rafmacalaba/datause-encoder-data huggingface dataset:
21 Table 7: State-wise Patterns in Inequality Per Capita Consumption Theil Index 1973-74 to 1986-87

||||||Annual Gr|owth Rat|es (%)||
|---|---|---|---|---|---|---|---|---|
||Theil In|dex (%)|||<br>73-74|<br>77-78|<br>83|73-74|
|States|..||||to|to|to|to|
||73-74|77-78|83|86-87|77-78|83|86-87|86-87|
|ANDHRA PRADESH|<br>14.6|<br>15.7|14.4|18|1.8|' -1.6|6.6|1.6|
|ASSAM|7.2|6.2|6.4|7.6|-3.7|0.6|5|0.4|
|BIHAR|13|<br>11.3|11|9.9|-3.4|-0.5|-3|-2.1|
|GUJARAT|".4|13.9|10.9|18.9|10.3|-4.3|17|5.5.|
|HARYANA|14.3|<br>14.7|12|14|0.7|-3.6|4.5|-0.2|
|JAMMU-KASHMIR|8.6|8.7|8.4|13.3|0.3|-0.6|14|3.4|
|KARNATAKA|13.4|<br>17.7|15|13.6|7.2|-3|-2.8|0.1|
|KERALA|16.6|<br>21.5|18.9|<br>20.5|6.7|-2.3|2.3|1.6.|
|MADHYA PRADESH|<br>13.8|<br>19.5|14.2|<br>16.8|9|-5.6|4.9|1.5|
|MAHARASHTRA|11.8|<br>14.2|13.3|<br>15.3|4.7|-1.2|4.1|2|
|ORISSA|1.4|15.7|12.2|<br>12.4|8.3|-4.5|0.5|0.6|
|PUNJAB|12.3|<br>19.2|13.4|<br>15.1|11.8|-6.3|3.5|1.6|
|RAJASTHAN|12.9|<br>18.3|19.7|<br>15|9.1|1.3|-7.5|1.2|
|TAMIL NADU|12.1|<br>19.3|14.1|<br>15.2|12.4|-5.5|2.2|1.8|
|UTTAR PRADESH|9.5|<br>15.5|14.1|<br>15.3|13|-1.7|2.4|3.7|
|WEST BENGAL|15.1|<br>14.6|13.9|<br>11.6|-0.8|-0.9|-5|-2|
|ALLINDIA|13.2|<br>17.1|15.4|<br>15.6|6.7|-1.9|0.4|1.3|

Source: NSS Various Rounds The results show a wide variation in inequality across states as well as over time. In 11 out of 16 states, inequality measured by the Gini index increased, from 1973-74 to 1977-78. Theil's measure, which gives greater weight to income transfers at the bottom end of the disLribution than the Gini index, shows that the inequality increased in 13 out of 16 states. Two states where inequality declined significantly are Assam and Bihar. However, this situation changed dramatically from 1977-78 to 1983, when 14 out of 16 states showed a decrease in inequality (all but Assam and Rajasthan). In the most recent period between 1983 and 1986-87, inequality increased in 12 states.
The results for the Index outcome data are shown in figure 9 for both 2019 and 2020 for the policymaker priors These figures show that the majority of the firms have ITEs centered at zero, with limited potential for heterogeneous effects in both 2019 and 2020. There is a reasonably high probability that at least some firms experienced positive effects, but the same is true of negative effects, and most firms have ITEs estimated to be almost exactly zero. Both the spread of effects and the uncertainty in these tails is more pronounced in 2020 relative to 2019. Overall however it seems the majority of the firms in the sample were unlikely to have seen any results from the intervention, and while some are likely to have benefited, others are likely to have experienced negative effects. This is most likely a result of combining the data with a prior positive correlation on the potential outcomes (so firms who did well in treatment would also have done well in control, and vice versa). We obtain similar results with the academics’ priors, shown in the additional results Appendix F, figure A9.

