rafmacalaba/datause-encoder-data huggingface dataset:
**Table B4: Parameter Estimates of Equation (1) and Stationarity Properties of Equation (2) between Tanzanian Markets and Nairobi**

||**-------------**|**----------- Regr**|**ession results**|**, eq. (1) --------**|**----------------**|**--- Price spr**|**ead, eq. (2)---**|
|---|---|---|---|---|---|---|---|
||μ|pt<br>W|_R-square_|ADF|PP|ADF|PP|
|_Arusha_|-0.28<br>(0.84)|1.01***<br>(32.24)|0.87|-5.38***|-3.73***|-5.36***|-3.72***|
|_Bukoba_|0.19<br>(0.68)|0.97***<br>(37.49)|0.88|-2.30|-4.20***|-2.45|-4.20***|
|_Dar es Salaam_|-0.07<br>(0.17)|0.99***<br>(24.56)|0.82|-3.65***|-3.98***|-3.69***|-3.99***|
|_Dodoma_|-0.99**<br>(2.30)|1.08***<br>(26.28)|0.81|-4.88***|-3.33**|-4.39***|-3.21**|
|_Iringa_|0.08<br>(0.17)|0.96***<br>(21.70)|0.73|-3.54***|-3.31**|-3.69***|-3.32**|
|_Lindi_|0.46<br>(0.79)|0.94***<br>(17.34)|0.69|-3.54***|-4.52***|-3.71***|-4.52***|
|_Mbeya_|-0.63<br>(1.34|1.03***<br>(22.90)|0.77|-3.56***|-3.22**|-3.41**|-3.17**|
|_Morogoro_|0.19<br>(0.43)|0.97***<br>(23.24)|0.78|-4.50***|-3.46**|-4.54***|-3.49***|
|_Moshi_|-0.64*<br>(1.88)|1.05***<br>(32.54)|0.88|-4.29***|-3.94***|-4.19***|-3.79***|
|_Mtwara_|1.19*<br>(1.87)|0.87***<br>(14.49)|0.65|-4.45**|-4.58***|-3.58***|-4.81***|
|_Musoma_|0.28<br>(0.92)|0.97***<br>(33.51)|0.87|-3.63***|-4.69***|-3.74***|-4.73***|
|_Mwanza_|-0.54<br>(1.55)|1.05***<br>(31.79)|0.87|-3.34**|-4.44***|-3.18**|-4.35***|
|_Shinyanga_|-0.06<br>(0.14)|0.99***<br>(22.66)|0.81|-3.84***|-3.94***|-3.86***|-3.94***|
|_Singida_|-0.66<br>(1.46)|1.04***<br>(24.42)|0.81|-5.38***|-3.71***|-5.15***|-3.68***|
|_Songea_|0.76<br>(1.46)|0.88***<br>(17.61)|0.64|-3.90***|-3.77***|-4.15***|-3.80***|
|_Sumbawanga_|0.90*<br>(1.66)|0.87***<br>(16.79)|0.66|-3.25**|-3.38***|-3.76***|-2.46**|
|_Tabora_|0.61<br>(1.35)|0.93***<br>(21.52)|0.74|-4.07***|-3.50***|-4.24***|-3.52***|
|_Tanga_|-0.45<br>(0.98)|0.93***<br>(23.40)|0.78|-4.76***|-3.49***|-4.67***|-3.48**|

**Notes** : ADF and PP denote the Augmented Dickey-Fuller and Phillips-Perron statistic for unit roots, respectively. The numbers in parentheses are absolute (robust) t-statistics. Significance levels: * = 10 percent, ** = 5 percent, *** = 1 percent .

47
- 28 -

# **REFERENCE**

- ADB. 2006. _Standard Prequalification Document: Prequalification of Bidders User’s Guide_ . Asian Development Bank.

- Bailey, Elizabeth. 2007. Are private equity consortia anticompetitive? The economics of club bidding. _The Antitrust Source_ , Vol. 6(4), pp. 1-8.

- Estache, Antonio, Atsushi Iimi. 2008. Procurement efficiency for infrastructure development and financial needs reassessed. Mimeograph.

Geene, William. 1997. _Econometric Analysis_ . Prentice Hall: New Jersey.

- Gupta, Srabana. 2002. Competition and collusion in a government procurement auction market. _Atlantic Economic Journal_ , Vol. 30, pp. 13-25.

