rafmacalaba/datause-encoder-data huggingface dataset:
40

# **Table 8: Regressions with individual variables on women’s work and gender roles on the sample of female respondents**

|**Dependent Variable**|(1)<br>**Working Female**|(2)<br>**Working Female**|(3)<br>**Working Female**|(4)<br>**Working Female**|(5)<br>**Working**<br>**Female**|
|---|---|---|---|---|---|
||**Working outside home**|**Married women**<br>**work**|**Returning after**<br>**5pm**|**Leaving child**<br>**with relative**|**Age of**<br>**child to**<br>**return to**|
|**Topic**|||||**work**|
|Social Empirical||||||
|Expectation|0.0757***|0.0754***|0.00885|0.0477**|0.0114|
||(0.0181)|(0.0179)|(0.0149)|(0.0187)|(0.00954)|
|Social Normative||||||
|Expectation|-0.0408***|-0.0353**|-0.0334*|-0.0212|-0.00614|
||(0.0154)|(0.0175)|(0.0199)|(0.0191)|(0.0109)|
|Personal Belief|-0.0405|0.00714|-0.00389|0.0467**|0.0147|
||(0.0287)|(0.0222)|(0.0196)|(0.0230)|(0.0110)|
|Counterpart Belief|0.000487|-0.00604|0.0548***|0.0286|-0.00502|
||(0.0168)|(0.0174)|(0.0195)|(0.0196)|(0.0106)|
|**Controls**||||||
|Age|0.0139***|0.0138***|0.0142***|0.0148***|0.0150***|
||(0.00218)|(0.00224)|(0.00225)|(0.00235)|(0.00271)|
|Yrs of Education|0.0294***|0.0291***|0.0321***|0.0331***|0.0330***|
||(0.00379)|(0.00379)|(0.00354)|(0.00391)|(0.00418)|
|Married|-0.148**|-0.151**|-0.108*|-0.101|-0.0901|
||(0.0611)|(0.0635)|(0.0644)|(0.0660)|(0.0746)|
|Erbil|0.0885*|0.0666|0.0614|0.0598|-0.00333|
||(0.0462)|(0.0462)|(0.0501)|(0.0570)|(0.0591)|
|Suli|0.0378|0.0147|0.0150|-0.0267|-0.0468|
||(0.0436)|(0.0424)|(0.0486)|(0.0527)|(0.0608)|
|Young child|0.0556|0.0489|0.0389|0.0352|0.0399|
||(0.0404)|(0.0415)|(0.0416)|(0.0428)|(0.0476)|
|HH Income|0.0512***|0.0512***|0.0605***|0.0533***|0.0563***|
||(0.00908)|(0.00939)|(0.00931)|(0.00980)|(0.0107)|
|Num cars|-0.0101|-0.00310|0.000631|0.00497|0.0342|
||(0.0316)|(0.0322)|(0.0324)|(0.0351)|(0.0377)|
|Constant|-0.563***|-0.556***|-0.662***|-0.668***|-0.621***|
||(0.0931)|(0.0961)|(0.0917)|(0.0999)|(0.137)|
|Observations|546|537|533|480|398|
|R-squared|0.332|0.326|0.315|0.329|0.313|
|Robust standard errors|in parentheses|||||
|*** p<0.01, **||||||
|<br>p<0.05, * p<0.1||||||
The multiple-channel pattern of technology transfer that has emerged after 1980s reforms not only converges to patterns in other developed countries but also facilitates technology transfer from those countries. With similar institutional patterns for in Turkey as in other developed countries, Turkish companies, universities, and other institutions can more easily access technology from a similar array of foreign institutions.

Despite convergence in regulations, Turkey may have gone backward in another respect, cutting budgets for public research below levels in developed market economies. Whether and where (GDAR, universities, etc) to rebuild budgets and public research capacity are questions for further review.

