rafmacalaba/datause-encoder-data huggingface dataset:
**Appendix Table 7: Average effects of preprimary interventions on participation and skills during preprimary and post-preprimary**

||School<br>p<br>|participati<br>rogression<br>|on and<br> <br>|C<br>|**Preprimar**<br>ognitive ski<br>|**y**<br>lls<br>|Executiv<br>e<br>|e functions a<br>motional ski<br>|nd social-<br>lls<br>|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|_Preprimary program_|0.3030**|----|----|0.1474***|0.1222***|0.1915***|0.1209***|0.1150***|0.1576***|
||(0.0932)|||(0.0259)|(0.0332)|(0.0397)|(0.0279)|(0.0331)|(0.0305)|
|_High-income_||----|----||0.0541<br>(0.0537)|||0.0138<br>(0.0586)||
|_Expansion_||----|----|||-0.0957**<br>(0.0482)|||-0.1066*<br>(0.0514)|
|N|22|----|----|131|131|131|114|114|114|
||School<br>p|participati<br>rogression|on and<br>|**P**<br>C|**ost-preprim**<br>ognitive ski|**ary**<br>lls|Executiv<br>e|e functions a<br>motional ski|nd social-<br>lls|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|_Preprimary program_|0.0736<br>(0.0539)|----|----|0.0718***<br>(0.0236)|0.0863**<br>(0.0311)|0.0856**<br>(0.0342)|0.0686***<br>(0.0190)|----|----|
|_High-income_||----|----||-0.0260<br>(0.0487)|||----|----|
|_Expansion_||----|----|||-0.0239<br>(0.0481)||----|----|
|N|12|----|----|62|62|62|70|----|----|

Notes: Standard errors estimated using small sample adjustments as in Tipton (2015) are in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Number of observations (N) refers to number of study-specific average effects used in the specification. Specifications that pool skills use one large category for extracted outcomes - either cognitive skills (which combines language, literacy, math, and general cognitive skills) or what is typically referred to as non-cognitive skills (executive functions, social-emotional skills, and behaviors). "High income" indicates whether the study reports results from high-income countries, while "Expansion" indicates whether the preprimary intervention increased preschool coverage as opposed to improving preschool quality. Cells are blank when the sample size is too small to be able to obtain trustworthy estimations, when the degrees of freedom fall below 4 or when all observations came from a single study.
industry (but are not direct competitors). All startups were deemed by Vilcap to be of high quality and at similar stages. Baseline studies suggest that all startups accepted into the Vilcap program have similar observable characteristics, which does not differ by founder gender (Burns et al. 2019). Vilcap also provided access to a curated set of investors that had expressed an interest in startups at an early-stage and in a specific industry, which limits the sample to investors that have the potential to be interested in this stage of startup. Any differences in results should not be driven by differences in startup quality, nor investor interest.

Vilcap’s evaluation process is typical of the average investment organization in several ways. The average investment organization employs a collective evaluation process to decide whether to invest their organization’s funds into startups (Tyebjee and Bruno 1984, Fried and Hisrich 1994), which typically takes about 90 days (Gompers et al. 2020). Investment organizations assess startups using organization-level criteria which typically include assessments of the founding team, market size, product and business model (Cohen et al. 2019b, Gompers et al. 2020). As shown in a simplistic model in Figure 1, when evaluating, investors typically receive information such as a pitch deck or overview, meet founders, and then make a decision on whether to continue to diligence (or inquire about) the startup. This process repeats, as investors progress through deeper stages of diligence before they invest in a startup.

In Vilcap, this process unfolds with two types of investors. Professional investors,<sup>1</sup> embedded in a range of investment organizations, meet startups once. They receive a venture overview, written by Vilcap, meet approximately three founders for thirty minutes, and evaluate startups using a Vilcap survey where they decide if they wish to conduct due diligence, and ask for additional information

> 1 Professional investors were invited by Vilcap and included other accelerator managers, investors from angel groups, and early-stage venture capital funders.

13
preferences and local governments need to prioritize public expenditures in times of fiscal stress.

