rafmacalaba/datause-encoder-data huggingface dataset:
Table 3 OLS and Olley-Pakes Regression with Contemporaneous Spillover Variables with Tariff controls: with vs. without firm-fixed effects (sample: all firms)

|||Dependent v|ariable: logY|Dependent va|riable: lnTFP|
|---|---|---|---|---|---|
|LogL||0.0914***<br>(0.0036)|0.0755***<br>(0.0041)|||
|LogK||0.0278***<br>(0.0016)|0.0347***<br>(0.0024)|||
|LogM||0.766***<br>(0.0069)|0.865***<br>(0.0042)|||
|Foreignshare|(by|-0.0018|-0.0018|-0.00353|0.0231***|
|HK-Taiwan-Macau)||(0.0031)|(0.0052)|(0.0031)|(0.0049)|
|Foreignshare<br>(by|other|0.0076**|0.0644***|0.0054*|0.115***|
|countries)||(0.0032)|(0.0057)|(0.0032)|(0.0089)|
|Stateshare||-0.0168***|-0.0586***|-0.0201***|-0.126***|
|||(0.0036)|(0.0060)|(0.0032)|(0.0108)|
|Horizontal||0.162*|0.128|0.164*|0.0771|
|||(0.088)|(0.116)|(0.0871)|(0.110)|
|Backward||0.813***|0.956***|0.807***|1.096***|
|||(0.259)|(0.331)|(0.256)|(0.328)|
|Forward||0.163*|0.185|0.160*|0.190|
|||(0.0869)|(0.114)|(0.0865)|(0.119)|
|lnTariff||-0.0385**|0.00198|-0.0381**|-0.0129|
|||(0.0150)|(0.0319)|(0.0151)|(0.0319)|
|lnTariff_backward||-0.0276|-0.0061|-0.0289|-0.0236|
|||(0.0188)|(0.0312)|(0.0189)|(0.0319)|
|lnTariff_forward||-0.0064|-0.0396**|-0.0066|-0.0410**|
|||(0.0080)|(0.0173)|(0.0079)|(0.0178)|
|Constant||1.921***|0.956***|1.721***|1.749***|
|||(0.0704)|(0.123)|(0.0577)|(0.118)|
|Firm-fixed effect||Yes|No|Yes|No|
|Observations||1,545,626|1,545,626|1,545,626|1,545,626|
|R-squared||0.831|0.949|0.179|0.237|

_Notes_ : Robust clustered standard errors are presented in parentheses. In estimates using logY (i.e., in column (1) and (2) as the dependent variable, logL, logM, and logK are included as regressors along with the firm-level controls, sector-level FDI and tariff variables. When the dependent variable is lnTFP (i.e. as in column (3) and (4)), the estimation procedure is two-stage. In the first stage, we use the OP regression method to obtain estimates for the input coefficients and then calculate lnTFP (the residual from the production function). In the second stage, we regress lnTFP on the remaining controls (firm-level foreign share, state share, sector-level FDI variables, and tariff variables).

*Significant at 10-percent level

**Significant at 5-percent level

***Significant at 1-percent level

38
Figure 4: Evolution of Employment at Low- and High-Productivity Firms <!-- Start of picture text --> 2010 2011 2012 2013 2014 2015<br>Year<br>High TFP Low TFP<br>.02<br>.015<br>.01<br>.005<br>0<br>Private sector employment rate<br>-.005<br>-.01<br><!-- End of picture text --> _Note_ : Figure shows the evolution of the employment rate at low- and high-productivity (TFP) firms over time. We report the difference in employment between the 55-57 age group that was affected by the payroll tax cut and the 52-54 age group that was not affected by the tax cut relative to the difference in 2012. In particular we report _βh_ coefficient of the regression equation (10) where the outcome variable is being employed at an above-median (in red) or at a below-median (in blue) TFP firm. 95% confidence intervals are reported with standard errors clustered at the age _×_ period level.

47
# **3.2 Outcomes**

The primary outcomes in this paper are intimate partner violence and sexual activity. For sexual activity, we focus on behaviors that may be mechanisms through which the interventions operate, such as changes in sexual partnerships (both quantity and quality). We also explore gender attitudes and risk perceptions around violence, SRH, and STIs. These outcomes are measured at both baseline and endline.

For each group of outcomes, we followed Kling, Liebman and Katz (2007) to create an overall index by (1) redefining each outcome in the group to be oriented in the same direction; (2) standardizing each outcome at baseline and endline separately to the mean and standard deviation among adolescents in control communities who were not assigned to the _Goal_ treatment; and (3) taking the unweighted mean across all standardized outcomes in the outcome group.

