rafmacalaba/datause-encoder-data huggingface dataset:
**Table 23. Governance-related drivers of legal and regulatory obstacles: Administrative complexity**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.110*|0.0953*|||0.175|0.156|||
||(0.0582)|(0.0570)|||(0.111)|(0.115)|||
|Export % of sales|-0.00125|-0.000267|-0.00129*|-0.000309|-0.00253*|-0.000512|-0.00277*|-0.000736|
||(0.000768)|(0.000767)|(0.000779)|(0.000774)|(0.00145)|(0.00152)|(0.00153)|(0.00158)|
|Share of sourcing via imports|0.00224***|0.00101|0.00225***|0.000966|0.00417***|0.00183|0.00440***|0.00193|
||(0.000835)|(0.000829)|(0.000859)|(0.000847)|(0.00161)|(0.00169)|(0.00168)|(0.00175)|
|High-income source country|-0.106|-0.0122|-0.114|-0.00328|-0.213|-0.0324|-0.245|-0.0193|
||(0.0853)|(0.0848)|(0.0861)|(0.0841)|(0.167)|(0.177)|(0.175)|(0.180)|
|>250 employees|0.108|0.0835|0.0802|0.0596|0.179|0.133|0.130|0.0892|
||(0.0698)|(0.0693)|(0.0693)|(0.0689)|(0.129)|(0.137)|(0.132)|(0.140)|
|Cumulative inv. >US$10M|-0.000665|-0.0742|-0.0116|-0.0816|0.000262|-0.154|-0.0292|-0.181|
||(0.0702)|(0.0692)|(0.0690)|(0.0684)|(0.130)|(0.139)|(0.132)|(0.141)|
|Years in country|-0.00136|-0.00122|-0.00102|-0.000964|-0.00226|-0.00258|-0.00194|-0.00231|
||(0.00248)|(0.00233)|(0.00233)|(0.00224)|(0.00465)|(0.00483)|(0.00457)|(0.00476)|
|Level of foreign ownership (%)|-0.00244**|-0.000784|-0.00273**|-0.000805|-0.00479*|-0.00201|-0.00547**|-0.00204|
||(0.00123)|(0.00126)|(0.00120)|(0.00121)|(0.00248)|(0.00263)|(0.00249)|(0.00258)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.018|0.123|0.049|0.143|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Complexity of administrative procedures]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

40
- 33 -

second block rate was decreased. These were consistent changes with the above finding to curb the electricity demand. However, the marginal rate was raised only for large-volume consumers in 2008. This created a differential between the two block rates once again. In addition, the threshold between the two rates was also raised. Therefore, the implicit income transfers were increased. According to our estimation results, the increased Nordin’s differential variable would push up the demand for electricity, not bring down.

||**Table 7.**<br>|**Electricity ta**<br>|**riff structure**|**in Albania**<br>||
|---|---|---|---|---|---|
||t||||t|
|Year|s oc<br>rate|n oc<br>rate|Threshold<br>(kWh)|orma<br>consumption|ve<br> (kWh)1/|
||(lek/kWh)|(lek/kWh)||Urban|Rural|
|Sample|period:|||||
|2002|<br>4.0|8.0|300.0|356.9|244.4|
|2003|<br>4.0|8.1|300.0|356.9|244.4|
|2004|<br>4.5|8.9|220.0|363.8|237.0|
|2005|<br>4.5|9.0|210.0|363.8|237.0|
|Beyond|the sample pe|riod:||||
|2006|<br>7.0|7.0|n.a.|363.8|237.0|
|2007|<br>7.0|7.0|n.a.|363.8|237.0|
|2008|<br>7.0|12.0|300|376.5|246.8|
|2009|<br>7.0|12.0|300|376.5|246.8|

1/ Average across districts for the summer period. Source: Electricity Regulatory Entity, Albania.