To examine heterogeneous demand for public services, we estimate a nonparametric hedonic function and allow the structural demand parameters to vary with household characteristics. The value of public services and other dwelling unit attributes are recovered non-parametrically, without resorting to restrictive functional forms. In fact, linear or even systematically (e.g., logarithmic or exponentiated) parametric nonlinear forms may be significantly biased: the χ2 test of linearity in parameters is rejected in100 percent of the bootstrapped samples. For robustness, we re-ran the regressions and simulations in OLS (results available). The OLS first-stage regression had consistently smaller – and most likely underestimated – standard errors. Conversely, because we do not impose parametric restrictions on the 1<sup>st</sup> stage, we achieved a much better fit in the 3<sup>rd</sup> -stage regressions using the GAM estimates. The OLS 3<sup>rd</sup> -stage parameter estimates were on average one-third smaller than the GAM estimates, and a quarter of them also had different signs.

Housing stock comprises large fraction of wealth, and housing services are a substantial part of consumption. Households place great value on certain attributes: living space, public services, housing quality, neighborhood attributes, and so on. The value of these attributes differs significantly among households, by wealth, education, social status (caste), and other characteristics. Although wealthier households receive a greater share of value of public services (water, transport, education, safety), poorer households also benefit. And when expressed in terms of the price of housing or income, the value of publicly-provided services accruing to the poor is greater than that going to 26
regions.<sup>24</sup> These indicators are derived under the assumption of no economies of scale (θ = 1) using the base poverty line.

The benefits of growth were not equally distributed across geographical regions and the different income groups. The rise in household income was more rapid in urban areas, where the monthly average per capita expenditure reached 82 LVL, representing a 35% increase. Similarly, a more rapid decline in poverty and improvement in living standards was recorded in urban areas where the incidence of poverty, already low, fell to an even lower level (9.2%, from 15.6% in 1997, representing more than 41% decline). The largest improvement in welfare was recorded in Riga, where the incidence of poverty fell to 6.7%, reaching an all time low level. A large reduction in poverty was also recorded in other cities where the headcount fell from 19 to 12%, reflecting a concurrent rise in income. However, the decline in poverty was less rapid in rural areas where household income grew at a slower rate, hence widening the urban-rural income and poverty gaps. The incidence of poverty remains relatively high, at about 26%, barely representing a 9% decline from 1997.<sup>25</sup> The decline in the poverty gap, an indicator of vulnerability, was even lower, suggesting that a large fraction of poor households continues to have per capita income falling significantly below the poverty line.

**Table 2: Poverty by Geographical Regions under the Assumption of no Economies of Scale (** θ = 1 **)**

||**Hou**<br>**Sur**|**sehold B**<br>**vey 1997 (**<br>|**udget**<br>1<br>=<br>θ<br>**)**|**Ho**<br>**Sur**|**usehold**<br>**vey 2000**|**Budget**<br>**(**<br>1<br>=<br>θ<br>**)**|
|---|---|---|---|---|---|---|
|**Economic Regions**|**P0**|**P1**|**P2**|**P0**|**P1**|**P2**|
|Riga|12.6|3.5|1.5|6.76|1.93|0.82|
|Kurzeme|24.5|8.0|3.8|16.61|4.50|1.79|
|Vidzeme|24.1|6.9|2.9|23.31|7.14|3.41|
|Zemgale|20.6|5.2|2.0|15.14|4.36|1.85|
|Latgale|30.0|8.4|3.6|24.17|7.24|3.11|
|Rural Areas|28.5|8.3|3.6|25.86|7.93|3.56|
|Urban Areas|15.56|4.39|1.87|9.15|2.51|1.03|
|Other Urban|19.5|5.3|2.2|12.05|3.22|1.26|
|**Latvia**|**19.4**|**5.5**|**2.4**|**13.99**|**4.09**|**1.76**|

The widening urban-rural poverty and income gap over time is illustrated by the poverty incidence curves depicted in Figure 1. This figure compares the poverty incidence curve in

> <sup>is the consumption of the i</sup><sup>_th_individual below the poverty line.</sup>

> <sup>_y_</sup> _i_

> 25 The urban-rural contrast in the poverty rates and welfare gaps is pervasive in most Eastern European and Former Soviet Union countries, including the great reformers. In Bulgaria and Poland for instance, rural poverty rates are almost twice the urban rates (Braithwaite, Grootaert and Milanovic [2000]).

