rafmacalaba/datause-encoder-data huggingface dataset:
# _2.1. Consistent dynamic conditional correlation_

The dynamic correlations are estimated by the cDCC. At first, we take the daily changes in bond yields (BY) as the first differences for each member country _i_ i.e.

For a precise model estimation, we take out the days when more than half of the markets are closed. Moreover, the highest and lowest five daily changes of each market are removed from the dataset to get rid of outliers.

To remove the serial correlation effects (which is present in all daily changes) and obtain the zero mean residuals, we first estimate the following mean equation where _rt_ = [ _r_ 1 _,t, ..., rn,t_ ]<sup>_′_</sup> is the vector of _n_ bond yield daily changes, _μ_ is a vector of constants with length _n_ , _φ_ is the coefficient vector corresponding to autoregressive terms and _εt_ = [ _ε_ 1 _,t, ..., εn,t_ ]<sup>_′_</sup> is the vector of residuals.

In the next step, we obtain the conditional volatilities _hi,t_ from the univariate FIAPARCH(1, _d_ ,1) model for an extended flexibility. In particular, we estimate the following where _ω ∈_ (0 _, ∞_ ), _|β|_ and _|φ| <_ 1, 0 _≤ d ≤_ 1, _γ_ is the leverage coefficient and _δ_ is the parameter for the power term that takes finite positive values, while (1 _− L_ )<sup>_d_</sup> is the financial differencing operator expressed in terms of a hypergeometric function (see Conrad et al. (2008) for the expression of this function).

To consider cDCC modeling, we start by reviewing the DCC model of Engle (2002). Assume that _Et−_ 1[ _εt_ ] = 0 and _Et−_ 1[ _εtε_<sup>_′_</sup> _t_<sup>]=</sup><sup>_Ht_,where</sup><sup>_Et_[</sup><sup>_·_]isthe</sup> 4
without consequence or controversy. As Figure 1 shows, the five years from 2012-2016 in our security data has marked a steady increase in the number of confirmed fatalities due to the ongoing conflict and a corresponding increase in the number of districts perceived as insecure. This recent trend of increasing violence motivates our interest in how the Afghan private sector responds to rising insecurity and further emphasizes the need for novel measurement strategies given the challenging setting for data collection.

# **3 Data**

In this study, we combine three complementary data sources to achieve a uniquely detailed perspective on the economic behavior of private firms in Afghanistan: administrative mobile phone records, administrative government records, and original firm survey data. Since 2002, mobile phone penetration in Afghanistan has grown rapidly, with four private operators and one public operator serving a total market of over 19.7 million subscribers out of an estimated population of 21.5 million adults (World Bank 2015).

## **3.1 Summary Statistics**

As a measure of firm activity, we use anonymized call detail records (CDR) covering April 2013 - August 2016 from the one of the countrys largest mobile network operators. As in prior studies using CDR data (c.f. Blumenstock et al. (2015)), we do not observe the identity of the calling or called parties, nor do we observe the content of their communication. We do observe a record of each call that was initiated, including anonymized identifiers for the calling and called parties, the date and time of the call, and the coordinates of the cell phone tower of the calling party. Unlike prior work, we focus our attention on CDR data of corporate line customers, a subset of subscribers who have signed up for a corporate pricing plan in which multiple phones belong to a single account. We observe the company names of corporate line customers, and screen these to remove public or non-profit organizations, 10
average size of exporters in their entry year could be underestimated. We investigate this possibility in the case of Peru, for which we have access to monthly export transaction data and find that in any given year (i) entrants are very small (relative to incumbents) regardless of the month when they start exporting and (ii) the probability of entering export markets is quite similar across months. Appendix B provides further details.

A final element in the analysis of export entrepreneurship at the firm-level is the contribution of new exporters to short-run export growth. Figure 4 shows the average annual growth rate of total exports for each country in the period 2005-2007 in Panel A and 2008-2009 in Panel B. The average contribution of the three terms in the export growth decomposition -- for new, incumbent, and exiting exporters; see Eq. (1) -- is presented for each country with Panel C covering 2005-2007 and Panel D covering 2008-2009.<sup>22</sup> During the steady growth period, incumbent exporters played a dominant role in explaining export growth in all LAC countries, which was also the case for all comparator countries in the groups of natural resource countries, processing countries and broad manufacturing export base countries. This finding confirms the evidence found for individual rich countries (such as the U.S. by Bernard, Jensen, Redding, and Schott, 2009). New exporters contributed very little to export growth in Chile, Peru, and Costa Rica but played a non-negligible role in the Dominican Republic, and to a lesser extent in Brazil, Ecuador, Guatemala, Nicaragua as well as in Bangladesh and Cambodia, the comparators for processing countries. Exiting exporters reduced total export growth importantly in Costa Rica and Ecuador, as well as in the comparator for broad manufacturing export base countries, Egypt. All in all, the contribution of new exporters was more important in LAC countries experiencing moderate export growth (Costa Rica, Dominican Republic) than in LAC countries experiencing fast export growth (Chile, Peru) between 2005 and 2007.

