rafmacalaba/datause-encoder-data huggingface dataset:
22

# **BIBLIOGRAPHY**

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24 unexplained by the regression. The leverage chart shows that most of the explanatory power of the regression derives **from** post 1977 data. As was noted earlier this coincides with the period of most rapid growth In replantling.

As a part of the diagnostic checking of the equation tests of parameter stability based on the CUSUJM and CUSUNSQ of the recursive residuals were also carried out and these also Indicated that the basic equation was satisfactory. Figures 9-12 provide plots of the coefficients of 10 significant variables based on recursive regression . These plots show considerable stability in the signs of the coefficienrts post 1976.

CompR ison with some variants: Before turning to an analysis of the policy implications of the results It Is useful to compare the basic equation with some of the variants. In one two-period moving averages of PRICOCO and OLDPROD were used In place of the one-period lagged values; In another the surrogate variable AGEINDEX was substituted In place of OLDPROD. In yet another only one of the two variables REALINT and REALSUJB was tried.

First, consider the role of subsidies on Interest payments of the replanters . Observe from Figure 13 that RSUBRATE and REALINT were stziff'iy negatively related over the period of subsidy. Note that the period over which the subsidies weere given was quite short compared with the sample The results were less satisfactory when the dependent variable RPBAIRR was used, which again suggests that deflation by OLDAREA rather than TOTAREA is more appropriate.

10 See Dufour (1982) for a survey of recursive regression techniques for the regression model. The calculations reported here were executed using DATAFIT program by Pesaran and Pesaran (1987).

11 The figures for interest rate subsidies were provided by CEPLAC.
McFadden(eds.), _Handbook of Econometrics,_ Vol. 4, North Holland, Amsterdam, 1993) Calvo, G. (1995) "Varieties of Currency Crises" mimeo, University of Maryland.

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Dornbusch, R., I .Goldfajn, and R. Vald6s (1995), "Currency Crises and Collapses," _Brookings Papers on Economic Activity, 2:219-295._ Edin, P and A. Vredin, "Devaluation Risk in Target Zones: Evidence from the Nordic Countries," The _Economic Journal,_ Vol 103 (January 1993) pp 161-75.

Edwards, S., _Real Exchange Rates, Devaluation and Adjustment: Exchange Rate Policy in Developing Countries_ (Cambridge, MA: MIT Press, 1989).

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Flood, R and P. Garber (1984) "Collapsing Exchange Regimes: Some Linear Examples," _Journal of International Economics,_ 17:1-13.

Frankel, J. and A. Rose, (1996) Exchange Rate Crises in Emerging Markets, _Journal of International Economics,_ forthcoming.

Froot, K. and M. Obstfeld (1989) Exchange Ral:e Dynamics under Stochastic Regime Shifts: A Unified Approach, NBER Working Paper no. 2835.

Garber, P. and L. Svensson (199?) "The Operation and Collapse of Fixed Exchange Rate Regimes," _Handbook ofInternational Economics,_ Vol III, North Holland.

International Monetary Fund, _Exchamge Arrangements and Exchange Restrictions, Annual Report,_ various issues. Washington.

Johansen, S.(1995) _Likelihood-Based Inference in Cointegrated Vector Auto-Regressive Models,_ New York, Oxford University Press Kamninsky, G. and C. Reinhart, "The Twin Crises: The Causes of Banldng and Balance of Payment Problems" Working Paper.

Kaminsky, G, Lizondo, S, Reinhart C. "Leading Indicators or Currency Crises" Mimeo, 1997.

17
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Columbia. Available at: https://github.com/acoppock/Green-Lab<u>SOP/blob/master/Green_Lab_SOP.pdf.</u>

- Martínez, J. F., Myers, R., & Linares, M. (2004). ¿Todos los pollos son amarillos?: En búsqueda de la

- calidad educativa en centros preescolares. [Are all chickens yellow?  An investigation into the

- quality of preschool centers.]. Mexico: Dirección General de Investigación Educativa.  Secretaría de Educación Pública.

