rafmacalaba/datause-encoder-data huggingface dataset:
triangulation and comparison of these ALPHA site data against nationally reported HIV prevalence and modeled Spectrum incidence trends has been published. **2.1.2.2. Laboratory Assays and Laboratory-based Testing Algorithms** HIV incidence can be inferred in clinical trials and cohort studies through the use of laboratory assays detecting recent infection, including the BED capture enzyme immunoassay (BED-CEIA), which was, until recently, the only commercially and globally available assay. This assay measures a biomarker that rises during early infection, and can be used cross-sectionally to discriminate between recent (less than six months) and older HIV infections. However, this assay is prone to misclassification of individuals with older infections (Hargrove et al. 2008) and cross-sectional BED incidence estimates have been higher than those obtained from longitudinal follow-up (Karita et al. 2007; McDougal et al. 2006), thus raising concerns on the use of this assay to accurately measure incidence despite locally established correction factors.

Other laboratory-based assays used for measuring incidence exist, and can be used in combination with the BED-CEIA. Instead of measuring only biomarker quantity, the strength, or avidity, of the biomarker in an assay can also be measured, as avidity increases through early HIV infection. The Multi-Assay Algorithm (MAA) measures both avidity and quantity of the biomarker/antibody response, using the BED-CEIA assay along with a second assay that specifically measures binding strength. Additionally, the Limiting Antigen Avidity (LAg-Avidity) assay has been recently developed, improving upon the measurement of low and high avidity antibodies and using a more crossreactive marker to eliminate bias from populations with different HIV subtypes (an issue with the BED-CEIA). Both the MAA and LAg-Avidity assays have been shown to have lower false recent rate than the BED-CEIA (Eshleman et al. 2013; Laeyendecker et al. 2013; WHO and UNAIDS 2013). The LAg-Avidity assay is now commercially available, and the United States Centers for Disease Control and Prevention (CDC) Division of Global HIV/AIDS (DGHA) has been recommended it for incidence surveillance, instead of the BED_CEIA, in countries outside of the US supported by President’s Emergency Plan For AIDS Relief (PEPFAR) funding (WHO and UNAIDS 2013). In the 2012 HIV populationlevel HIV survey in South Africa, a HIV incidence algorithm was used based on the LagAvidity assay in combination with a negative ARV test and a viral load >1,000 copies/ml (Shisana et al., 2014). At the same time, indirect HIV incidence estimates were generated using the inter-survey mathematical modelling method according to Hallett et al. 2008 and 2010.

Current research and development efforts are focused on developing assays able to use dried blood spot specimens in validated incidence assays, which would make HIV detection more rapid and logistically feasible in resource-limited settings.

15
Table 7: Treatment Effects: Product Adoption Experiment

||Person|al loan|Non-cred|itproduct|
|---|---|---|---|---|
|||Accep|ted offer||
||(1)|(2)|(3)|(4)|
|[1] Relationship moratorium offer|0.076|0.119|-0.056|-0.080|
||(0.060)|(0.115)|(0.050)|(0.076)|
|[2] Branded product offer|0.070|0.286|-0.047|-0.043|
||(0.060)|(0.255)|(0.050)|(0.108)|
|[1]x[2]|-0.094|-0.153|0.152<sup>_∗∗_</sup>|0.206<sup>_∗∗_</sup>|
||(0.085)|(0.133)|(0.073)|(0.100)|
|Month FE|No|Yes|No|Yes|
|Day and caller FE|No|Yes|No|Yes|
|R-squared|0.004|0.542|0.008|0.450|
|Observations|528|528|632|632|

Notes: This table shows treatment effect estimates from the cross-selling experiment. Columns (1) and (2) display the results of the product adoption experiment for the credit product. Columns (3) and (4) display the results for the non-credit product. Each column reports results from a separate regression. The sample in all regressions is restricted to borrowers who received a debt moratorium offer in the main experiment and were approached with a cross-sell offer no more than one year after receiving the moratorium offer. The dependent variable in all regressions is a dummy equal to 1 if a participant accepted the cross-sell offer. * p < 0.10, ** p < 0.05, *** p < 0.01.

