rafmacalaba/datause-encoder-data huggingface dataset:
19 was found earlier that the positive effects of education on fertility are reduced as the overall literacy rate rises, but it is not possible to immediately quantify these effects. Second, the costs of birth averted in Table 10 are underestimated relative to those for family planning programs because the family planning program has fairly immediate effects while the effects of education on fertility take 10 to 20 years to materialize and thus the births averted by education should probably be discounted. Nonetheless, the primary message is that the effectiveness of different policies and their costs differ greatly from country to country and the policy recommendations need to differ also.

The recommendation to bring about fertility decline through mortality decline is also frequently mentioned as an alternative to family planning. Table 11 contains estimates of the costs of averting a birth by averting an infant or child death. These estimates are based on the effect of reducing a death shown in Table 6 and costs of $250 per death averted from Walsh and Warren (1979) in addition to some country specific estimates of mortality reduction catalogued in Cochrane and Zachariah. The high and low estimates differ depending on whether the highest or lowest estimate of mortality's effect on fertility is used.

Comparing the costs in Table 11 with those in Table 9, one finds that family planning is more cost effective than mortality reduction in every case where the $250 per death averted is used. The costs of deaths averted through measles immunization in Kenya, however, is so low and the costs per birth averted through family planning is so high in Kenya that measles immunization is a more cost effective way to reduce fertility in Kenya than is family planning. In no other country is that true. If one compares costs
<!-- Start of picture text --> lo}<br>oO<br>t e<br>g e<br>e e<br>e e e e<br>° e ; g°.<br>oa e? 3e 3° e ec (Je ° ee<br>[o)<br>e ee<br>°e<br>I<br>e<br>oe<br>%<br>e<br>1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Year of procurement<br>BR<br>Peite) 8 83°<br>e @ e<br>o 8 e ee<br>e<br>Bid s °<br>= e<br>Aw e<br>e e<br>re) ° e<br>+ e r) e<br>e ®@ e<br>t © e e<br>wo<br>io)<br>1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Year of procurement<br><!-- End of picture text -->
markets for insurance and credit are largely missing or imperfect.

**Table 8:** Months in Operation - by Location

||Ni<br>rural|ger<br>urban|Nig<br>rural|eria<br>urban|Tan<br>rural|zania<br>urban|Ug<br>rural|anda<br>urban|
|---|---|---|---|---|---|---|---|---|
|_<_6|23|9|10|7|23|12|19|7|
|6-11|24|16|26|24|29|25|24|16|
|12|53|75|64|69|48|63|57|77|

_Notes_ : Survey weights included.

Figure 4 depicts the months of enterprise operation over time using data from Uganda.<sup>14</sup> We notice the difference between rural and urban enterprises. In all four survey years, the share of enterprises operating throughout the year is lower in rural than in urban areas. We may posit that seasonal work, especially in agriculture, is a possible explanation for this finding, which can cause a mismatch between household consumption and income. While household labor is needed on the fields during planting and harvest, surplus labor can be allocated to non-farm enterprises during the low season. Engaging in non-farm activities can therefore be a consumption-smoothing strategy.

**Figure 4:** Months in Operation - by Location

<!-- Start of picture text --> (a) Uganda - Urban (b) Uganda - Rural<br>64.45 72.71 77.13 65.91 53.91 58.31 56.95 64.13<br>24.20<br>24.50 23.79<br>19.05 15.62 21.33<br>16.56<br>16.02 21.88<br>16.49 18.47 17.19 19.26 14.55<br>10.74<br>6.84<br>2005-06 2009-10 2010-11 2011-12 2005-06 2009-10 2010-11 2011-12<br>< 6 months 6-11 months 12 months < 6 months 6-11 months 12 months<br>100 100<br>80 80<br>In Percent 60 In Percent 60<br>40 40<br>20 20<br>0 0<br><!-- End of picture text --> _Notes_ : Survey weights included.

The surveys also capture information about the numbers of non-farm enterprises operated across the waves. Figure 5 shows the number of enterprises counted for Nigeria and Uganda in each of the survey years. In Nigeria the number of new business activities is accounted for in one specific question, in Uganda enterprises from previous rounds that are no longer operating are included in the consecutive rounds as “empty” observations. The data can be used to obtain insight of entry and exit numbers into and from the non-farm enterprise sector. In the second, third and fourth survey rounds we include the number of enterprises that “survived” in between the surveys, with the previous survey round as the baseline survey.

> 14 For this figure we can also use data from the 2005/2006 survey. In the panel regressions we did not include this survey round due to data discrepancies.