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impact evaluation of early childhood development in rural Mozambique. Available at <u>http://www.3ieimpact.org/media/filer/2013/04/11/3ie_mozambique_ie001.pdf (Accessed August</u> 9, 2013)

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Mejia, J. (2010). Early Grade Reading Assessment: National Baseline Report.

Ministry (2010). Annual Report for Early Childhood Development. Ministry of Gender, Children, Disability and Social Welfare (MoGCDSW).

Munthali, A., Mvula, P., & Silo, L. (2008). Community-based childcare centres in Malawi: a national inventory. . University of Malawi, Zomba: Centre for Social Research.

Munthali, A. C., Mvula, P. M., & Silo, L. (2014). Early childhood development: the role of community based childcare centres in Malawi. . _SpringerPlus,_ 3, 305.

Neuman, M. J., McConnell, C., & Kholowa, F. (2014). From Early Childhood Development Policy to Sustainability: The Fragility of Community-Based Childcare Services in Malawi. _International Journal of Early Childhood,_ 46(1), 81-99.

Rao, N., et al. (2012). Is something better than nothing? An evaluation of early childhood programs in Cambodia. _Child Dev,_ 83(3), 864-876.

38
Le Dem (1996), and the World Bank (1995)), the treatment of this market in _applied_ policy models has focused on only a narrow set of its well-documented features-such as an economy-wide rigid minimum wage. Except for a few exceptions (see for instance Maechler and Roland-Host (1995, 1997)), insufficient attention has been paid to the macroeconomic implications of alternative sources of labor market segmentation, differences in wage formation across various labor categories, inter-sectoral wage rigidity (as opposed to aggregate wage rigidity), and feedback effects between relative prices and wage decisions by price-setting firms. All of these features may have important implications for understanding the impact of policy and exogenous shocks on poverty. Labor market segmentation, in particular, tends to restrict labor mobility and can be associated with persistent wage differentials; these, in turn, may prevent the reallocation of resources necessary to cope with external and policy-induced shocks. Because the poor in many countries generate a significant fraction of their income from labor services, modeling these features of the labor market is crucial for understanding the impact of pretty much any type of shocks on poverty in the short, medium, and long run.

Our analysis captures some of the important institutional characteristics of the urban labor market observed in low- and middle-income developing countries.<sup>3</sup> In these countries it is important to distinguish, in analyzing the functioning of the urban labor market, between formal employment (such as employment in large private enterprises and the public sector) and informal employment. In many cases (particularly in sub-Saharan Africa) public sector employment represents a very large share of formal sector employment. Formal employment in many countries (in both Latin America and sub-Saharan Africa) has increased only slowly in recent decades, whereas migration to urban areas has been extensive. Informal urban employment has thus increased dramatically in size.<sup>4</sup> In Kenya, for instance, the share of the informal sector in employment outside agriculture is currently about 60 percent, whereas in Cameroon in 1993 it was estimated that 57 percent of the employed labor force worked in the informal sector (Fortin, Marceau

> 3See Agenor (1996) for a detailed overview of the literature, and Bigsten and Horton (1998) for a survey of labor markets in sub-Saharan Africa. See also the World Bank (1995).

> 4The informal sector can be defined in various ways; one common definition is that it includes self-employed workers (except for professionals) unpaid family workers, workers employed in small firms (less than, say, 5 or 6 workers), and those working in the trade and services sector without a legally-binding contract.

