rafmacalaba/datause-encoder-data huggingface dataset:
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26
**Table 4:** Correlational relationship between female agent and administrative sales outcomes

||Sales (48|h)||Sales (2|4h)|
|---|---|---|---|---|---|
|Any<br>(1)|Total<br>(2)|Total price<br>(3)|Any<br>(4)|Total<br>(5)|Total price<br>(6)|
|_Panel A: Co_|_rrelation_|_al estimates_||||
|Female<br>0.003|0.005|-1.577|0.002|0.001|-2.110|
|(0.008)|(0.008)|(1.542)|(0.007)|(0.008)|(1.508)|
|Control Mean (wt)<br>0.024|0.026|4.644|0.021|0.022|4.416|
|Observations<br>8,863|8,863|8,863|8,863|8,863|8,863|
|_Panel B: Ex_|_periment_|_al estimates_||||
|Female<br>-0.020<sup>_∗∗_</sup>|-0.022<sup>_∗∗_</sup>|-3.495<sup>_∗∗∗_</sup>|-0.018<sup>_∗∗_</sup>|-0.018<sup>_∗∗_</sup>|-3.432<sup>_∗∗∗_</sup>|
|(0.010)|(0.011)|(1.240)|(0.009)|(0.009)|(1.166)|
|Control Mean (wt)<br>0.030|0.030|4.898|0.030|0.030|4.639|
|Observations<br>2,653|2,653|2,653|2,653|2,653|2,653|
|Equality b/w Exp/Non-exp (p)<br>0.047|0.044|0.324|0.067|0.094|0.481|

This table shows correlational and causal effects of female agent on sales outcomes from administrative records. Panel A shows correlational estimates using the non-experimental sample, while Panel B shows causal estimates from the experimental sample. Any represents any sale, Total represents number of sales, and Total price is the cumulative price of all sales in EUR. All outcomes based on customer purchases only. Sales are measured within 24 or 48 hours of the start of the chat. Female indicator in Panel B determined in customer’s first chat of the day. Controls include month, customer location, customer purchase history, and customer chat history fixed effects. The experimental estimates additionally include agent fixed effects. The control group means are reweighted to match the OLS weights. For the correlational estimates, the control mean is reweighted by month fixed effects, while in the experimental estimates it is reweighted by agentmonth fixed effects. Standard errors two-way clustered at the agent-day and customer-day level.<sup>_∗_</sup> _p <_ 0.10, _∗∗ p <_ 0.05, _∗∗∗ p <_ 0.01.

24
<!-- Start of picture text --> however, that Nelson<br>sample F-tests of the ,<br>12. A further tes<br>whether the time tre<br>in its slope. Accoi<br>for a change in the E.<br>this change is not<br>I(1). The test proc<br>the following regress:<br>X = a + bDU + cDT<br>t<br>where DU = 1 for<br>otherwise, and t* den(<br>occurred. The t-stat:<br>from the Student's t<br>l/ Perron's test fo<br>terms of the inclus<br>break occurred. Hot<br>for in this paper.<br>I/ See Perron (1989<br><!-- End of picture text -->
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34
Shah, Anwar (2003). Fiscal Decentralization in Transition Economies and Developing Countries. Progress, Problems and the Promise. In Raoul Blindenbacher and Arnold Koller, editors, _Federlaism in a Changing World: Learning From Each Other_ . Pp. 432-460. Montreal and Kingston: McGill-Queen’s University Press

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28
|EC Institutional Reform|Edgardo Mosqueira Medina|
|---|---|
|EC Institutional Reform|Edgardo Mosqueira Medina|
|China: Fifth Inland Waterways|Wenlai Zhang|
|China: Fifth Inland Waterways|Wenlai Zhang|
|China: Fifth Inland Waterways|Wenlai Zhang|
|China: Fifth Inland Waterways|Wenlai Zhang|
|China: Fifth Inland Waterways|Wenlai Zhang|
|China: Fifth Inland Waterways|Wenlai Zhang|
|China: Fifth Inland Waterways|Wenlai Zhang|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|CTC Sector Phaseout Project - ODS IV|Laurent Granier|
|ILO Labour Market Indicators Library|Sulekha Patel|
|ILO Labour Market Indicators Library|Sulekha Patel|
|ILO Labour Market Indicators Library|Sulekha Patel|
|ILO Labour Market Indicators Library|Sulekha Patel|
|ILO Labour Market Indicators Library|Sulekha Patel|
|ILO Labour Market Indicators Library|Sulekha Patel|
|ILO Labour Market Indicators Library|Sulekha Patel|
|ManagingHigher Education for Relevance and Efficiency|Ratna Kesuma|
|ManagingHigher Education for Relevance and Efficiency|Ratna Kesuma|
|ManagingHigher Education for Relevance and Efficiency|Ratna Kesuma|
|ManagingHigher Education for Relevance and Efficiency|Ratna Kesuma|
|ManagingHigher Education for Relevance and Efficiency|Ratna Kesuma|
|ManagingHigher Education for Relevance and Efficiency|Ratna Kesuma|
|ManagingHigher Education for Relevance and Efficiency|Ratna Kesuma|
|ManagingHigher Education for Relevance and Efficiency|Ratna Kesuma|
|ManagingHigher Education for Relevance and Efficiency|Ratna Kesuma|
|ManagingHigher Education for Relevance and Efficiency|Ratna Kesuma|
|ManagingHigher Education for Relevance and Efficiency|Ratna Kesuma|
# **Appendix B: Bias correction procedure**