Competitive and Private Inputs Industries: Regulatory reforms favor expansion of private inputs industries in Turkey, moving the country towards common OECD patterns. With reforms, new companies have been established, and private producers and traders have taken larger market shares. Inputs firms have joined into trade associations, for which one of the major objectives has been to further company influence in policy formulation. Seed companies in 1986 created the Seed Industry Association (TEBD), which reported 29 members in 1994. Trade associations for other inputs include Tarmakbir, an association for agricultural machinery producers, and TISIT, the Association of Pesticide Manufacturers, Importers and Agents, formned in 1985. In 1994 TISIT had 28 members and was an associate member of GIFAP, the international industry association for pesticides. Stronger companies and new trade associations reinforce reform processes, pushing Turkey further toward convergence with common OECD patterns.

# **4. IMPACT ON PRODUCTIVITY AND INCOMES**

Addressing the third sub-hypothesis in Section 1.1, this Section presents evidence that Turkey's regulatory reforms have had a "significant net positive impact on producer and/or consumer incomes." To prove this point, we show that a partial measure of benefits less all costs is significantly positive: Benefits: Benefits are uneven across outputs. Following the train of evidence from reforns through inputs to outputs, Section 4.2 estimates partial benefits -- from selected outputs only -- of 1980s reforms. In Turkey, new technology after reforms had a relatively bigger impact on inputs for maize, sunflower, vegetables, and poultry than on inputs for some other outputs, such as cotton or barley.

Costs: Section 4.3 considers all costs of regulatory reform.

Net Benefits: Subtracting estimated total costs from partial benefits, Section 4.4 presents a lower bound estimate for net benefits.

Section 4.1 presents an overview of changes in agricultural production from the early 1960s through early 1990s. The impact of regulatory reforms cannot be determined from such aggregate

-20-
# **C Additional Numerical Illustration**

Figure 1 summarizes the distribution of cluster sizes in five published studies employing partial population designs: Cr ́epon et al. (2013), Gin ́e and Mansuri (2018), Haushofer and Shapiro (2016), Ichino and Sch ̈undeln (2012) and Imai, Jiang and Malani (2021).

For this numerical illustration, we calculate the estimators standard errors and minimum detectable effects based on our formulas from Section 3 using the cluster size distribution of these four studies. We refer to these magnitudes as “adjusted” standard errors and MDEs, since they are adjusted for cluster size variation. For comparison, we also calculate the “unadjusted” standard errors and MDEs using average cluster size and assuming that the variance of group size is equal to zero, that is, ignoring cluster size heterogeneity. To make the results comparable, we use as a benchmark the design in our application to tax compliance, which has four saturations: _p_ 0 = 0, _p_ 1 = 0 _._ 2, _p_ 2 = 0 _._ 5, _p_ 3 = 0 _._ 8. We compute the optimal probabilities _{q_ 0 _, q_ 1 _, q_ 2 _, q_ 3 _}_ using Theorem 2. We assume for simplicity that outcomes are homoskedastic with _σ_<sup>2</sup> ( _dt, dt_ ) = 1 for all _d, t_ so that effects are measured in standard deviations, and consider four values for the intraclass correlation, _ρ ∈{_ 0 _._ 1 _,_ 0 _._ 2 _,_ 0 _._ 5 _,_ 0 _._ 8 _}_ . The parameter of interest is the spillover effect on untreated units in groups with 80% treated.

Table A6: Numerical results

||**Sta**|**ndard er**|**ror**||**MDE**||
|---|---|---|---|---|---|---|
||Adj.|Unadj.|Ratio|Adj.|Unadj.|Ratio|
|_ρ_= 0_._1|||||||
|GM|0_._1262|0_._1181|1_._0687|0_._3536|0_._3308|1_._0689|
|HS|0_._1053|0_._0932|1_._1307|0_._2951|0_._2610|1_._1307|
|IS|0_._1768|0_._1667|1_._0608|0_._4954|0_._4670|1_._0608|
|IJM|0_._0569|0_._0497|1_._1453|0_._1595|0_._1393|1_._1450|
|_ρ_= 0_._5|||||||
|GM|0_._2593|0_._2393|1_._0835|0_._7265|0_._6705|1_._0835|
|HS|0_._2098|0_._1783|1_._1761|0_._5877|0_._4997|1_._1761|
|IS|0_._3437|0_._3171|1_._0840|0_._9630|0_._8884|1_._0840|
|IJM|0_._1136|0_._0950|1_._1961|0_._3183|0_._2661|1_._1962|
|_ρ_= 0_._8|||||||
|GM|0_._3252|0_._2997|1_._0851|0_._9112|0_._8397|1_._0851|
|HS|0_._2622|0_._2218|1_._1818|0_._7345|0_._6215|1_._1818|
|IS|0_._4284|0_._3941|1_._0869|1_._2002|1_._1042|1_._0869|
|IJM|0_._1420|0_._1181|1_._2024|0_._3979|0_._3309|1_._2025|