7
<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>ee ee<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>40<br>20 ee<br>0<br>2015 2020 2025 2030<br>———RICUT ——R2CUT — — F2CUT<br><!-- End of picture text -->
# **Appendix Table I. 6: Unorganized Sector Log (Employment) and Log (Output) - Differential Effect of Labor-Intensity and Skill-Intensity**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Labor-Intensive<br>Industries|Skill-Intensive<br>Industries|Labor-Intensive<br>Industries|Skill-Intensive<br>Industries|
||Employmen|t Spillovers|Output S|pillovers|
|il ill f||<br>_Other-sect_|<br>_or Spillovers_||
|Horzonta Spovers rom|||||
|Organized Sector|0.1769***|0.1434***|0.1008***|0.0996***|
||(0.0275)|(0.0272)|(0.0236)|(0.0241)|
|Above Median * Horizontal|||||
|spillovers from Organized Sector|0.2416***<br>|0.2706***|0.1897***|0.1906***<br>|
||(0.0325)|(0.0344)|(0.0247)|(0.0263)|
|Backward Spillovers from|||||
|Organized Sector|1.4551***|1.4631***|2.8403***|2.5363***|
||(0.1771)|(0.1608)|(0.4755)|(0.4812)|
|Above Median * Backward|||||
|spillovers from Organized Sector|1.4108***|1.6084***|1.9300***|2.6851***|
||(0.1839)|(0.2327)|(0.4856)|(0.5452)|
|Forward Spillovers from Organized|||||
|Sector|-0.4225++|0.0313|0.8438|2.0268***|
||(0.3076)|(0.2748)|(0.7931)|(0.7227)|
|Above Median * Forward|||||
|spillovers from Organized Sector|0.4629*|0.2746|2.6550***|2.1767***|
||(0.2721)|(0.2757)<br>|(0.7437)<br>|(0.8241)|
|||_Own-secto_|_r Spillovers_||
|Forward Spillovers from|||||
|Unorganized Sector|1.0197***|0.4043++|1.1842***|0.1095|
||(0.3291)|(0.3034)|(0.3701)|(0.3059)|
|Above Median * Forward|||||
|spillovers from Unorganized Sector|0.0980|0.3180|-0.5905*|0.3331|
||(0.3263)|(0.3162)|(0.3468)|(0.4116)|
|Backward Spillovers from|||||
|Unorganized Sector|0.9400***|1.0007***|0.1152|0.9684***|
||(0.1783)|(0.1638)|(0.2496)|(0.3064)|
|Above Median * Backward|||||
|spillovers from Unorganized Sector|1.0372***|0.6859***|0.8501***|0.3668++|
||(0.1567)|(0.1840)|(0.2574)|(0.2662)|
|||_Other-sector Prod_|_uctivity Spillovers_||
|Average TFP of Horizontal-linked|||||
|Organized Plants|0.0876++|0.0765|0.1166|0.0833|
||(0.0610)|(0.0599)|(0.1549)|(0.1548)|
|Average TFP of Backward-linked|||||
|<br>Organized Plants|0.6104***|0.5543***|0.7161**|0.5153+|
||(0.1441)|(0.1384)|(0.3485)|(0.3516)|
|Average TFP of Forward-linked|||||
|Organized Plants|0.5485***|0.5218***|1.2917***|1.1668***|
||(0.1477)|(0.1491)|(0.3877)|(0.4058)|
|Constant|-34.0763***|-32.4809***|-113.3159***|-128.3803***|
||(5.2618)|(5.2698)|(17.1542)|(17.2434)|
|Observations|5888|5888|5888|5888|
|R-squared|0.1569|0.1588|0.1405|0.1358|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. All specifications also include state-year fixed effects. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***._ 59
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.7. Benchmarking air transport in ECOWAS and other regional economic communities**

||ECOWAS|CEMAC|EAC|SADC|
|---|---|---|---|---|
|Annual seats, domestic ( ̳000s)|2,034|235|1,345|3,076|
|Annual seats, international within Sub-Saharan<br>Africa ( ̳000s)|362|187|1,196|964|
|Domestic city pairs served (number)|8|4|13|17|
|International city pairs served (number)|20|15|29|26|
|Seat-km in old aircraft (% of total)|43|30|33|29|
|Seat-km in recent aircraft (% of total)|57|70|67|71|
|Domestic market Herfindahl Index|0.84|0.83|0.64|0.73|
|International market Herfindahl Index|0.19|0.24|0.25|0.34|
|Market Herfindahl Index (domestic and<br>international)|0.21|0.30|0.27|0.42|

_Source:_ AICD database.

