rafmacalaba/datause-encoder-data huggingface dataset:
clearly indicate, as expected, a serious problem of heteroskedasticity. Second, as suggested by Greene (1980), we apply a transformation to the original data and re-estimate the model in order to get the GLS estimates. The results are very satisfactory. Out of six explanatory variables, four are significant. Capital input is not significant across all the estimations and has an unexpected sign. The explanation may be that the selected functional form is not adequate; and/or that there is a great heterogeneity among firms within the African water industry. It may also reflect to trend to focus more on small scale operations discussed earlier.

Results confirm the non-endogeneity of labor as seen from the Hausman simultaneity test. The estimation with the GMM has given similar results. Note also that the GLS is the favorite due to the absence of correlation between the individual effects and the explanatory variables confirmed by the Hausman test.

Table 2: Estimation Results and Diagnostic Tests

|Dependent|variable:Totalwaterp|roduction||
|---|---|---|---|
|Regressor<br>Specificati<br>|on(standard deviation<br>|*<br>||
|WG|GLS|GMM|IV|
|Constant<br>1.7128<br>(0.133)|0.001a<br>(0.125)|1.7358<br>(0.145)|1.775a<br>(0.135)|
|Capital<br>0.0017<br>(0.00037)|0.00016<br>(0.00034)|0.00015<br>(0.00033)|0.00015<br>(0.00034)|
|Materials<br>0.00092a<br>(0.00031)|0.00013a<br>(0.00030)|0.00014a<br>(0.0004)|0.00015a<br>(0.0005)|
|Labor<br>0.0016a<br>(0.00046)|0.0017a<br>(0.00044)|0.0016a<br>(0.00045)|0.0018a<br>(0.00047)|
|EnergyCosts<br>-0.00006<br>(0.00018)|-0.00007<br>(0.00017)|-0.00006<br>(0.00019)|-0.0008<br>(0.0002)|
|Technology<br>-0.00037<br>(0.00039)|-0.00037<br>(0.0004)|-0.00037<br>(0.0005)|-0.00039<br>(0.0006)|
|<br> <br>|<br>Diagnostics:<br>|<br>||
|DegreeofFreedom<br>31|31|31|31|
|_~**2**<br>0.38|0.39|0.27|0.28|
|S.e.e<br>2.30%|2.11%|2.40%|2.43%|
|Error<br>auto-1.81<br>correlation***|1.86|1.83|1.79|
|Durbin'h'||||
|LM(1)<br>F6,31 =0.63|0.54|0.72|0.81|
|ARCH(1)**<br>F(1,68=0.05|0.02|0.04|0.05|
|Normality<br>Z(2)=0.76|0.75|0.77|0.78|
|Reset(1)<br>F(153)**=**0.22|0.18|0.25|0.27|
||HausmanTest|||
|x2(5)<br>6.86|1.21|2.23|NO|
|<br> <br>Notes:*'t'statistics are derivedusingheterosce<br>**the errors auto-correlation tests andtheARCH|<br>dastic-consistent estim<br> tests**are**alladjusted for|<br>atesofstandard-errors.<br>gaps.||

13
_ GJ _

# market premia and the sustainability variable.

Project sustainability concerns the beneficial effects of a social project in the longer term. It is rated as "likely", "marginal", "uncertain" and "unlicely" in the OED data set. On this basis, we divided the fifty-six projects into three categories: i) likely to be sustainable; ii) marginal or uncertain in sustainability; and iii) unlikely to be sustainable.

