rafmacalaba/datause-encoder-data huggingface dataset:
# **Replicating Replication:  Due Diligence in Roodman and Morduch’s Replication of Pitt and Khandker (1998)**

Mark M. Pitt and Shahidur R. Khandker

## **1.  Introduction**

“The Impact of Microcredit on the Poor in Bangladesh: Revisiting the Evidence,” by David Roodman and Jonathan Morduch (2011), hereafter referred to as RM, is the most recent of a sequence of papers and postings that seeks to refute the findings of Pitt and Khandker (PK) 1998, which found that microcredit for women had significant poverty-reducing impacts.

In the most recent iteration of their papers and web postings, RM claim that they have found three substantive and related weaknesses in PK: weak instruments, non-normality, and bimodality of the log-likelihood function. RM state that bimodality is “the most striking” finding, and implies that the PK estimates are inconsistent. They claim that this can be explained ‐ by “instrument weakness caused by disaggregating credit by gender” and that “non normality appears to interact with the instrument weakness to generate the bimodality.” They present test statistics to back up their claim of instrument weakness and non-normality, and suggest that instrument weakness and non-normality cause bimodality. They further suggest that the parameters estimated by PK lack validity based upon these three claims; additionally, using their preferred two-stage least squares (2SLS) linear LIML estimator, they fail to find the positive and statistically significant female credit effects found by PK, which they attribute to instrument weakness and non-normality. Like the earlier critiques by RM, we believe that these claims are based on flawed evidence.

This paper demonstrates that the tests of instrument weakness and non-normality presented by RM are invalid. As we will show, they have chosen testing procedures that necessarily tend to invalidly accept the hypothesis of weak instruments and reject the hypothesis of normal errors. Hidden from view in the paper are methods and results that reflect a serious lack of diligence in responding to and reporting errors that are plainly and repeatedly reported in the Stata output generated by the commands contained in the authors’ Stata “do” files.

In the weak instrument case, RM “pad” the matrix of instruments they use for the test so that it is over 58 times larger than that actually used by PK. We show that this augmented matrix of instruments biases standard 2SLS coefficients in the direction of the least squares bias; that is, they underestimate women’s credit effects. We provide simulation results that demonstrate that the PK model is very robust to variation in the weakness of the instruments, with instrument weakness measured using the methods of RM. The PK maximum likelihood approach recovers 2
connected firms has a real impact on real economic activity.

Table 9 presents the results of the impact of political lending on firm-level economic activity. Though the merge with Orbis data set leaves fewer observations, we still find that the preferential treatment does translate into real effects on firms. The excess credit supply due to political connection has a large effect on employment outcomes, which is statistically significant at the 1 percent level. The economic magnitude of the estimate is also meaningful. Having at least one senator serving as the chairman of any committee results in an additional increase in employment of 2.1 percentage points in the ordinary least squares specification. This estimate is comparable to that of Chodorow-Reich (2014), examining the impact of a reduction in credit supply due to the financial crisis on firm-level employment.

With higher credit availability, firms become more capable of taking full advantage of profitable opportunities; thus, they can increase investment that leads to better performance. In columns 3 to 8 we also examine the impact of excess credit supply on firm’s financial information. More specifically, political lending increases total assets, liabilities and revenue of firms by 1.2, 1.0 and 0.8 percent respectively. However, the last two estimates are no longer statistically significant. The “small versus large” literature has documented that due to greater asymmetric information or smaller buffer savings, smaller borrowers exhibit greater sensitivity to changes in credit supply (Gertler and Gilchrist, 1994; Chodorow-Reich, 2014). Therefore, we conduct a subsample analysis by only looking at the sample of small firms (firms with less than 50 employees), and document a much stronger effect. For example, political connection leads to 2.5 percent increase in employment and 2.7(2.4) percent rise in total assets (liabilities). This greater sensitivity for small borrowers provides supporting evidence of asymmetric information in the lending markets.

# **VI. Extension and Robustness Tests**

## **_VI_ .A. Endogenous Selection of the Chairman**

One threat to the identification strategy is an endogenous selection hypothesis. More concretely, the possibility that the committee chair is selected based on the economic performance of the corresponding sector in his or her state. For example, if a senator from Tamaulipas is selected to chair the energy committee because of the outperformance, or expected outperformance, of the energy sector in Tamaulipas, the empirical strategy would confound the effect of the chairman with the fact that the energy sector of Tamaulipas would have bloomed regardless of who the chair was.

Such an explanation is unlikely given the process by which the committee members are selected, as noted in Section II.

