rafmacalaba/datause-encoder-data huggingface dataset:
**Table 13. Voluntary Downsizing and Re-Hiring**

|||||**Deoenden**|**tVanable**<br>|**:re-hire**<br>||||
|---|---|---|---|---|---|---|---|---|---|
||Probit||**dF/dX**|**Probit**||dF/dX|Probit<br>||dF/dX|
||**(1)**|||**(2)**|||(3)|||
|Voluntarydownsizing|0.3476|(0.170)<br>|[0.1292]|0.3741|(0.198)<br>|[0.1374]|0.4027|(0.240)**c**|[0.1581]|
|Union.|0.7863|(0.270)a|[0.2373]|0.5006|(0.313)<br>|[0.1598]|1.1636|(0.888)<br>|[0.3492]|
|ILOConventons||||-0.0002|(0.004)<br>|[-0.0001]||||
|LaborFinngCost|||||||-0.1199|(0.096)|[-0.0468]|
|Constant|-1.947|(1.334)||-3.9402|**(2.0 52) b **||-1.1906|(0.617)**b**||
|Observations|292|||214|||146|||
|Loglikelihood<br>|-179.87|||-130.22|||-94.92|||
|Waldchi2|16.8|||9.19|||7.92<br>|||
|**Prob> **chi2|0.010|||0.031|||0.054|||
|||||**Dependent V**<br>|**ariable:r **<br>|**e-hiresane**<br>||||
||**Probit**<br>**(lb)**||dF/dX|Probit<br>(2b)||dF/dX|Probit<br>(3b)||dF/dX|
|Voluntarydownsizinw|0.0758|(0.253)|[0.0068]|0.0897|(0.289)<br>|[0.0087]|0.1010|(0.111)|[0.0187]|
|Union|0.2795|(0.458)|[0.0204]|0.1313|(0.486)<br>|[0.0113]|0.1728|(0.346)|[0.0123]|
|ILOConventions||||-0.0030|**(0.004)**<br>|[-0.0003]||||
|LaborFinngCost|||||||**_-0.0730_**|(0.152)|[-0.0072]|
|Constant|-4.9733|(1.680)a||-3.3286|(2.328)||-3.4810|(2.012)'||
|Observations|292|||214|||146|||
|Loglikelihood|-51.89|||-39.81|||-37.12|||
|Waldchi2|7.03|||6.23|||6.03|||
|Prob**>**chi2|0.060|||0.07|||0.08|||

All **Regression** include: **partial** privatzation<sup>**dunmry,**sectoraldummies,cotry</sup><sup>**macro**controls,and</sup><sup>**continental**dumrnes.</sup> Standard emors and nral effects are given in parentheses and brackets respectively. **a** significant at **I** pere **b** significant at **5** percent; **c** sigrificant<sup>at 10 paent</sup>

**36**
Similar to the SD identification strategy, the matching strategy compares beneficiaries to nonbeneficiaries with similar pre-program characteristics (Caliendo & Kopeinig, 2008; Rosenbaum & Rubin, 1983). The propensity to participate to the program ���� is estimated via a probit regression including pre-program baseline characteristics as covariates �. Then, impacts are estimated via PSM: where ��1 indicates treatment, ��1� is the outcome of interest for treated observations (beneficiary households) and ��0� for untreated observations (non-beneficiary households).<sup>26</sup> The propensity score (propensity to be treated, i.e. beneficiary of the project) is computed from the PMT score, since it is known that project eligibility is determined by this PMT score. Oneon-one matching without replacement is performed for all households, not only those in the PMT ‘common support’ sample.<sup>27</sup> A test of balance between treated and control units is performed for unmatched (before PSM is used) and matched observations (after PSM weighting) and illustrates the reduction in the standardized percentage bias obtained by the matching procedure (Figure 2). The test shows that standardized percentage bias is relatively small, except for household size and the PMT score itself (which is expected) and that the PSM weighting slightly reduces this bias.<sup>28</sup> Common support is found for all but for 89 beneficiary households, which means that most observations are used.