Given the somewhat negative effects on average in 2020, it may be surprising that there is still some potential for positive tail effects for other firms. This result may be an artefact of the likelihood model chosen: the Gaussian enforces symmetric tails in the outcomes, which propagates to a Gaussian and thus symmetric distribution of individual treatment effects. The Gaussian may be a particularly constraining assumption in our case especially because the outcome data is not symmetric – it has a large discrete spike at zero and then a positive continuous mass situated away from zero. While we could build a likelihood model to accommodate this, in our case the priors on treatment effects will have a oneto-many mapping to priors on parameters of such a model (because a treatment effect could be achieved by moving firms out of the spike, or from changing the distribution of outcomes for firms already out of the spike in the control state). Indeed, this is a general issue for our Bayesian analysis preventing the use of more complex likelihoods without substantial additional effort; further details of this problem are discussed in appendix G. Overall, however, we still find the analysis informative, and it is worth noting that the results of the current approach do suggest only a limited potential for heterogeneous effects.

# **5.5 Why were impacts not as large as expected?**

We see that, contrary to the priors of academics, policymakers, the participating firms, and the existing literature, the program did not succeed in increasing exports, and in fact appears to have actually reduced exporting in 2020. We believe that these results stem from a combination of the type of consulting advice given, the intensity of the intervention, and the quality of this advice. In addition, the heterogeneity of the firms in the program makes it harder to detect impacts, and likely made it harder to offer a standardized program.

27
<!-- Start of picture text --> Portugal's income gap was mostly in productivity... ...feflecting weak TFP growth...<br><!-- End of picture text --> <!-- Start of picture text --> 20 5<br>10 PercentaggContnbutionsgap of Realwith respectIncome Differences,toUS GDP per2007 capita Percent)FP Growth 1/<br>| 1/<br>0 | | | 4 1995-2000<br>-10 | — LJ — | 2001-2005<br>H<br>| | | 3<br>-20 | rl<br>-40 | 1<br>-50 , =<br>-60 | ;<br>OContnbution of Labor Resource Utilization<br>70 O Contribution of Labor Productivity 1<br>=X¥FzZzeo£Rseteo-etmee Ww ag"2092320752RE=~ WmwWws>aasz” F432z2aeFueEEruw 24545= © tertEBSao =a<br>.<br>45 ..-and low labor productivity growth. ; Productivity growth fell in most sectors of the<br>Labor Productivityper Unit Labor Input 1/<br>AQ (Percent, Annual Growth Rate)<br>0.2<br>3.5<br>3.0<br>0.0<br>2.5<br>2.0<br>-0.2<br>1.5<br>1.0<br>-0.4<br>0.5<br>Changes in labor productivity growth 2/,<br>oo; -0.6 avg(Percentage .2001-2006points) vs avg. 1996-2000<br>-0.5 1995 gecSscseseregesshessesCP RsoaePa tPesg4ta -9a BG<br>1997 1999 2001 2003 2005 2007 Sse RuUPSHeae sxe ccSeagss<br>aooS250pez sPsolgS4158 gS5LgstVEG oaeRe"raa<br>8 RETOE GegesiT} FF F£<br>s<br><!-- End of picture text -->

Source: OECD, BoP, and IMF Staff Calculations. 1/Data are from OECD. The latest available data for Portugal's total factor productivity growth were upto 2005. 2/Data are from BoP.
General trading companies had a significafit advaiftagc over commercial banks in lending to small and new companies at the periphery of the keiretsu groups. By being involved in marketing and production decisions, they had access to better and more reliable information on the prospects and perfomiance of individual companies. They could also take corrective action more promptly when problems arose. The decline in the relative role of general trading companies over time suggests that this comparative advantage was more valuable during the reconstruction and high growth era.

Restrictions on housing loans and consumer credit. An important feature of the Japanese financial system during the high growthi era was the low level of lending to households for either consumer credit or hiousing finance. Data for 1965 show that the main source of credit for households was trade credit for consumer goods and employer loans for housing. Total loans to households amounted to 3% of all lending by financial institutions and corresponded to only 4% of GNP. Both housing finance and consumer credit started to increase in the 1970s, reaching 17% of GNP by 1975 and 29% by 1985 **(Tables** 11 and 12). Although these levels are lower than those prevailing in the United Kingdom and the United States, they are comparable to those found in many continental European countries. The increasing importance of lending to households and small firms is also shown in the changing structure of bank loans over time **(Table** 13). Lending to households rose fiom 6% of all bank loans in 1975 to 13% in 1990. At the same time, lending to small firms increased from 33% to 57%, while lending to large enterprises fell fi-om 61% to 30%.