41
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# **1. Introduction**

Access to the formal financial system can increase asset ownership and serve as a catalyst to greater economic empowerment among women.<sup>1</sup> Even a basic financial tool such as a deposit account at a formal financial institution can be of great value. A formal account provides a safe place to save and creates a reliable payment connection with family members, an employer, or the government. It can also open up channels to formal credit critical to investing in education or in a business. Yet more than 1.3 billion women worldwide remain largely outside the formal financial system (Demirguc-Kunt and Klapper, 2013). Efforts to improve gender parity in the formal financial system have been hindered by the lack of systematic indicators on the use of different financial services—both formal and informal—in most economies.

This paper documents and analyzes gender differences in the use of financial services using the Global Financial Inclusion (Global Findex) database, which provides indicators measuring how people in 148 economies around the world save, borrow, make payments, and manage risk. These new indicators are constructed with survey data from interviews with more than 150,000 nationally representative and randomly selected adults. The data show a persistent gender gap in developing countries in the ownership of bank accounts and savings and borrowing behavior. Even after controlling for a host of individual characteristics including income, education, employment status, rural residency and age, gender remains significantly related to access to and use of financial services.

We also explore the degree to which country-level legal discrimination against women and cultural norms of gender equality are related to differences in account penetration and

> 1 The lack of access to financial services is often a critical element underlying persistent income inequality, as well as slower economic growth. For additional discussion see, for example, Beck, Demirguc-Kunt and Levine (2007), Beck, Levine and Loayza (2000), Demirguc-Kunt and Levine (2009), Klapper, Laeven, and Rajan (2006) and World Bank (2008).

2
systematically between the treated and non-treated sectors with respect to tax incentives. Nonetheless, we measure access to electricity, road and railway station at the town and village-level using data from the India Census 2011 and introduce these variables as additional controls in Equ.(3) to check the robustness of our estimates. The results regarding total employment are reported in the top panel of table 10. Both _Target_ and the interaction term _Target X Treat_ remain positively associated with employment. And the magnitudes of the estimates are comparable to those reported in table 9.

**Table 10. Within-sector effects with heterogeneous central excise tax incentives, robustness checks**

**<u>(2013, town-sector level)</u>**

||Log (e<br>|mployment),<br>|controllingf<br>|or access to i<br>|nfrastructure<br>|<br>||
|---|---|---|---|---|---|---|---|
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|0.2191***<br>(5.31)|0.1556***<br>(4.23)|0.2215***<br>(6.33)|0.1630***<br>(5.16)|0.1519***<br>(5.16)|0.1844***<br>(6.63)|0.2061***<br>(7.76)|
|_Target X Treat_|0.5212**|0.6356***|0.6451***|0.6674***|0.7130***|0.7090***|0.6876***|
||(3.28)|(4.55)|(4.94)|(5.55)|(6.22)|(6.54)|(6.57)|
|Infrastructure|Y|Y|Y|Y|Y|Y|Y|
|_No. of_|11984|14248|16223|18215|20296|22356|24547|
|_observations_||Log (<br>|employment)<br>|,micro firms<br>|<br>|||
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|-0.0383|0.0030|0.0423*|0.0220|0.0382*|0.0663***|0.0683***|
||(-1.51)|(0.14)|(2.05)|(1.18)|(2.24)|(4.14)|(4.50)|
|_Target X Treat_|-0.0615|-0.0771|-0.1029|-0.0826|-0.0804|-0.0906|-0.1112*|
||(-0.83)|(-1.18)|(-1.68)|(-1.46)|(-1.53)|(-1.84)|(-2.36)|
|_No. of_<br>_observations_|34516|41975|48325|54640|61601|67649|74058|

_Note:_ All regressions include a _constant_ , and a linear function of _Distance_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

As another robustness check, we restrict to a sample of micro firms—those with fewer than five workers, and replicated the analysis of Equ.(3). We expect the estimated impact of the New Industrial Policy to be small among these firms, in contrast to the impact on the larger firms. It is because most of the micro firms are subsistence entrepreneurship and they tend to stay small regardless the benefits offered by the New Industrial Policy. In addition, we expect the impact of exercise tax incentives to be particularly limited as these micro firms are much less likely to comply with tax codes than the larger firms. The results regarding total employment are reported in the bottom panel of table 10. Indeed, we find no impact of exercise tax incentives and limited impact of other interventions on employment outcomes among micro firms in the border area. While the coverage of micro firms by the Economic Census is of lower quality, 29
Policy Research Working Paper 5452

# **Abstract**

Existing wealth estimates show that in most countries intangible capital is the largest share of total wealth. Intangible capital is calculated as the difference between total wealth and tangible (produced and natural) capital. This paper uses new estimates of total wealth, natural capital, and physical capital for a panel of countries to shed light on the constituents of the intangible capital residual. In a development-accounting framework, the authors show that factors of production are very successful in explaining the variation in output per worker when they use intangible capital instead of human capital as a factor of production. This suggests that intangible capital captures a broad range of assets typically included in the total factor productivity residual. Human capital is an important factor, both in statistical and economic terms, in regressions decomposing intangible capital.