_Note:_ ECOWAS = Economic Community of West African States; CEMAC = Economic and Monetary Community of Central Africa; EAC = East African Community; SADC = Southern African Development Community. The SADC aggregates include South Africa and Tanzania and the EAC includes Tanzania, which are not a part of COMESA.

Air transport connectivity in Central Africa has fallen steadily since 2001. This overall pattern is consistent with air transport connectivity in ECCAS and CEMAC, which have both fallen, albeit at different rates (figure 2.12). The air transport connectivity for ECCAS in 2007 was only 40 percent of the connectivity of 2001 and the number of seats in 2007 only a little over 600,000. In CEMAC the air transport connectivity in 2007 was only 30 percent of the connectivity in 2001. The total number of seats in 2007 was a little over 300,000, compared with almost a million in 2001.

**Figure 2.12. Seats for intra-REC travel within ECCAS and CEMAC**

**a. ECCAS b. CEMAC**

<!-- Start of picture text --> 2,000,000<br>2,000,000<br>1,500,000<br>1,500,000<br>1,000,000<br>1,000,000<br>500,000 500,000<br>0 0<br>2001 2004 2007 2001 2004 2007<br><!-- End of picture text --> _Source:_ Derived from Bofinger 2009. _Note:_ REC = regional economic community; CEMAC = Economic and Monetary Community of Central Africa.

26
# Academic Peer Effects with Different Group Assignment Policies: Residential Tracking versus Random Assignment<sup>_∗_</sup>

## Robert Garlick<sup>_†_</sup>

February 25, 2014

**Keywords:** education; inequality; peer effects; South Africa; tracking **JEL classification:** I25; I25; O15

> _∗_ This paper is a revised version of the first chapter of my dissertation. I am grateful to my advisors David Lam, Jeff Smith, Manuela Angelucci, John DiNardo, and Brian Jacob for their extensive guidance and support. I thank Raj Arunachalam, Emily Beam, John Bound, Tanya Byker, Scott Carrell, Julian Cristia, Susan Godlonton, Andrew Goodman-Bacon, Italo Gutierrez, Brad Hershbein, Claudia Martinez, David Slusky, Rebecca Thornton, Adam Wagstaff, and Dean Yang for helpful comments on earlier drafts of the paper, as well as conference and seminar participants at ASSA 2014, Chicago Harris School, Columbia, Columbia Teachers College, CSAE 2012, Cornell, Duke, EconCon 2012, ESSA 2011, Harvard Business School, LSE, Michigan, MIEDC 2012, Michigan State, NEUDC 2012, Northeastern, Notre Dame, PacDev 2011, SALDRU, Stanford SIEPR, SOLE 2012, UC Davis, the World Bank, and Yale School of Management, I received invaluable assistance with student data and institutional information from Jane Hendry, Josiah Mavundla, and Charmaine January at the University of Cape Town. I acknowledge financial support from the Gerald R. Ford School of Public Policy and Horace H. Rackham School of Graduate Studies at the University of Michigan. The findings, interpretations and conclusions are entirely those of the author. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.