Table 3 presents the crosstabulations of project sustainability (measured by the three categories) by several policy indicators. Among all evaluated projects, 59 percent is sustainable, 29 percent mediocre, and 12 percent a complete failure. Currency overvaluation, no matter how measured (and what benchmark is used), show a significant negative impact on sustainability. The higher the black market premium in the country, the lower the likelihood for the project to be sustainable. For comparison purposes, we also present the sustainability for nonsocial projects. Among 267 nonsocial projects, 51 percent was evaluated as "likely to be sustainable', a slightly lower than that for social projects (59 percent). Similar association between sustainability and policies are found: A project is more likely to have sustainable positive effects when the foreign exchange premium and price distortions were low, when the fiscal deficit was small, and when the GDP growth rate was high.
Figure 4: Growth versus selection on initial size, 2005-2009 <!-- Start of picture text --> all entrants, empl. 2005<br>survived through 2009, empl. 2005<br>survived through 2009, empl. 2009<br>0 2 4 6<br>log employment<br>all entrants, sales 2005<br>survived through 2009, sales 2005<br>survived through 2009, sales 2009<br>0 5 10 15 20<br>log sales<br>all new exporters, exports 2005<br>survived through 2009, exports 2005<br>survived through 2009, exports 2009<br>0 5 10 15 20<br>log exports<br>.4<br>.3<br>.2<br>density<br>.1<br>0<br>.3<br>.2<br>density<br>.1<br>0<br>.2<br>.15<br>.1<br>density<br>.05<br>0<br><!-- End of picture text --> Notes: The figure depicts non-parametric estimates from a kernel density smoother with bandwidth 0.7, using data from 2005 and 2009.

38
<!-- Start of picture text --> indicator survey gov cbip ngo<br>cc 11214 6592 12521 10837<br>ge 8865 6588 12521 3434<br>pv 3569 6592 16374 1700<br>rl 9347 13284 12521 13821<br>rq 4899 7125 12521 8600<br>va 7820 6745 10531 23356<br>Total 45714 46926 76989 61748<br><!-- End of picture text -->
million. However, the risk of sampling is also large – if the selection of households or individuals is not properly done, statistics constructed from the sample do not represent the characteristics of the population of interest. This is the reason why sampling is so critical when NSOs are designing a survey.

This paper describes the sampling design for the 2016/17 Bangladesh Household Income and Expenditure Survey (HIES). The HIES, a large household survey collected by the Bangladesh Bureau of Statistics (BBS), is the main government instrument for monitoring welfare and living conditions in Bangladesh. The HIES is a well-established sample household survey in Bangladesh and has been conducted about every five years since 1991-92, with the latest three rounds collected in 2000, 2005, and 2010.<sup>1</sup> BBS decided to redesign the sample of the HIES 2016/17 to produce more frequent and more disaggregated poverty estimates than the previous rounds. Specifically, the samples of the latest three rounds of the HIES survey are designed to provide reliable annual poverty estimates at the division level for both urban and rural areas.<sup>2</sup> But in the HIES 2016/17, BBS decided to produce reliable annual district poverty statistics and quarterly national poverty statistics in addition to annual poverty estimates at the division level for urban and rural areas. As a result, BBS needed to change the design of sampling for the HIES 2016/17 significantly. For example, the sample size for the HIES 2016/17 needed to increase almost fourfold compared to the HIES 2010. This substantial increase in the sample size also forced a revision of the sampling frame to accommodate the larger number of household field interviews.

The main challenge when designing the HIES 2016/17 was how to achieve multiple goals with one sampling design. As mentioned above, the objective of the HIES 2016/17 is three-fold: (i) producing reliable annual poverty and welfare statistics at the district level; (ii) producing reliable quarterly poverty and welfare statistics at the national level; and (iii) producing reliable

> 1 In addition to collecting detailed household consumption and income data, the HIES collects detailed information on household demographics, education, health, livelihoods and economic activities, quality of housing, asset ownership, migration, social safety-nets, micro-credit, access to services, and household shocks and coping strategies.

> 2 Divisions are the higher level of geographical divisions in Bangladesh. As of 2010, there were a total of seven divisions in the country, including Dhaka, Chittagong, Barisal, Khulna, Sylhet, Rangpur, and Rajshahi.

