rafmacalaba/datause-encoder-data huggingface dataset:
|Partial<br>statistic<br>Hansen<br>statistic<br>p-value<br>_F_<br>_J_|**0.009**<br>**349.15**<br>0.26|0.107<br>**539.62**<br>0.52|0.950<br>**250.82**<br>0.21|HI dummy and an indicator for whether SHI<br>dummy variable. Significant at**5%**and**_10%_**.<br>ments in the first stage regressions. In the last<br>hat the instruments are uncorrelated with the|
|---|---|---|---|---|
|M estimates<br>SE<br>|**0.362**|0.050|0.352|e first lag of the S<br>usted) of the SHI<br>of excluded instru<br>ull hypothesis is t|
|GM<br>Coef|**1.043**|-0.084|0.023|instruments are th<br>or (SE, cluster-adj<br>test of the subset<br>, where the joint n|
|timates<br>p-value|**0.014**|**_0.082_**|0.983|and the excluded<br>f) and standard-err<br>oint insignificance<br>ctions are reported<br>in equation.|
|least squares es<br>SE|**0.374**|**_0.056_**|0.354|variable is used,<br>coefficient (Coe<br>st) refer to the j<br>identifying restri<br>ded from the ma|
|Two-stage<br>Coef|**0.997**|**_-0.102_**|0.008|the dependent<br>sults refer to the<br>tics (cluster-robu<br>the test of over<br>e correctly exclu|
|log)|ment|MONEE||the natural logarithm of<br>rior to communism. Re<br>d_t_-tests. Partial_F_statis<br>Hansen’s_J_statistics for<br>excluded instruments ar|
|ariable (|nemploy|- Trans|nomy|models,<br>country p<br>two-side<br>r-robust<br>that the|
|Dependent v|Registered u|Empl-to-pop|Informal eco|_Notes:_For all<br>existed in the<br>P-values from<br>column, cluste<br>error term and|
Beshears, John, James Choi, David Laibson, Brigitte Madrian, and Jung Sakong (2011). "Self Control and Liquidity: How to Design a Commitment Contract." Mimeo, Stanford University.

Brune, Lasse, Xavier Giné, Jessica Goldberg and Dean Yang (2011). Commitments to Save: A Field Experiment in Rural Malawi. Mimeo, University of Michigan.

Bryan, Gharad, Dean Karlan and Scott Nelson (2010). Commitment Devices. Mimeo. Yale University.

Choi, Syngjoo, Ray Fisman, Douglas Gale and Shachar Kariv (2007). Consistency and Heterogeneity of Individual Behavior Under Uncertainty, _American Economic Review_ 97(5), 1921-1938.

Choi, Syngjoo, Shachar Kariv, Wieland Muller, and Dan Silverman (2011). Who Is (More) Rational? Mimeo, University of Michigan.

DellaVigna, Stefano (2009). Psychology and Economics: Evidence from the Field. _Journal of Economic Literature_ , 47(2), 315-372.

Fang, Hanming and Dan Silverman (2009). Time-Inconsistency and Welfare Program Participation: Evidence from the NLSY, _International Economic Review_ , 50(4), 1043-1077 FinScope (2008), “FinScope Malawi 2008”. Available at: http://www.finscope.co.za/documents/2009/Brochure_Malawi08.pdf Frederick, Shane, George Loewenstein and Ted O’Donoghue (2002). Time Discounting and Time Preference: A Critical Review, _Journal of Economic Literature,_ 40(2), 351-401.

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____, (2010) Timing and Self-Control, mimeo, Harvard University Department of Economics.

Gul, Faruk, and Wolfgang Pesendorfer (2001). Temptation and Self-Control, _Econometrica_ , 69 (6), 1403-1435.

Giné, Xavier, Jessica Goldberg and Dean Yang (forthcoming). Credit Market Consequences of Improved Personal Identification: Field Experimental Evidence from Malawi. _American Economic Review_ .

Halevy, Yoram (2011), “Time Consistency: Stationarity and Time Invariance,” working paper, University of British Columbia.

Harrison, Glenn W., Morten Lau and Elisabet Rustrom (2005). Dynamic Consistency in Denmark: A Longitudinal Field Experiment, Mimeo, Georgia State University.

Hausman, Jerry A. (1979) Individual Discount Rates and the Purchase and Utilization of Energy-using Durables. _Bell Journal of Economics_ 10(1): 33-54.

Kaur, Supreet, Michael Kremer, and Sendhil Mullainathan (2011), “Self-Control at Work,” working paper, Harvard University.

Laibson, David (1997). Golden Eggs and Hyperbolic Discounting. _Quarterly Journal of Economics_ , 112(2), pp.443-77.