26
Policy Research Working Paper 8029

# **Abstract**

The 2015 United Nations resolution on Financing for Development stresses the importance of effective resource mobilization and use of domestic resources to pursue sustainable development. The first Sustainable Development Goal is to eradicate extreme poverty for all people everywhere by 2030. This paper proposes an accounting exercise to assess whether it is feasible for countries to eliminate poverty using only domestic resources, in other words, by mere redistribution. Moreover, the paper argues that the concentration of resources in the hands of fewer individuals in the society may hinder the feasibility of implementing effective fiscal policies (from the revenue side and the social spending side) to reduce poverty. The paper provides a new tool to assess the capacity of countries to eliminate poverty through redistribution, and a new tool to approximate the concentration of political influence in a country. The new methodologies are applied to the most recent surveys available for more than 120 developing countries. The findings show that countries with the same fiscal capacity to mobilize resources for poverty eradication differ widely in the political feasibility of such redistribution policies.

This paper is a joint product of the 2017 World Development Report Team, Development Economics; and the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at lceriani@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 13: IV regression IT use on firm-level productivity considering China’s competition and initial IT interactions

|Dependent variable: log(sales/worker)|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|IT (Computers-per-worker)|-1.095|-0.932|-1.248|-1.315|-1.962|-2.075|
||(1.236)|(1.381)|(1.357)|(1.352)|(1.413)|(1.762)|
|Competition*IT|16.144***|15.181***|14.396***|14.337***|14.147***|14.050**|
||(5.752)|(5.280)|(4.968)|(4.922)|(4.906)|(5.750)|
|Competition|-3.216*|-3.137*|-2.188|-2.343|-1.794|-3.164|
||(1.950)|(1.898)|(1.657)|(1.625)|(1.628)|(2.287)|
|Initial IT*Competition|3.181|3.193|-0.296|-0.124|-2.141|4.212|
||(7.073)|(6.828)|(6.529)|(6.521)|(6.653)|(6.689)|
|IT*initial IT|1.984|1.869|1.780|1.975|2.176|3.594|
||(1.491)|(1.725)|(1.650)|(1.677)|(1.670)|(2.295)|
|IT*initial IT*Competition|-17.030*|-16.074*|-12.698|-12.553|-10.196|-15.739*|
||(8.983)|(8.806)|(8.026)|(7.943)|(8.025)|(9.096)|
|F-frst stage IT|13.4|14.4|14.2|14.8|13.6|14.7|
|F-frst stage IT*competition|11.0|12.3|13.6|14.2|13.9|13.3|
|F-frst stage IT*initial IT|14.8|13.7|13.8|13.5|13.3|13.1|
|F-frst stage triple interaction|26.0|27.1|26.1|26.2|26.1|26.2|
|Sargan|0.59|0.47|0.45|0.44|0.61|0.35|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

34
interpretation of transformed variables would not be possible. Omitting certain collinear variables can help, but it weakens the empirical framework and introduces omitted variables bias. Shah (1988, 1989) advocates the use of canonical analysis to form composite indices of collinear variables to overcome multicollinearity. The canonical analysis is preferable to principal component analysis, because the latter technique attempts to maximize the explained variance in these variables, ignoring correlation with the dependent variable. Canonical analysis, on the other hand, forms linear combinations of original variables in such a way that the resulting composite indexes are maximally correlated with the dependent variable. We use canonical analysis to form composite indices of competitive market structure (based upon data for state owned enterprises, state ownership of banks and price controls), democratic institutions (comprising political rights, civil rights and free media variables) and bureaucratic culture (by combining state as a helping hand, credibility of announced policies, efficiency in service delivery and informed and demanding citizenry based upon percent Protestant population). The formation of these composite indices helped us to retain the richness of a large data set while overcoming multicollinearity problems in econometric estimation.

# **Econometric Results on the Causes of Corruption**

Since reliability of corruption indices is our main concern, we weight observations by their standard deviation in our regression analysis. These standard deviations published by the Transparency International measure the dispersion of corruption ratings for each country across different polls. Although the White test rejects the existence of heterogeneity, we still use White-corrected standard errors to address country specific characteristics. The estimated model is presented in Table 1.

Table 1 shows that lack of openness of the economy is significantly related to corruption, whereas public sector size is insignificant and contrary to our a priori expectations has a negative sign. Complexity of the tax system also contributes to corruption (significant at the 10 percent level only). The lack of competitive market structure is identified as a significant source of corruption. Weakness in bureaucratic controls lessens the incidence of corruption although this coefficient is insignificant.