3
Davis SJ, Caldeira K, Matthews HD (2010) Future CO2 emissions and climate change from existing energy infrastructure. Science 329(5997):1330 1333

- Dixit A, Lambertini L (2003) Interactions of commitment and discretion in monetary and scal policies. The American Economic Review 93(5):pp. 15221542

- Ekins P, Kesicki F, Smith AZ (2011) Marginal abatement cost curves: A call for caution. Tech. rep., UCL Energy Institute

Gilotte L (2004) Incertitude, inertie et choix optimal. PhD thesis, Cired

- Goulder LH, Mathai K (2000) Optimal CO2 abatement in the presence of induced technological change. Journal of Environmental Economics and Management 39(1):138

- Greening LA, Greene DL, Diglio C (2000) Energy eciency and consumption the rebound eect a survey. Energy Policy 28(6-7):389401

- Grubb M, Chapuis T, Duong MH (1995) The economics of changing course : Implications of adaptability and inertia for optimal climate policy. Energy Policy 23(4-5):pp. 417431

- Guivarch C, Hallegatte S (2011) Existing infrastructure and the 2C target. Climatic Change Letters accepted

- Gusdorf F, Hallegatte S, Lahellec A (2008) Time and space matter: How urban transitions create inequality. Global Environmental Change 18(4):708719

- Ha-Duong M, Grubb M, Hourcade J (1997) Inuence of socioeconomic inertia and uncertainty on optimal CO2-emission abatement. Nature 390(6657):270273

- Hammitt JK, Lempert RJ, Schlesinger ME (1992) A sequential-decision strategy for abating climate change. Nature 357(6376):315318

- Jaccard M, Rivers N (2007) Heterogeneous capital stocks and the optimal timing for CO2 abatement. Resource and Energy Economics 29(1):116

- Jackson T (1991) Least-cost greenhouse planning supply curves for global warming abatement. Energy Policy 19(1):3546

- Jae AB, Newell RG, Stavins RN (2005) A tale of two market failures: Technology and environmental policy. Ecological Economics 54(2-3):164174

- Johnson TM, Alatorre C, Romo Z, Liu F (2009) Low-Carbon development for mexico. Low-Carbon Development for Mexico 1:1156

- Kesicki F (2010) Marginal abatement cost curves for policy making: Expert-Based vs. Model-Derived curves. In: IAEE's 2010 International Conference. Rio de Janeiro

19
migratory herding necessary to deal with variation in water availability and forage, and those with smaller herds than this size must stay near their base camps, where land degradation means only a small herd size can be supported. Moreover, Santos and Barrett (2011) show that these asset dynamics are known to the farmers, and that those too close to the low-level steady state are excluded from informal credit markets. So in this remote location, with only a single non-convex production technology, and no access to credit, some pastoralists appear trapped in poverty as theory would suggest. The key question, to which we will return, is then why more people do not leave such areas.

In contrast, in most of the world, individuals face multiple production technologies. Individuals have a choice over many different types of sectors (e.g. make clothing, sell food, sell other goods), and within a sector, a choice over which items to make and sell. Empirically this gives rise to several facts that are difficult to reconcile with models of poverty-traps like that in Figures 3 and 4.

The first two facts relate to the capital needed to start a business. In practice, it seems individuals do not need a lot of capital to start a business; many business owners start with very low levels of capital, and some start with zero capital. Second, the amount of capital needed to start a business appears relatively continuous – it does not seem that business owners only have a small number of production technologies they can choose among. As an example, Figure 5 plots the distribution of start-up costs of 2,200 urban Sri Lankan microenterprises in the baseline of the Sri Lanka Longitudinal Survey of Enterprises (SLLSE), described in Suresh de Mel et al. (2009). Start-up capital includes land, buildings, machinery, and working capital. We see a wide range of initial start-up levels of capital, with 10 percent of firms starting with US$100 or less, and 23 percent with $500 or less. David McKenzie and Christopher Woodruff (2006) show similarly that there is a range of different starting capital levels for Mexican microenterprises, with many starting with low amounts.

15
is perhaps mostly due to this latter group that model uncertainty is such an important feature of growth regression.

We hope the simple jointness measure introduced here can provide a useful tool for further exploration of the posterior distribution in problems involving Bayesian variable selection. In particular, we hope it may contribute to our understanding of the growth of countries or regions.

# **5. References**

- Brock, William, and Steven Durlauf (2001), “Growth Empirics and Reality,” _World Bank Economic Review_ , 15: 229–72.