12
panoply of laws as well as government departments to monitor and control the private sector on the one hand, and to promote and finance small enterprises through government assistance on the other. Creditor and investor rights were largely unprotected in practice, with banks having little bargaining power against willful defaulters. Large corporate houses often got away with default, or got poor projects financed through the state-owned banking sector, often by using connections with influential politicians and bureaucrats.

To summarize, despite strong protection provided by the law, legal protection is considerably weakened in practice due to an inefficient judicial system, characterized by overburdened courts, slow judicial process, and widespread corruption within the legal system and government.

# **II.3 The Financial Sector**

Despite the history of India’s stock markets and large number of listed firms, the size and role in terms of allocating resources of the markets are dominated by those of the banking sector, similar to many other emerging economies. From Table 3-A, total bank deposits (of over $385 billion dollars) are equivalent to 50% of GDP in 2003, and constitute three-quarters of the country’s total financial assets. The efficiency of the banking sector, measured by the concentration and overhead costs, is ahead of the world average. On the other hand, total capitalization of the stock market is 34% of GDP, while the size of the (private) corporate bond market is miniscule.

In Table 3-B we compare India’s financial system (2003 figures) to those of the LLSVsample countries (LLSV, 1997a, 1998), using measures from Levine (2002). In terms of the size (bank private credit over GDP), India’s banking sector is much smaller than the (value-weighted) average of LLSV sample countries, even though its efficiency (overhead cost as fraction of total banking assets) compares favorably to most countries. The size of India’s stock market, measured by the total market capitalization as fraction of GDP, is actually slightly larger than that of the banking sector, although this figure is still below the LLSV average. However, in terms of the 12
**<u>Table 4. Household-level Cross-sectional Estimates for Impact of Tenure Status on Revenue and Investment</u>**

||Gross r<br>Rs/|evenue<br>ac|Net re<br>Rs|venue<br>/ac|Inves<br>Rs|tment<br>/ac|
|---|---|---|---|---|---|---|
|Pure Tenancy (α)|-0.182***<br>(7.83)||-0.172***<br>(6.98)||-0.011*<br>(1.86)||
|Owner-cum-tenant (β)|-0.116***||-0.142***||0.005||
||(6.34)||(5.95)||(0.80)||
|Share of barga land in||-0.202***||-0.198***||-0.008**|
|total land||(9.35)||(8.22)||(2.03)|
|Head’s educ (years)|0.007***|0.007***|0.008***|0.008***|0.002***|0.002***|
||(4.70)|(4.63)|(4.99)|(4.93)|(4.47)|(6.36)|
|ST/SC caste|-0.098***|-0.096***|-0.078***|-0.076***|-0.007*|-0.007***|
||(4.65)|(4.53)|(3.29)|(3.22)|(1.70)|(2.71)|
|Head's age|0.000|0.000|0.001|0.001|0.001***|0.001***|
||(1.10)|(1.01)|(1.32)|(1.25)|(3.34)|(5.01)|
|Total area cultivated|0.709***|0.707***|0.610***|0.608***|0.026***|0.026***|
|(log)|(47.71)|(48.07)|(37.33)|(37.58)|(8.49)|(19.67)|
|Household size (log)|0.095***|0.095***|0.077***|0.077***|-0.006|-0.005*|
||(9.33)|(9.28)|(6.86)|(6.79)|(1.07)|(1.77)|
|Own livestock|0.131***|0.131***|-0.013|-0.014|0.021***|0.021***|
||(11.40)|(11.26)|(0.56)|(0.61)|(4.02)|(8.42)|
|No. of households|41,031|41,031|41,031|41,031|34,046|34,046|
|R-squared|0.69|0.69|0.93|0.93|0.13|0.13|

_Note:_ Dependent variable is log of gross or net value of revenue as explained in the text. Robust t statistics in parentheses. *, **, and *** denote significance at 10%, 5%, and 1%, respectively. For the investment regression, livestock ownership is for 1978 and the share of irrigated land in 1978 and the year dummies for household formation are included but not reported.