- Hendricks, Kenneth, Robert Porter. 1992. Joint bidding in Federal OCS auctions. _The American Economic Review_ , Vol. 82, pp. 506-511.

- Hoffman, Elizabeth, James Marsden, Reze Saidi. 1991. Are joint bidding and competitive common value auction market compatible? Some evidence from offshore oil auctions. _Journal of Environmental Economics and Management_ , Vol. 20, pp. 99-112.

- Iimi, Atsushi. 2004. (Anti-)Competitive effect of joint bidding: evidence from ODA procurement auctions. _Journal of The Japanese and International Economies_ , Vol. 18, pp. 416-439.

- Iimi, Atsushi. 2006. Auction reforms for effective official development assistance. _Review of Industrial Organization_ , Vol. 28, pp. 109-128.

- Mares, Vlad, Mikhael Shor. 2008. Industry concentration in common value auctions: theory and evidence. _Economic Theory_ , Vol. 35, pp. 37-56.

- Moody, C.E., W.J. Kruvant. 1988. Joint bidding, entry, and the price of OCS leases. _RAND Journal of Economics_ , Vol. 19, pp. 276-284.

- Porter, Robert, Douglas Zona. 1993. Detection of bid rigging in procurement auctions. _Journal of Political Economy_ , Vol. 101, pp. 518-538.
<!-- Start of picture text --> B<br>58 B<br>= 5<br>x (=<br>ag<br>ge3-<br>: Ee<br>5 2:<br>ot ral<br>£o 3}<br>g E<br>As) BS<br>3z<br>i<br>oS ~@<br>—(<b) (=oD<br>8 @<br>22<br>oi ql °<br>1990 1995 2000 2005 2010 2015<br>MO deaths ©—— investment<br><!-- End of picture text -->
For the family labor input regressions in the lower panels of Table 2, we use essentially the same approach as we did with yields.<sup>12</sup> Based on the household fi xed effects estimates, we fi nd that total family labor per acre is about 6% lower on sharecropped plots than on owner-cultivated plots, but this difference is not signi fi cant. Unlike the case of yields, the estimated magnitude of the labor differential falls considerably after adding the controls. The village fi xed effects estimate is somewhat more precise than that based on household fi xed effects, but in this case a Hausman test rejects the former speci fi cation (p-value = 0.006). Conservatively, then, our test of the null of zero moral hazard has high power against family labor use differentials on the order of 20%.

A more powerful test of moral hazard focuses on those tasks that are difficult to contract. Data from the farm wage employment section of PRHS-01 indicate that harvesting effort is much easier to monitor than effort in land preparation, weeding, and so forth. Two-thirds of all annual paid labor days in agriculture are for harvesting, compared to just 7% for weeding and 4% for tilling, and harvesting jobs are much more likely to pay piece rates, which rely on directly observing output or effort. Thus, using a family labor regression that excludes days spent harvesting and threshing (almost half of all family labor devoted to the average plot), the null hypothesis of zero moral hazard should be more likely to be rejected if it is indeed false. These regressions are estimated on exactly the same sample as for total family labor (see Table 1), and the results are reported in the bottom panel of Table 2. Surprisingly, the differential between sharecropped and owner-cultivated plots in terms of noncontractible family labor is only 2% (column (3)). This leads us to suspect that even the 6% overall family labor differential may overstate the extent of moral hazard.

# 2.7 Discussion

To sum up, we fi nd no signi fi cant yield shortfall on share-tenanted land vis à vis owner-cultivated/rented land. Using similar procedures and data, Shaban (1987) obtains a signi fi cant 16% yield differential in six south Indian villages. We can rule out an effect of such magnitude with virtual certainty in neighboring Pakistan. Shaban also estimates that owner-cum-sharecroppers allocate 21% less male family labor overall (47% less female labor) to their sharecropped plots than they do to their owned plots (ICRISAT labor data

> 12 We do, however, take logs of labor hours instead of levels, as the former gives higher within R2s in the fi xed effect regressions and thus considerably lower standard errors. The standard errors of the percentage changes are calculated using the approximation formula given by van Garderen and Shah (2002).

9
damaging) for economic development if a government enforces them in a mechanistic and rigid manner. Indeed, the incentive structures and modes of government control are changing so fundamentally in contemporary China that the authorities' ability accurately to forecast future skill needs, and to use the results as the basis for reliable planning of VTE must be seriously doubted. In general, the climate of economic reform poses a particular challenge for VTE planning and development, mandating the need for a system able to adapt in fundamental ways to changes in economic management.