This paper—a product of the Environment and Energy Team, Development Research Group—is part of a larger effort in the department to extend the national accounts to include more comprehensive measures of the wealth of nations. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at khamilton@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table A2: Loan availability and loan application: Restricting the sample to firms with no loan in _t −_ 1

|Dependent variable:|Loan availability<br>(1)|Loan application<br>(2)|
|---|---|---|
|Constrained by informal|-0.0714***<br>[0.0269]|-0.0831***<br>[0.0278]|
|Account|-0.0285|-0.00862|
||[0.0250]|[0.0283]|
|No need|-0.00700|-0.0163|
||[0.0223]|[0.0246]|
|Age|-0.00498|0.00962|
||[0.0158]|[0.0180]|
|Size|0.0277***|0.0231**|
||[0.00847]|[0.00995]|
|Export|0.0356|0.0452|
||[0.0293]|[0.0375]|
|N competitors|-0.00816<br>[0.00604]|0.00353<br>[0.00722]|
|Model|Logit|Logit|
|Time FE|yes|yes|
|Geographic area FE|yes|yes|
|Additional controls|yes|yes|
|Observations|1002|1067|
|Pseudo R2|0.162|0.220|

_Notes:_ logit marginal effects. This table replicates the analysis in Column 3 of Tables 4 and 5, while restricting the sample to firms with no access to credit in the previous wave (i.e., switchers only). All regressors are lagged once. Unreported controls follow the specification in Table 4. Measures are defined in Table B4. Robust standard errors in brackets. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

Table A3: Loan availability: Restricting the timing of the issuance

|Dependent variable:||L|oan availabilit|y||
|---|---|---|---|---|---|
|Issuance:|10 years<br>(1)|7 years<br>(2)|5 years<br>(3)|2 years<br>(4)|1 year<br>(5)|
|Constrained by informal|-0.0764***<br>[0.0222]|-0.0730***<br>[0.0219]|-0.0690***<br>[0.0216]|-0.0582***<br>[0.0203]|-0.0663***<br>[0.0254]|
|Account|0.00839|0.00602|0.00274|0.0215|0.0457|
||[0.0279]|[0.0280]|[0.0281]|[0.0283]|[0.0388]|
|No need|-0.0464**<br>[0.0192]|-0.0462**<br>[0.0191]|-0.0487***<br>[0.0188]|-0.0439**<br>[0.0183]|-0.0599**<br>[0.0233]|
|Age|-0.00655|-0.00700|-0.00600|-0.00281|0.0135|
||[0.0157]|[0.0156]|[0.0156]|[0.0148]|[0.0198]|
|Size|0.0382***<br>[0.00792]|0.0374***<br>[0.00779]|0.0364***<br>[0.00768]|0.0289***<br>[0.00734]|0.0235**<br>[0.00962]|
|Export|0.0195|0.0222|0.0154|0.0103|0.0265|
||[0.0259]|[0.0254]|[0.0252]|[0.0237]|[0.0304]|
|N competitors|-0.00520|-0.00621|-0.00683|-0.00548|-0.00959|
||[0.00558]|[0.00549]|[0.00537]|[0.00509]|[0.00650]|
|Model|Logit|Logit|Logit|Logit|Logit|
|Time FE|yes|yes|yes|yes|yes|
|Geographic area FE|yes|yes|yes|yes|yes|
|Additional controls|yes|yes|yes|yes|yes|
|Observations|1371|1362|1351|1277|906|
|Pseudo R2|0.197|0.202|0.199|0.227|0.209|

_Notes:_ logit marginal effects. This table replicates the analysis in Column 3 of Table 4, while restricting the availability of loans to an issuance occurring within the last 10, 7, 5, or 2 years (respectively in columns 1, 2, 3, and 4). All regressors are lagged once. Unreported controls follow the specification in Table 4. Measures are defined in Table B4. Robust standard errors in brackets. *, **, *** indicate statistical significance at the 10%, 5%, and 1%, respectively.