**Intimate partner violence.** For females, we measured intimate partner violence (IPV) based on the responses to three questions that capture her experience of violence from their most recent partner within the last two years. Violence categories included physical (pushing, shaking, or throwing something at her), psychological (threatening to hurt or harm her or someone she cares about), and sexual (being physically forced to have sexual intercourse). These questions are standard questions on IPV and are used in the Tanzania Demographic and Health Surveys (DHS, 2016). The response options are often, sometimes, not in the past 12 months, or never. Interviews were conducted in private and confidentiality was assured to participants. In cases where females reported violence, they were provided a violence hotline number to seek support. We generated two sets of indicators to capture whether the violence happened (i) often or (ii) at all in the last year. We generate an index for each frequency set of psychological abuse, physical abuse, and forced sex—IPV often and IPV in last year—by standardizing each component to the mean and standard deviation among adolescents in control communities who were not assigned to the _Goal_ treatment at baseline and endline separately and averaging across items. For males, the survey instead asked if he perpetrated such violence on his most recent partner within the last two years, rather than experiencing it. We generated the

13
the MDGs. The assumption we have used here is that donor grants will increase by 10% a year in dollar terms, using the 2007 budget projection as a base, which is consistent with the commitment made by the G8 countries in 2005 to increase their aid to poor countries by 60% by 2010. We assume that the 10% growth in grants will be sustained through to 2016. As shown in table 3, the projected average level of budget grants during 2007-11 is 2% of GDP and that during 2012-2016 is 2.2% of GDP. This represents an increase in budgetary resources of approximately 1.4% of GDP from the 2006 level. However, these projections are unavoidably highly speculative and depend to a large extent on decisions which are outside the control of the Tajikistan government.

# _Domestic Borrowing_

The main constraint on government domestic borrowing is the need to maintain macroeconomic stability without crowding out private sector borrowing from the banking system. Government’s domestic debt stock is small, at around 4% of GDP, hence the sustainability of the domestic debt stock is not the binding constraint on domestic borrowing. Given the low level of domestic debt, government could issue more domestic debt and remain solvent, but the issuance of more government debt faces three constraints: the very narrow domestic monetary system (broad money was only 8.5% of GDP at the end of 2006), the high level of dollarization of the banking system (foreign currency deposits are 69% of total bank deposits), and the lack of significant non bank financial institutions. The poorly developed domestic financial sector also constrains the use of alternative macroeconomic instruments to create fiscal space. If government were to fund a more expansionary fiscal policy by borrowing from the central bank the latter would then face the need to sterilize the base money thus created, but its ability to do so is constrained by the same characteristics of the domestic financial system which restrict the scope for government borrowing from the domestic market.

To derive projections of domestic borrowing consistent with macroeconomic stability we assume that only borrowing from the central bank and commercial banks is possible: this is because the non bank financial sector is too small to hold any significant amount of government debt. Borrowing from the central bank is driven by the imperative of controlling reserve money growth and accumulating foreign 22
_5_ The first, Alansoor et al. (1989), argues for the existence of sizable unexploited potential trade within SSA. They define potential trade as '* e value of imports currently coming from the rest of the world for which at least one SSA country is making significant exports (at least one percent of its exports) to the rest of the world" (p.45). This potential trade was computed using UN COMTRADE 1983 trade flows at the four digit SITC level of disaggregation. Potential intra-regional trade for SSA as a whole was _S4.5_ billion, 16 percent of the region's total exports for that year.

However, this calculation alone cannot suggest that intra. SSA trade is too small. One needs a model for the expected value of this "potential' trade. While one can perhaps define "potential" trade, as total within region intra-product-category trade, associating that with "unexploited potential" trade implicitly assumes that the expected, or even attainable, value of intra-product-category trade is zero. A vast literature on intra-industry trade shows there are sound reasons for the simultaneous import and export of similar goods even in the most successfully integrated regions. For example, intra-product-category trade for the USA and the EC10..' in 1983 was equal to 22 and 14.5 percent of their non-oil imports respecti- ely, roughly the same as that for SSA. Given the complete (or nearly so) absence of barriers to trade in goods within the US and the EC, one would be hard pressed to identify this trade as "unexploited" regis..ial trade potential or to argue that these levels are higher than should be expected. Therefore, the calculation of "potent.a;' regional trade cannot begin to address the question of expected regional trade unless in addition to calculating potential trade, some reasonable notion of expected potential trade is also created.

