 5.8 + .43AGY R<sup>2</sup> - .56 (.22) (2) Sierra Leone RNFY - 4.2 + .14AGY R<sup>2</sup> - .31 (.09) It would be improper to infer strict causality from these relationships, because there may be other exogenous sources of growth in rural nonfarm income. Yet given the overwhelmingly agricultural character of rural Togo, the results in equation (1) suggest that in rural Togo a $1 increase in agricultural income leads to a $.43 increase in regional nonfarm earnings. But the Sierra Leone estimate, even after removing the outlier region, is far lower. It implies that a $1 increase in farm income generates only an additional $.14 in nonfarm earnings. Closer inspection of the survey methodology reveals the reason for this low figure. The Sierra Leone data exclude income from households primarily engaged in trad-
> Journal of Econometrics, 19: 233-238.

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> [27] Meeusen W, and van den Broeck J (1977) Efficiency Estimation from Cobb

- -Douglas Production Functions with Composed Error. International Economic Review, 18: 435-444.

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- [31] Schmidt P, and Lovell CAK (1979) Estimating Technical and Allocative Inefficiency Relative to Stochastic Production and Cost Frontiers. Journal of Econometrics, 9: 343-366.

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[35] Shepard RW (1970) Theory of Cost and Production Functions. Princeton: Princeton University Press.

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- [37] Taylor, WE (1980) Small sample considerations in estimation from panel data. Journal of Econometrics 13, 203-223.

20
**Table 7**

|**The cha**<br>**Year**|**nges in aggregate**<br>**Total number**|**number of regist**<br>**Year-on-year**<br>**rate (%)**|**ered forei**<br>**Index**|**gners(as of end of eachyear)**<br>**The share in total**<br>**population of Japan (%)**|
|---|---|---|---|---|
|1978|766,894||39|0.67|
|1979|774,505|1.0|39|0.67|
|1980|782,910|1.1|40|0.67|
|1981|792,946|1.3|40|0.67|
|1982|802,477|1.2|41|0.68|
|1983|817,129|1.8|41|0.68|
|1984|840,885|2.9|43|0.70|
|1985|850,612|1.2|43|0.70|
|1986|867,237|2|44|0.71|
|1987|884,025|1.9|45|0.72|
|1988|941,005|6.4|48|0.77|
|1989|984,455|4.6|50|0.80|
|1990|1,075,317|9.2|54|0.87|
|1991|1,218,891|13.4|62|0.98|
|1992|1,281,644|5.1|65|1.03|
|1993|1,320,748|3.1|67|1.06|
|1994|1,354,011|2.5|69|1.08|
|1995|1,362,371|0.6|69|1.08|
|1996|1,415,136|3.9|72|1.12|
|1997|1,482,707|4.8|75|1.18|
|1998|1,512,116|2.0|77|1.20|
|1999|1,556,113|2.9|79|1.23|
|2000|1,686,444|8.4|85|1.33|
|2001|1,778,462|5.5|90|1.40|
|2002|1,851,758|4.1|94|1.45|
|2003|1,915,030|3.4|97|1.50|
|2004|1,973,747|3.1|100|1.55|

Sources: Japanese Ministry of Justice.

30
through the banking system. Since 2007-8 the NAIS has been supplemented by the Weather Based Crop Insurance Scheme (WBCIS).<sup>2</sup> The NAIS is based on an “area yield” indexed approach: if the observed seasonal area yield per hectare of the insured crop for the defined insurance unit falls below a specific threshold yield, all insured farmers growing that crop in the defined area will receive the same claim payment (per unit of sum insured). The insurance unit size is chosen by the state and is often chosen to be a subdistrict.

The seasonal area yield estimate for a given crop in a given insurance unit, the _actual yield_ , is determined by harvested production measurements taken at a series of randomly chosen Crop Cutting Experiment (CCE) locations. Approximately 500,000 CCEs are conducted across India every year (Joint Group 2004) with the number of CCEs increasing in the size of the insurance unit; for example a minimum of 8 CCEs are to be conducted if the insurance unit is a village Panchayat and 16 if the insurance unit is an administrative block (AICI 1999). The claim payment per unit of sum insured depends on the actual yield and the contractual threshold yield (TY) as follows: The threshold yield is calculated as where the probable yield is calculated for each season and each insurance unit as and the indemnity level is typically uniform across each state for a given crop, and based on the ten year coefficient of variation of actual yields (CV):<sup>3</sup> For example, if the most recent five year average actual yield for groundnut in a particular insurance unit is kg/ha and the average ten year coefficient of variation for groundnut across the state is then the threshold yield for the insurance unit is given by kg/ha.