9. There are some solid grounds for optimism in this respect. Planning for manpower match and even for some aspects of the financing of the VTE system in China is already quite decentralized, incremental, and based on a good deal of formal and informal consultation and consensus building. In the current circumstances, therefore, China's present mode of "bottom-up' manpower forecasting, with only the most general guidelines from Beijing as to how many and what types of skilled workers to train, is to be welcomed as being likely to contribute to successful economic outcomes. The process, while structurally appropriate, could be improved through better information at all levels. Data on employment outcomes would be especially useful in policy and planning decisions.

10. In addition, the curricula of the SVS have been able to provide skills relevant to the service sector and to self-employment. These schools are scheduled for large increases in enrollment. In combination with evolving linkages with employers and considerable school initiative in finding work for graduates, SVS have the potential for providing large-scale, flexible, and relatively low cost formti of skills training to support the government's goal of expanding service sector and self-employment.

11. The present report has focused on the external efficiency of the VTE system in China. However, the internal efficiency of the system (that is, the effectiveness with which available resources are utilized) suffers from a number of weaknesses:

- (a) training units are often too small;

- (b) curricula are very narrowly specialized;

- (c) course lengths are unusually long for the material covered.

It is beyond the scope of the present report to suggest ways in which the internal efficiency of the VTE system might be improved, although such improvements will have important implications for external efficiency.

12. China is not alone among the major countries of the industrialized world in seeking to improve the VTE system. Knowledge of the past and present experiences of those other countries is likely to be useful to Chinese VTE personnel, even though foreign models and

- 51 -
2009 and Trannoy et al. 2010 for adults’ self-assessed health studies; for comparisons between different countries, see Lefranc et al. 2008 for income-based outcomes; for comparisons between regions, see Peragine and Serlenga 2008 for education-based outcomes). Our paper makes three primary contributions to this literature. First, and most important, we conduct our evaluation by establishing the effect of Oportunidades on children’s health opportunities. Second, we consider opportunity in the health of young children because their health is crucial for their adult outcomes (see, e.g., Black et al. 2007 and Alderman et al. 2006) and because it is important in its own right. Third, in contrast to previous literature that tested for stochastic dominance in the context of equality of opportunity, our test procedure is based on Davidson and Duclos (2009) and Davidson (2009). Thus, we test the null of nondominance against the alternative of dominance so that rejection of the null logically entails dominance.

Most of the literature on program evaluation focuses on estimating average treatment effects. However, we are interested in establishing or rejecting stochastic dominance between the distributions of health outcomes of children when their parents are either in or out of the program. This exercise is not trivial because we cannot observe the same child both in and out of the program; in other words, we cannot simply resort to a comparison of the cumulative distributions of treatment and control types without making additional assumptions (Heckman 1992). One such assumption is perfect positive quantile dependence (see Heckman et al. 1997), which stipulates that those who are at the _q_ th quantile in the distribution with treatment would have been at the _q_ th quantile in the distribution without treatment. Roemer’s identification axiom (Roemer 1993) is usually invoked in empirical applications of equality of opportunity when responsibility characteristics are unobserved. This axiom posits that the parents of children who are at the same percentile of their type distribution have exercised comparable responsibility. We 4
Table 1: Normalized Wages and Number of Observations

|||Wag|es|Numbe|r of obs|ervations|
|---|---|---|---|---|---|---|
|||_Mean_|_SE_|_Mean_|_Min_|_Max_|
|_White_|_Aggr/Cons_|1.19|0.05|252|172|410|
||_Manuf_|1.22|0.06|587|309|1002|
||_Non-traded_|0.99|0.05|603|322|1214|
||_Traded_|1.08|0.05|934|612|1439|
|_Tech/Sales_|_Aggr/Cons_|0.97|0.08|63|36|95|
||_Manuf_|0.95|0.03|424|240|631|
||_Non-traded_|0.85|0.04|840|619|1076|
||_Traded_|0.95|0.04|1052|703|1390|
|_Service_|_Aggr/Cons_|0.51|0.03|341|226|412|
||_Manuf_|0.71|0.06|101|41|151|
||_Non-traded_|0.74|0.04|670|558|744|
||_Traded_|0.56|0.03|627|480|758|
|_Craft_|_Aggr/Cons_|0.89|0.07|1096|844|1496|
||_Manuf_|0.95|0.04|1331|661|2051|
||_Non-traded_|0.88|0.03|652|362|937|
||_Traded_|0.86|0.06|863|586|1209|
|_Laborer_|_Aggr/Cons_|0.78|0.06|461|288|702|
||_Manuf_|0.78|0.05|1844|984|2616|
||_Non-traded_|0.69|0.05|433|280|644|
||_Traded_|0.79|0.05|1026|721|1219|

14
remittances that are predominantly consumption oriented having more of an appreciating impact on the real exchange rate.