PSM estimates are very close to those obtained from the SD identification strategy (Table 9), with two main exceptions. First, PSM estimates also suggest a significant, negative effect in terms of housing quality, which confirms that most households have not invested in their dwelling. Second, the magnitude of the estimated effects tends to be slightly larger in PSM estimates than in DID and SD estimates. Overall, PSM results support the findings obtained from the DID and SD specifications. While the PSM specification does not address all the limitations

> 26 The PSM estimator is implemented using Stata (Leuven & Sianesi, 2014).

> 27 Several other PSM specifications are tested, including: a specification for households in the common support sample; a specification using all baseline (2010) variables instead of the PMT score only; and a specification using a different matching algorithm, i.e. radius matching. Results are relatively similar across specifications, but results from the common support sample are much closer to results from the main identification strategy.

> 28 Indeed selection into beneficiary status is based on PMT scores. Besides, PMT targeting is known to select larger households in general (Stoeffler, Mills & del Ninno, 2015).

21
**<u>Table 2: Description of dependent & independent variables</u>**

|**Variable Name**|**Description of Variable**|**unwei**|**n=22**<br>**ghted**<br>|**,158**<br>**wei**|**ghted**<br>|
|---|---|---|---|---|---|
|||_mean_|_std.err._|_mean_|_std.err._|
|infant death|Children died within 12 months after birth|0.053|0.225|0.053|0.002|
|neonatal death|Children died within 2 months after birth|0.045|0.207|0.045|0.002|
|gwec|Groundwater electricity connectivity in mS/cm|1.916|2.747|1.568|0.048|
|male|Child is male|0.510|0.500|0.510|0.004|
|birth order|The birth order of the children|2.493|1.717|2.514|0.021|
|maternal age at delivery|Age of mother when the children was delivered<br>Mother's education converted by education level|23.623|5.880|23.444|0.055|
|mother's years of education|achieved and grade completed into number of<br>years|4.960|4.061|4.637|0.054|
||Mother's partner's education converted by|||||
|partner's years of education|education level achieved and grade completed|||||
||into number of years|4.963|4.688|4.499|0.056|
|mother currently married or living in union|Mother currently married or living with a partner|0.985|0.120|0.986|0.001|
|urban residence|Household resides in urban area<br>Asset index computed by Principle Component|0.305|0.460|0.160|0.005|
|standardized asset indices|Analysis using household asset ownership|||||
||information and standardized at [0, 1] interval|0.267|0.232|0.240|0.003|
|arsenic|Groundwater arsenic concentration in ug/L|62.273|93.576|66.276|2.394|
|gwd|Depth to ground water level in meters<br>Household having access to improved sanitation|7.282|5.796|7.095|0.093|
|improved household sanitation|including septic tank/toilet, water sealed/slab<br>latrine|0.333|0.471|0.293|0.006|
|Brahmaputra-Jamuna basin|Household clusters located within Brahmaputra-<br>Jamuna basin|0.460|0.498|0.449|0.012|
||Household clusters located within Ganges-Padma|||||
|Ganges-Padma basin|<br>basin|0.486|0.500|0.489|0.010|
|Meghna basin|Household clusters located within Meghna basin|0.055|0.228|0.062|0.007|

24
**Table A.36. The district fixed-effect RDD-DD regressions of households’ income with clustering the standard error at the village** **<u>level</u>**

||||D<br>|ependent variable<br>|s<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program * Post-program period|-0.0181|0.4116|0.8039***|-0.3774***|0.5492**|0.8739***|0.9973***|
||(0.0517)|(0.3108)|(0.2190)|(0.1403)|(0.2655)|(0.1978)|(0.2159)|
|(Poverty rate – 50) * Post-|0.0052|-0.0756**|-0.0658**|0.0099|-0.0303|-0.0566***|-0.0153|
|program period|(0.0057)|(0.0330)|(0.0279)|(0.0188)|(0.0253)|(0.0199)|(0.0251)|
|Program * (Poverty rate – 50) *|-0.0092|0.0284|0.0511*|-0.0052|-0.0064|0.0444*|0.0215|
|Post-program period|(0.0061)|(0.0360)|(0.0294)|(0.0192)|(0.0280)|(0.0238)|(0.0268)|
|Age of household heads|0.0262***|0.0485***|0.0565***|0.1330***|-0.0451***|-0.0931***|-0.0598***|
||(0.0016)|(0.0099)|(0.0064)|(0.0065)|(0.0073)|(0.0083)|(0.0083)|
|Age squared of household heads|-0.0002***|-0.0008***|-0.0007***|-0.0013***|0.0006***|0.0012***|0.0007***|
||(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Gender of household head|0.0053|-0.0124|0.2031***|0.6791***|-0.4089***|-0.0837*|-0.3800***|
|(male=1, female=0)|(0.0123)|(0.0721)|(0.0504)|(0.0426)|(0.0461)|(0.0439)|(0.0547)|
|Ethnic minorities (yes=1, Kinh=0)|-0.6139***|-0.2264**|-1.7972***|0.8630***|-0.5437***|0.6164***|-0.7285***|
||(0.0232)|(0.1125)|(0.0980)|(0.0755)|(0.0848)|(0.0733)|(0.0792)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.6202***|3.0156***|1.6227***|3.7472***|4.8958***|1.9215***|3.0930***|
||(0.0492)|(0.2853)|(0.2004)|(0.1945)|(0.2174)|(0.2131)|(0.2141)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.364|0.164|0.107|0.148|0.228|0.149|0.091|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