During the global recession, average export growth was lower (as expected) in fact negative for the most developed LAC countries, Brazil, Chile, Costa Rica, Colombia, Mexico, and Peru. Incumbent exporters played a dominant role in explaining the export decline in those countries (except Costa Rica) and in explaining export increases in Ecuador and Nicaragua. Exiting exporters contributed importantly to the export decline in Brazil and Costa Rica and to reduce export growth in the Dominican Republic, Guatemala, and Nicaragua during the crisis.

> 22 Several comparator countries included in Figures 1-3 are excluded from Figure 4 due to data limitations.

19
We find that many emerging economies have large deficits in terms of housing adequacy already today. Across the 64 countries in the sample, we estimate that about 26 percent of HHs, comprising a total of 268 million housing units, currently live in inadequate housing. This number is projected to grow by more than 40 million units until 2030 if governments and private sector actors along the housing value chain are not able to act swiftly. The poorest countries face the direst housing deficits in both housing stock and flow.

With this paper, at a more disaggregated level, we show that housing conditions correlate highly with income level of HHs. Yet, inadequacy of housing is not only confined to low-and lowermiddle income HHs. Middle and upper-middle income HHs often also live in inadequate housing conditions, especially in low and lower-middle income countries. This vividly shows that efforts to address the adequate housing gap need to take the specific needs of different segments of the population into consideration in order to cater housing interventions appropriately. The results of the paper suggest that the income group requiring external interventions by governments and development finance institutions does not necessarily comprise only the lower income segment. Disaggregated within-country analysis along income and other dimensions such as gender or location could be a basis for tailoring external interventions. As the prevalence of inadequate housing seems to be disproportionally affecting low-income countries, particularly in Sub-Saharan Africa, addressing the adequate housing deficit is imminent to enhance economic growth and mitigate income disparities.

A further challenge is that, even in countries where the right to adequate housing is enshrined in the constitution or ordinary legislation, poor or rural families, uneducated HHs, workers in the agriculture sector, ethnic minorities, or other disadvantaged groups are often unable to exercise that right. According to our estimations, these groups are significantly more likely to find themselves in inadequate, low-quality housing with minimal access to improved facilities. Future research needs to investigate why that is the case.

Our results also indicate that countries need to be more innovative in providing adequate housing to everyone – particularly to vulnerable groups. Creative approaches in housing finance to improve existing housing stock, unlocking land supply, and inclusive urban planning will be required to provide adequate housing to all segments of the current population and the 1.6 billion people who will be added to urban areas by 2030.

The cross-country nature of the analysis and the reliance on household surveys entails several caveats. While household surveys provide a great source of micro-level data, they are expensive to execute and are only available for certain years. As of now, we only take the most recent HH survey into consideration for the computation of the _AHI_ . A next step would be to apply our
estimated at φ0 are insignificant. There are, however, some positive and significant effect of 0.098 and 0.069 of a standard deviation on Spanish and the overall test score, respectively. However, these results are very unstable and hold only for the specifications of the first model of participation in the matching analysis.

In general, findings from the second research design (RDD) on the ―SIFA + _Sisben_ ‖ sample, in Table 7, also indicate that program recipients who graduate from high school perform at the same level as equally poor non-recipient graduates. The graphical analysis<sup>32</sup> does not provide visual evidence of a jump in the regression function at the threshold. Overall, econometric results based on functional forms with second, third and fourth-order polynomials in general show that participant children do as good as the children in the control group in their math test scores. There is a partial negative effect of the program on Spanish (significant at the 5 percent level) but not consistent throughout all the functional form specifications. Furthermore, the findings indicate that there is no systematic evidence of differential performance between participant and non-participant children based on the scores in the overall test. The non-parametric estimations (estimated on an optimal bandwidth close to the cutoff following Imbens and Kalyanaraman (2010)) consistently show that test scores are lower for treated children. Nevertheless we should take into that account that we were not able to correct for the negative selection bias, given that the controls appear to be over-represented in this data. Additionally, results coming from the non-parametric estimation use a local sample very close to the threshold assigned by the optimal bandwidth. When compared against the little evidence available, the absence of impacts of FA on learning outcomes reported in this paper mirrors the findings available from previous evaluations of comparable interventions in Mexico and Cambodia (Behrman and others, 2005; Filmer and Schady, 2009).