The numerical results are shown in Table A6. When the intraclass correlation is low ( _ρ_ = 0 _._ 1), accounting for cluster size heterogeneity increases standard errors and MDEs between 6.8% and 14.5%. The problem worsens for larger intraclass correlations. When _ρ_ = 0 _._ 5, adjusted standard errors and MDEs are between 8.3% and 19.6% larger, and between 8 _._ 5% and 20 _._ 2% larger when _ρ_ = 0 _._ 8.

D-31
**Table 1:  Summary statistics, 2000-2012**

|Variable||Obs.<br>Mean|Std. Dev.|Max.|Min.|
|---|---|---|---|---|---|
|cbagdp||356<br>-5.86|9.12|33.68|-50.60|
|psbgdp||344<br>-0.24|3.47|17.66|-9.21|
|open||350<br>101.98|31.77|199.68|41.62|
|nfagdp||320<br>11.77|12.90|41.18|-37.95|
|debtgdp||302<br>50.03|33.05|190.26|1.34|
|tot||306<br>104.84|22.71|252.10|25.63|
|cbagdp|=|Current account balance as a|ratio to GDP,|||
|psbgdp|=|Cyclically adjusted primary f|iscal balance as a|ratio to GDP,||
|open|=|(Exports+Imports)/GDP,||||
|nfagdp|=|Net Foreign Assets as a shar|e of GDP,|||
|debtgdp|=|External debt as a ratio to G|DP,|||
|tot|=|Index (goods and services, 2|000 = 100).|||

Figure 1 shows a measure of fiscal policy behavior during the decade. It graphs growth in real expenditures against the output gap in ECA countries. The scatterplot indicates that real expenditure growth and the output gap have a positive association – real expenditures growing more when the output gap is larger (and positive). Figure 2 shows the output gap as a ratio to GDP graphed against the primary structural balance (oil exporters excluded). These figures suggest that public finances were not stabilizing output changes in the region during the period under consideration.

**Figure 1:  Real expenditures and the output gap show a positive association during 2000-2012**

<!-- Start of picture text -->
-.1 -.05 0 .05 .1 .15<br>Output gap as a ratio to GDP<br>60<br>40<br>20<br>0<br>Growth rate of real expenditures<br>-20<br><!-- End of picture text -->

_Source:_ World Bank staff estimates based on government accounts. _Note:_ Oil Exporters are excluded from this graph.

8
with loan penetration. In column (8) we include contract enforcement days as a proxy for the efficiency of the legal system. In principle, loans should be more prevalent in more efficient legal systems. While this variable usually enters significantly in other work using private credit to GDP as dependent variable<sup>34</sup> , the coefficient here is not statistically significant. A possible explanation is that the quality of courts matters for business loans, but not so much for individual loans which constitute a large proportion of loans granted by commercial banks. Column (9) shows no significant relationship between political violence and number of loans. Finally, columns (10) and (11) consider the role of financial infrastructure which creates mechanisms for lenders to screen borrowers and enforce repayment. As proxies for such mechanisms, we use the Creditor Right Index developed by Djankov, McLiesh, and Shleifer (2007) following La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1997, 1998) and the Credit Information Index introduced by Djankov, McLiesh, and Shleifer (2007) and updated in “ _Doing Business 2009_ ” [World Bank (2009)]. The results show that the number of loans per thousand adults increases as the contractual and informational frameworks, as measured by these two indices, improve.