Under NAIS, premium rates paid by farmers in respect of food crops are determined by the following rule.<sup>4</sup> For Kharif crops the farmer premium rate is 3.5% for all oilseed crops and bajra and 2.5% for all other food crops. For Rabi crops the farmer premium rate is 1.5% for wheat and 2% for all other food crops. Premium rates paid by farmers in respect of commercial and horticultural crops are determined at the state level for each crop using a Normal Theory Method, under which yields are assumed to be normally distributed with mean and variance calculated using ten years of data for that insurance unit. For all crops, small and marginal farmers receive a 10% premium rate subsidy, and therefore only pay 90% of the above rates.

> 2 See Clarke et al. 2011b for a description of the WBCIS.

> 3 The coefficient of variation is the ratio of the standard deviation to the mean. Kharif and Rabi calculations are performed separately, with Kharif calculations using historic Kharif data only and Rabi calculations using historic Rabi data only.

> 4 A premium rate is the ratio of the premium to the sum insured, where the sum insured is the maximum possible claim payment. Under NAIS, the maximum possible claim payment occurs when the actual yield is zero.

4
**Table 4: Impact on Employment**

|||**New Fir**|**ms**|||**Existing Fi**|**rms**||
|---|---|---|---|---|---|---|---|---|
||Own<br>Employment|Total<br> Employment|Firm of<br>10 +|Firm of<br>25+|Own<br>Employment|Total<br> Employment|Firm of<br>10 +|Firm of<br>25+|
||||workers|workers|||workers|workers|
|Experimental impacts:|||||||||
|First‐Follow‐up|0.074***|1.426*|0.024|0.007|0.046**|1.461*|0.055|0.007|
||(0.025)|(0.732)|(0.020)|(0.008)|(0.019)|(0.808)|(0.041)|(0.019)|
|Second Follow‐up|0.128***|6.012***|0.288***|0.022**|0.066***|2.521*|0.211***|0.008|
||(0.017)|(0.412)|(0.026)|(0.009)|(0.018)|(1.366)|(0.041)|(0.018)|
|Third Follow‐up|0.119***|5.227***|0.229***|0.025**|0.069***|4.391***|0.206***|0.027*|
||(0.018)|(0.469)|(0.028)|(0.011)|(0.021)|(0.674)|(0.040)|(0.015)|
|PSM impacts for National/Zonal winners|||||||||
|First‐Follow‐up|0.072**|2.707***|0.142***|0.011|0.043**|3.149***|0.102**|0.061***|
||(0.036)|(0.683)|(0.042)|(0.016)|(0.021)|(0.844)|(0.050)|(0.020)|
|Second Follow‐up|0.141***|11.162***|0.344***|0.097***|0.070***|5.739***|0.347***|0.100***|
||(0.019)|(2.240)|(0.050)|(0.028)|(0.017)|(1.478)|(0.038)|(0.020)|
|Third Follow‐up|0.137***|7.007***|0.261***|0.037*|0.072***|7.338***|0.349***|0.094***|
||(0.024)|(1.277)|(0.053)|(0.022)|(0.023)|(0.784)|(0.042)|(0.018)|
|Control Mean: First follow‐up|0.787|3.618|0.083|0.010|0.938|6.852|0.212|0.032|
|Control Mean: Second follow‐up|0.841|3.305|0.088|0.009|0.922|8.134|0.231|0.038|
|Control Mean: Third follow‐up|0.831|3.773|0.114|0.014|0.906|5.571|0.170|0.014|
|Obs: First follow‐up|1021|987|987|987|432|422|422|422|
|Obs: Second follow‐up|1181|1159|1159|1159|505|500|500|500|
|Obs: Third follow‐up|1085|1044|1044|1044|477|461|461|461|

Notes: Robust standard errors in parentheses, *, **, *** indicate significance at the 10, 5, and 1 percent levels. Experimental estimates are ITT estimates and control for randomization strata. Propensity score matching (PSM) estimates compare non‐experimental winners to experimental control group. Obs is the number of observations in the experimental sample.

41
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21
We attempt to overcome some of the issues discussed above with the use of cross-sectional data at the school and household levels from 2003 through 2009 and quasi-experimental methods. More specifically, the analysis contrasts cohorts of girls in treatment and control districts in double-difference and RDD frameworks.<sup>16</sup> While double-differencing compares the changes in the outcomes of analysis between stipend and non-stipend districts overtime, RDD exploits the literacy rate cut-off for program eligibility across districts. The RDD models are also implemented within a DD framework as done by Chaudhury and Parajuli (2008). Therefore, both models compare the trends in stipend and non-stipend districts, which helps address possible selection bias introduced by time-fixed characteristics related to nonrandom program placement. In order to check the sensitivity of the findings to the issue of participation bias, additional models were estimated with samples that do not condition on enrollment in middle school.<sup>17</sup> The validity of the identifying assumptions of the DD models and the implications on the expected selection on returns to schooling are discussed in the robustness section.