However, and while the institutional background makes it difficult to believe the endogenous selection hypothesis, we present several checks disproving this explanation. To test whether the selection of chairman actually depends on the relative performance of the sector in his or her state, 20
|**Country**<br>**Code**|**Year**|**No. Obs.**|**Frac. FDI**<br>**Obs.**|**Avg. FDI**<br>**(%)**|**Frac. For.**<br>**Input Use.**<br>**Obs.**|**Avg. For.**<br>**Input**<br>**(%)**|**Frac. Exp.**<br>**Int. Obs.**|**Avg. Exp.**<br>**Int.**<br>**(%)**|
|---|---|---|---|---|---|---|---|---|
|SVN|2009|276|1.00|10.49|0.35|44.84|1.00|23.92|
|SWZ|2006|307|1.00|34.15|0.23|45.06|1.00|7.94|
|TCD|2009|150|1.00|22.01|0.39|52.25|1.00|6.19|
|TGO|2009|155|1.00|27.05|0.28|54.84|1.00|24.56|
|TJK|2008|360|0.99|4.85|0.30|35.16|1.00|4.17|
|TLS|2009|150|1.00|16.33|0.43|24.28|0.98|0.78|
|TON|2009|150|1.00|6.07|0.51|38.55|1.00|4.56|
|TUR|2008|1152|0.99|2.09|0.71|23.85|0.99|24.11|
|TZA|2006|419|1.00|9.54|0.65|24.93|1.00|2.85|
|UGA|2006|563|1.00|14.88|0.55|21.50|1.00|4.26|
|UKR|2008|851|0.98|5.02|0.55|22.43|0.99|9.66|
|URY|2006|621|1.00|10.32|0.56|53.64|0.99|13.63|
|URY|2010|607|0.99|8.89|0.57|51.08|1.00|14.59|
|UZB|2008|366|1.00|8.10|0.33|14.60|1.00|3.93|
|VEN|2006|500|0.00||0.00||1.00|2.02|
|VEN|2010|320|0.99|11.53145|0.27|35.13|1.00|0.99|
|VNM|2009|1053|1.00|11.33429|0.74|35.35|1.00|23.25|
|VUT|2009|128|1.00|30.32031|0.17|62.95|0.11|23.29|
|WSM|2009|109|0.96|12.90476|0.24|65.04|0.99|8.60|
|YEM|2010|477|0.99|1.716102|0.50|42.51|1.00|3.35|
|ZAF|2007|937|1.00|10.25053|0.73|16.04|1.00|5.23|
|ZAR|2006|340|1.00|15.87059|0.44|29.73|1.00|1.62|
|ZAR|2010|359|1.00|11.47207|0.33|26.89|1.00|2.99|
|ZMB|2007|484|1.00|20.77355|1.00|34.81|1.00|2.94|

Source: Author’s calculations based on data from the World Bank’s Enterprise Surveys. See text for details.

25
significantly across the PKO deployment and non-deployment communities.

Furthermore, perceptions from an auxiliary survey with ex-combatants also support the intuition that UNMIL was viewed as a credible deterrent force. We focus here on the responses of the 275 respondents from the sample of ex-combatants that were identified via the national reintegration program. Similar to their civilian counterparts, 74% of ex-combatants stated that they were confident that UNMIL was capable of both preventing fighting among armed groups and protecting civilians, and 94% said “yes” when asked whether UNMIL helped to end the war. Nearly all excombatant respondents stated that UNMIL made it more difficult for them and “their enemies” to launch attacks (98% and 94% respectively), and nearly all (90%) stated that third party peacekeeping was crucial to their decision to disarm. Among the respondents who agreed that peacekeeping was crucial for their decision to disarm, 72% stated that only UN peacekeeping forces (in contrast to ECOWAS forces, as an alternative) could have played this role. When asked why, the most common reasons were about evenly split between some expression of“more trustworthy”, “more powerful”, or “they represent the world.” Given these results, we have a bit of a contradiction. On the one hand, it is clear that PKO deployments are _appreciated_ by the public, and they appreciated specifically the security role that UNMIL plays. On the other hand, there is no indication that individuals react _locally_ to PKOs as security providers. Rather, the function of the PKOs seems to be appreciated by local communities as a distant guarantor rather than an immediate provider of security.