19
The SDI surveys collect a broad set of school, teacher, and student specific information, with an approach that relies as much as possible on direct observation rather than on respondent reports. Data are collected through visual inspections of fourth-grade classrooms and the school premises, direct physical verification of teacher presence by unannounced visits, and teacher and student tests. Bold et al. (2017) document how African teachers perform along three dimensions: Time spent teaching, teachers’ knowledge of the subject they are teaching, and teachers’ pedagogical skills. Teachers, on average, are absent from class 44 percent of the time and about half of that classroom absence is due to teachers not being at the school during regular teaching hours (Table 1). As a result, while the scheduled teaching time for fourth graders is relatively long—5 hours and 25 minutes—the actual time students are taught is about half that time—2 hours and 46 minutes. Pedagogical knowledge is low, with only one in ten teachers deemed to have minimum pedagogical knowledge, and even fewer teachers are judged to be able to properly assess students’ learning shortcomings and progression.

In each school, 10 students are sampled from a randomly selected grade 4 classroom. The choice to test students who had completed the third grade was made with the following objectives in mind: on the one hand a desire to assess cognitive skills at young ages when these are most malleable; and on the other hand a desire to assess the learning outcomes of students who have completed at least some years of schooling and to assess language learning at a time when all children would have had lessons in the official language of their country (English in Nigeria and Uganda, English and Swahili in Kenya and Tanzania, French in Senegal and Togo, and Portuguese in Mozambique). In each school, the students’ current, and to the extent possible, previous language and mathematics teacher are selected for testing.<sup>8</sup> In more than 80 percent of the schools surveyed in the seven countries, and for 65 percent of the students, data were collected on both the current and previous teachers, i.e., the teachers in grade 4 and in grade 3.

The student test was designed as a one-on-one evaluation, with enumerators reading instructions aloud to students in their mother tongue. This was done to build up a differentiated picture of students’ cognitive skills; i.e., oral one-to-one testing allows one to test whether a child can solve a mathematics problem even when his/her reading ability is so low that he/she would not be able to attempt the problem independently.

> 8 In five of eight surveys, teachers in other grades were also sampled.

6
Venables. One difference is that increases in exports from trading partners do not increase the number of varieties supplied by these countries, and hence do not generate benefits from the preference for variety assumed in the new economic geography models. For trading partners where welfare declines, but exports increase, our formulation omits a positive effect that may reverse the very small estimated overall negative impact. For markets where both welfare and exports decline, our analysis excludes a negative welfare impact contained in the new economic geography models.

A recent paper by Amiti and Freund (2007) found that new products and new markets were important to China’s recent export success but much less so than in Hummels and Klenow (2006). Using highly disaggregated time series data of China’s exports to the US (10-digit HS data with over 16,000 codes) and 6-digit HS data on China’s export to all countries for the period 1992-2005 and Feenstra’s index of variety, they estimated an extensive margin of 31 percent – 15 percent associated with growth in new varieties and 16 percent associated with growth of existing varieties to new markets. This is 35 percentage points lower than the estimate in Hummels and Klenow, but their estimate based on crosssection estimation is not directly comparable with that of Amiti and Freund since the forces that generate product distribution across countries may be quite different from forces at work within countries over time. In addition, the estimate in Hummels and Klenow is an average effect for the 126 exporting countries in their U.N. data for 1995. The time period of the Amiti and Freund (2007) study may also be important. Manole and Martin (2007) found that the growth of non-traditional exports was much more rapid between 1980 and 1992 than in the post-1992 period.

The extensive margin in developing countries other than China is likely to be much stronger than the extensive margin observed for China’s exports because in 1992 China’s export bundle was already very large and diverse, and overlapped with that of the developed countries much more substantially than one would expect given its level of development or its size (Schott 2007). It is quite plausible that the size of the extensive margin used for India is closer to the estimate in Hummels and Klenow (2006), while that for China may be closer to Amiti and Freund (2007). In addition to the uncertainty about the size of the extensive margin and the strength of purchasers’ love of variety there is also uncertainty about the size of the elasticity of substitution between different varieties. Hummels and Klenow (2006) consider estimates that vary between 5 and 10 based on estimates in Hummels (1999), but they also discuss the much lower value of 2.6 in Acemoglu and Ventura (2002) which is close to more recent estimates in 16
Table 5-1 shows the GMM-IV and OLS growth results pooling 1991-1980 and 2000-1991 differenced equation years for equation (7). For instruments, we add to the IV list of Table 4 ln(distance to São Paulo), ln(transport costs to São Paulo, 1968), and ln(transport costs to state capital, 1968). All covariates, except changes in rural income opportunities, have strong and expected sign coefficients. The poor performance of rural income opportunities is most probably due to the limited variance in the data over time, as discussed next.