17
A further difference relates to the choice of control variables. In the standard regression method one looks for predictors of outcomes, and preference is given to variables that one can argue to be exogenous to outcomes. In PSM one is looking instead for covariates of participation. It is clearly important that these include those variables that also matter to outcomes. However, variables with seemingly weak predictive ability for outcomes can still help reduce bias in estimating causal effects using PSM (Rubin and Thomas, 2000).

It is an empirical question as to how much difference it would make to mean-impact estimates by using PSM rather than OLS. Comparative methodological studies have been rare. In one exception, Godtland et al. (2004) use both an outcome regression and PSM for assessing the impacts of field schools on farmers' knowledge of good practices for pest management in potato cultivation. They report that their results were robust to changing the method used. However, other studies have reported large differences between OLS with controls for _Z_ and PSM based on _P_ ( _Z_ ) (Jalan and Ravallion, 2003a; van de Walle and Mu, 2006).

**_How well does PSM perform?_** Returning to the same data set used by the Lalonde (1986) study (described in section 3), Dehejia and Wahba (1999) found that PSM achieved a fairly good approximation — much better than the NX methods studied by Lalonde.  It appears that the poor performance of the NX methods used by Lalonde stemmed in large part from the use of observational units outside the region of common support.  However, the robustness of the Dehejia-Wahba findings to sample selection and the specification chosen for calculating the propensity scores has been questioned by Smith and Todd (2005a), who argue that PSM does not solve the selection problem in the program studied by Lalonde.<sup>35</sup>

Similar attempts to test PSM against randomized evaluations have shown mixed results. Agodini and Dynarski (2004) find no consistent evidence that PSM can replicate experimental results from evaluations of school dropout programs in the US. Using the _PROGRESA_ data base, Diaz and Handa (2004) find that PSM performs well as long as the same survey instrument is used for measuring outcomes for the treatment and comparison groups. The importance of using the same survey instrument in PSM is also emphasized by Heckman et al. (1997a, 1998) in the context of their evaluation of a US training program. The latter study also points to the importance of both participants and non-participants coming from the same local labor markets,

> 35 Dehejia (2005) replies to Smith and Todd (2005a), who offer a rejoinder in Smith and Todd (2005b).  Also see Smith and Todd (2001).

26
<mark>TABLE 5. Comparison of Characteristics across Households That Do and Do Not Abandon the Study at Any Time after Baseline</mark>

|Abandon the Studyat Any|Time after Baseline<br>Did|<br>Did||
|---|---|---|---|
|Baseline Characteristics|not abandon|abandon|P value difference|
|Age of head|42.1123|42.3617|0.5057|
|Age of spouse|37.1413|37.4586|0.2987|
|Head no education|0.2309|0.2497|0.2263|
|Spouse no education|0.1927|0.2120|0.1889|
|Head some education|0.6321|0.6208|0.5251|
|Spouse some education|0.6579|0.6655|0.6668|
|Head high education|0.1365|0.1260|0.4047|
|Spouse high education|0.1493|0.1225|**0.0398**|
|Treated area|0.6844|0.6756|0.6064|
|Altitude|577.69|726.43|**0.0000**|
|Crop loss at first survey|0.1339|0.1249|0.4708<br>**0.0000**|
|Owns house|0.6466|0.5321||
|N|5,289|857||

_Source: Familias en Accion_ Evaluation Data.

_<mark>Note:</mark>_ Sample consists of households in which both parents are present at the baseline and there is at least one 7- to 14-year-old child. N denotes the number of households at baseline. P values are based on standard errors clustered at the municipality level. Figures in bold in column (4) indicate that the correlation of the figures in columns (2) and (3) are significant at the 5 percent level or less.

36
Commission (Economist Intelligence Unit, 1999a). Similarly, after the National Assembly banned the MCP leader from the Assembly for one<sup>yearfor boycottedparliamentarysessions,</sup> the High Court of Malawi overtumed the ban.<sup>4</sup> 1

## V. **IMPACT OF REFORM** ON **SECTOR ORGANIZATION**<sup>**ANDPERFORMANCE**</sup>

# V.1 **Institutional Changes**

Although the 1998 Communications Sector Policy<sup>advocated theissuanceof asecond</sup> cellular license, the Govemment actually started the<sup>processof doing thisbeforeitissuedthe</sup> Policy. To offer a second license, the Govermment first had<sup>tore-negotiatetheallocationof</sup> spectrum with TNM, which had been allocated the<sup>entire GSM900bandin itsprovisional</sup> license. Further, since the negotiations for the second<sup>licenseprecededtheestablishmentof</sup> MACRA and the 1998 Communications Act, the<sup>licensehadtobebid undertheearlier1994</sup> Telecommunications Act and was offered by the Ministry<sup>of Information on the recommendation</sup> of the Post Master General, the head of MPTC.