Another issue for further discussion concerns the seeming inconsistent socioeconomic effects of UNMIL, which often run counter to our hypotheses. We hypothesized positive socioeconomic effects from improved security conditions as well as from direct socioeconomic programing undertaking by the peacekeeping mission. Indeed, there is a small, but growing empirical literature that demonstrates some positive economic (and social) effects of peacekeeping missions at the loxxiii
<!-- Start of picture text --> Cases Involving SC/ST/OBC<br>Percentage of Cases in Favor of SC/ST/OBC Complainants vs Others<br>100<br>80<br>60<br>40<br>20<br>0<br>1950 1960 1970 1980 1990 2000 2010<br>Year<br>Not SC/ST/OBC SC/ST/OBC<br>Percentage<br><!-- End of picture text --> 21
Sivadasan (2013), Amiti and Khandelwal (2013), Caron, Fally, and Markusen (2014), Harrigan, Ma, and Shlychkov (2015), Fan, Li, and Yeaple (2015), Bas and Strauss-Khan (2015), Atkin, Khandelwal, and Osman (2017), Bastos, Silva, and Verhoogen (2018) and Bastos, Dias, and Timoshenko (2018). Second, we contribute to the literature on impacts of the effects of international trade agreements on trade flows. Our analysis follows the methodology of Hoekman, Ng, and Olarreaga (2002), who draw on a simple and transparent partial equilibrium model to estimate the impact of duty free access for developing countries to high-income markets (Canada, EU, US and Japan) and compares this with a non-discriminatory reduction of tariff peaks to 5 percent.

Partial equilibrium models are static in nature, allowing only for a comparative static comparison of pre/post policy change when all other variables are held constant. Thus, the dynamics which affect the change are not explicitly modeled, nor can complex variations in the setup be considered. Since these models look at the partial effects – i.e. for one set of markets – of a policy change, they do not capture important feedbacks between markets. Dynamic linkages and market feedbacks can be captured in Computable General Equilibrium (GE) models. However, CGE models are much more complex to set up and require more information than partial equilibrium models, e.g. up-to-date input-output tables, or social accounting matrices. They also provide more aggregate information than partial equilibrium models. Since we focus on a relatively small subset of imports (digital goods imports), for which we require disaggregated data by product and country, we rely only on estimates from a partial equilibrium model, leaving a more comprehensive examination of GE effects for further research.

The rest of the paper is organized as follows. Section 2 describes the sources of data used in the paper, as well as the definition of digital goods. Section 3 documents basic facts about Africa’s trade over the period of analysis. Section 4 explains the methodology for estimating quality and quality-adjusted prices, and presents the corresponding estimates on average import prices, quality and quality adjusted prices. Section 5 examines the likely impacts of AfCFTA on the imports of digital goods of member countries. Section 6 compares this scenario with that of a full tariff liberalization on digital goods imports. Section 7 concludes the paper.

# **2 Data**

The empirical analysis in this paper exploits and combines several sources of data, including international trade flows, tariffs, a set of gravity-like variables and a definition of digital goods based on the Bureau of Economic Analysis’s (BEA’s) Digital Economy list of Digital Inputs. In 4
86 would depend, in the first instce, on the share of energy costs in the total cost of production in the various subsectors of the industrial sector. In particular, one would expect greater output effects for the energyinive subsectors, Table 7.2 gives ihe changes in output of selected industries during the period 1986-90.

_Rgure 7.1: Energy Price Changes in Turkey (percentage p.a. in real terms)_ <!-- Start of picture text --> 2D0%-<br>150%<br>100%<br>. 50% _ IA<br>0%<br>-50%<br>-100%<br>1986 1987 1988 1989<br>* Diesel O Electricity [l Gasolie U Kerosene<br><!-- End of picture text --> A marked downtun in growth is seen for the years of adjusament, 1988-89, as can be expected Also, industia t generaly are less energy-intensive, such as food and texfiles, fare better than sectors such as macinery and transport, basic metals, paper and printing and forestrI products, all of -which experience declines at least one of the two years. Importantly, output rebounded in 1990, and markedly more so for the sectors wth the lower growth rates in 1988-89. What was theinpact on energy memand in he industial sector dunng fts peiod? Of puticular importan is th **e** ffect on demand for pteuin the indusia sector. Petroleum consumption as a e oftotd ameW conspion by the indusa sector feR from nearly 45 percent in 1985 to 41 percent by 1989 and natal gas consption aed. Share of all the other fuels remained relatvely stable ding this period, so t change in petroleun product pnces was successful in terms of the overall Turish energ policy objectve to reduce dependence on impo.ted oiL
mainly renewables and biomass, as in the REMIND model. Or the same targets can be met using a combination of renewables, nuclear and fossil fuels with CCS, as in the GCAM model.