"This is the current European Community minus Portugal and Spain.
predicted or shadow sovereign ratings for several Sub-Saharan African countries that are currently unrated are in a similar range as some established emerging markets (table 5).<sup>37</sup> Table 4: Rated Sub-Saharan African countries (December 2007)

||**S**|**&P**|**Mo**|**ody’s **|**F**|**itch**|**Predicted**|
|---|---|---|---|---|---|---|---|
||**Rating**|**Date**|**Rating**|**Date**|**Rating**|**Date**|**shadow rating* **|
|Botswana|A|Apr 2001|Aa3|May 2006|||AA to AAA|
|South Africa|BBB+|Aug 2005|Baa1|Jan 2005|BBB+|Aug 2005|BBB to BBB+|
|Mauritius|||Baa1|May 2006|||BBB+ to A-|
|Namibia|||||BBB-|Dec 2005|BBB- to BBB|
|Lesotho|||||BB-|Nov 2005|BB to BB+|
|Gabon|BB-|Nov 2007|||BB-|Oct 2007|BBB- to BBB|
|Nigeria|BB-|Feb 2006|||BB-|Jan 2006|BB to BBB-|
|Cape Verde|||||B+|Aug 2003|BBB-|
|Ghana|B+|Sep 2003|||B+|Sep 2007|BB- to BB|
|Kenya|B+|Sep 2006|||||B to B+|
|Senegal|B+|Dec 2000|||||BB to BB+|
|Seychelles|B|Sep 2006|||||BB to BBB-|
|Cameroon|B|Feb 2007|||B|Jun 2006|BB- to BB|
|Benin|B|Sep 2006|||B|Sep 2004|BB- to BB|
|Burkina Faso|B|Mar 2004|||||B to B+|
|Madagascar|B|May 2004|||||B to B+|
|Mozambique|B|Jul 2004|||B|Jul 2003|B+ to BB-|
|Uganda|||||B|Mar 2005|BB-|
|Mali|B|May 2004|||B-|Apr 2004|BB|
|Malawi|||||B-|Mar 2007|CCC+ to B|
|The Gambia|||||CCC|Dec2005|B+toBB-|

* These shadow ratings are based on forecasts of explanatory variables for 2007 for the benchmark sovereign rating model of Ratha, De and Mohapatra (2007). Source: Rating from Standard and Poor’s, Moody’s and Fitch.

Sub-Saharan African countries with large remittance inflows can leverage those inflows for raising the sovereign rating (Ratha 2006). Preliminary estimates indicate that including remittances in the debt-to-exports ratio in creditworthiness assessments would result in an improvement in sovereign ratings by up to two notches (World Bank 2005a). The securitization of future receivables, including trade payments and future remittances, can further improve the rating of the transaction, typically to investment grade (BBB). For example, the spread saving from improving ratings from B to BBB would be in the range of 320 to 450 basis points (figure 5).

> 37 This literature models sovereign ratings as a function of macroeconomic and institutional variables (See Cantor and Packer 1995, Mora 2006). Interestingly, the benchmark model of Ratha, De and Mohapatra (2007) performs quite well for Sub-Saharan African countries. The predicted or shadow ratings for the 11 Sub-Saharan African countries rated under the recent United Nations Development Programme (UNDP) initiative were within one to two notches of the actual rating assigned by Standard and Poor’s, as of the end of 2006. The model successfully predicted the rating of the recent bond issue from Ghana.

27
<!-- Start of picture text --> Default window Non-default<br>°<br>°<br>><br>£<br>oO<br>><br>fo)<br>aw<br>% °<br>8 ° ° 01320 °° o<br>Co —2 0 0° ° 28 002 Tia on<br>ta ere ____°9 Eg 228<br>> o (? © % ee 09 °<br>G ° oo<br>oo ° °<br>oO<br>fe)<br>=<br><x °<br>°<br>' rsssa Ss ie<br>-40 -20 0 20 40 -40 -20 0 20 40<br>Avg. Growth in Mean income<br>© Avg. change in Excess Poverty —— — Fitted values<br><!-- End of picture text -->
16 financial management did not deserve too much attention.9 Alfonso Inostroza, the President of the Central Bank, stated in early 1971 that the main objective of the monetary policy was to "transform it into a key instrument ... to achieve the complete mobilization of productive resources, and their allocation to those areas that the government gives priority to ...