# **5. Robustness Checks**

This section discusses the robustness of the findings regarding a number of identification issues that may affect the internal validity of the analysis. We start by

> 32 Not presented, but available upon request to the Authors.

26
TABLE 6: regressions on binary ratings of outcome and borrower

||2|SLS (1)|2|SLS (2)||2SLS (3)|
|---|---|---|---|---|---|---|
||Outcome|Borrower|Outcome|Borrower|Outcome|Borrower|
|Borrower|1.009***<br>(0.027)||1.027***<br>(0.029)||1.027***<br>(0.029)||
|Size<br>GDP p.c.<br>Volatility<br>Growth|-0.007<br>(0.008)<br>-0.00001<br>(0.0005)<br>-0.00001<br>(0.00002)<br>-0.002<br>(0.052)|-0.009<br>(0.008)<br>0.00001<br>(0.00005)<br>-0.00001<br>(0.0002)<br>0.003<br>(0.059)|-0.007<br>(0.009)<br>-0.00004<br>(0.0002)<br>-0.00003<br>(0.00003)<br>0.052<br>(0.087)|-0.001<br>(0.009)<br>0.00004<br>(0.00002)<br>0.00003<br>(0.00003)<br>0.035<br>(0.086)|-0.005<br>(0.009)<br>-0.00001<br>(0.0002)<br>-0.00003<br>(0.00003)<br>0.032<br>(0.093)|0.007<br>(0.009)<br>0.00001<br>(0.00002)<br>0.00004<br>(0.00003)<br>0.036<br>(0.095)|
|Infrastructure|0.265<br>(0.266)|0.324<br>(0.279)|-0.645<br>(0.614)|0.300<br>(0.648)|-0.805<br>(0.639)|0.334<br>(0.657)|
|Bureaucracy|0.007<br>(0.015)|-0.0004<br>(0.013)|-0.046*<br>(0.025)|0.005<br>(0.032)|-0.040<br>(0.026)|0.005<br>(0.033)|
|Corruption|-0.010<br>(0.014)|0.019<br>(0.015)|-0.020<br>(0.024)|-0.007<br>(0.025)|-0.014<br>(0.025)|-0.004<br>(0.026)|
|Account.|-0.006<br>(0.006)|-0.010<br>(0.007)|0.041***<br>(0.014)|-0.024*<br>(0.014)|0.039***<br>(0.014)|-0.026*<br>(0.015)|
|Stability<br>Ethnic|-0.013*<br>(0.007)<br>-0.001<br>(0.008)|0.003<br>(0.007)<br>-0.003<br>(0.008)|-0.009<br>(0.007)<br>0.004<br>(0.023)|0.005<br>(0.010)<br>-0.009<br>(0.023)|-0.016*<br>(0.009)<br>0.005<br>(0.023)|0.002<br>(0.010)<br>-0.011<br>(0.024)|
|Ext. conflict|-0.005<br>(0.007)|0.004<br>(0.008)|-0.016*<br>(0.009)|0.021**<br>(0.009)|-0.015<br>(0.011)|0.018*<br>(0.011)|
|Instruments|||||||
|Bank||0.667***(0.02)||0.661***(0.02)||0.661***(0.02)|
|Bank 1||3.48***(0.61)||2.53***(0.77)||2.58***(0.79)|
|Bank 2||16.3***(2.48)||12.3***(3.30)||12.1*** (3.36)|
|Borr 1||-4.49***(0.74)||-3.74*** (1.05)||-3.84*** (1.10)|
|Borr 2||-21.9***(3.15)||-19.0*** (4.75)||-18.9*** (4.96)|
|Country FE|||Yes|Yes|Yes|Yes|
|Year FE|||||Yes|Yes|

||||Other S<br>|tatistics<br>|||
|---|---|---|---|---|---|---|
|Observ.<br>|1710|1710|1710|1710|1710|1710|
|Adj. R<sup>2</sup>|0.436|0.462|0.439|0.470|0.440|0.470|
|Prob(F)|0.00|0.00|0.00|0.00|0.00|0.00|
|Sargan Test||0.116||0.741||0.635|
|Basman Test||0.118||0.766||0.673|

Note: both the project outcome variable and the borrower performance variable are reported on a 0 (unsuccessful) and 1 (successful) scale. For further references, see the text. The number of observations (1709) is lower than the sample because some instruments do not present a total coverage of the sample.
POLICY RESEARCH WORKING PAPER 2622

# Summary findings

Using household data specifically collected for the purpose of evaluation, Coady, Dai, and Wang empirically evaluate the impact on household income of a rural program in China that focuses on increasing women's economic and social participation in the local community. They find that the program substantially increases women's participation and household income, and also generates positive social benefits.