Relative to the levels equation in Table 4, the growth equation coefficients reported in column 1 are similar for market potential and (change) in schooling. However results for changes in rural situation variables and transport costs differ in magnitude. For Δln(rural population supply) and Δln(rural income opportunities), not only is there little variation, the two variables are strongly negatively correlated.<sup>10</sup> So the high coefficient on Δln(rural population supply) may be picking up some of the effect of Δln(rural income opportunities). For the inter-city transport cost variable, differences over time may be poorly measured. While we instrument for this variable, the instruments include historical levels of the same measure, and therefore may be subject to the same measurement issues. As a result, reductions in inter city transport costs have a much smaller effect in the growth estimation. Nevertheless coefficients are consistent in sign with those of the level equation in Table 4.

In examining the results in Table 5, we focus on column 3. The main difference between the GMM results in columns 1 and 3 is that we introduce base period population and manufacturing to service ratios in the latter specification. Controlling for population allows for dynamic adjustment to steady state levels from the base, and introducing

> 10 The correlation coefficients are -.719 (for 1991-1980) and -.481 (for 2000-1991).

17
# **I. INTRODCUTION**

## **1. Background**

The Republic of Korea became one of the world’s earliest adopters of value-added tax (VAT) when it introduced a uniform 10 percent invoice-credit VAT as a general consumption tax in 1977. Since then, VAT has grown to become a mainstay of the government’s tax revenues, reaching KRW58.9 trillion or 29 percent of the entire tax revenues for 2013.<sup>1</sup> However, the need for effective VAT enforcement—especially in respect of curbing VAT refund frauds—remains unabated.

Many countries that have adopted an invoice-credit VAT face more or less the same difficulties with their VAT refund administration when giving credit for input VAT or sometimes refunding excess credits to taxpayers. The difficulty arises from the intrinsic administration of the invoice-credit VAT system, which determines tax liability by deducting tax assessed on a purchase (“input VAT”) from tax assessed on a sale (“output VAT”). Compared to a sales tax or an income tax, VAT is said to be more vulnerable to fraud because of the input VAT credit mechanism (United States Government Accountability Office [GAO], 2008, p. 14). Those critical of VAT contend that the refund mechanism leaves the invoice-credit VAT particularly vulnerable to fraud (Keen, 2007, p. 4). VAT fraud has in fact been observed throughout the developed and the developing countries (Harrison & Krelove, 2005). Across the EU, the phenomenon of missing trader intra-community (MTIC) fraud exploiting VAT refunds for zero-rated exports and deferred VAT payments on imports within the multijurisdictional block has been well established.<sup>2</sup> Moreover, tax authorities in many developing countries with governance constraints grapple with substantial backlogs of VAT refund applications because of the time taken (varying from several months to more than a year) to verify the legitimacy of VAT refund claims and deter fraud (Harrison & Krelove, 2005, p. 5).

Fraud involving input VAT credit can take various forms. For instance, businesses can file false claims for credit by presenting fictitious invoices, inflating the credit amount by altering invoice information, claiming zero-rating for nonexistent exports or credits on purchases that are not creditable, or even setting up bogus companies to

> 1 Five percent of the VAT revenues were allocated to the local governments in 2013, meaning KRW56.0 trillion accounted for 29.4 percent of the total national tax revenue of KRW190.2 trillion for 2013  ( _2014 Statistical Year Book of National Tax_ , the National Tax Service, 2014).

> 2 MTIC is also called “carousel fraud” because it occurs when an importer pays no VAT on imports but collects VAT on sales and disappears without remitting the VAT collected to the tax authority. Multiple transactions then ensue before goods are exported again to other EU countries with zerorating refund, through which a whole new process starts again. The U.K. estimated that the size of MTIC fraud varied from £3.5 billion to £4.75 billion in June 2005, and Germany estimated MTIC fraud at €4.5 billion in 2002 (United Kingdom House of Lords European Union Committee, May 2007,  pp.12-13).

1