86
inefficiency inasmuch as a fixed transfer of groundwater necessarily leads to a misallocation across farms once the state of nature is revealed. Our model yields the sharp prediction that as groundwater supply uncertainty increases, long-term contracts become unattractive relative to spot arrangements.

The assumption that long-term (ex-ante) contracts deter holdup appeals to the referencepoint insight of Hart and Moore (2008) and Hart (2009) wherein a contract establishes what each party in the transaction is entitled to; opportunism thus leads to deadweight losses (see also Herweg and Schmidt, 2014, for a related model).<sup>4</sup> In the earlier property rights theory of the firm associated with Grossman and Hart (1986) and Hart and Moore (1990), renegotiation is efficient so that hold-up is virtually inevitable (Hart, 1995). Since there is, consequently, no functional difference between contracts agreed upon ex-ante and those agreed upon ex-post, _payoff_ uncertainty, in Hart’s (2009) terminology, can play no role.

A key contribution of this paper lies in quantifying the contracting distortion, as well as its impact on the return to water-saving technology, using a structural econometric model. A rather unique feature of a groundwater economy that allows us to do this is that buyers and sellers are _both_ agricultural producers, cultivating side-by-side with the same technology. Our model of agricultural production under stochastic groundwater supply accounts for the choice between seasonal contracts and per-irrigation sales, for water transfers through leasing, as well as for the area irrigated under each such arrangement. We use data from a large sample of borewell owners across six districts of Andhra Pradesh and Telangana states. The specially-designed survey instrument takes particular care to elicit from each well owner a subjective probability distribution of their borewell’s discharge near the end of the season conditional on its initial discharge. The structural parameters of the model are identified principally from the variation across borewells in this conditional probability distribution.

To assess external validity of the structural model, we retain two _nonrandom_ holdout samples corresponding to two of the six surveyed districts; borewells from the remaining four districts constitute the estimation sample on which we fit the model. Keane and Wolpin (2007) argue for choosing “a [holdout] sample that differs significantly from the estimation sample along the policy dimension that the model is meant to forecast (p. 1352).” The analogue, in our setting, to a policy regime “well outside the support of the data” are the large differences in first and second moments of groundwater supply between estimation and holdout districts.

> 4Fehr et al. (2011), Hoppe and Schmitz (2011), and Bartling and Schmidt (2014) corroborate the reference-point idea experimentally.

2
**ash**

- 1/ In 1981 approximately 63 per cent of the Bahama's total exports went to Canada which accounts for

the sizeable difference between the

- "all developed" share (93.0) and that for the EC, Japan and United States.

2/ The 1981 to 1983 doclIne In HaitI s exports Is confined almost exclusively to STIC 28 (Metal Ores) and is due to the cIo-. Bouxit. mine.

ng of a large

- 3/ In 1981 approximately 9 per cent of Jamaica's exports went to Canada and this share had grown to 17 per cent in 1985.

4/ In 1981 the combined share of Canada and the United States In Caribbean exports was 59.7 per cent. **its** 1983 the corresponding 53.6 per cent while In 1985 It was 63.1 per cent.

share was Source: Computed directly from each countries' reported exports as recorded on United Nations Series D Coidoity Trade Tapes (SITC Revision 2).