We construct treatment and comparison groups of girls at both the school and household levels. At the school-level, the analysis draws upon administrative data from school censuses to estimate the annual average changes in total female enrollment at public schools in stipend districts (treatment schools) for the period between 2004 and 2010 relative to the pre-program level (2003-04 academic year). These changes are then compared with the corresponding annual changes in female enrollment observed for public schools in non-stipend districts (control schools) during the same period. This approach is limited, however, by the fact that schools are the units of analysis, and it is not possible to tease out other behavioral responses regarding the investments in the human capital of girls.

To overcome that, we also use household surveys to estimate changes at the individual level. The research design for this approach follows for the most part the same identification strategies and assumptions as the school-level analysis. However, the richness of information of the household-level data, as compared to school censuses, allows the analysis to take advantage

> 16  It was not possible to also use matching techniques with the pool of cross-sectional data available. The only common level of aggregation between baseline data —that would be used to predict program participation and construct the propensity score for each household— and follow up data was the district. This implies that matching is only possible at the district level and the province has only 34 districts.

> 17 These samples are restricted to girls who finished primary school (having at least five years of schooling). Additional econometric exercises also show that the GSP program did not have indirect effects on either enrollment in grades 4 and 5 or completion of primary school.

12
# **Figure 20: Leading, Middle-range, and Lagging Areas, by Total Population,**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30 Lagging (lower subset)<br>20<br>10 Lagging (upper subset)<br>0<br> Middle‐range<br>Leading<br>Percent<br><!-- End of picture text --> _Source_ : Rosstat.

0
rates, and also a narrowing of the urban-rural wage gap (Hnatkovska and Lahiri, 2013).<sup>18</sup> Three factors appear to be in play here. First, schooling has expanded, thus reducing the supply of unskilled labor, especially in rural areas. Second, there has also been a decline in female laborforce participation rates (Klasen and Pieters, 2015). Third, there has been a construction boom across India, especially in (rural and urban) infrastructure, which had been neglected for a long period. In 1993-94, the construction sector accounted for only 3.2% of employment for rural males, but by 2011-12 this had risen to 13%.<sup>19</sup> <mark>The shift of labor out of agriculture to the nonfarm sectors has been more rapid since the 1990s.</mark><sup>20</sup> <mark>The shift has been to construction and services, and also to manufacturing, to a smaller degree. Jacoby and Dasgupta (2015) suggest that rising labor demand from construction has contributed to higher wages of unskilled labor relative to skilled labor within rural areas, as well as rising rural relative to urban wages (for male workers).</mark> The combination of a lower supply of un-skilled labor and rising demand for that labor in construction, transport and other services is likely to have been a driving force in higher casual wages, in both farm and non-farm sectors and compressing the urban-rural wage gap. It is unclear how permanent this change will prove to be. It may be conjectured that (like China) India has reached its Lewis Turning Point. However, that is a conjecture, since there may well be other factors leading to higher wages even while there is still rural underemployment. And temporary reversals might be expected, notably if the current construction boom does not continue.<sup>21</sup> With the backdrop of this history of recent economic change in India, this paper revisits the implications for poverty of both the higher rate of growth and the pattern of growth. The sectoral imbalance in India’s post-reform growth would be a concern for poverty reduction if the model linking poverty to growth had remained the same, notably with the rural and agricultural sector contributing most to poverty reduction. However, previous research by Datt and Ravallion

> 18 Hnatkovska and Lahiri (2013) also show that the narrowing of the wage gap persists when one controls for education and occupation.

> 19 These are our estimates based on the NSS 50th and 68th employment-unemployment survey rounds.

> 20 The share of agriculture, forestry and fishing in total (usual status) employment declined from 76% in 1993-94 to 49% in 2011-12 (Jacoby and Dasgupta, 2015).

> 21 At the time of writing (late 2015) lifted by public infrastructure investment, the construction sector has continued to expand overall, despite a slow-down in the residential building construction sector (Wilkes and Kumar, 2015). However, wage growth for construction workers has been lower than in other occupations.

5
**Figure 1: Total Factor Productivity, base year 1960 = 1**

<!-- Start of picture text --> 5<br>4.5<br>4<br>3.5<br>3 China<br>India<br>2.5<br>Indonesia<br>2<br>Nigeria<br>1.5 United States<br>1<br>0.5<br>0<br><!-- End of picture text --> _Source: Bosworth & Collins, 2003._ 6