The discouragement of lending to households during the high growth era had two important results: first, it forced households to maintain a high rate of saving since lack of access to credit required accumulatioi. of a higher downpayment for the purchase of consumer durables and houses; second, the increased household savings were available to be lent to industry and thus to finance from domestic sources the large investment funding needs of the high growth era. The financial assets and liabilities of the personal sector and sources and uses of funds for five year periods from 1961/65 through 1986/90 are shown in Tables 14 and 15. These tables cover both households and unincorporated enterprises and thus tend to exaggerate the size of financial assets of Japanese households. Nevertheless, the vast accumulation of financial assets over time is evident from the data. The willingness of Japanese households to invest in low-yielding financial assets, even when the Japanese yen was not appreciating, explains the absence of a capital flight problem for Japan. This is attributed to the success of the Japanese authorities in protecting the safety of bank deposits and other traditional financial assets not only from bank failures but also from the vagaries of inflation.

17
previous sections, education has positive impacts on non-farm activity participation, which implies that a person with better education is less likely to participate only in farm activity. However, the effects of education on pure farm income are still positive. In other words, education increases one’s earning potential. A person with better education is not only more capable to participate in non-farm activity, but also more likely to have higher incomes if he or she participates in farm activity. Only in case the return of farm income is high (high enough compared to the opportunity costs), the households with well-educated members will participate only in farm activity (Taylor & Yunez-Naude, 1999). As expected, households rich in land have higher income.

Based on of regression 4, we simulate the total income of the households that actually participate in non-farm activity if they had participated only in farm activity. Table 5 shows that, for all the samples, the Gini index of the distribution of the simulated income (in the absence of access to non-farm income) is about 30% higher than that of the observed income at the household level as well as at the per capita level.<sup>31</sup> In other words, the participation in non-farm activity lowers the rural income inequality.<sup>32</sup>

**Table 5 Comparison of the distribution of the observed household income and the simulated -** **<u>household income (in the absence of non farm activity)</u>**

||Total household<br>income|Household income<br>percapita|
|---|---|---|
|Observed income|0.493|0.495|
|Simulated income (if the household had participated only in<br>farmactivity)|0.609|0.621|

It is true that the distribution of non-farm income is unequal among rural households, because not all rural households participate in non-farm activity, while most rural households participate in farm activity. Because of the non-zero opportunity costs of rural labor, the participation in non-farm activity and farm activity is not independent. Non-farm income is a substitute for farm income. The non-farm activity participation provides the possibility for the households with low marginal labor productivity in farm activity to diversify their production in

> 31 According to the research by the Ministry of Agriculture of China, the Gini index increased from 0.3-0.4 in 1980s to over 0.4 in the mid 1990s (Rural Economic Research Center, Ministry of Agriculture of China, 2003). Based on the LSMS data, the Gini index in rural Hebei and Liaoning is relatively high, around 0.5.

> 32 Our results on the comparison of the inequality of the distribution of the observed income and the simulated income are robust using other measures of inequality. See annex 2 for details.

19
This study offers two sets of recommendations: The government has already conducted the analyses needed to achieve the first set. Work on many of these recommendations is ongoing. All that remains is an allocation of necessary resources and a commitment to action. Those in the second set require further analysis and input.

The first set of recommendations is that the government take the following actions as soon as possible:

- Establish an IP policy think tank staffed with Indian and foreign IP scholars, and charged with assessing the likely impact on India’s public of contemplated IP policies. As part of the think tank, an expert committee on patent guidelines should be created to consider frequent revisions, amendments, and additions.

- Complete the statutory framework of India’s new patent laws.

- Finish modernizing the Patent Office. Efforts should focus on enabling automated applications and online searching and on launching a prior art database and connecting these databases and search engines with as many foreign patent offices as possible.