The bottom part of Table 21 explores the relationship between policies that may broaden access and the penetration of loans in the population across countries. In column (12), we include into our baseline regression our proxy for bank concentration. Concentration tends to be negatively and significantly associated with the supply of credit. The ownership structure of the banking sector, in contrast, is insignificantly correlated with the loan penetration. Columns (15) – (18) of Table 20 add to our baseline regression four measures of non-price barriers to accessing credit: (1) The number of locations to submit a loan application, (2) the minimum amount of consumer loan banks make expressed as a percent of GDP per capita, (3) the average fees banks charge on consumer loans expressed as percentage of GDP per capita, and (4) the average number of days banks take to process a typical consumer loan application. All these figures are from Beck, Demirgüç-Kunt and Martinez-Peria (2008). Holding per capita income constant, these barriers are not significantly correlated with the penetration of loans in the population. The last column

> 34 See for instance Djankov, McLiesh, and Shleifer (2007)

32
Java and Sumatra. The ban was subsequently extended to the whole country in 1974 and supplemented with tariff advantages for completely knocked down (CKD) kits. In 1976, the program was expanded to include a deletion program under which specified groups of components were to be produced locally for specific vehicle groups. The policy was “progressive” in that it envisaged a gradually expanding list of increasingly complex parts to be produced locally. In the early 1980s, the emphasis on local production was supplemented by pressure on assemblers to limit in-house parts production and to develop subcontracting relationships with SMEs.

This overall strategy clearly failed. Its problems were in part a function of overambitious goals and a recession-induced fall in demand for autos in the mid 1980s. More critically, given tariff protection and their political influence, brand holders had little incentive to expand and deepen the base of local suppliers. Also impeding the development of local production capacity was the lack of synergy among brands (noted earlier), as well as a tariff structure that raised the cost of imported components and, as a result, discouraged the growth of an efficient domestic support industry (Aswicahyono and Feridhanusetyawan 2004: 21). And finally, the ability of local firms to absorb new automotive technologies was limited by the country's poor education and skills levels: In 2000, Indonesia scored the lowest of East Asian market economies with regard to secondary and tertiary education enrolments and among the lowest in terms of technology imports and scientists and engineers as a percentage of population (Aswicahyono and Feridhanusetyawan 2004: 22, based on World Bank data).

Indonesia was certainly not the only Southeast Asian country to undertake an interventionist, protectionist automotive strategy based on production for the domestic market. Even Thailand, Southeast Asia’s best automotive performer, implemented an ambitious local content program backed up by high tariffs. What distinguishes Indonesia has been the degree and length of protection afforded to a small number of influential, local interests. Indeed, the 58
60

# TABLE 1: VARIATION IN THE INTENSITY OF LAND REFORM BY PRESIDENTIAL TERM, 1900-1988.

|Period|Land redistributed<br>(Hectares)|Percent of total<br>area redistributed<br>by 1988|
|---|---|---|
|1900-1914|194,495|0.2|
|1915-1934|11,543,833|11.0|
|1935-1940|18,786.131|17.9|
|1941-1946|7,277.697|6.9|
|1947-1952|4,590,381|4.4|
|1953-1958|6,056,773|5.8|
|1959-1964|8,870,430|8.4|
|1965-1970|24,738,199|23.5|
|1971-1976|12,773,888|12.1|
|1977-1982|6,097,005|5.8|
|1983-1988|4,167,588|.<br>4.0|
|TOTAL|105,167,588|4.0|