- Brock, William, Steven Durlauf and Kenneth West (2003), “Policy Evaluation in Uncertain Economic Environments,” _Brookings Papers of Economic Activity_ , 235–322.

- Doppelhofer, Gernot, and Melvyn Weeks (2005), “Jointness of Growth Determinants,” unpublished (University of Cambridge: CWPE #0542).

- Doppelhofer, Gernot, and Melvyn Weeks (2006), “Jointness of Growth Determinants,” unpublished (University of Cambridge: mimeo).

- Fern ́andez, Carmen, Eduardo Ley and Mark F.J. Steel (2001a), “Benchmark Priors for Bayesian Model Averaging,” _Journal of Econometrics_ , 100: 381–427.

- Fern ́andez, Carmen, Eduardo Ley and Mark F.J. Steel (2001b), “Model Uncertainty in CrossCountry Growth Regressions,” _Journal of Applied Econometrics_ , 16: 563–76.

- George, Edward I., and Robert E. McCulloch (1997), “Approaches for Bayesian Variable Selection,” _Statistica Sinica_ , 7: 339–373.

- Hoeting, Jennifer A., David Madigan, Adrian E. Raftery and Chris T. Volinsky (1999), “Bayesian model averaging: A tutorial,” _Statistical Science_ 14: 382–401.

- Jeffreys, Harold (1961), _Theory of Probability_ , 3rd ed., Clarendon Press: Oxford.

- Le ́on-Gonz ́alez, Roberto and Daniel Montolio (2004), “Growth, Convergence and Public Investment: A BMA Approach,” _Applied Economics_ , 36: 1925–36.

- Levine, Ross, and David Renelt (1992), “A Sensitivity Analysis of Cross-Country Growth Regressions,” _American Economic Review_ , 82: 942–963.

- Papageorgiou, Chris, and Winford Masanjala (2005), “Initial Conditions, European Colonialism and Africa’s Growth,” unpublished (Baton Rouge: Department of Economics, Louisiana State University)

- Raftery, Adrian E. (1995), “Bayesian Model Selection in Social Research,” _Sociological Methodology_ , 25: 111–63.

- Raftery, Adrian E., David Madigan, and Jennifer A. Hoeting (1997), “Bayesian Model Averaging for Linear Regression Models,” _Journal of the American Statistical Association_ , 92: 179–91.

15
# **7. BIBLIOGRAPHY**

Baker, Michael and Dwayne Benjamin. 1994. “The Performance of Immigrants in the Canadian Labor Market.” Journal of Labor Economics, 12(3): 369-405.

Baltagi, Badi H. 2001. “Econometric Analysis of Panel Data.” Second Edition. John Wiley & Sons, Ltd. West Sussex England.

Barro, Robert J. 1991. “Economic Growth in a Cross-Section of Countries.” Quarterly Journal of Economics 106: 407-433.

Barro, Robert J. 1997. “Determinants of Economic Growth: A Cross-Country Empirical Study.” Cambridge, MA: MIT Press.

Ben-Porath, Yoram. 1967. “The Production of Human Capital and the Life Cycle of Earnings.” Journal of Political Economy 75: 352-365.

Bloom, David E. and Morley Gunderson. 1991. “An Analysis of the Earnings of Canadian Immigrants.” In J.M. Abowd and R.B. Freeman (eds.): _Immigration, Trade and the Labor Market_ , 321-42. Chicago: University of Chicago Press.

Borjas, George. 1982. “The Earnings of Male Hispanic Immigrants in the United States.” Industrial and Labor Relations Review, 35(3): 343-353.

Borjas, George. 1985. “Assimilation, Changes in Cohort Quality, and the Earnings of Immigrants.” Journal of Labor Economics 3(4): 463-489.

Borjas, George. 1987. “Self-Selection and Earnings of Immigrants.” American Economic Review 77(4): 531-553.

Borjas, George. 1989. “Immigrant and Emigrant Earnings: A Longitudinal Study.” Economic Inquiry 27(1): 21-37.