Note: Bilateral trade flows that account for less than one-tenth of a per cent are recorded as 0.0 in this table.
||No. of stud|ents|**Table 32:**|**IV Estimation **|**(7)**||||
|---|---|---|---|---|---|---|---|---|
||Total||Boys||Girls||Preschool||
|D|1.003|(0.608) *|0.739|(0.533)|0.878|(0.535) *|-1.191|(0.437) ***|
|lnHH size|0.860|(0.190) ***|0.584|(0.166) ***|0.649|(0.167) ***|0.040|(0.136)|
|Adult share|-3.082|(0.398) ***|-2.593|(0.348) ***|-2.161|(0.350) ***|-1.829|(0.286) ***|
|HH head sex: Male|0.016|(0.229)|-0.042|(0.200)|0.169|(0.201)|0.056|(0.164)|
|HH head education: Elementary|0.018|(0.164)|0.099|(0.144)|-0.074|(0.144)|0.010|(0.118)|
|HH head education: Secondary|-0.587|(0.268) **|-0.393|(0.234) *|-0.405|(0.235) *|0.313|(0.192) *|
|HH head education: University|-0.466|(0.542)|-0.422|(0.474)|-0.429|(0.477)|0.215|(0.389)|
|Appliance ownership: Refrigerator|-0.245|(0.248)|-0.369|(0.217) *|-0.107|(0.218)|-0.124|(0.178)|
|Appliance ownership: Color TV|0.309|(0.247)|0.083|(0.216)|0.428|(0.217) **|-0.305|(0.177) *|
|Appliance ownership: Radio|-0.409|(0.153) ***|-0.149|(0.134)|-0.265|(0.134) **|-0.171|(0.110)|
|Appliance ownership: Gas stove|-0.149|(0.257)|-0.009|(0.225)|-0.240|(0.226)|0.030|(0.184)|
|Appliance ownership: Washing|||||||||
|machine|0.228|(0.179)|0.318|(0.157) **|-0.042|(0.157)|0.232|(0.128) *|
|Gas use for cooking|0.499|(0.173) ***|0.246|(0.151) *|0.275|(0.152) *|-0.053|(0.124)|
|Power use for lighting|0.551|(0.262) **|0.440|(0.229) *|0.458|(0.230) **|-0.054|(0.188)|
|Tap water for cooking|-0.136|(0.166)|0.021|(0.145)|-0.305|(0.146) **|-0.007|(0.119)|
|Constant|-2.566|(0.909)***|-2.932|(0.795)***|-3.513|(0.799)***|-1.846|(0.652)***|
|Obs.|911||911||911||911||
|R-squared|0.259||0.213||0.184||||
|Wald stat|21.30||16.93||14.69||7.71||
|Sargan overidentifying restriction test|0.24||0.29||0.22||0.01||
|Hausmanexogeneity test|14.57*|**|16.58*|**|11.52 *|**|22.74 *|**|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

51
# _2005 Slowdown_

GDP growth in 2005 was projected to be in the range of 6 to 7 percent, although the data for the first half of the year suggest growth of now more than around 4 percent. Year-onyear quarterly growth fell consecutively from 7.7 percent in the last quarter of 2003 to 3.3 percent in the first quarter of 2005, before recovering to 4.4 percent in the second quarter of 2005 (Figure 4.A). A number of idiosyncratic factors play a minor role in this reduction: the tsunami in early 2005, its impact on tourism, and a severe drought which counterbalances the buoyancy of agricultural prices. More important is the rise in oil prices that have fueled the costs of imports. But the key change is the performance of exports whose year-on-year growth dropped close to zero in the first six month, compared to expanding by 8 percent in the first half of 2004. The combination of higher import and lower export growth has contributed to turning the current account negative. This slowdown is linked to lower import growth in China and underlines the three principal risks to Thailand’s growth: rising costs of oil and capital import, a “hard landing” in China and a “disorderly unwinding” of global current account imbalances.