23
# **<u>Figure 5:</u>**

<!-- Start of picture text --> Internet Use in Upper-Middle and High Income Countries<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>35<br>30<br>25<br>20 Actual data<br>Predicted<br>15<br>10<br>5<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Year<br>Percentage of Internet Users<br><!-- End of picture text --> A striking result of Table 1 is the estimate of δ 0 . According to the results from Table 1, there is no long term convergence in Internet diffusion between low and high income countries. Upper-Middle and high income countries converge to an adoption rate of 40.9 percent, whereas low and lower-income ones reach a much lower percentage in the long run (16.3 percent). This result is explained by the short time period of available data – fifteen years, and the significant current difference in percentage of users in low and high income countries. Provided the estimation indicates low income countries are catching up as the speed of adoption is higher, a relevant question would be: how many years would it take low and lower-middle income countries to reach the long term adoption rate of upper-middle and high income countries? To answer this question we estimate equation (1) imposing the restriction that the δ 0 coefficient of low and lower-middle income countries equals 40.9, the estimated long run adoption rate of upper-middle and high income countries. The result of the estimation is shown in Figure 6. Using the actual available diffusion data, it is estimated that lower-middle income countries would take 8 years, i.e. in the year 2015, to catch up to the 90 percent long term level of upper-middleincome countries.

In this section we showed that Internet adoption follows an S-shape pattern, but this pattern is different for low and high income countries. Internet diffusion in the former group started with a lag<sup>13</sup> , but is enjoying a higher adoption speed. However, the digital divide, in absolute terms, is impressive. A simplified simulation indicates it would take low income countries more than 50 years to close the digital divide. These results motivate the need to further study the determinants of Internet adoption, a task we take in the following section.

> 13 While upper-middle income countries reach a 2 percent usage of Internet in 1995, low and lower-middle income countries reached the same usage rate in 2002.

10
**Bratkowski, A., I. Grosfeld, and J. Rostowski** , “Investment and finance in de novo private firms,” _Economics of Transition_ , 2000, _8_ (1), 101–116.

- **Burkart, Mike and Tore Ellingsen** , “In-Kind Finance: A Theory of Trade Credit,” _The American Economic Review_ , Jun 2004, _94_ (3), 569–590.

- **Cabral, Luis M. B. and Jose Mata** , “On the Evolution of the Firm Size Distribution: Facts and Theory,” _The American Economic Review_ , nov 2003, _93_ (4), 1075–1090.

- **Carpenter, R.E. and B.C. Petersen** , “Is the Growth of Small Firms Constrained by Internal Finance?,” _Review of Economics and Statistics_ , 2002, _84_ (2), 298–309.

- **Cooley, Thomas F. and Vincenzo Quadrini** , “Financial Markets and Firm Dynamics,” _The American Economic Review_ , dec 2001, _91_ (5), 1286–1310.

- **Fazzari, S.M. and B.C. Petersen** , “Working Capital and Fixed Investment: New Evidence on Financing Constraints,” _The RAND Journal of Economics_ , 1993, _24_ (3), 328–342.

- **, R.G. Hubbard, and B.C. Petersen** , “Investment-Cash Flow Sensitivities are Useful: A Comment on Kaplan and Zingales,” _The Quarterly Journal of Economics_ , 2000, _115_ (2), 695–705.

- **Fazzari, Steven M., R. Glenn Hubbard, Bruce C. Petersen, Alan S. Blinder, and James M. Poterba** , “Financing Constraints and Corporate Investment,” _Brookings Papers on Economic Activity_ , 1988, _1988_ (1), 141–206.

- **Fisman, R.** , “Trade Credit and Productive Efficiency in Developing Countries,” _World Development_ , 2001, _29_ (2), 311–321.

- **and I. Love** , “Trade Credit, Financial Intermediary Development, and Industry Growth,” _The_

- _Journal of Finance_ , 2003, _58_ (1), 353–374.

- **Fisman, Raymond and Inessa Love** , “Financial Development and Growth in the Short and Long Run,” January 2004, (10236).

- **Ganesh-Kumar, A., K. Sen, and R.R. Vaidya** , “Outward orientation, investment and finance constraints: a study of indian firms,” _The Journal of Development Studies_ , 2001, _37_ (4), 133–149.

- **Hallward-Driemeier, M. and W.P. Smith** , “Understanding the Investment Climate,” _Finance and Development_ , 2005, _41_ , 40.