An advertisement for bids for a second cellular<sup>license wasputintothenationaland</sup> intemational press in March 1998. In addition to the<sup>amount tobe paidfor thelicenseand radio</sup> frequencies, the bidders were asked for details<sup>on networkdevelopmentplans,qualityservices</sup> targets, public pay phone targets, schedule for network<sup>coverage, the shareholdingof the bidders,</sup> and provisions for Malawian involvement. From the first round of bids, the government committee selected six bidders and proceeded to negotiate<sup>overexpansion plans,license feesand</sup> other areas. The committee then ranked the candidates,<sup>reducingthelist firstto three candidates</sup> and then to a single candidate. The committee then reported its choice to the Minister<sup>of</sup> Information, after which the Privatization Commission<sup>entereddetailedlicensenegotiations with</sup> the winning bidder.

The fifteen-year license was awarded to Celtel Limited<sup>onDecember25,1998.Celtelis</sup> 80 percent owned by Mobile Systems International<sup>(MSI)of theUnitedKingdom,10percent</sup>

> **41** Pan Aftican New Agency, "Malawian Parliament<sup>atOdds withJudiciary",September30, 2000.</sup> As discussed below, a recent report by the International Bar Association<sup>(2002)suggested thatthe independenceof the judiciary</sup>

_35_
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43
further robustness checks to select a preferred model and argue that none of these models is completely satisfactory.

# _Validation Checks_

To validate the results of the survey-to-survey imputation, we use the CES 2011/12 as training data to project poverty rates backward and compare them against the poverty rates observed in Health SCS 2014/15, and CES 2009/10 and CES 2004/05.<sup>15</sup> Table 3 compares the poverty estimates observed in CES 2009/10 and CES 2004/05 to predicted poverty rates based on the consumption model estimated on the CES 2011/12 as training data and CES 2009/10 and CES 2004/05 as target data. In both cases, predicted poverty based on our model is considerably lower than observed poverty. In 2009/10, our estimates hardly vary across models with national poverty rates between 17.23 and 17.65 percent, although the differences are somewhat larger for urban areas (13.04 to 15.65 percent). In all cases, predicted poverty rates are substantially lower than the poverty rates observed in the 2009/2010 survey (31.7 percent nationally) (see Table 3 middle panel). In 2004/05, the predicted national poverty rates range from 28.40 percent to 30.38 percent, which are up to 10 percentage points lower than the observed poverty rate (38.9 percent). The difference is wider in rural areas, whereas some of our estimates for urban areas overlap with the 95 percent confidence interval of the observed poverty rates (see Table 3 top panel).

In 2014/15, we compare the poverty rates that we predict using our four models against the poverty rates predicted by Newhouse and Vyas (2019) (see Table 3 bottom panel). Since the CES 2014/15 does not include actual consumption data, we rely on the estimates by Newhouse and Vyas (2019). In our prediction, we use the Health SCS 2014/15 applied to a consumption model estimated over CES 2011/12. In this comparison, we thus compare predictions across different surveys, while the earlier backcasts compared actual and predicted poverty in the same survey (various rounds of the CES). The 2014/15 CES and SCS, both official nationally representative surveys, show broadly similar socio-economic indicators (Tables 2 and 3). Across all four models, our national poverty estimates (between 16.8 and 20.93 percent) are consistently higher than the estimates from Newhouse and Vyas (2019) (14.6 percent), although the confidence intervals for our estimates in models 2 and 4 would include Newhouse and Vyas’ estimates. These differences are mostly driven by rural areas, while for urban areas models 1, 2 and 4 are not very different from Newhouse and Vyas (2019). There is thus an interesting contrast between the three validation tests: While our model underpredicts poverty in 2004/05 and 2009/10, it overpredicts in 2014/15. Again, this is likely because coefficients estimated using 2011 data are applied to earlier data, while in reality the coefficients may vary over time.

> 15 For an overview of these kinds of validation methods, see James et al. (2013). Similarly, microsimulation exercises are validated by “back-casting” reforms that occurred in the past, for example see Figari et al. (2015).

10