The authors' results also suggest that the income gains accrue only to participants, and partly at the expense of nonparticipants. They find that the magnitude of the program's impact depends sensitively on the program's ability to increase participation rates within villages.

In the presence of the program, individual participation helps to prevent negative externalities and to buy into the positive gains accruing to participants.

The authors' results support the view that effectively implemented gender-focused interventions can have substantial social benefits when supported by the necessary legal and institutional framework.

This paper-a product of the Poverty Reduction and Economic Management Sector Unit, East Asia and Pacific Regionis part of a larger effort in the region to better understand the impact of gender-focused policies on development. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Limin Wang, room MC5-208, telephone 202-473-7596, fax 202-522-1735, email address lwangl@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. David Coady may be contacted at d.coady@cgiar.org. June 2001. (37 pages) _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 represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
savings, albeit with much greater geographic spread. Future endeavors for implementation at scale needs to include larger class sizes with the best trainers to drive down costs. We found microenterprise owners did implement some of the practices learned, resulting in short-run gains, but they dropped some of these practices and the control group had caught up by 6 months. While there is a tendency to want to spend time on diagnostics and strategic planning, making sure training includes immediately actionable and specific advice that entrepreneurs can use to ensure their business looks different tomorrow than it does today is needed to improve the effectiveness of training content.

# **References**

- Anderson, S., Chintagunta, P., and Vilcassim, N. (2022). Virtual collaboration technology and international business coaching: Examining the impact on marketing strategies and sales. _Marketing Science, forthcoming_ .

- Asiedu, E., Lambon-Quayefio, M., Truffa, F., and Wong, A. (2023). Female entrepreneurship and professional networks. _Mimeo_ .

- Behaghel, L., Crépon, B., Gurgand, M., and Barbanchon, T. L. (2015). Please call again: Correcting nonresponse bias in treatment effect models. _Review of Economics and Statistics_ , 97:1070–80.

- Belloni, A., Chernozhukov, V., and Hansen, C. (2014). Inference on treatment effects after selection among high-dimensional controls. _Review of Economic Studies_ , 81:608–650.

- Broderick, T., Giordano, R., and Meager, R. (2023). An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference? _Working paper_ .

- Bryan, G., Karlan, D., and Osman, A. (2021). Big loans to small businesses: Predicting winners and losers in an entrepreneurial lending experiment. _Working paper_ .

- Campos, F., Frese, M., Goldstein, M., Iacovone, L., Johnson, H., McKenzie, D., and Mensmann, M. (2017). Teaching personal initiative beats traditional training in boosting small business in west africa. _Science_ , 357:1287–90.

- Carpena, F., Cole, S., Shapiro, J., and Zia, B. (2019). The ABCs of financial education: Experimental evidence on attitudes, behavior, and cognitive biases. _Management Science_ , 65:346–69.

- Chernozhukov, V., Demirer, M., Duflo, E., and Fernández-Val, I. (2020). Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments with an Application to Immunization in India. _Working paper_ .

- Cusolito, A., Darova, O., and McKenzie, D. (2023). Capacity building as a route to export market expansion: a six-country experiment in the Western Balkans. _Journal of International Economics, forthcoming_ .

- Estefan, A., Improta, M., Ordoñez, R., and Winters, P. (2023). Digital training for microentrepreneurs: Experimental evidence from Guatemala. _Mimeo._

13
36

# DATA APPENDIX

Data on the composition of labor force were drawn from an IIO data tape prepared on November 12, 1985 in Geneva, which parellels the figures published in the ILO Yearbook of Labour Statistics up to 1984. Several additional countries and observations were added, or ILO data tape figures were corrected if inconsistencies emerged. Countries were included if they reported figures similar to those in Table 2 of the Yearboo which cross tabulates the labor force by sex, industry, and job status (i.e.: employer or own account, wage and salary, family worker, and other--inclusive of unemployed). Seventy-five countries provided this detail for at least two years. The final working sample of 61 countries is a subset of these for which real GNP per adult was available from World Bank sources. this sample and the initial and final year of the observation is listed in Appendix Table A-1. Missing figures could arise for a certain sector because no workers are employed, say of women in mining, or it is more likely that given the size of the labor force sample and small population in cell, the mean is not reported because of sampling variability. In a number of other instances, a small sector, such as mining or electricity (utilities) Is combined with manufacturing in one year and disaggregated in another year. In these cases the same aggregation is retained for both years at the cost of eliminated, for that country, the smaller sector (see footnote 10).