SOURCE: 1900-1982: SPP-INEGI, Estadisticos Hist6ricos de Mdxico, Tomo I, Mexico D.F., SPP, 1986.1983-1988: Sexto Informe Presidenclal, 1988 (Anexo Estadistico), Mexico D.F., SPP, 1988.
|(10)|6<br>Offcial|0.122***<br>(0.012)|103,199|0.075<br>0.263|59.81|of 2003, 2004,<br>in the intensive<br>ar of residence<br>erence between<br>ity of residence||
|---|---|---|---|---|---|---|---|
|(9)|200<br>Effective|0.217***<br>(0.012)|96,830|0.061<br>0.239|130.5|or the years <br>nnot re-enrol<br>unicipality-ye<br>ed as the diff<br>at municipal||
|(8)|05<br>Offcial|0.101***<br>(0.010)|276,733|0.064<br>0.244|54.70|otherwise (f<br>us years ca<br>to CS and m<br>toff is defn<br>ets clustered||
|(7)|20<br>Effective|0.166***<br>(0.009)|256,517|0.065<br>0.246|135.3|given and 0 <br>in the previo<br>h eligibility<br>stance to cu<br>ted in brack||
|(6)|4<br>Offcial|0.087***<br>(0.008)|296,529|0.049<br>0.216|48.16|CS in a <br>e entrants<br>action wit<br>variable di<br>s are repor|nt at 1%.|
||0|||||ed<br>c<br>er<br>e<br>r|a|
|(5)|20<br>Effective|0.153***<br>(0.008)|264,205|0.057<br>0.232|160.5|family start<br>variable sin<br>off, their int<br>toff, and th<br>tandard erro|*** signifc|
|(4)|03<br>Offcial|0.058***<br>(0.007)|315,641|0.044<br>0.206|42.23|lue 1 if the <br>e dependent<br>stance to cut<br>he offcial cu<br>ff. Robust s|cant at 5%;|
|(3)|20<br>Effective|0.140***<br>(0.007)|267,339|0.064<br>0.244|175.7|hat takes va<br>missing in th<br>adratic in di<br>ective and t<br>offcial cuto|%; ** signif|
|(2)|02<br>Offcial|0.018***<br>(0.003)|326,324|0.029<br>0.167|13.92|indicator t<br>years have<br>e include qu<br>both the eff<br>effective or|fcant at 10|
|(1)|20<br>Effective|0.121***<br>(0.008)|229,804|0.057<br>0.232|99.54|ariable is an<br>in previous<br>ed from tabl<br>mates using<br>mily and the|ated. * signi|
||entry|le]|tions||Eligibility|dependent v<br>06 entrants<br>trols exclud<br>present esti<br>ore of the fa|ility is evalu|
||r of<br>off|ligib|erva|an|t on|The <br>d 20<br> Con<br> We<br>S-sc|ligib|
||Yea<br>Cut|1[E|Obs|Me<br>SD|Tes<br>F|Note: <br>2005 an<br>phase). <br>effects. <br>the CA|when e|

22
omitted, again suggesting that the result is not merely reflecting India.

# **2.2.2 A statistical coincidence?: Omitted and placebo variables**

Across rich and poor places, good conditions are often found together, and problems are often found in places with other problems. Would _any_ measure of infrastructure, governance, or welfare be as correlated with height as is sanitation?

Column 6 of table 1 adds time-varying controls. These include female literacy, which is an important predictor of child welfare, and accessibility of water supply, all also from the DHS.<sup>6</sup> Development outcomes are often attributed to good “institutions;” the set of controls includes the polity and autocracy scores from the Polity IV database. With these controls added, the association between height and sanitation is essentially unchanged.<sup>7</sup> Table 3 isolates each of these alternative independent variables in turn. None of these “placebo” predictors matter for child height, conditional on sanitation and and GDP.<sup>8</sup> In particular, conditional on sanitation and GDP, child height is not associated with other types of infrastructure (electrification, water), governance (a democratic polity, autocracy), female literacy, or nutritional measures such as food availability, the breastfeeding rate,<sup>9</sup> or the fraction of infants who are fed “other liquids” beyond breastmilk in the last 24 hours.

> 6Despite the frequency of undifferentiated references to “water and sanitation,” improving water supply and reducing open defecation have very different effects on child health and other outcomes and should not be conflated (Black and Fawcett, 2008).

> 7If controls for the fraction of infants ever breastfed, the fraction of infants breastfed within the first day, and the fraction of infants fed “other liquids” in the past 24 hours are further added as measures of the quality of infant nutrition, the coefficient on open defecation in panel A with all controls becomes _larger_ in absolute value, -1.59, with a standard error of 0.82. This is not statistically significantly larger than the other estimates.