> _†_ Postdoctoral Researcher in the World Bank Development Research Group and Assistant Professor in the Duke University Department of Economics; rob.garlick@gmail.com

1
# **Table 12. Contemporaneous and Lagged Effects of US-Partnered IPAs on U.S. MajorityOwned Affiliate Sales in Manufacturing**

||(1)|(2)|(3)|
|---|---|---|---|
|US IPAt|–0.043|0.052|0.029|
||(0.088)|(0.139)|(0.139)|
|High-IP × US IPAt|0.371***|0.124|0.137|
||(0.117)|(0.137)|(0.144)|
|US IPAt–1||–0.120|0.035|
|||(0.141)|(0.166)|
|High-IP × US IPAt–1||0.295**<br>(0.146)|0.102<br>(0.183)|
|US IPAt–2|||–0.202<br>(0.134)|
|High-IP × US IPAt–2|||0.257<br>(0.164)|
|**Total effect time t (****_β_IPA +****_β_High-IP,IPA)**|**0.327*****|**0.176****|**0.166****|
|_F_-test of total effect|15.55|5.29|4.52|
|**Total effect t–1**||**0.175****|**0.137**|
|_F_-test of total effect||4.11|1.08|
|**Total effect t–2**|||**0.055**|
|_F_-test of total effect|||0.16|
|Observations|5,523|5,283|5,042|
|_R_<sup>2</sup>|0.906|0.912|0.920|
|Country-sector FEs|Y|Y|Y|
|Year FEs|Y|Y|Y|

Robust standard errors clustered by country-industry in parentheses. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1 34
Table 7 : Using predicted bilateral stocks - All countries <u>(Dep=Log</u> of Fertility rate)

||OLS|IV|OLS|IV|
|---|---|---|---|---|
|Constant|0.890|0.977|1.138|0.849|
||(4.16)***|(3.70)***|(4.72)***|(3.88)***|
|Log of fertility at dest|0.352|0.406|0.398|0.445|
||(2.19)**|(1.91)*|(2.39)**|(1.71)*|
|Log of p0|-0.050|-0.024|-0.032|-0.019|
||(2.82)***|(0.61)|(1.76)*|(0.46)|
|Urbanization|-0.006|-0.006|-0.004|-0.005|
||(3.75)***|(3.23)***|(2.47)**|(3.15)***|
|GDP per capita|-0.059|-0.053|-0.081||
||(2.38)**|(1.92)*|(2.61)**||
|Adult’s education|||-0.449<br>(2.40)**|-0.723<br>(4.25)***|
|East Asia & Paci...c|0.275|0.228|0.391|0.271|
||(3.71)***|(2.32)**|(4.51)***|(2.45)**|
|Sub-Saharan Africa|0.546<br>(6.77)***|0.546<br>(6.96)***|0.487<br>(5.58)***|0.457<br>(5.24)***|
|Latin America & Carib|0.364<br>(5.04)***|0.400<br>(6.60)***|0.350<br>(3.98)***|0.297<br>(3.96)***|
|Mena||0.230|0.150|0.185|
|||(2.48)**|(1.48)|(1.78)*|
|Muslims|0.005|0.241|0.004|0.213|
||(6.16)***|(4.35)***|(4.55)***|(4.01)***|
|Catholics|0.002<br>(2.70)***||0.002<br>(2.32)**||
|Partial Corr First Stage||0.353||0.323|
|F-stat First Stage||18.14||14.51|
|Hansen J Test (p-value)||0.932||0.667|
|R-squared|0.75|0.740|0.76|0.731|
|Observations|174|174|142|146|

Robust z statistics in parentheses signi...cant at 10%; ** signi...cant at 5%; *** signi...cant at 1% Col (1)-(3): OLS; C (2)-(4): IV Instruments for ln(p0): island, ln(size), ln(distance to main destination) Col (1)-(2) : all countries; Col (3)-(4) : developing countries only 30
# **Probit Estimation of the Probabilit** **<mark>y</mark> of a Worker Chan** **<mark>g</mark> in** **<mark>g</mark> Sector of Em** **<mark>p</mark> lo** **<mark>y</mark> ment**