Turning to the selection equation associated with metering, the Nelson-Olsen estimator shows that low-volume consumers are more likely to be equipped with meters (Table 8). This is also consistent with the theoretical prediction. There is little incentive for low-volume customers to stay unmetered. Moreover, the effects of _~~q~~_ and _q_ 0 on _I_ are quite suggestive, though not statistically significant. It is indicated that the higher norm might promote meter installation. This is reasonable because the possibility of overconsuming electricity would shrink if the norm is raised. Further, the increased block tariff threshold might also help to increase metering. This is because more people would become able to take advantage of the lower marginal tariff, if the threshold is increased. But in order to benefit from this privilege, it is required to install a meter, because the norm is set above the threshold in most cases in Albania. Therefore, people would be willing to install electric meters. These are statistically insignificant, but the Nordin’s difference has a significant impact on metering. Again, if the threshold increases, the Nodrin’s difference increases, which would result in higher metering.
# **_2.4 Evidence from the International Adult Literacy Survey_**

The preceding analyses for both developed and developing countries rely largely on panel data that follow individuals from school into the labor market. The alternative approach as found in the International Adult Literacy Survey (IALS) is to test a sample of adults and then to relate these measures to labor market experiences.<sup>19</sup> An advantage of this data collection approach is that it provides information about the labor market experiences across a broader range of age and labor market experience.<sup>20</sup> Consistent data on basic skills of literacy and numeracy for a representative sample of the population aged 15-65 were collected for a sample of countries between 1994 and 1998.<sup>21</sup> These data permit direct comparisons of the relative importance of quantity and quality of schooling across countries, although the bias toward developed economies remains. Hanushek and Zhang (2006) estimate returns to school attainment and to literacy scores for the 13 countries where continuous measures of individual earnings are available.<sup>22</sup> Figures 2.2 and 2.3 provide the relevant summary information on the returns to skills, estimated in a model that jointly includes school attainment and literacy scores. As in the prior analyses, both school attainment and cognitive skills enter into the determination of individual incomes. With the exception of Poland, literacy scores have a consistent positive impact on earnings (Figure 2.2). The (unweighted) average of the impact of literacy scores is 0.093, only slightly less than found previously for the U.S.

19 This design was subsequently repeated in 2003 with the Adult Literacy and Lifeskills Survey (ALL), but only six countries participated, and the data were unavailable for this study.

20 This approach does also present some complications, because the individuals of different ages have both different adult learning experiences and different times of attending school of possibly different quality. Hanushek and Zhang (2006) consider these alternatives, but they do not change the qualitative results about the impact of cognitive skills that are presented here.

21 Twenty-three countries and regions participated in one of three different waves of surveys conducted in 1994, 1996 and 1998. Country participation included some sub-country divisions (e.g., Flemish speaking Belgium and Italian speaking Switzerland). Not all countries provided background information and wage information. IALS provides information on the skills and attitudes of adults aged 16-65 in a number of different areas, and the analysis here combines the scores in a single measure of literacy.

22 Samples include full-time workers between 26 and 65 years of age. In all the models, the dependent variable is the logarithm of annual earnings from employment; control variables are gender, potential experience and its square, and an indicator for living in rural area.

14
The types of products described above are different from what one would expect from relationship lending.<sup>44</sup> Banks that can sell these products on a large scale tend to benefit the most, by having branches and SME account managers that act as the “personalized” point of contact for SMEs but that deliver mostly generic (yet somewhat customized by sector or group) products that have been planned and designed centrally, at headquarters. In Chile and Argentina many, especially large, banks have created business centers that service various branches within a geographic area. This helps them reduce costs by centralizing some functions that are subject to economies of scale, such as back-office functions. The account manager in a branch reaches out to new SMEs and manages the daily operations with existing SMEs, relying on the business center for specific back-office work. As a consequence, account managers are in high demand because their human capital is highly portable, generating job hopping and poaching. In Colombia, by contrast, the concept of a “business center” is embryonic and the figure of “SME account manager” is incipient.

Under this model for engaging SMEs, headquarters have an advantage in designing the strategy of which group or sub-sector of SMEs to target. Headquarters are better equipped at taking advantage of synergies arising with “supply chains” and outsourcing arrangements, given the close connection with large corporations, which help them sort out which SMEs are worth approaching. Moreover, headquarters can design the array of products to be offered to SMEs to exploit cross-selling potential. These products

> 44 We are not saying that relationship lending implies that only loans are offered to SMEs. US studies focusing on relationship lending by community banks show that banks tend to offer multiple products (Prager and Wolken 2007). Our point is that large and foreign banks, which are less likely to engage in relationship lending, have a comparative advantage in offering the breadth and types of products described here. Also, our interviews of banks reveal that cross-selling is at the heart of their strategy in catering to SMEs and not a by-product of the lending relationship.