**Figure 4: Expenditure Composition of GDP by Quarter, Q1 2004 to Q2 2005**

## A. <u>Quarterly Real Growth, Year-on-Year</u>

<!-- Start of picture text --> 8<br>7<br>6<br>5<br>4<br>3<br>2<br>01Q3 01Q4 02Q1 02Q2 02Q3 02Q4 03Q1 03Q2 03Q3 03Q4 04Q1 04Q2 04Q3 04Q4 05Q1 05Q2<br>GDP<br>B. Value-Added, 1988 Constant Prices<br>2004Q1 | 2004Q2 | 2004Q3 | 2004Q4 | 2005Q1 | 2005Q2<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br>Private Consumption Public Consumption Investment Exports Imports<br><!-- End of picture text -->

12
<!-- Start of picture text --> n<br>m=<br>eee<br>"+ *<br>.<br>S2 “: 1“ om *“<=Rsom 6 ‘ome4<br>. “ ou epee% e sa ine<br>5 4 . : .<br>bo - og Ty<br>i wa Be<br>s+ om, —<br>‘sdes * abrte oy,<br>*a"we -- *2 oe =<br>‘. ' " * ‘a<br>aCe een of etacanen tame<br><!-- End of picture text --> <!-- Start of picture text --> n<br>-<br>“mm<br>"+ *<br>“Lan *<br>=2 ~~ =wer?*. m7ee= ‘ eo<br>“: 2 ta we neti. nS inert<br>.5iJ ve- 8“ oer ce8 eeheel<br>re Pa<br>t+ , and -o-<br>our oe. ve<br>om“E, -oe =<br>: ‘ ‘ ' * “a<br>(ag eseme pe ofehcen sees<br><!-- End of picture text -->
# **_Table 2: Selection into screenings_**

||**Tot**|**al**|**Participated i**|**n screening**|**Did**|**not particip**|**ate**|
|---|---|---|---|---|---|---|---|
|Variable|**N**|**Mean**|**N**|**Mean**|**N**|**Mean**|**P-value**|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|_Personal characteristics_||||||||
|Age of respondent|1946|37.63|1242|38.26|704|36.52|**0.000*****|
|Gender (male)|1984|0.29|1260|0.28|724|0.30|0.368|
|<br>Married|1983|0.84|1259|0.85|724|0.82|**0.054***|
|Widowed|1983|0.02|1259|0.02|724|0.01|**0.031****|
|Single|1983|0.14|1259|0.13|724|0.17|**0.004*****|
|Muslim|1983|0.36|1260|0.35|723|0.39|0.112|
|Christian|1983|0.63|1260|0.64|723|0.61|0.111|
|Can speak English|1974|0.70|1255|0.72|719|0.66|**0.005*****|
|Igbo|1982|0.19|1260|0.20|722|0.18|0.149|
|Yoruba|1982|0.75|1260|0.75|722|0.75|0.965|
|Other ethnicitiy|1982|0.05|1260|0.04|722|0.07|**0.012****|
|_Education_||||||||
|No completed school education|1983|0.07|1260|0.06|723|0.10|**0.006*****|
|Primary school education|1983|0.22|1260|0.22|723|0.24|0.386|
|<br>High school diploma|1983|0.50|1260|0.50|723|0.49|0.843|
|<br>Diploma|1983|0.10|1260|0.11|723|0.09|0.137|
|Graduate school|1983|0.10|1260|0.11|723|0.09|0.101|
|_Household characteristics_||||||||
|Household (HH) size|1972|4.51|1251|4.52|721|4.51|0.873|
|Number of children below 12 in HH|1948|1.30|1234|1.31|714|1.29|0.761|
|Number of dependents in HH|1954|2.41|1241|2.47|713|2.31|**0.090***|
|Number of dependents outside the HH|1862|1.53|1179|1.52|683|1.54|0.882|
|_Business characteristics_||||||||
|Months in operation|1947|98.59|1235|98.76|712|98.30|0.917|
|Has a savings account|1977|0.56|1260|0.59|717|0.52|**0.002*****|
|Keeps written financial records|1968|0.37|1254|0.39|714|0.32|**0.002*****|
|Operating inside main market|1979|0.25|1260|0.28|719|0.20|**0.000*****|
|Number of employees|1980|1.45|1259|1.45|721|1.45|0.987|
|Business experience inyears|1977|10.70|1256|10.88|721|10.40|0.218|

*** p<0.01, ** p<0.05, * p<0.1

27
infinitely elastic, then the rents attributable to the variable factor, _e.g.,_ the fish, would be only partly dissipated. The Appendix shows that this result also carries over to the investment process analyzed here.