Here we briefly summarize the bias correction proposed by Shi and Lee (2017). In order to derive the limiting distribution of the estimators _η_ ˆ _T_ and the associated <u>1</u> _n_ asymptotic bias, consider _G_ ( _η_ ) = _nT_ � _i_ = _r_ +1<sup>_μi_(</sup><sup>_S−_�</sup><sup>_K_</sup> _k_ =1<sup>_Zkδk_)(</sup><sup>_S−_�</sup><sup>_K_</sup> _k_ =1<sup>_Zkδk_)</sup><sup>_′_from</sup> equation (5) is expressed around the initial value _η_ 0. Under appropriate assumptions, using the perturbation theory of linear operators the limiting distribution of _η_ ˆ around _η_ 0 can be derived as where _φ_ = ( _φβ, φλ,_ 01 _×_ ( _K −_ 2) _, φρ_ )<sup>_′_</sup> , with _φβ_ = _−_ _~~√~~_<sup>_σ_</sup> _nT_ <u>0</u><sup>2</sup> � _Th_ =1 _−_ 1<sup>_tr_(</sup><sup>_J_0</sup><sup>_PF_</sup> _T_<sup>_J_</sup> _h_<sup>_′_)</sup><sup>_tr_(</sup><sup>_Ah−_1</sup><sup>_S−_1)</sup> <u>0</u> _T −_ 1 _φλ_ = _−_ _~~√~~_<sup>_<u>σ</u>_2</sup> _nT_ � _h_ =1<sup>_tr_(</sup><sup>_J_0</sup><sup>_PF_</sup> _T_<sup>_J_</sup> _h_<sup>_′_)</sup><sup>_tr_(</sup><sup>_WAh−_1</sup><sup>_S−_1)</sup><sup>_,_</sup> <u>0</u> _T −_ 1 _<u>T</u> φρ_ = _−_ _~~√~~_<sup>_σ_</sup> _nT_<sup>2</sup> � _h_ =1<sup>_tr_(</sup><sup>_J_0</sup><sup>_PF_</sup> _T_<sup>_J_</sup> _h_<sup>_′_)</sup><sup>_tr_(</sup><sup>_βG_+</sup><sup>_λGW_)</sup><sup>_Ah−_1</sup><sup>_S−_1 +</sup> � _n_<sup>_σ_</sup> 0<sup>2(</sup><sup>_<u>r</u>_</sup> _n_<sup><u>0</u></sup><sup>_tr_(</sup><sup>_G −tr_(</sup><sup>_PψG_))</sup><sup>_,_</sup> _A_ = _S_<sup>_−_1</sup> ( _βI_ + _λW_ ) _, S_ = _I − ρW, G_ = _WS_<sup>_−_1</sup> _, Jh_ = (0 _T ×_ ( _T −h_ ) _, IT ,_ 0 _T ×h_ )<sup>_′_</sup> _,_ where _πT_ = ( _π_ 1 _. . . πK_ +1 0), with _πk_ = _vec_ ( _MT ZkMF_ ) _, k_ = 1 _, . . . , K_ + 1; Υ _K_ +1 _,K_ +1 = _n_<sup><u>1</u></sup><sup>_tr_(</sup><sup>_GG′_) +</sup> _n_<sup><u>1</u></sup><sup>_tr_(</sup><sup>_G_2)</sup><sup>_−_2(</sup> _n_<sup><u>1</u></sup><sup>_tr_(</sup><sup>_G_))2</sup><sup>_,_</sup> Υ _K_ +1 _,K_ +2 = _n_<sup><u>1</u></sup><sup>_tr_(</sup><sup>_G_ ̃</sup><sup>_G_) +</sup> _n_<sup><u>1</u></sup><sup>_tr_( ̃</sup><sup>_G_2)</sup><sup>_−_2(</sup> _n_<sup><u>1</u></sup><sup>_tr_( ̃</sup><sup>_G_))2</sup> Υ _K_ +2 _,K_ +2 = _n_<sup><u>1</u></sup><sup>_tr_( ̃</sup><sup>_G′_ ̃</sup><sup>_G_) +</sup> _n_<sup><u>1</u></sup><sup>_tr_( ̃ ̃</sup><sup>_G_2)</sup><sup>_−_2(</sup> _n_<sup><u>1</u></sup><sup>_tr_( ̃</sup><sup>_G_))2,andtheexpressionforΣisgiveninShi</sup> and Lee (2017).