A third possibility for remittances to affect the real exchange rate is through their impact on growth (Acosta, Calderon, Fajnzylber, and Lopez, 2007), although in this case the impact on the exchange rate is likely to be uncertain. On the one hand, an acceleration in the growth rate would lower the stock of net foreign assets as a percentage of GDP and hence this would lower the real exchange rate (i.e. growth would have the same impact as an increase in the liabilities of the country). If on the other hand, the net foreign asset position of the country is negative _vis a vis_ the rest of the world, the increase in the rate of growth would lower the liabilities to GDP ratio and hence lead to an appreciation.

**<u>Table 1. Remittances and the real exchange rate.</u>**

||**REER 1/**<br>**Growth**|**Initial**<br>**Remitta**|**Final**<br>**nces 2/**|
|---|---|---|---|
|Argentina|-1.6|0.7|0.5|
|Belize *|32.6|3.4|1.7|
|Bolivia|-0.3|0.1|1.5|
|Brazil|-5.3|0.1|0.6|
|Chile|0.1|0.0|0.0|
|Colombia|0.5|1.1|4.0|
|Costa Rica|0.3|0.2|1.8|
|Dominican Republic|-0.4|5.0|12.7|
|Ecuador|3.0|0.5|5.7|
|El Salvador|3.4|7.4|16.1|
|Guatemala|3.3|1.6|8.7|
|Haiti|3.0|2.7|41.2|
|Honduras|1.1|2.1|12.5|
|Jamaica|1.0|4.4|17.7|
|Mexico|1.8|1.2|2.4|
|Nicaragua **|-1.3|0.5|10.6|
|Panama|-1.2|0.3|0.7|
|Paraguay|-1.6|0.6|3.7|
|Peru|-0.6|0.3|1.4|
|Venezuela ***<br>1/ percent<br>2/ percent og GDP|-1.5|0.1|0.2|

2/ percent og GDP Initial Period is 1990 an <mark>d Final Period is 2003</mark>

- Belize Initial Period=1990, Final Period=2002

- ** Nicaragua, Initial Period=1992, Final Period=2003

- *** Venezuela, Initial Period=1997, Final Period=2003

Source: Author’s calculations.

On the internal front, faster growth would be associated with a real exchange appreciation. Higher growth would lead to higher internal demand and the mechanism described above in the description of the internal adjustments will apply (i.e. the Balassa-Samuelson argument). Thus on the whole, the impact of a growth acceleration could go in either direction or even cancel each other and have no impact.

7
# **Reference** s

Aker, Jenny C., Rachid Boumnijel, Amanda McClelland, and Niall Tierney (2011), "Zap it to me: The short-term impacts of a mobile cash transfer program. Center for Global Development Working Paper No. 268 Aker, Jenny C., Rachid Boumnijel, Amanda McClelland, and Niall Tierney (2013) “How do electronic transfers compare? Evidence from a Mobile Money cash transfer experiment in Niger.” Tufts University Working Paper.

Alphonse, Pascal, Jacqueline Ducret, and Eric Séverin (2006) "When trade credit facilitates access to bank finance: evidence from US small business data." Unpublished working paper, Université de Nancy.

Bångens, Lennart and Björn Söderberg, B. (2011). “Mobile Money Transfer and Usage Among Micro and Small Businesses in Tanzania” SPIDER, The Swedish Program for Information and Communication Technology in Developing Regions.

Batista, Catia and Pedro C. Vicente (2013) "Introducing Mobile Money in rural Mozambique: Evidence from a field experiment" NOVAFRICA Working Paper Series No. 1301 Beck, Thorsten, Haki Pumak, Ravindra Ramrattan and Burak R. Uras (2015) “Mobile money, trade credit and economic development: Theory and evidence”, CentER Discussion Paper 2015023.