Figure 3 illustrates the erosion of the potential gains from technological progress when the low-ability type is at the margin of entry. In this example<sup>6</sup> , _r_ = 1, _W_ = 0.7, _e_ = 0.36, _pL_ = 0.2, _pH_ = 0.8, and λ = 0.8. The upper panel plots _q_ against _R_ .

FIGURE 3. Effect of an increase in _R_ on entry and the social surplus <!-- Start of picture text --> 1<br>q in equilibrium<br>0.5<br>q in first-best economy<br>0<br>4 4.5 5<br>Success return ( R )<br>0.3<br>0.2 y in first-best economy<br>0.1<br>y in equilibrium<br>0<br>-0.1<br>4 4.5 5<br>Success return ( R )<br>) q<br>Fraction of type-L who borrow (<br>)<br>y<br>Social surplus (<br><!-- End of picture text --> The lower panel plots _y_ against _R._ The figure shows that in equilibrium, when _q_ is increasing, _y_ is flat: over this range, all potential gains from the increase in _R_ are eroded.<sup>7</sup> These curves can be contrasted with the dotted curves for a first-best economy (in which banks can distinguish between good and bad types).

> 6 The Matlab programs to produce Figures 3 and 5 are at www.econ.worldbank.org/staff/khoff.

> 7 The same kind of analysis shows that if the innovation in the success returns of projects _R_ is biased toward type _L_ , then social surplus may fall as a result of the innovation. There are many instances in which firms at the margin are different from the best firms, and so a non-uniform technological change is natural. For example, in the trucking business the marginal firms may be those whose entrepreneurs have the lowest ability to repair trucks.  In that case, the marginal firms will benefit the most from technological change in the form of an improvement in publicly provided roads.

12
the second‐ to fourth‐ lagged levels of right‐hand‐side variables in the differenced equations, and first‐ lagged differences in the levels equation.

In the second and third columns of Table 2, 3 and 4, I unbundle the SYS‐GMM estimator into the corresponding differenced and levels equations, and report 2SLS estimates of each, using the same internal instruments for each equation as those used in the SYS‐GMM estimator. In all three papers, the within‐country variation exploited by the differenced equation suggests a positive relationship between inequality and growth, although this effect is statistically significant only in HOZ. On the other hand, the findings from the SYS‐GMM estimator of a negative effect of inequality and growth appear to be driven primarily by the cross‐country variation captured in the levels equation. For all three papers, the levels equation gives estimates of a negative effect of inequality on growth that are similar to, or slightly larger than, the SYS‐GMM estimates. Accordingly, in much of the discussion below I will focus on instrument strength in the levels equation as it provides the best evidence in support of the main conclusions in these three papers.

I now turn to the question of instrument strength in these specification.<sup>11</sup> I first test the null hypothesis of underidentification using the Kleibergen and Paap (2006) rk‐LM statistic. Specifically, the null hypothesis is that the matrix of coefficients from the first‐stage regressions is not full rank, signaling a complete failure of identification. This is a fairly undemanding null hypothesis that, even if rejected, may still be consistent with the instruments having very weak explanatory power for the endogenous variables. In the case of OBT, the p‐value for the null hypothesis of underidentification is 0.18 in the differenced equation, and 0.17 in the levels equation. Similarly, the null of underidentification is not rejected in both the differenced and levels equations for HOZ. Only in the case of CC is the null of underidentification rejected at the 5 percent level. The failure to reject even this undemanding null hypothesis of underidentification at conventional significance levels in these first two studies is a first indication of weak instrument problems.

I next report the Cragg‐Donald statistic, and the corresponding critical values for the Stock and Yogo (2005) test of weak instruments. The critical values are for a test of the null hypothesis that the instruments are weak, where “weak” is defined in terms of a maximal bias of the 2SLS estimates relative

> 11 As noted earlier, I take as given the identifying assumptions that generate the internal instruments in the SYS‐ GMM estimator.  However, in the case of OBT it is worth noting that the Hansen test of overidentifying restrictions casts some doubt on these identifying assumptions, in the case of the levels equation which drives their results – the p‐value for the null hypothesis that the instruments are orthogonal to the error term in the levels equation is just 0.036.

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