Equation (A1) indicates that the limiting distribution of _η_ ˆ may deviate from _η_ 0 with an asymptotic bias term ( _σ_ 0<sup>2</sup><sup>_Q_)</sup><sup>_−_1</sup><sup>_φ_.In our SDPD the bias comes from the predetermined</sup> control variables and the interactions of the spatial effects and factor loadings.

Under some additional assumptions, the bias corrected estimator is given by 38
benefits after the change of the law. Recall that under the new law, the replacement rate falls after three months from 70 to 60 percent; there was no such a gradation before the change of the law.

The results show that the difference in the hazard for the unemployed due to bankruptcy who registered after the cliange in the law is indeed positive. significantly so for the two high-end experience groups.' Changes in the replacement rate. however, are found not to influence the jobtaking rate. The drop of replacemenit rate from 70 percent to 60 percent after the third month has no effect on the hazard; in fact, the coefficient on the interaction between the dummy for receiving unempiloyment comiipenisationi in the third month and the period dummy is negative, even significantly so.

How should we interpret these results'? Of course, one can make a case that it is not the disincentive et'fects. but r ather heterogeneity -- because we control for observed heterogeneity, it would be the unobserved heterogenieity this time -- that is responsible for a lower overall hazard of exit of the recipients of insurance benefit. But the identitied effects of duration by spline dummies coniclusively confirm the waiting pattern detected by empirical hazard.

# _5._ POLICY IMPLICATIONS

The paper has shown that during 1990-92 there was a tendency among the recipients of unemployment compenisation **i** n Slovenia to stay (tormally) unemployed until their benefits expired betfore taking a job. Institutional set-up suggests -- and labor surveys show -- that many of the recipienits were actually working while collecting Ul benefits. In spirit, if not in the letter of the law, the Ul system was abused.

A hetter specificatioin of the test would compare differences in the hazard rate throughout time distanice fronm the exihaustioni, readjusting the potential eligibility for the treatment group that registered after the chanige of the law so as to iginore the chaniges in the law. Such specification, however, would be conmputationally **Iimuch** more deinanidiiig.

20
The depth of participation -- as measured by degree of compliance -- is analyzed in Table 7. Twio measures are used: the fraction of the firm's paid workers registered with IMSS (for the subset of firms with any paid workers), and the fraction of all institutions in which the firm participates. Both variables are bounded below by zero and above by one, so the estimation used double-censored tobits.<sup>26</sup> The second and third rows of the table report two different specifications for the fraction of all institutions in which the firm participates. The first encompasses all seven institutions in Table 4. The second excludes business guild registration and dues payment because not all firms may have access to such institutions; i.e. differences between firms in participation along this dimension may simply represent cross-industry differences in production technology or market structure.

The patterns in Table 7 again are consistent with our prediction that the depth of participation is an increasing function of both firm size and age. Excluding business guild registration and dues payment in the third row makes these relationships stronger. The inclusion of industry dummies (not reported) leaves the results largely unchanged.

# IV. Relation to **Previous Informal Sector Research**

The framework offered here departs from the premise that the small scale firms found in developing countries are fundamentally different from those in industrialized countries. Thus, it is solidly in the spirit of Hart (1972) and de Soto who stressed the intrinsic dynamism of the sector. In conceiving of formality as an input into the production function and that firms choose the optimal level along a continuum, we provide a theoretical underpinning both for Tokman's grey areas of partial compliance, and for de Soto's view that a lack of access to institutions is a binding constraint on firm growth.

De Soto claimed that onerous compliance costs prevent firms from becoming formal. This concern was echoed by Porter (1995) who cited high and uncertain regulatory costs as barriers to firm growth in United States inner cities. Both of these perspectives are easily nested in our framework as a case where the government sets po so high that for most firms the 26 Ordinary least squares regressions yielded comparable results.

18
**Figure 1: Mineral depletion (% of GDP) in Ghana, Liberia and Sierra Leone**

Estimates and Central Projections, 2006-2015 <!-- Start of picture text --> 20.0%<br>18.0%<br>16.0%<br>14.0% Ghana<br>12.0% Liberia<br>10.0%<br>Sierra Leone<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Source: World Bank staff calculation.

In Ghana, the depletion of mineral resource between 2006 and 2011 was not met with equivalent investments. With quicker mineral depletion, Ghana’s economic growth accelerated (to reach a peak of 14 percent in 2011, up from 6 percent in 2006), as did national savings and investments, though not fast enough to replace the depleted mineral capital. While gross national savings, measuring the nation’s capacity to invest, increased from 15.6 percent of Gross National Income (GNI) to 17.9 percent between 2006 and 2011, adjusted net savings decreased from 7.7 to 2.0 percent of GNI over the same period of time. Thus, in order to maintain investment at higher levels, Ghana decided to significantly increase its external borrowing during the period 2012-15.<sup>4</sup> Such as decision brings additional challenges in terms of debt sustainability, i.e. sophisticated debt management and the need to ensure high economic rates of returns for investment projects.

> 4 In 2011, Ghana borrowed US$3.0 billion from the China Development Bank, at non concessional terms to finance public investment projects over the period 2012-15.

- 4 -
30

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