Laibson, David, Andrea Repetto and Jeremey Tobacman (2007). Estimating Discount Functions with Consumption Choices over the Lifecycle. Mimeo, University of Pennsylvania. Loewenstein, George (1987). Anticipation and the Valuation of Delayed Consumption, _Economic Journal_ 97, pp. 666-684.

Loewenstein, George, and Jon Elster, _Choice Over Time (_ Russell Sage: New York, 1992).

36
**Table 5. Exporting a container can cost up to three times more...**

|**_20-footer container_**|**US Dollars**<sup>**26**</sup>|
|---|---|
|**Philippines**|**1,336**|
|Thailand|848|
|China|335|
|Singapore|<br>382|

_Source:_ World Bank, 2007 on Cross-Border Trading, 2006.

**Port operations largely remain a monopoly.** Philippines’ ports are rated as the least competitive among those in eight major Asian countries (World Economic Forum, 2004). Until the reforms in the 1990s, port development and operation was a government monopoly; only a few private ports were allowed to operate commercially. Today, most ports are public, administered by the Philippine Ports Authority (PPA). The sector suffers from a lack of credibility in pursuing open and fair competition between ports and among port operators.

**The airline sector is also highly concentrated and oligopolistic.** Today, air transport carries about 70 percent of exports<sup>27</sup> and 98 percent of visitor arrivals. The country is endowed with a (mostly underutilized) network of airports, but the development of the sector is limited by a dominant conglomerate; indeed, the national flag carrier enjoys the government’s protection and preferential allocation of state aid, subsidies, and airport slots. In the past, liberalizing entry of foreign carriers into the local market proved successful: the entry of three competitors<sup>28</sup> in 1995 and of two low-cost carriers<sup>29</sup> in 2005 lowered fares and increased choice.

**Electricity and cement are oligolipolistic markets.** The electricity industry is dominated by three large companies, with around 70 percent of the total electricity generated by the largest supplier.<sup>30</sup> The cement market structure is highly concentrated<sup>31</sup> , and - reflecting lack of

> 26 The cost does not include ocean freight and consists of several items/charges – documentation, inland transportation, customs clearance and technical control, ports and terminal handling. 27 By value.

> 28 Cebu Pacific, Asian Spirit and Air Philippines. The major beneficiary of this entry was the domestic passenger market, with a wider range of choices (e.g., airlines, flights, fares) and growth in traffic.

> 29 Air Asia and Tiger Airways. Between 2004  and 2006, passenger traffic increased from less than 50,000 to more than 470,000.  In 2006, the two low-cost carriers, accounted for 83 percent of the passenger traffic. 30 By 2006, only 11 percent of power generation capacity had been privatized, compared to the original target of 70 percent, mandated by the Electric Power Industry Reform Act.

> 31 The top three firms - the multinationals Holcim, LaFarge and Cemex - account for nearly 90 percent of installed clinker capacity of 22mn tones, while four independent producers account for the rest. Between 2000 and 2005, when demand fell, cement producers increased prices by as much as 75 percent.

21
Figure 9. The Share in Global Death Toll: Current versus Simulated <!-- Start of picture text --> 78.9<br>69.3<br>Reported share<br>60 Simulated share<br>40 38.8<br>30.7<br>26.6<br>20 21.1<br>17.8<br>3.1 0.2 3.9<br>0<br>High Upper−middle Lower−middle Low Entire<br>income income income income developing<br>countries countries countries countries world<br>Share in global death toll (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization; World Population Prospects; Verity and others (2020); Dowd and others (2020).

Figure 9 shows the counterfactual distribution of COVID-19 fatalities based on the above assumptions. The results indicate that demography does not explain the heavy concentration of COVID-19 fatalities in HICs. In fact, demography contradicts it

- The share of HICs in the global death toll drops by a factor of 2.6 (from 79 to 31 percent). This is mainly the result of the developing world seeing its death toll rise despite also being influenced by the fact that other HICs that have not yet seen an increase in the death toll may raise the overall HIC tally.

- The share of UMICs falls by a factor of 2 (from 18 to 39 percent). Note that the share in the death toll rises above that of HICs. This is mainly the result of UMICs counting over 400 million 60+ people (of which China makes up 250 million), which is larger than the 300 million residing in HICs.

- The share of LMICs rises by a factor of 9 (from 3 to 27 percent). This group of countries has 281 million 60+ people, which is similar to the HICs combined. Of the 281 million, India claims exactly half. The 70+ cohorts are over 100 million. As in UMICs, they are highly underrepresented in the share of global COVID deaths relative to what their age distribution would suggest.