The final component of the short run program referred to reducing Chile's external vulnerability. The UP basic program stated that, along with a reduction in import dependence, a priority of the new government would be to "execute a foreign trade policy tending to expand and diversify our exports ... I (Unidad Popular 1969, p. 24). What is interesting, however, is that this objective was to be achieved without providing any price incentives. Quite the contrary, the UP economists thought that changes in the exchange rate had very little, if any, effects on exports or imports. In fact, in the same paragraph where the program called for expanding and diversifying exports, it was stated that an important goal of the policy was to "avoid the scandalous devaluations of [the] ... currency" (p. 24). In line with this belief, one of the first measures undertaken by the Allende administration was to eliminate the system of crawling peg that had been adopted, with great success, by the Frei administration. It was expected, then, that expansion of exports 9See, for example, the 1971 CIAP Report reproduced in Panorama Economico, No.260 Feb.-March 1971, p.<sup>36</sup> .

l°See Inostroza (1971,p.8).
**Table 9: Technology Adoption and External Training**

|**Variable**||**19**|**93**||||**19**|**99**||
|---|---|---|---|---|---|---|---|---|---|
|**Tatrx**|**Wage**|**P>Izl**<br>|**Pro**|**ductivity**|**P>Izl**|**Wage**|**P>Izl**<br>|**Producfivity**|**P>fzl**|
|Pooled|0.08|0.03||-0.11|0.08|0.03|0.65|-0.23|0.13|
|Micro|0.19|0.44||-0.55|0.22|0.18|0.34|-0.50|0.29|
|Small|0.02|0.82||0.03|0.81|-0.15|0.17|-0.89|0.00|
|Medium|0.03|0.60||-0.25|0.00|-0.09|0.41|-0.06|0.83|
|Large<br>1|<br>0.12|0.04||-0.02|0.85|0.14|0.30|-0.67|0.07|

_Source._ Author's calculations based on ENTRAM-ENESTYC.

# **Endogenizing Training**

Following Nielsen and Rosholm (2002), we control for selectivity bias of training. A Treatment Effects Model (Treatreg) was estimated to endogenize the training variable. Outcome P (wages or productivity) is a function of explanatory variables in Z (workers' characteristics) and X (firms' characteristics). Training _(TR),_ that enters in vector Z in equation (1) is considered as an endogenous variable (our treatment variable). The binary decision to obtain the treatment _TR_ is modeled as the outcome of an unobserved latent variable which represents the expected present value of returns to training, _TR *._ It is assumed that _TR *_ is a linear function of the exogenous covariates Q that includes the variables in Z and X and the instruments (marital status of the worker and number of economic dependents of the employee)<sup>10</sup> , and a random component u. The outcome and training equations can be written as a system of equations: Where the worker trains according to this rule: Table 10 summarizes the training results of the treatreg and sureg estimations. In 1999, the coefficient of the training variable in the wage equation increased from 0.13 to 0.78. The coefficient of the external training variable increased from 0.26 to 0.77. In the

> **10** Test of the instruments were performed, results are available upon request.

14
Figure 16. Direct and indirect value‐added exports of business services as shares of GDP, 2011 <!-- Start of picture text --> 5<br>4.5 LUX<br>CHE<br>4<br>3.5<br>3<br>HUN<br>2.5 IRL BEL<br>SVN DEU<br>CHL FRA ISRSVK NLD SWE<br>2 ISL EST<br>NZL DNK SGP<br>1.5 ITA KORCZE HKG AUT<br>FIN<br>PRT HRVPOL LVA GBR<br>AUS BGR<br>1 MEX NOR ESP<br>PER ARG USC A N CRI MLT<br>ZAFJPN LTUBRA CYP<br>0.5 RT U RSCOL CHNTHA MYS MAR<br>GRC<br>KHM BRNROU PHL<br>0 SAU VNM TUN IND<br>IDN<br>0 0.5 1 1.5 2 2.5 3 3.5 4<br>Direct Value Added Exports/GDP (percent)<br>Indirect Value Added Exports/GDP (percent)<br><!-- End of picture text --> Source: OECD TiVA and authors’ calculations.

# **iv. Challenges in services reform**

The significant benefits of services openness cannot be achieved by a mechanical opening of markets. Flawed reform programs can substantially reduce gains from liberalization (World Bank 2002, chapter 3). While precise recommendations require detailed analysis of each service sector in each country, three broad types of action are likely to be desirable: emphasizing competition, strengthening the regulatory environment, and creating mechanisms to improve access to essential services in the poorest areas. Regional cooperation and international support could help in each of these respects.