31
**Table A15: Treatment - Additional Fixed**

|Be|fore delegat|ed randomi|zation of inspec|tor assignmen|t|
|---|---|---|---|---|---|
|**Dependent variable:**|**Time**|**Fraud**|∆**log value**|∆**log tax**|**Hyp. tax**<br>**revenue**<br>**losses**|
|**A. Controlling **|**for Inspec**|**tor-Semes**|**ter and Brok**|**er-Semester **|**Fixed Efects**|
||(1)|(2)|(3)|(4)|(5)|
|Excess interaction share|-1.841***<br>(0.354)|-0.232**<br>(0.093)|-0.067***<br>(0.018)|-0.081***<br>(0.022)|0.310*<br>(0.153)|
|Observations|41,121|44,522|44,434|40,471|44,497|
|R-squared|0.340|0.235|0.173|0.153|0.223|
|**B. Contr**|**olling for **<br>**Imp**|**Inspector-**<br>**orter-Seme**|**Semester, Br**<br>**ster Fixed E**|**oker-Semest**<br>**fects**|**er, and**|
||(6)|(7)|(8)|(9)|(10)|
|Excess interaction share|-1.855***<br>(0.372)|-0.104*<br>(0.060)|-0.045**<br>(0.020)|-0.053**<br>(0.022)|0.223***<br>(0.080)|
|Observations|38,624|41,972|41,885|38,045|41,952|
|R-squared|0.461|0.412|0.383|0.381|0.477|
|**C. Controlli**|**ng for Insp**|**ector-Mo**|**nth and Brok**|**er-Month Fi**|**xed Efects**|
||(11)|(12)|(13)|(14)|(15)|
|Excess interaction share|-1.810***<br>(0.317)|-0.184**<br>(0.087)|-0.057***<br>(0.018)|-0.068***<br>(0.023)|0.255<br>(0.151)|
|Observations|41,098|44,510|44,422|40,449|44,485|
|R-squared|0.386|0.281|0.219|0.208|0.258|
|**D. **|**Controllin**|**g for Imp**|**orter-Broker **|**Fixed Efect**|**s**|
||(16)|(17)|(18)|(19)|(20)|
|Excess interaction share|-1.995***<br>(0.316)|-0.132*<br>(0.076)|-0.054**<br>(0.022)|-0.061**<br>(0.026)|0.236***<br>(0.084)|
|Observations|39,422|42,761|42,674|38,803|42,738|
|R-squared|0.411|0.349|0.326|0.327|0.447|

_Notes:_ Standard errors clustered two-way by inspector and by broker presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. _Excess interaction share_ is the difference between the share of given broker’s declarations handled by an inspector in a given semester and the hypothetical share she would be expected to handle if the allocation of declarations to inspectors were random conditional on their productivity, as prescribed by official rules, calculated using calibration methods (as explained in Section 4). All specifications include the tax rate, the risk score, a dummy for the red channel, the share of value accounted for by differentiated products, a dummy indicating whether the declaration was mixed, a dummy indicating the declaration was subject to valuation advice, and source country and HS2-product fixed effects. Specifications in panels A, B, and D also include month-year fixed effects. Specifications in panel D also include inspector fixed effects. "Observations" refers to the number of non-singleton observations. OLS estimation is used. The sample covers the period January 1, 2015 to November 17, 2017 (i.e., before the delegated randomization intervention).

75
The Cull et al. (2017) findings on modest but persistent subsidy suggest that the conversation needs to shift toward measured impact so the costs analyzed by Cull et al. (2017) can be compared to benefits. Ultimately, cost-benefit ratios are of key interest for policy makers, and— even with modest benefits—microfinance cost-benefit ratios can compare favorably to those for other interventions. However, systematic work here has hardly started.

# **Part 4: Microfinance as Liquidity Services**

Most of the discussion above is framed in terms of impacts on business and entrepreneurship. It was posited that through business comes increases in income and, from that, social gains. This, though, is a particular (and narrow) view of finance. Finance is also needed by households to purchase consumer goods, however, and to help with basic, week-by-week financial management. The kinds of questions that researchers asked with RCTs followed from the business focus, and the same perspective was carried forward by the Microcredit Summit Campaign, donors, and investors.