- The share of LICs rises by a factor of 22 (from 0.2 to 3.9 percent). LICs are less populous as a group and their age distributions are heavily tilted towards the younger cohorts. While the simulations suggest that the death toll would rise significantly based on demography alone, the increase is from a very low base and in absolute terms the contribution to the global death toll remains limited.

18
apparel into the US and Canada). This is followed by Korea and Hong Kong. The ASEAN countries have the highest growth rates over this period. For example, Indonesia's cumulative quota growth rate to the US and Canada is 113% for textiles and 1 14% for clothing.

Returning to table 6, the column headed "2005" reports the projected tax equivalents associated with the data base in that year. These represent the combined effect of economywide growth and structural change, on the one hand, and the MFA quota growth rates from table 7 on the other. A comparison of the entries for 1992 and 2005 clearly shows that the quotas are projected to become more binding in all but a few circumstances, including USC imports from Latin America and ROW where the export tax equivalents decline. In some cases the increase in the tax equivalent is quite substantial. This is particularly true for exports of wearing apparel from China, Indonesia, Malaysia and the Philippines. Meanwhile, the share of exports from these markets to the restricted North American and European markets also declines over this period. This is a logical consequence of the binding quotas and more rapid income and population growth in other parts of the world. Quite simply put, the global market for textiles and wearing apparel has shifted away from the traditional importers and towards the Asian countries. One of the goals in this paper is to evaluate the consequences of these changes in the MFA for an analysis of its elimination.

_Non-MFA Reforms:_ Table 8 reports information relevant to the non-MFA portion of the Uruguay Round Agreement. This includes weighted averages for the pre-Round and post-Round tariffs as well as trade-weighted average price cuts (change in tariff/one plus the initial tariff) for manufactures imports, by the regions used in the analysis. The trade-weighted average protection rates for manufactures were calculated using disaggregated tariff and trade data obtained from the WTO's Integrated Data Base. The pre-UR estimates are either the bound tariff rate, in cases where the tariff was bound at the beginning of the Uruguay Round, or the tariff rate applied in September 1986 if the tariff rate was not previously bound. In most developed countries, the preRound, bound tariff rate was the same as the applied rate for the vast majority of commodities, and so the rate reported is _both_ the bound and the applied rate. In developing countries, by contrast, only around a fifth of industrial products were subject to bound tariffs prior to the Round (GATT, 1994a), and so the average tariffs reported in the first column of table 8 are more typically based on applied rates. Comparable data for food imports are also reported (see 10
Figure 6: Effective tax rates, 2019 <!-- Start of picture text --> Mean (%)<br>2<br>Empirical ETR<br>Statutory ETR<br>1.5<br>1<br>.5<br>0<br>0 10 20 30 40 50 60 70 80 90 100<br>Percentiles (assessed value)<br><!-- End of picture text --> _Note:_ This figure presents mean effective tax rates (ETR) for residential properties only, across percentiles of assessed property values in _Tres de Febrero_ municipality for 2019. Dark navy dots report ETR as the tax liability to property value ratio, while blue dots report the ratio between paid taxes and property value. The figure does not include taxpayers who paid the annual tax liability in one go. We also exclude the first percentile for visualization purposes.

Figure 7: Effective tax rates, by gender, 2019 <!-- Start of picture text --> Average (%)<br>1.5 Statutory ETR<br>Empirical ETR<br>Diff:0.38 Diff:0.36 Diff:0.30<br>1 0.96<br>0.89 0.87<br>0.58 0.57<br>0.53<br>.5<br>0<br>Women Men Co-Owned<br><!-- End of picture text --> _Note:_ This figure presents mean effective tax rates (ETR) in 2019 for residential properties only and across gender ownership. Dark navy bars report ETR as the tax liability to property value ratio, while blue dots report the ratio between paid taxes and property value. The figure does not include taxpayers who paid the annual tax liability in one go. We also exclude the first percentile for visualization purposes.

23
(June 2011), so treated children in the treatment villages had 9 months of preschool participation on average, with some children having only a few months of exposure.

**Table 1: Village participation—formal sample (village survey)**

||N|Av|C|T|T-C|SE|P|
|---|---|---|---|---|---|---|---|
|Presence of a primary school in the village|1,549|1|1|1|0|.|0|
|Preschool was constructed in the village|1,731|.548|.131|.817|0.686***|(.125)|0|
|Preschool classes were given in the village|1,731|.504|.09|.772|0.682***|(.105)|0|
|Presence of a community-based preschool|1,549|.094|.167|.047|-0.12|(.103)|.252|
|Presence of an HBP in the village|1,549|.17|.135|.193|0.058|(.12)|.634|

Note: Column T-C corresponds to results of the regression of the dependent variable on the treatment status (FPS); N presents the number of observations; Av, the average in the whole sample; T, the average in the treatment group; and C, the average in the control group. Each regression is robust to the heteroskedasticity and accounts for intra-village correlation (109 clusters in total). * 10% significance; ** 5% significance; *** 1% significance.