The largest gains come from eliminating barriers to entry and _enhancing competition_ . Some East Asian developing countries like Malaysia and Thailand have given priority to a change in ownership from public to private or national to foreign hands, while retaining limitations on new entry. These restrictions can lead to a transfer of rents not just to domestic firms but also to foreign firms at the expense of consumer welfare. It is sometimes suggested that unrestricted entry is not always desirable, and in areas like banking might threaten bank viability and financial 33
While much attention has traditionally been paid to large firms, and their potential to contribute to employment and productivity growth, the focus is shifting away from size and towards the age of firms, particularly to the contributions of young enterprises, and the life cycle of firms. Technology is also changing the landscape for entrepreneurs. The entry of new firms is driving job creation in the US, much of Europe and many developing countries. A strong link has been found between the initial levels of young and small firms and subsequent job growth (Ghani et al, 2011). The policy message is clear: if job creation is a priority, policy makers should focus on promoting entrepreneurship locally. How does one promote entrepreneurship?

Education and physical infrastructure matter greatly for creation of new firms. These patterns are true for manufacturing and services. These relationships are much stronger in developing than in developed economies. So investment in people is an easy call for policymakers. Likewise, local areas must improve their infrastructure – electricity, roads, telecoms, and water/sanitation facilities. Their elasticity is in fact stronger in economic magnitude in developing than in developed economies. Agglomeration economies too operate as strongly for entrants in developing countries as they do in advanced economies. Most strikingly, the importance of the Chinitz effect is concentrated among small entrants.

**Figure 11—Global Convergence in Urbanization**

<!-- Start of picture text --> Change in urbanization rate<br>1990-2010<br>30<br>25<br>China<br>Malaysia<br>20<br>15<br>10 Brazil<br>5 Ethiopia Tanzania India Thailand<br>0<br>-5<br>-10<br>-15<br>0 20 40 60 80 100 120<br>initial urbanization rate, 1990<br>Source: World Development Indicators.<br>population)<br>CHange in urbanization rate (urban population / total<br><!-- End of picture text --> 16
to the relationship between governance and debt maturity, depends on the larger institutional environment and in particular the extent of investor rights in a given country.

<mark>Our paper complements studies that examine the determinants of short-term debt.</mark> Barclay and Smith (1995), Guedes and Opler (1996), Stohs and Mauer (1996) and others find that debt maturity is determined by firm characteristics such as asset maturity, growth opportunities and firm size. Datta, Iskandar-Datta, and Raman (2005) provide evidence that managerial ownership is an additional determinant of corporate debt maturity. Custodio, Ferreira and Laureano (2013) show that corporate use of long-term debt has decreased in the United States over the past three decades. They find that the decline was driven by firms with higher information asymmetry and new firms issuing public equity. More directly related to our study are two papers that have examined the impact of corporate governance on debt maturity. Harford, Li and Zhao (2008) find that stronger boards force the firm to hold more debt and more short-term debt. Senbet and Tosun (2019), on the other hand, find that board independence is associated with lower use of short-term debt. Both papers examine US firms and do not examine the impact of institutional environment on the governance-debt maturity relationship, which is the focus of our paper.

Our paper also complements the literature that examines country-level determinants of debt maturity. Demirguc-Kunt and Maksimovic (1999) find that the size of the banking sector, stock market development levels, inflation and legal institutions are important determinants of debt maturity. Fan, Titman, and Twite (2012) find that a country’s legal and tax system, corruption, and the preferences of capital suppliers explain a significant portion of the variation in leverage and debt maturity ratios. Booth et al. (2001) find that firms have longer duration debt in countries where the legal system provides greater investor protection. Diamond (2004) argues that the extent to which creditors are legally protected should have an impact on the optimal maturity structure of debt. Consistent with this view, Bae and Goyal (2009) report evidence of shorter debt maturity in countries where contracts are poorly enforced. Qian and Strahan (2007) and Giannetti (2003) examining bank loans and private companies respectively, show that debt maturity is longer in countries that offer greater creditor rights.

The effect of governance quality on debt maturity is documented by a number of papers using a country-specific setting. Using data from Turkey, Arslan and Karan (2006) show that <mark>both concentrated ownership structure and the presence of large shareholders are associated with the use of long-term debt.</mark> Kim (2015) finds that Korean firms with poor governance tend to have a 5