- Identify the extent to which enforcement of court-ordered IP remedies fall short of general enforcement standards, and provide training for customs and police personnel engaged in enforcing orders related to IPRs.

- Implement the government’s plan to upgrade its IP training institute into a full-blown staff training college. Curriculum should be developed for staff—examiners and enforcers—but private practitioners should be encouraged to enroll as well.

- Adopt the current government proposal to launch a management-oriented education and research facility. This facility’s curriculum should focus on issues related to patent exploitation—licensing, negotiating, and litigating—to convert patents into revenue streams.

- Launch the TKDL as soon as it is complete and suitable for international consumption. In addition, negotiations should continue on building international respect for IPRs for traditional knowledge.

The second set of recommendations requires more thought before the best courses of action become clear. This study outlines many questions and delineates many tradeoffs, but resolution must lie with a trusted cadre of IP advisers who combine an understanding of IP theory with an intimate awareness of India’s sociopolitical environment. The proposed think tank would thus begin with many critical questions. Once such an institution exists, the government should charge it with:

- Assessing the relative merits of a patent registration system and a patent examination system—determining the class of patents most likely to best serve India’s needs and drafting substantive and procedural guidelines for the Patent Office oriented toward that class.

- Developing a workbook, or catalog of illustrations, drawn from a variety of disciplines, taken from foreign experiences, to accompany the guidelines.

xi
_Table 9. Effect of the treatment on asset accumulation_

|Dependent varia|ble.Log oftota<br>|lassets value. <br>|OLS estimatio<br>|n.<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||Buying<br>value|Buying<br>value|Resale<br>value|Resale<br>value|
|Endline*Treated|-0.00419<br>(0.0710)||-0.0336<br>(0.0789)||
|Endlne* 0 GNF||-0.0392||-0.0880|
|||(0.0848)||(0.0959)|
|Endline * 70k GNF||-0.0145||-0.0576|
|||(0.0806)||(0.0895)|
|Endline * 140k GNF||0.0527<br>(0.0999)||0.0722<br>(0.103)|
|Endline|0.0623|0.0622|-0.0655|-0.0655|
||(0.0523)|(0.0523)|(0.0600)|(0.0600)|
|Constant|6.541<br>(0.0518)***|6.541<br>(0.0517)***|6.291<br>(0.0603)***|6.291<br>(0.0602)***|
|Observations|9,360|9,360|9,318|9,318|
|R-squared|0.159|0.159|0.144|0.144|
|HH controls|Yes|Yes|Yes|Yes|
|Village Fixed effects|Yes|Yes|Yes|Yes|
|Clustered SE|Villages|Villages|Villages|Villages|
|Robust standard errors<br>Household controls inc<br>effects.|in parentheses.<br>lude household|*** p<0.01, *<br>size and ethni|* p<0.05, * p<<br>city fixed|0.1|
# **I.  Introduction**

Evidence of perceived financing constraints often comes in the form of enterprises’ reliance on informal financing sources, rather than formal sources such as commercial banks. Since informal financing is generally considered an imperfect substitute for formal finance, the literature assumes that enterprises that heavily rely on informal credit do so primarily because they are rationed out of formal credit markets.

The segmentation of credit markets between the formal and informal is largely attributed to credit rationing by lenders as a result of information asymmetries (Stiglitz and Weiss, 1981; Bell et. al., 1997). For example, deficiencies in the legal and institutional environment can make it difficult for banks to enforce contracts (La Porta et. al., 1997, 1998; Jappelli and Pagano, 2002; Jappelli et. al., 2005; Djankov et. al., 2007). Poor financial information makes it more challenging for formal lenders to sort and monitor borrowers (Love and Mylenko, 2003). In rural areas distance to formal providers is a key barrier to lending (McMillan and Woodruff, 1999). The result is that banks exclude some borrowers, and the informal sector serves those borrowers who are excluded by banks.