An alternative view emerges from financial diaries. The diaries are most closely associated with the work of Collins et al. (2009), which details the financial lives of low-income families in Bangladesh, India, and South Africa. Stepping away from large-scale statistical efforts, Collins et al. (2009) take a close-to-the-ground approach, aiming to track the entire financial lives of a small set of households in both rural and urban areas. They use the tools of empirical corporate finance to create linked balance sheets and income statements for the households.<sup>16</sup> Their focus is on the complete set of household financial transactions connected to

> 16 For a related method, see the important work of Samphantharak and Townsend (2009).

32
imports from many countries, in East Asia as well as Europe and the United States. Spillovers from China to the rest of the world have been strong in both the short and long run (Arora and Vamvakidis 2010), and strong trade ties with China contributed to East Asia’s resilience to the global financial crisis (Broadman 2010). It was estimated that China’s fiscal stimulus was particularly effective in stimulating demand for commodity exports by Australia, (Day 2011). According to an estimate by the International Monetary Fund (IMF), the resilience of the BRIC economies (Brazil, Russia, India, China) during the financial crisis may have added 0.3–1.1 percentage points to growth by low-income countries.

**Table 1 Key macroeconomic indicators in China, 2007–12**

|_Indicator_|_2007_|_2008_|_2009_|_2010_|_2011_|_2012_<br>_(projected)_|
|---|---|---|---|---|---|---|
|GDP growth (annual percentage change)|14.2|9.6|9.2|10.5|9.2|8.2|
|Inflation, consumer prices (annual percentage change)|4.8|5.9|–0.7|3.3|5.4|3.3|
|Current account balance (percent of GDP)|10.1|9.1|5.2|5.2|2.8|2.3|
|General government gross debt (percent of GDP)|19.6|17.0|17.7|33.5|25.8|22.0|
|General government balance (percent of GDP)|0.9|-0.7|-3.1|-1.5|-1.2|-1.3|
|Central government fiscal balance (percent of GDP)|6.1|6.1|6.1|6.6|—|—|
|Local government fiscal balance (percent of GDP)|–5.5|–6.5|–8.3|–8.3|—|—|
|Total investment in fixed assets (percent of GDP)|51.6|54.8|65.8|69.0|—|—|

_Source_ : Authors’ compilation based on data from IMF World Economic Outlook April 2012, IMF Article IV Consultation with China July 2012, and China Statistical Yearbooks of recent years. IMF estimates of GDP growth, inflation, and general government gross debt start after 2011; current account balance estimates start after 2010. _Note_ : — Not available.

# **Effects of China’s Fiscal Stimulus**

China’s fiscal stimulus helped prevent a drastic slowdown of its economy, which had been growing by double digits for three decades. Had the economy suddenly slowed significantly, latent issues could have surfaced, such as a sharp increase in unemployment which would have undermining the “harmoniousness” of the society Several recent studies provide a range of estimates of the effects of China’s fiscal stimulus. The IMF estimates that it raised China’s GDP by 1.5–2.25 percentage points in the first half of 2009 7
- [19] Ghosal, V. (2003b). Firm and establishment volatility: the role of sunk costs, pro...t uncertainty and technological change. CESifo Working Paper No. 980. Retrieved from http://www.cesifo-group.de/DocCIDL/cesifo_wp980.pdf.

- [20] Ghosal, V. (2007). Small is beautiful but size matters: the asymmetric impact of uncertainty and sunk costs on small and large businesses." MPRA Paper No. 5461. Retrieved from http://mpra.ub.unimuenchen.de/5461/1/MPRA_paper_5461.pdf.

- [21] Ghosal, V. (2009). The role played by sunk costs in real-options models. 10<sup>th</sup> CESifo Venice Summer Institute, 6-11 July 2009. Retrieved from http://www.cesifo-group.de/link/vsi09_ou_Ghosal.pdf.

- [22] Hagerman, R., & Senbet, L. (1976). A test of accounting bias and market structure. Journal of Business, 46, 509-514.

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- [26] Kelly, W. (2001). A generalized interpretation of the Her...ndahl index. Southern Economic Journal, 48 (1), 50-57.