**Table 3.a Representation of energy, transport sectors and technological change for General Equilibrium**

|**Model**|**Represented Energy End**<br>**Use Sectors**|**Represented Transport sector**|**Technological change**|
|---|---|---|---|
|**MERGE-ETL**|Energy intensive sector|No disaggregation|Endogenous|
|**MESSAGE**|Industry, feedstocks (non-<br>energy use of fuels),<br>residential, commercial,<br>transport|Transport sector representation is very stylized and<br>essentially includes fuel switching and price-elastic<br>demands|Exogenous|
|**ReMIND**|Stationary (aggregating<br>industry, buildings), transport|Mobility demands for the 4 modeled transport sub-<br>sectors (Passenger-light duty vehicles (LDV), Freight,<br>Electric Rail, Passenger-Aviation and Buses) are<br>derived in a top-down fashion, since they are input to<br>a nested CES production function that ultimately<br>produces GDP. For the LDV mode, three different<br>technology options (internal combustion engine,<br>battery electric vehicle, and fuel cell vehicle) compete<br>against each other in a linear bottom-up technology<br>model.|Endogenous LBD|
|**WITCH**|transport, industry and<br>residential/commercial|Passengers and freight vehicles (both distinguished<br>according to traditional combustion, hybrid, plug in<br>hybrid and electric). The composition of vehicles is<br>determined by a Leontief function of a range of costs<br>(including battery, O&M, fuel and carbon costs)|Partly endogenous|
|**C3IAM**|transport, industry and<br>residential/commercial|Intercity passenger transport, urban passenger<br>transport and freight transport. Urban passenger<br>transport is divided into public transport (bus and rail<br>transit), taxi, and private vehicles (cars and electric<br>bikes). Intercity passenger transport is divided into<br>private vehicles and four main business intercity<br>passenger transport (road transport, railway<br>transport, aviation and waterway transport). Freight<br>transport is divided into five main types: road<br>transport, railway transport, aviation transport,<br>waterway and pipeline transport.|Exogenous|
|**BET EMF33**|Transport, Power, Heat|Passenger and freight|Exogenous|
|**AIM-CGE**|Industry, residential,<br>commercial, transport|Road, Railway, Waterways, Air transport|Exogenous|
|**GEM-E3-ICCS**|Firms, households|As regards the transport modes, the sectorial split<br>follows and further extends the classification used by<br>the GTAP database. This extension splits the GTAP<br>data in passenger and freight transport by mode and<br>further disaggregates themodesintorailand roadas|Exogenous|

16
would be a gross underestimation of their actual needs<sup>19</sup> . Nor is it possible to define physical norms for such a category.

Because of these problems we have estimated the expenditure needs for individual ULBs based on exogenously given norms. First, we estimate the current expenditure needs for various services based on norms relating to population and other characteristics of the ULBs<sup>20</sup> . The need in respect of “other expenditures” has been estimated by taking the median value of other expenditure component of each ULB during the last five years. These values are added to the aggregate expenditure needs for the core services to get total current expenditure needs of a ULB.

The next step is to estimate the capital expenditure needs of ULBs. In the absence of disaggregated data and problems with lumpy capital expenditures mentioned in the previous section, we have used the ratio of capital to revenue expenditures of all the ULBs in the respective states<sup>21</sup> in which the UAs are located to derive the capital expenditure needs on the basis of revenue expenditure needs estimated by us. The assumption is that the prevailing ratio of capital expenditure to current expenditure will hold. The last step would be to add the two categories of expenditure needs, capital and revenue, to arrive at aggregate expenditure needs of ULBs. Table 10 summarizes the estimated expenditure needs of UAs.

A review of studies on financial norms show that the norms set up by the Zakaria Committee (1963) have been updated by all subsequent studies and working groups of the government by simply adjusting for price increases. Mathur et al (2007) gives a comprehensive summary of the literature on norms for Indian ULBs. They have considered the norms suggested from time to time, important among which are those by Zakaria 19 A set of reduced form regressions for Kolkata with a time series data was attempted to estimate the expenditure needs on only the five services at the ULB level. See NIPFP 2007(a) for details.

20 For Delhi the proportion of Education on other expenditures is around 22% which is quite high. On the basis of the norm suggested by National Policy of Education (1986) in terms of minimum average salary of teachers and with the help of the existing teacher student ratio and the proportion of salaries of teachers in total expenditures in schools in Delhi, we can convert these norms into per capita expenditures and take out Education from the other services by specifying a norm.

21 According to the Report of the Twelfth Finance Commission the ratio of total capital expenditure to total revenue expenditure in West Bengal, Andhra Pradesh, Maharashtra, and Tamil Nadu are 15%, 26%, 43%, and 52% respectively, while the all India figure stands at 33%. This ratio for Delhi is not available from any reliable source. We have assumed this ratio to be 50% in Delhi, higher than the national average and at par with the better performing states like Maharashtra.