||Movement from<br>Agricultural to Non-<br>Agricultural Sectors|Movement from<br>Informal to Formal<br>Activities|
|---|---|---|
|Urban|-1.463<br>(309.49)**|-0.324<br>(64.32)**|
|Household Head|0.180<br>(31.54)**|-0.359<br>(66.20)**|
|Gender|1.197<br>(186.35)**|-0.689<br>(123.53)**|
|Years of Schooling|-0.139<br>(147.78)**|-0.041<br>(40.62)**|
|Experience|-0.012<br>(24.96)**|-0.034<br>(64.00)**|
|Experience Squared|0.000<br>(23.66)**|0.001<br>(79.14)**|
|Household Members|0.024<br>(32.35)**|-0.006<br>(6.62)**|
|Self-employed|0.403<br>(89.11)**||
|Other HH member's income|0.000<br>(16.10)**||
|Sectoral Dummies||YES|
|Constant|-0.354|1.830|
||(29.53)**|(140.72)**|
|Observations|5762|3201|
|Pseudo-R2|0.41|0.12|

Absolute value of z statistics in parentheses * significant at 5%; ** significant at 1% 31
**Figure 3.9. Headcount poverty by simulation (%‐age point deviation from base)**

<!-- Start of picture text --> ‐2 ‐1.5 ‐1 ‐0.5 0 0.5 1 1.5 2<br>ginv‐tdir<br>ginv‐dbor<br>ginv‐fbor<br>pwe<br>remit<br><!-- End of picture text --> Source: Authors’ calculations based on simulation results.

**Figure 3.10. Sectoral GDP growth by simulation (%‐age point deviation from base)**

<!-- Start of picture text --> ‐0.3 ‐0.2 ‐0.1 0 0.1 0.2<br>ginv‐tdir<br>ginv‐dbor<br>ginv‐fbor<br>pwe<br>remit<br>Private activity Government activity GDP factor cost<br><!-- End of picture text --> Source: Authors’ calculations based on simulation results.

‐26‐
# 1.2 Bootstrapped standard errors

Dyadic observations are typically not independent since residual u_{ij} is likely to be correlated with u_{ik}.This complicates the computation of standard errors. In particular, robust standard errors must correct for cross-observation correlation in the error terms involving the same enterprises. To obtain consistent standard errors, we use bootstrapping, implemented as follows:

- (i) Draw a random sample s of n firms from the firm-level dataset with replacement;

- (ii) For this artificial sample of n firms, construct the corresponding n(n-1) dyadic dependent and independent variables;

- (iii) Estimate the regression for this sample and store the resulting parameter estimates;

- (iv) After repeating the process (i)-(iii) _J_ times, use the standard deviations of the estimated parameters as estimates of the standard errors.
> <sup>JMP refer to the first term as the “observed-X’s” effect. This term reflects the change in</sup> the gender wage gap resulting from changes in the earnings generating characteristics, for example educational qualifications, over time. The second term is the “observed prices” effect and measures changes in the prices of the observed earnings generating characteristics of men.  A change in the return to an educational qualification would serve as an example of this kind. The third term is the “gap effect” and measures the effect of the changing differences in the relative position of women in the male wage distribution. In other words it captures what would happen if the residual male inequality were to be held constant but the percentile ranking of the female wage residual changed. In general, females would move up this distribution if they were faced with less discrimination over the two periods or if they improved their unobserved (to the researcher) characteristics that are nevertheless rewarded in the labor market. The final term is the “unobserved prices” effect and measures the change in the gender wage gap attributable to the change in the spread of the male wage function holding constant the gap in male-female unmeasured endowments. In other words it gives the contribution to the change in the gender gap between _t_ -1 and _t_ that would result if the percentile ranking of the female wage distribution stayed the same but the male residual distribution changed.

The data used in this paper is drawn from the 1988, 1992 and 1998 PNADs, and we will report the decomposition for changes between 1988 and 1992 and 1992 and 1998, as well as for both sub-periods combined. The purpose of looking at these two sub-periods is to investigate the impact of the period of concentrated trade and financial liberalization 9