International standard industrial sector codes (ISIC) were adopted in 1958 and revised again in 1968. The important distinction added in 1968 is between personal and business services, which could not be retained here in order to include the earlier observations. For this study business and personal services are thus combined. In either the 1958 or 1968 schema the residual category is not homogeneous across countries, containing persons not able to answer the
0 0 0 0 0 cubic

|meter/ye<br>Year(26)|ar<br> <br>house<br>yard|public|non-|total (press F9)|
|---|---|---|---|---|
||nntin|standi|e resid||
||coeco|p|.||
|no. of<br>persons|0<br>0|<br>0|<br>n/a|0|
|cubic|0<br>0|<br>0|<br>0|<br>0|
|meterlyea|r||||
|2.|Investment cost (conn<br> <br>|ection excl<br>|uded) on co<br>|nstant prices:<br>total<br> <br>|
||year(-5)<br>year(-4)|year(-3)|year(-2)|year(-1) year(O)<br>value<br>|
|||||In year(O)|
|million|0<br>0|<br>0|<br>0|<br>0<br>0<br>0|
|rupiah|||||
|3.|Connection cost (incl|uding house|hold storag|e tank):<br>total|
|MRp|year(-4)|year(-3)|year(-2)|year(-1)<br>year(O)<br>value|
|||||in year(O)|
|house|0|<br>0|<br>0|<br> <br>0<br>0<br>0|
|yard|0|<br>0|<br>0|<br>0<br>0<br>0|
|standpipe|<br>0|<br>0|<br>0|<br>0<br>0<br>0|
|non-res.|<br>0|<br>0|<br>0|<br>0<br>0<br>0|
|MRp|year(1)<br>year(6)|year(11)|year(16)|total<br>year(21) year(26) value<br>|
|||||in year(O)|
|house|0<br>0|<br>0|<br>0|<br>0<br>0<br>0|
|yard|0<br>0|<br>0|<br>0|<br>0<br>0<br>0|
|standpipe|<br>0<br>0|<br>0|<br>0|<br>0<br>0<br>0|
|non-res.|0<br>0|<br>0|<br>0|<br>0<br>0<br>0|
|4.<br>rupiah/<br>cub.met.|Operation and mainte<br>company overhead co<br>0|nance cost<br>st) on a cub|of supply sy<br>ic meter del|stem (including<br>ivered basis:|
|5.|Do water vendors or th<br>from standpipes to the<br>consumers, change in|e consum<br>place of co<br>dicator to '1|ers themsel<br>nsumption ?<br>, if vendo|ves carry the water<br>(if answer is<br>rs, leave as**'0**<br>**)**|

30
Table 5: Total Factor Productivity Growth: Average in 1950-1995 and Change During Periods of Reform(%), Pooled OLS and Fixed Effects with Five-Year Averages and Annual Data (Percent)

|Recessions Dummy|None||DREC1|_DREC2|||
|---|---|---|---|---|---|---|
|Method / Period|1950-95|Reform<br>(change)|1950-95|Reform<br>(change)|1950-95|Reform<br>(change)|
|Pooled OLS<br>5-Year Averages(*)|-0.8<br>(0.003)|1.3<br>(0.000)|_<br>_||-||
|Fixed Effects<br>5-Year Averages()|-0.9<br>(0.001)|1.5<br>(0.000)|||||
|Pooled OLS<br>Annual Data|-1.0<br>(0.000)|1.5<br>(0.000)|1.1<br>(0.000)|0.6<br>(0.007)|2.0<br>(0.000)|0.9<br>(0.000)|
|Fixed Effects<br>Annual Data|-1.2<br>(0.000)|1.7<br>(0.000)|1.0<br>(0.000)|0.9<br>(0.001)|1.9<br>(0.000)|1.0<br>(0.000)|

Notes: -- Not available; (*) The average for the period 1950-95 is the coefficient on the constant in equation (3), while the change during the periods of Reform is the coefficient on the variable DREF in equation (3); (**) the variable DREF is activated in the periods in which the economy is open most of the time; P-values in parenthesis; DREC is activated when GDP growth is negative; DREC2 is activated when TFP growth (calculated with a capital share of 0.4) is negative. Source: Authors' calculations.