This paper focuses on reasons other than rationing that affect enterprises’ source of financing choices. In particular, we test the hypothesis that enterprises choose informal sources of financing over formal ones in order to avoid the additional regulatory scrutiny and harassment that engaging with the formal financial sector might invite. Dealing with banks can leave enterprises vulnerable to predatory regulators and onerous regulations. We test this hypothesis using enterprise-level data on over 3,500 enterprises spanning 29 countries.

In overregulated economies, enterprises have incentives to hide assets and to circumvent burdensome rules and regulations. Examples of this behavior can be found by looking at the negative correlations between burdensome business registration procedures and enterprise registration, or complex tax procedures and the collection of public revenues.

2
<u>Table 5. Indonesian Exports, ICT use, and Log of Skills Measures. All workers. 2005-2010</u>

||||||No|n-routine<br>|skill measur<br>|es|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||Ana|lytical|||Interp|ersonal|||Ma|nual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.000294<br>(0.000217)||0.000266<br>(0.000222)|-0.000194<br> (0.000350)|0.000314<br> (0.000216)||0.000285<br>(0.000222)|-0.000199<br> (0.000354)|-0.000325<br> (0.000214)||-0.000293<br>(0.000221)|0.000213<br> (0.000359)|
|Share of hhlds with computer||0.0813|0.106|0.118||0.0619|0.0876|0.101||-0.0470|-0.0758|-0.0907|
|||(0.0657)|(0.0553)*|(0.0582)**||(0.0608)|(0.0477)*|(0.0510)**||(0.0605)|(0.0448)*|(0.0498)*|
|IDN exports per worker *||||0.00267||||0.00281||||-0.00292|
|Share of hhlds with computer||||(0.00112)**||||(0.00115)**||||(0.00118)**|
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|
|R-squared|0.879|0.878|0.879|0.879|0.504|0.502|0.505|0.503|0.875|0.875|0.875|0.875|
|||Ma|nual|Routine skil|l measures|Cog|nitive||||||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|||||
|IDN exports per worker|-0.000272||-0.000239|0.0000931|-0.000245||-0.000221|0.000361|||||
||(0.000245)||(0.000247)|(0.000362)|(0.000224)||(0.000230)|(0.000393)|||||
|Share of hhlds with computer||-0.0518|-0.0809|-0.0989||-0.0198|-0.0413|-0.0482|||||
|||(0.0763)|(0.0693)|(0.0749)||(0.0557)|(0.0411)|(0.0396)|||||
|IDN exports per worker *||||-0.00182||||-0.00348|||||
|Share of hhlds with computer||||(0.00126)||||(0.00138)**|||||
|Observations|3,123|3,123|3,123|3,123|3,123|3,123|3,123|3,123|||||
|R-squared|0.976|0.976|0.976|0.976|0.870|0.870|0.870|0.870|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and province linear time trends. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

26
as quickly as it disappears and contemporaneously with the rebound in GDP growth. Still, it takes more than three years for pre-downturn levels of openness to be reached. As a result of the collapse in trade, crises moderate global imbalances. Since trade contracts by more than GDP, a country’s trade balance as a share of GDP (whether surplus or deficit) typically declines in absolute value. Moreover, because of falling commodity prices during downturns, the deceleration in trade value tends to be far greater than in trade volumes. We show that the reversal of trade deficits tends to persist for Asia, Europe, Latin America, and the Middle East. In North America, the improvement in the trade balance has been temporary, with deficits worsening following the downturn. In contrast, surplus regions tend to see only a temporary reversal.

We also examine characteristics across industries and regions during the slowdown. While all industries tend to decline, it is least severe in food and beverages. Growth returns after one year in most products, though iron and steel and commodities exhibit more protracted slowdowns. By region, countries in Europe and Central Asia were relatively less affected during previous downturns; while those in the Middle East and North Africa were the most affected. By income level, the high income OECD countries were least affected; while middle income countries were the most affected.

The results have important implications for trade during the current financial crisis. Given current GDP forecasts and trade data available through June, our results imply that the decline in real trade in 2009 could well exceed 15 percent. In addition, global imbalances are likely to be mitigated during the crisis, and this may persist even after the crisis is resolved. Finally, food and beverages are likely to be the least affected products in the coming months.

3