Bencivenga, Valerie R., Bruce D. Smith, Ross M. Starr (1995), "Transactions costs, technological choice, and endogenous growth," _Journal of Economic Theory_ 67(1): 153–177 Burkart, Mike, and Tore Ellingsen (2004) "In-Kind finance: A theory of trade credit," _The American Economic Review_ 94(3): 569-590 Blumenstock, J. E., Callen, M., Ghani, T., & Koepke, L. (2015). Promises and pitfalls of mobile money in Afghanistan: Evidence from a randomized control trial. In Proceedings of the Seventh International Conference on Information and Communication Technologies and Development (p. 15). ACM. Blumenstock, J., & Eagle, N.

Cull, Robert, and Lixin Colin Xu (2005) "Institutions, ownership, and finance: the determinants of profit reinvestment among Chinese firms" _Journal of Financial Economics_ 77(2005): 117-146 De Long J. Bradford and Lawrence H. Summers (1993) “How Strongly Do Developing Economies Benefit from Equipment Investment?” _Journal of Monetary Economics_ , 32(3): 395– 415 Demirguc-Kunt, Asli and Vojislav Maksimovic (1998) "Law, finance, and firm growth," _The Journal of Finance_ 53(6): 2107-2137 Domar, Evsey D. (1946) “Capital expansion, rate of growth, and employment.” _Econometrica_ 14, 137–147.

16
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Table A.15: Robustness checks, pooling the 2012 and 2018 surveys. Estimating the effect of migrants’ legal status on wages after return, unconditional on return

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age|0.005***|-0.071***|0.007|-0.002|
||(0.001)|(0.021)|(0.005)|(0.004)|
|Age squared|-0.000**|0.001***|0.000|0.000**|
||(0.000)|(0.000)|(0.000)|(0.000)|
|No education|0.006*|0.450***|0.042**|-0.391***|
||(0.003)|(0.082)|(0.021)|(0.018)|
|Primary or preparatory|0.022***|0.399***|0.092***|-0.373***|
||(0.003)|(0.091)|(0.024)|(0.018)|
|Secondary|0.017***|0.346***|0.068***|-0.257***|
||(0.003)|(0.072)|(0.017)|(0.014)|
|Rural|0.012***|0.120**|-0.034**|-0.014|
||(0.002)|(0.061)|(0.016)|(0.011)|
|Oil prices|0.014***<br>(0.000)||||
|Diplomatic exchange||-0.431***<br>(0.066)|||
|Confict|||0.118***<br>(0.020)||
|Illegal status||||-0.337***<br>(0.046)|
|Legal status||||0.129***<br>(0.040)|
|Observations|28,982|3,927|3,920|16,323|
|Year fxed efect|Yes|Yes|Yes|Yes|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Non-mig.=1.|911; Illegal mig|.=1.445; Legal|mig.=2.040||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : We rely on pooled cross-sectional data from the 2012 and 2018 ELMPS. Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use diplomatic exchange between Egypt and the countries of destination of Egyptian migrants at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange neither in Egypt, nor in the country of destination and is equal one if there is evidence of diplomatic exchange either in Egypt or in the country of destination at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

62
questions compared to more objective questions. The respondent’s characteristics explain the majority of the variation, even taking into account the fact that the explained variance of the interviewer effects is likely to be overstated due to confounding with geographic effects. The findings on the first order effects of the interviewer support the hypothesis that respondents are more likely to give pro-state answers to historically disenfranchised groups, but the evidence is not conclusive. Similarly the findings suggest women respondents are more susceptible to the influence of the interviewer’s opinions on subjective questions.

The above results must be interpreted with the caveat that the study was one of convenience and is vulnerable to multicollinearity and omitted variable bias. The likely correlation between the interviewer’s opinions and other covariates such as the interviewer’s age, gender, education, and ethnic group raises the potential impact of multicollinearity of the estimates and significance generated with age, gender, and interviewer opinion, as well as omitted variable bias for education and ethnic group.

The results from this study, while not definitive, may provide a basis for future work more purposely designed to study interviewer effects. A design may employ an interpenetrating design, such as the one used in Schnell & Kreuter (2005), or a cross-classified estimation model, to separate the geographic and interviewer effects. The topics covered in a methodological study could also be selected to have more straightforward expectations and to have a sufficiently large sample size to explore a wider range of interactions. Even if future work is not forthcoming, the results highlight the need to consider more fully the impact of traditional cultural norms when conducting quantitative surveys in the developing world on topics that are outside the objective survey questions.

12