# <u>CBPs</u>

Deviations from the experimental design in the informal sample severely limited the statistical power and ability to draw clear conclusions on program impacts (Table 2).<sup>6</sup> In control villages, 28 percent of households had access to HBPs, while 11 percent had access to a CBP. Monitoring data show that only 56 percent of CBP villages actually benefited from a CBP treatment. These problems result in part from the failure to comply with assignment to control and treatment groups, as villages originally designated as control villages became treatment villages. To understand why the quality of implementation suffered, it is important to note that CBP teachers were not paid the planned stipend during the first year of the evaluation (2009/10), and some were not paid until the beginning of the second school year (2010/11). Follow-up data for the informal sample were collected around January 2011, at which time CBP teachers had been paid only for a few months at most. Qualitative interviews further revealed that CBP teachers left either during or at the end of the 2009/10 school year because they were not paid. Some villages had trouble finding substitutes, possibly because the stipend was so low (US$ 8 a month). The small size of the stipend and its delayed, irregular payment seem to have limited children’s ability to participate in CBPs.

> 6 The data in table 2 reflect parents’ assessments of different preschool services during the intervention, and owing to confusion among the respondents, the numbers should be interpreted with caution. For example, parents tended to confuse CBPs and FPSs, especially when CBPs used the primary school premises or were held in the village pagoda. This confusion may explain the numbers in the second row; the “presence of a preschool in the village” should be balanced across all treatment groups, but it is higher for CBPs, probably because some parents said they had access to an FPS when in fact they had access to a CBP.

8
The sum of the headcount ratios also has a negative coefficient in the final model, though it is not significant.

**Table 1. Change in censored headcount ratio models results.**

||Change|_Depe_<br>in censor|_ndent vari_<br>ed headco<br>~~(~~<br>ΔHci(t)<br>Δt<br>~~)~~|_able:_<br>unt ratio p|er year|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Initial poverty centrality (Sc(t))<br>||-0.006<sup>***</sup>|-0.005<sup>***</sup>|-0.005<sup>***</sup>|-0.005<sup>***</sup>|
|||(0.001)|(0.001)|(0.001)|(0.001)|
|Initial censored headcount ratio (Hc(t))<br><br>|||-0.008<sup>**</sup><br>(0.004)||-0.009<sup>***</sup><br>(0.003)|
|Initial headcount ratios of all other indicators<br>(∑<br>H<br>i≠c<br>ci<sup>(t)) </sup>||||0.008<sup>**</sup>|-0.001|
|||||(0.004)|(0.001)|
|Constant|-0.002<sup>***</sup>|-0.002<sup>**</sup>|-0.001<sup>*</sup>|-0.006<sup>**</sup>|0.001<sup>**</sup>|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.000)|
|Observations|652|652|652|652|652|
|R<sup>2</sup><br>|0.370|0.441|0.447|0.447|0.447|
|Adjusted R<sup>2</sup><br><br>|0.288|0.367|0.373|0.373|0.373|
|Country dummy (αc)<br>|Yes|Yes|Yes|Yes|Yes|
|Indicator dummy (βc)<br>_Note: Robust_Standard errors in parentheses.<sup>*</sup>p<|Yes<br>0.1,<sup>**</sup>p<0.05|Yes<br>,<sup>***</sup>p<0.0|Yes<br>1.|Yes|Yes|

To ascertain the robustness of our findings, we use two different approaches.

First, we utilize data for 15 countries that have an additional survey conducted between the initial and final years of the survey period. For these nations, we estimate the Poverty Space (and the corresponding Poverty Centrality) for all three survey years and compute the change in headcount ratio between the first and second, as well as the second and third survey years. This enables us to construct a new unbalanced panel sample which is then used to re-estimate our model. To maintain the restrictiveness of the model we also include year dummies in this analysis. The results, presented in Table 2, affirm that Poverty Centrality remains a significant and negative predictor of changes in headcount ratios over time even within this more homogeneous sample.

22
# **6. Conclusion**

We have explored the causal impact of ICT adoption on firm performance and labor market outcomes. We find that at the firm level adoption of ICT leads to increases in firm productivity and wages, and that the effects are heterogeneous across firms, being larger for initially high‐ productivity and high‐skill firms. The increase in wage occurs even after controlling for skill composition, implying that there are productivity and rent sharing mechanisms in play. The increase in wages is twice as high for managers compared to other skilled and unskilled workers.