Borjas, George. 1992. “National Origin and the Skills of Immigrants in the Postwar Period.” In R.B. Freeman (ed.): “Immigration and the Work Force.” Chicago: University of Chicago Press.

Borjas, George. 1994. “The Economics of Immigration.” Journal of Economic Literature, 32(4): 1667-1717.

Borjas, George. 1995. “Assimilation and Changes in Cohort Quality Revisited: What Happened to Immigrant Earnings in the 1980s.” Journal of Labor Economics 13(2): 201-245.

Borjas, George. 2000. “The Economic Progress of Immigrants.” In G.J. Borjas (ed.): “Issues in the Economics of Immigration.” Chicago: University of Chicago Press.

Borjas, George J. and Bernt Bratsberg 1996. “Who Leaves? The Outmigration of the Foreign-Born.” The Review of Economics and Statistics, 78(1): 165-176.

30
**Table 4. Point estimates of other parameters. Asymptotic standard errors in parenthesis**

|G|ermany|France|Italy|Japan<br>|UK|
|---|---|---|---|---|---|
|,B,|0.266|1.506|1.686|1.240|0.395|
||(0.108)|(0.381)|(0.498)|(0.293)|(0.173)|
|**2**|1|1|1|0.0*|1.661<br>(0.680)|
|3|0.1*|0.104|0.039|0.060|0.074|
|||(0.012)|(0.0036)|(0.025)|(0.018)|
|14|1.0*|2.05|0.0*|13,|0.0*|
|**55**|0.073|13|0.117|0.069|0.122|
||(0.010)||(0.030)|(0.027)|(0.028)|
|**16**|135|13|0.0217<br>(0.0045)|3|135|
|137|0.031|0.016|0.058|0.070|0.072|
||(0.0043)|(0.0027)|(0.0019)|(0.0051)|(0.029)|
|_138_|0.014|0.007|0.005|0.006|0.0096|
||(0.0029)|(0.0012)|(0.0019)|(0.0011)|(0.002)|
|139|0.013|0.085|0.0*|0.0*|0.117|
||(0.0061)|_(0.015)_|||(0.035)|
|_010_|0.036|0.020|0.017|0.023|0.047|
||(0.0079|(0;0055)|(0.003)|(0.008)|(0.012)|
|**PH3**|0.5*|_0.5*_|0.5*|0.5*|0.5*|
|**P112**|**0-1***|0.1*|0.1*|0.1*|0.1*|
|013|0.047|0.068|0.025|0.051|0.067|
||(0.0106)|_(0.015)_|(0.0061)|(0.014)|(0.015)|
|**14**|0.451|0.122|0.281|0.151|0.211|
||(0.196)|(0.031)|(0.076)|(0.048)|(0.043)|

23
selected poor households that did not receive a cash transfer in the 15 control villages where no households received cash transfers.

Among households randomly assigned to a CCT scheme, for their children under age six, the conditions imposed were quarterly visits to the local health clinic for child growth monitoring, while for children age seven to fifteen, the conditions were school enrollment with an attendance rate above 90 percent each quarter. In the villages randomly assigned to conditional cash transfers, satisfaction of the conditions was assessed using a family booklet in which the school teachers and the health workers signed and stamped the booklet for a given child to confirm school attendance and health visits, respectively. Further, in the CCT villages, local village committees, which had received specific training, randomly selected 20 percent of the booklets and verified the information reported in those with data from the school attendance registers and the health center visits register.

For families randomly assigned to a UCT program, the mother or father received a quarterly stipend for each child without conditions. For each child under age six, in the CCT and UCT programs, the mother or father would receive 1000 FCFA per quarter, for a total of 4000 FCFA per year. Using the exchange rate during the 2008 baseline of 415 FCFA = $1 USD, the annual transfer was worth approximately $9.64, which is 9 percent of household per capita expenditures. For each child age 7 to 10 (or in grades 1 to 4), the mother or father would receive 2000 FCFA per quarter (8000 FCFA per year), while for each child age 11 to 15 (or in grades 5 or higher), the mother or father would receive 4000 FCFA per quarter (16,000 FCFA per year).<sup>4</sup> There was no household transfer cap amount, so that each age eligible child could participate irrespective of the composition of the family. The transfers were made in cash during a quarterly

> 4 To minimize child fostering in response to the program introduction and reduce any potential sample attrition (see Akresh 2009 for evidence on the relationship between income shocks and child fostering), eligibility for cash transfers was based only on the children present in the household at the time of the baseline survey.