- **Hubbard, R. Glenn** , “Capital-Market Imperfections and Investment,” _Journal of Economic Literature_ , mar 1998, _36_ (1), 193–225.

- **Kaplan, Steven N. and Luigi Zingales** , “Do Investment-Cash Flow Sensitivities Provide Useful Measures of Financing Constraints?,” _The Quarterly Journal of Economics_ , feb 1997, _112_ (1), 169–215.

- **and** , “Investment-Cash Flow Sensitivities are not Valid Measures of Financing Constraints,”

- _The Quarterly Journal of Economics_ , may 2000, _115_ (2), 707–712.

- **Lorenzoni, Guido and Karl Walentin** , “Financial Frictions, Investment and Tobin’s q,” Working Paper 13092, National Bureau of Economic Research May 2007.

21
inequality rose more rapidly within urban areas than within rural areas. Second, the level of rural inequality has been and remained higher than urban areas’. Finally, the contribution of within urban inequality to overall inequality increased sharply relative to that of within rural inequality, from 21% in 1989 to 36% in 2004, while the contribution of within coastal and within inland provinces remained unchanged.

**Table 1 – Distribution of household per capita income (1989-2004)**

||Average<br>inco<br>( _yu_|per capita<br>me<br>_an_)|Ginico|efficient|Theil Deco|mpositio|n|
|---|---|---|---|---|---|---|---|
||1989|2004|1989|2004|1989|20|04|
|Total|1044|2765|0.354|0.482|0.205 (100.0)|0.452|(100.0)|
|Decomposition by sector||||||||
|Rural areas|925|2445|0.396|0.487|0.150 (73.3)|0.258|(60.9)|
|Urban areas|1279|3411|0.251|0.452|0.042 (20.6)|0.153|(36.0)|
|Between groups|||||0.012 (6.1)|0.013|(3.1)|
|Decomposition by region||||||||
|Inland provinces|982|2360|0.365|0.497|0.129 (62.9)|0.257|(60.6)|
|<br>Coastal provinces|1149|3456|0.327|0.433|0.073 (35.7)|0.149|(35.2)|
|Betweengroups|||||0.003 (1.4)|0.018|(4.2)|

Note: Average income is measure in _yuan_ 1989 Liaoning urban price. The contributions to overall inequality (in percentage) are presented in parentheses.

Figure 4 summarizes the full Kernel density distribution of rural and urban income in 1989 and 2004 and shows the simultaneous increase of income and inequality. Starting with a lower level, income inequality within urban areas increased sharply over time and approached income inequality level within rural areas in 2004.

**Figure 4 – Kernel density of household per capita income, by sector, 1989 and 2004**

<!-- Start of picture text --> 4 5 6 7 8 9 10<br>Real per capita household income (logarithmic scale)<br>Rural, 1989 Urban, 1989<br>Rural, 2004 Urban, 2004<br>.8<br>.6<br>.4<br>Kernel density<br>.2<br>0<br><!-- End of picture text --> Figure 5 describes the growth incidence of the entire population. All segments of the population have experienced positive gains in average income. The poorest quartile gained less than 80% 8
**Table 4. Results from growth accounting (single-country OLS estimations)**

|Dependent variable :<br>TFPgrowth rate|**Indonesia**|**Thailand**|**Philippine**<br>**s**|**South**<br>**Korea**|**Singapore**|
|---|---|---|---|---|---|
|Constant|0.000430<br>(0.012378)|-0.003964<br>(0.013298)|-0.032361<br>(0.016032)|-0.011897<br>(0.049837)|0.013270<br>(0.019744)|
|Number of telephones|0.122859*<br>(0.055356)|-0.082006<br>(0.048992)|0.282970*<br>(0.152046)|-0.071994<br>(0.123365)|-0.090847<br>(0.107652)|
|Total roads (railways and<br>paved roads)|-0.119878<br>(0.129726)|0.470495**<br>(0.196164)|-0.017326<br>(0.221852)|0.539977<br>(0.534860)|-0.191091<br>(0.300689)|
|Electricity generating<br>capacity|-0.047187**<br>(0.015937)|0.027691<br>(0.052692)|0.062823<br>(0.123118)|-0.026010<br>(0.083775)|-0.003853<br>(0.076603)|
|R<sup>~~2~~</sup>|0.590285|0.486470|0.153937|0.069669|0.092980|
|Number of observations|14|11|28|21|18|

Standard errors in parentheses. ** significant at the 5% level; * significant at the 10% level.

24