We further find that adoption of ICT is associated with employment turnover as captured by the replacement of workers, elimination of occupations and creation of new occupations. There is a decrease in the participation of unskilled workers, supporting the view that ICT is complementary with skilled labor. The drop in the participation of unskilled workers is higher in districts where labor adjustment costs are lower. At the same time, the adoption of ICT leads to an increase in the number of workers across all categories. This effect is largest for skilled workers and for firms with high output growth during the sample period. The finding that some unskilled jobs are lost together with the finding that unskilled employment increases points towards job turnover within the unskilled group.

# **References**

Aboal, D., P. Garda, B. Lanzilotta, and M. Perera. 2011. “Innovation, Firm Size, Technology Intensity, and Employment Generation in Uruguay. The Microeconometric Evidence.” IDB Technical Notes No. IDB‐TN‐314. Washington, DC: IDB.

Acemoglu, D., and D. H. Autor. 2011. ‘‘Skills, Tasks and Technologies: Implications for Employment and Earnings,’’ in _Handbook of Labor Economics_ , vol. 4, O. Ashenfelter and D. E. Card, eds. (Amsterdam: Elsevier).

Akerman, A., Gaarder, I. and Mogstad, M., 2015. “The Skill Complementarity of Broadband Internet”. _The Quarterly Journal of Economics_ , 130(4), pp.1781‐1824.

Alvarez, R., J. M. Benavente, R. Campusano, and C. Cuevas. 2011. “Employment Generation, Firm Size, and Innovation in Chile”. IDB Technical Notes, No. IDB‐TN‐319. Washington, DC: IDB.

AutorD., L. F. Katz, and M. S. Kearney. 2006. "The Polarization of the US Labor Market", _The American Economic Review_ 96.2, 189‐194.

21
# **Figure 2 Bank exposure to SMEs (I)**

The information shown in this figure is derived from the data we collected from banks. The ratios presented in Figure 2 are calculated as the sum of loans offered to SMEs according to the data provided by the banks over the sum of loans offered to the private sector according to these same banks. Therefore, Figure 2 presents financing to SMEs as a share of total financing to the private sector for 2005 and 2006, for Chile and Argentina separately. In parentheses we present the share of total private sector loans that correspond to the banks that answered.

# **Financing to SMEs as a share of financing to the private sector**

<!-- Start of picture text --> 37%<br>40% 36%<br>35% Argentina Chile<br>30%<br>25%<br>20%<br>14%<br>13%<br>15%<br>10%<br>(49%) (72%) (64%) (80%)<br>5%<br>0%<br>2005 2006<br>SME loans over private sector loans<br><!-- End of picture text -->
2 Simulation models support the received wisdom that, in LDCs, liberalization of imperfectly competitive industries results in larger plants and higher efficiency (Condon and de Melo, 1986; Devarajan and Rodrik, 1988. 1989; de Melo and Roland-Holst, forthcoming). Disturbingly, however, there is very littie micro-econometric evidence confirming the adjustment mechanisms that these models assume. For example, Bhagwati (1988) concludes: "Although the arguments for the success of the [outward-oriente **d** evelopment scrategies] based on economies of scale and X-efficiency are plausible, empirical support for them is not available." Pack (1989) goes furthe., climing that the link between trade liberalization and productivity growth has not been established at all.' Given the lack of direct evidence regarding industrial adjustment in response to trade liberalization, this paper tackles some very basic questions. Specifically, in LDCs, how is trade orientation correlated with the size distribution of plants and with plant-level labor productivity? We begin with a simple model that summarizes some effects of trade exposure on producer size and productive efficiency that have been stressed in the recent analytical and simulation literature. We then examine annual plant-level data from Chile and Colombia to determine whether these effects can be confirmed.

The empirical results indicate that, over the long run, higher trade exposure is corre'.ated with smaller plant sizes, controlling for industry and country effects. However, the mix of high versus low productivity plants is not strongly associated with trade exposure. Both of these findings cast doubt on the mec'anisms linking trade, plant size, and productivity in a number of recent analytical and simulation studies.
<!-- Start of picture text --> 0.60<br>m@1970<br>@ 1980<br>0.50 01991<br>040<br>0.30<br>0.20<br>-0.00 |<br>& a@ SfRea ee a we> ew@ eeR oe ea oe ro. rs ra2<br>wos 3 oe<br>weoO eeS<br><!-- End of picture text -->