25
<!-- Start of picture text --> son Average 5-Year CDS Spread<br>7oo ee<br>ol500 Nae ve<br>inieealadl<br>aaa f aa .<br>2002 oe“ go a Ratsaay on, a<br>aa<br>2 8 2 2 2 2 8 » 8 es ee eRe en eean anne<br>T2PTP TPR PP PTET PP PP REE TS<br>aReea RESETi |Re SE ee EEE EET ES EG<br>Source: Bloomberg LP, Datastream<br>Note:ausilahle.AverageS84 exeludesS-yeer CDSKenya,spreadNigeria:of GemlocLAC ewsludesceuntriesCastaby region.Ries, Uruguaywhere data<br><!-- End of picture text -->

<!-- Start of picture text --> EMIBI Global Sovereign Spread Index<br>son | 7 aa<br>600 —_ «aWhis<br>ar, ee MSN)<br>400 aangLiggett | patent aue, -<br>eee —_<br>o<br>oO Oo O mm OO MNT HHH AAT<br>SEZSSESSSSESSSSSSSSSEZEES<br>am ei eR wo oF s a al aA me ea ew Ro Ss a al<br>. ASIA CCA Lac MNA sca,<br>Source: BloumbergLP, Datsotreerr<br><!-- End of picture text -->
33 government. This phenomenon was called flypaper effect since the money appears to stick where it hits without being redistributed. Such pattern is clearly inconsistent with basic economic theory which suggests that the government maximizing social welfare should allocate available resources optimally between the public and private sectors regardless of the composition of regional income. And yet the flypaper effect is almost universal, having been documented almost in every country.<sup>29</sup> Hines and Thaler (1995) discuss different explanations of this puzzling regularity. They show that misspecification of econometric models, confusion of average and marginal costs of local government spending projects, high political costs of attracting regional government revenue and other popular explanations advanced in the literature cannot account for the entire magnitude of the flypaper effect although they may have some explanatory power. Hines and Thaler then conclude that the flypaper effect must be a behavioral phenomenon of the “easy come easy go” type: individuals and corporations find it easier to spend quickly unexpected “windfall revenues”, and so do governments.

The analysis of fiscal decentralization in Russian regions provided an interesting indirect test of the hypothesis about the behavioral nature of the flypaper effect. Namely, if the theoretical argument of Hines and Thaler is valid, both windfall revenues of the richer regions (derived from natural resources) and those of the poorer regions (in the form of federal transfers) must be spent at the regional rather than municipal level (according to the “money sticks where it hits” principle), negatively affecting the extent of fiscal decentralization.

The estimation shows that it is indeed the case (Table 4). Moreover, under additional assumptions calculations show that effects of windfall revenues in the form of transfers and natural resource rents are comparable: a 1% increase in the former results in between 0.11 and

> 29 Hines and Thaler survey the literature studying the flypaper effect mostly in the United States and Canada. However many studies found similar results for other countries. See, for instance, Sagbas and Saruc (2004) on Turkey, Lago-Peñas (2003) on Spain.
<!-- Start of picture text --> m<br>oo ® == Vozambque<br>@® = Africa<br>4 Australasia x United<br>on Asia '<br>= + = Latin America x Canada<br>ao ||] —— managementx log of GDP PPP per capita rprestrele<br>c£ Mo:i @New Zealand & Seger<br>S +Chile @ PortugalSpain Ml Republicof Ie_<br><= AChina Feral Fetoentina Sere<br>Eeoi1 @ Kenys Alndia + Colombia<br>os3 + NicaraguaNicarag<br><= @ Ethiopia__-tf TanzanisZambia @ Ghana<br>n~ @ Mozambique<br>7 8 9 10 11<br>Log of 10-yr average GDP based on PPP per capita GDP(Current int'l $ - Billions)<br>Note: April 2013, World Economic Outlook (IMF) indicator<br><!-- End of picture text -->

<!-- Start of picture text --> + ~<br>Co} oO<br>Ssc | s<<br>wm GN<br>iywe ws<br>o oO<br>41 2 3 4 5 1 2 3 4 5<br>Management Management<br>= +<br>g<<Ss<br>SN SN<br>oc - °<br>we Ww<br>lo} o<br>1 2 3 4 5 1 2 3 4 5<br>Management Management<br>5 Japan [— 7] Mozambique 5) Germany =[2__] Mozambique<br><!-- End of picture text -->