7
Figures

- B.1 School Entry Probabilities from Three Census Rounds

- C.1 Effect of Reforms on Number of Births, by Age

- C.2 Effect of Reforms on Timing of First Birth, Marriage, and Intercourse, by Age

- D.1 Comparison of Pre-treatment Trends

# Tables

# A.1 Timing of FPE Reform with On Time Entry, by Birth Year

# Anderson-Rubin Weak IV Confidence Sets

- B.1 Effect of FPE on Years of Schooling and Number of Children Born

- B.2 Effect of Years of Schooling on Knowledge and Health

- B.3 Effect of Years of Schooling on Labor Market Outcomes and Fertility Preference

- B.4 Effect of Schooling on Beliefs Regarding Domestic Violence

- C.1 Oromia – MTI Implementation

- C.2 SNNPR – MTI Implementation

- C.3 Dire Dawa – MTI Implementation

- C.4 Tigray – MTI Implementation

- C.5 MTI+Script Regions: Timing of FPE and MTI Reforms with On Time Entry, by Birth Year

- C.6 Tigray: Timing of FPE and MTI Reforms with On Time Entry, by Birth Year

National Estimates with Separate Instruments: Accounting for FPE and MTI

- C.7 Effect of Reforms on Years of Schooling and Number of Births (Census + DHS)

- C.8 Effect of Reforms on Years of Schooling and Number of Births (Census)

- C.9 Effect of Reforms on Years of Schooling and Number of Births (DHS)

- C.10 Effect of Years of Schooling on Knowledge and Health

- C.11 Effect of Years of Schooling on Labor Market Outcomes and Fertility Preference

- C.12 Effect of Years of Schooling on Sector of Employment - Job Different than Husband

- C.13 Effect of Schooling on Husband’s Characteristics

- C.14 Effect of Schooling Regarding Domestic Violence (Married Women Only)

- C.15 Effect of Schooling on Beliefs Regarding Women’s Empowerment

A.2
controlled by the central government is high (3,,, > 0 ). The analysis of Table 10 suggests that the decision regarding membership is also affected by the age of the finn (3 > 0) and the change in prices (3, > 0 ). The probability of joining an association is greater for older firns than for younger ones, possibly because the former are more likely to have been influenced more bythe Soviet system and to have less knowledge of the market system rules. Moreover, since firm ijoins an association in an attempt to decrease its transaction costs, an increase in the price of firm i's product reduces the benefits from using this alternative form of coordinating activities and sharing infornmation.

The geographical distance of suppliers instead reduces the probability of joining an association. This somewhat surprising result, suggesting that associations among distant members are less likely to arise, reenforces our intuition that local incentives to coordinate are the triggering factor for the emergence of these institutions. Distance may in fact make it more difficult to coordinate activities with other members, reducing therefore the usefulness of business association. The other proxies of disorganization however are not statistically significant, though with the predtcted sign _(84 >o25, 8,_ > 0 ) This last set of results on the role and the emergence of associations is in line with the theoretical framework discussed in Section 2 and with the studies of Greif (1989, 1993, 1994) and Milgrom et al. (1990) on the importance of historical institutions for trade and growth: firms may attempt to resolve problems due to market failures - such as informnation asynmmetry and contract enforcement issues - by joining efforts with other firms. This strategy, though potentially socially sub-optimal, improves the perfornance of each single firn. Further work along these lines is necessary at this stage to better understand the evolution of these institutions.

> 24For a more detail dscussion on the link between coordination problems and output, see Recanatini and Rytenman (1999). 25 The positive coefficient of AU may also suggests that finns under an All-Union Ministry are more likely to join an association because of the larger information loss brought by the transition.

22