> 8Some of them do predict child height in a less thoroughly controlled specification. For example, in the first column, if GDP is removed the coefficient on female literacy almost doubles and becomes statistically significant at the 0.1 level.

> 9Breastfeeding is an especially important variable because India has high levels of open defecation, short children, and poor breastfeeding. In a regression with open defecation, country fixed effects, log of GDP, the fraction of children ever breastfed, and the fraction of children breastfed on the first day, neither breastfeeding variable is statistically significant ( _t_ of 0.23 and 0.6 respectively), but open defecation has a similar coefficient of -0.849, statistically significant at the two-sided 0.1 level ( _n_ = 139). If open defecators per square kilometer is used in place of the open defecation fraction, again it is statistically significant ( _t_ = _−_ 6) and the breastfeeding variables are not.

11
15 years old. Similarly, Foreign takes the value of 1 in enterprises with more than 10 percent participation of foreign capital and exporter identifies enterprises that export more than 10 percent of their sales at the time of the survey. Government identifies enterprises with participation of more than 10 percent from the government, and small_city identifies enterprises that are in small cities (less than one million habitants and not the capital city). A vector Ct captures country controls (GDP per capita, GDP per capita squared, average GDP growth over previous 3 year period, trade/GDP and inflation).

ICvariable~i,jksc thus refers to the set of investment climate variables, i.e. reliability of infrastructure services, access to finance, corruption or regulations. As discussed earlier, they are averages for the country(c)-city(k)-sector(j)-size(s) cell, excluding the enterprise’s own response and matched to enterprises based on their lagged size. Each of the investment climate variables are interacted with region dummies to test if the impact of these conditions are different by income group or between SSA and LOW regions. Table 5 column1 shows that there are significant regional differences in the impact of each of the four investment climate dimensions.<sup>21</sup> Greater outages are associated with reduced growth in LOW -- but with _higher_ growth in SSA. These results seem surprising; one would have expected that interruptions to power would make production difficult and would hamper enterprises’ growth prospects. To understand the pattern, the same specification is repeated in Col (3), this time allowing the results to vary by size within regions.

> 21 To save space, while all the HIGH interactions were included, they are not reported here.  Overall, access to finance is positive associated with growth in HIGH, weak infrastructure is negatively correlated.  Corruption is more positively associated with growth in HIGH, while regulations are more mixed.  The full set of results is available on request.

22
thus are more likely to stay in the market. However, once they have reached (or are very close to) the MES, they do not need to grow very fast.<sup>9</sup> Although all of the studies cited so far concern developed countries, the evidence from DCs is similar. For instance, Das (1995), examining the Indian computer industry, found a significant negative relationship between firm growth and initial firm size. McPherson’s (1996) study of five southern African countries detected a significant negative link between firm growth and both the firm’s size and age. Goedhuys and Sleuwaegen (2000) and Sleuwaegen and Goedhuys (2002) analyzed 141 and 129 manufacturing firms in Côte d’Ivoire, respectively, and found negative correlations between firm growth and both firm size and age. Finally, Bigsten and Gebreeyesus (2007) ran GMM-SYS panel estimates covering census-based Ethiopian manufacturing firms over the 1996–2003 period and showed that the negative relationship between size and age, on the one hand, and firms’ employment growth, on the other, is significant and robust to sample selection and unobserved firm heterogeneity.<sup>10</sup> In summary, a larger start-up size is reassuring in terms of the likelihood of survival and ensuring that job creation linked to the newborn firm is not transitory. In contrast, to survive, smaller new entrants must grow rapidly so they can contribute to employment growth. However, in the latter case, the job creation effect related to the surviving and fastgrowing small entrants must be compared with the massive job losses due to the early failure of most small newborn firms.

# _Entrepreneurial Learning_

From a theoretical point of view, Lucas (1978) was the first to propose a theory of the size distribution of firms based on the relative endowment of entrepreneurial talents. However, the first author to represent the post-entry evolution of newborn firms formally was 13