- [27] Kessides, I. (1986). Advertising, sunk costs, and barriers to entry. The Review of Economics and Statistics, 68 (1), 84-95.

- [28] Kessides, I. (1990). Towards a testable model of entry: a study of the US manufacturing industries. Economica, 57, 219-238.

- [29] Kwoka, J., & White, L. (2001). The new industrial organization and small business. Small Business Economics, 16 (1), 21-30.

- [30] Levy, M., & Solomon, S. (1996). Power laws are logarithmic Boltzmann laws. International Journal of Modern Physics, C 7 (04), 595-601.

- [31] Lotti, F., Santarelli, E., & Vivarelli, M. (2009). Defending Gibrat’s law as a long-run regularity. Small Business Economics, 32 (1), 31-44.

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24
A vast literature in the field of bibliometrics uses data on scientific publications and citations.<sup>10</sup> While peer review has traditionally been the main form of evaluating academic research, quantitative methods have become more important in the past 20 years (Hicks _et al_ . 2004). Within the field of economics, until recently most empirical studies of innovation used patent data, rather than publication data, as the measure of research output. While the use of publication data within the economic research community has recently increased, the focus has primarily been on using publication data to measure knowledge flows across institutions, rather than evaluating the effectiveness of research inputs.<sup>11</sup> Other options for measuring knowledge creation are also available to researchers. Some studies focus on the effects of innovation. For instance, Newell _et al_ . (1999) demonstrate a correlation between energy prices and the energy efficiency of home appliances available for sale between 1958 and 1993. Surveys are also used, such as in Lanoie _et al_ . (2007). In a survey of firms in seven OECD countries, they find that greater regulatory stringency induces a firm to perform more environmental R&D. Surveys are particularly useful in the case of process innovations, which are more difficult for the researcher to observe with secondary data.

The above data focus primarily on innovative activity. Focusing on the diffusion of technology is more difficult. In some cases, data are available at the macro-level for crosscountry comparisons. For instance, studies such as Popp _et al_ . (2011) and Brunnschweiler (2010) compare investments in renewable energy capacities across countries. However, for more micro-level studies, detailed data on the adoption of specific technologies is required, such as in Kemp (1997), Blackman and Bannister (1998), or Gallagher and Meuhlegger (2011). Data

> 10 Moed, Glänzel, and Schmoch (2004) provide a good introduction and overview of this literature.

> 11 Examples of the former include Adams, Clemmons, and Stephan ( 2004, 2006), Adams and Clemmons (2008), Jones _et al_ . (2008), Wuchty _et al_ . (2007), and Zucker _et al_ . (2007).  Doranova _et al_ . (2009) provide an example pertaining to green technologies.

9
to standard household surveys in many developing countries; because, in addition to the coresident children, the rural survey includes information on a significant proportion of the non-resident children. All of the non-resident children who had not been away for more than six months were included as household members. Among those who had been away for more than six months, the survey counted a child as part of the household if he/she had significant economic connection with the household.<sup>30</sup> The sampling procedure for the rural survey consists of two steps: first, sample villages are selected in each province, and then approximately 10 households are drawn from each village. The 2002 CHIP rural sample include 9200 households in 22 provinces.<sup>31</sup> Another important advantage of 2002 CHIP survey in this regard is that it also includes a migrant household survey of 2000 households that covers people residing in the urban areas with rural _Hukou_ . This allows us to add a random sample of the individuals who may be missing from the rural survey because of migration. The final sample of ‘parents-children’ analysis thus ensures that the estimates are not subject to any significant coresident sample selection bias (for more details, see below).

# **The Parents-Children sample**

Our parents-children sample is composed of two parts. The first subsample is extracted from the CHIP 2002 rural survey. Adult children in this paper are defined as individuals 18 years of age or older. We have a total of 5,909 rural adult children-parents pairs, with adult children’s age and education, parents’ age, education and occupation identified.

The second parents-children subsample comes from the rural-urban migrant survey which captures the long-term migrants who have left their rural home and live in urban

> 30For an excellent discussion on the 2002 CHIP survey, please see Shi et. al. (2013).

> 31The 22 provinces are Beijing, Hebei, Shanxi, Liaoning, Jilin, Jiangsu, Zhejiang, Anhui, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, and Xinjiang.

21