rafmacalaba/datause-encoder-data huggingface dataset:
Figure 11: Conditional independence assumption test - Comparing expected values at municipality level A estimated through a single model at state level R and multiple models estimated at A level <!-- Start of picture text --> 6.0<br>5.8<br>R 2 = 0.90<br>5.6<br>5.4<br>5.2<br>5.0<br>4.8<br>4.6<br>4.4<br>4.2<br>4.0<br>4.0 4.2 4.4 4.6 4.8 5.0 5.2 5.4 5.6 5.8 6.0<br>E[y/X, H(A)]<br>E[y/X, H(R)]<br><!-- End of picture text --> Source: Census and Authors’ calculation

# **2. Inter-cluster correlation of errors**

We next ask whether a good model specification in (1) addresses concerns about intercluster correlation of regression residuals. In principle, there can be many levels at which a location effect occurs. To see how such inter-cluster correlation matters, suppose we expand on (1) and consider the following: This specification allows for a separate ‘area level effect- η’ (e.g at the municipality level), and a ‘cluster level effect - _e_ ’ (enumeration area), alongside an idiosyncratic household level effect,

- ε.  The inter-cluster correlation coefficient and inter-area correlation coefficient can then be

important for the simulation phase of the ELL method. As emphasized by Tarozzi and Deaton (2007), the variance of the welfare predictions in the final phase can be understated when inter16
different sources of information on prices and expenditures, particularly for Africa. It will also highlight the value of ICP data for this purpose. However, we show that this is no mere statistical exercise -- previous evaluations of poverty in Cote d'Ivoire are affected significantly by the use of our new regional price index.

# 2. The **CPI and Previous Regional Price Indices in C6te d'Ivoire**

CMte d'Ivoire has traditionally compiled two different CPI series: one for African households and one for European households. Since 1985, a further disaggregation has become available for the former, based on the occupation of the head of househoid - the series is now available for "workers and traditional craftsmen" and for "professional and managerial occupations. " However, while even this disaggregation is not available in most - - African countries, this CPI like many others in Africa is inadequate because its coverage excludes rural households. Even for urban households, the data cover only Abidjan and four main cities. In particular, therefore, this information base cannot be used to construct a regional price index.

rhe construction of a regional cost of living index requires information on prices and on expenditure patterns across the country. With this information, a common operational procedure is to construct a Paasche cost of living index: where X is the region under consideration, A is the reference region, p is the price vector and q is the quantity vector. As is well known, dividing nominal expenditures in a region by this index will give the consumption basket of that region evaluated at the prices of the reference region.

2
Our results show that skilled labor is the most important determinant of backward and forward GVC participation in SSA. However, unskilled labor, which is the most available labor form in SSA economies, has adverse effects on both backward and forward GVC integration. This suggests that SSA countries need to improve their skilled labor content in exports to better engage in manufacturing GVCs. Public policies that upgrade unskilled to skilled labor should be encouraged in SSA countries. Investing in professional and skills developments that are required to support direct and indirect GVC related activities is needed for SSA countries to maximize the gains from GVC participation. In addition, our findings show that net migration as a share of population has a strong positive effect on backward and forward GVC participation. To this point, migration is an important determinant of integration in manufacturing GVCs for SSA countries. This is because skilled foreign workers contribute in increasing the participation of SSA countries in manufacturing GVCs especially in countries where there are skilled-labor shortages. Therefore, regional agreements that encourage and support intra-regional skilled mobility can be a great vehicle to upgrade countries’ integration in backward and forward global and regional value chains.

Moreover, our results suggest that countries with greater human capital endowment (high HCI) tend to engage more in manufacturing GVCs than those with low initial endowments. Since SSA countries lag behind the rest of world in initial human capital endowment, our results show that the impact of HCI on GVC participation is minimal in SSA compared to the rest of the world. This signals the need for policies to be directed towards improving the human capital index of SSA countries. Investing in children today is a great opportunity to boost long-term skilled endowment which has proven to register the highest impact on both backward and forward GVC participation in SSA. Finally, it is important to note that our analyses focus on manufacturing GVCs integration, but the methodology employed can also be extended to the primary and service sectors. Another limitation of this study is the small size of the SSA sample due to limited available skills related data. But future research on the role of skills in GVCs integration could explore other ways of collecting more longitudinal skilled/unskilled labor data for developing countries in general and explore how SSA would perform in comparison with other developing regions, especially those that are performing better.

31
For the most part, empirical studies investigating international conflict and cooperation over water have utilized static measures of water availability, which often proxy for scarcity. Interestingly, the work by Brochmann (2012), which focuses on the impact of treaties on basin cooperation, does not include any variables proxying for water scarcity or variability. Given that this study focuses on whether basin country dyads governed by treaties evince more cooperation than those basin country dyads that are not governed by treaties, specifically under conditions of variability, we use the coefficient of variation (CoV) for precipitation to measure climate-driven water variability in each basin dyad. Precipitation data from the Climate Research Unit TimeSeries (TS) Version 3.23 (Harris et al. 2014)<sup>7</sup> are the input values for the precipitation CoV. The CoV is calculated to measure inter-annual precipitation over all monthly observations for the relevant time-period (1901-2008).<sup>8</sup> Our rationale for calculating the CoV as a long-term measure is based on our empirical assumption that water variability is embedded in the basin’s history and that it is long-term water variability that affects conflict and cooperation over time in a given basin. Our CoV measure likewise follows precedent set by previous studies (Dinar et al., 2010; Dinar et al., 2011; De Stefano et al., 2012).

The literature touting a relationship between climate change and conflict would suggest that high water variability is not conducive for cooperation. However, some research has found that water scarcity can facilitate treaty formation (Tir and Ackermann, 2009). In fact, some studies have identified a more nuanced relationship between water scarcity (and variability) and treaty formation, whereby formal cooperation is enhanced only up to a certain level of scarcity (and variability), after which formal cooperation is negatively affected as scarcity and variability continue to increase (Dinar, 2009; Dinar et al., 2010; Dinar et al., 2011). In light of the findings in the extant literature, our model tests for both linear and nonlinear effects ( **_Precipitation CV and Precipitation CV Squared_** ) between variability and cooperative/conflictive behavior.

> 7 Accessed Version TS3.23 on 9 February 2016.

> 8 Using run‐off data provides another way to measure variability but run‐off data is not consistently available on a yearly basis like the precipitation data, which is also available across a much longer time frame. See World Bank Water and Climate Change Project (World Bank, 2009) and CLIRUN‐II (Strzepek et al., in preparation).

8
**Table 20: Wage and productivity premiums combining age, occupation, education, and union status**

|||All countries|||Anglophone|||Francophon|e|
|---|---|---|---|---|---|---|---|---|---|
||Production<br>function|Wage<br>equation|Equal premia<br>P-value|Production<br>function|Wage<br>equation|Equal premia<br>P-value|Production<br>function|Wage<br>equation|Equal premia<br>P-value|
||N=1438|N=7726||N=583|N=3210||N=855|N=4516||
|Unionized firm|0.011|0.011||0.057|**0.109**||0.197|-0.047||
||(0.105)|(0.038)||(0.154)|(0.062)||0.137|0.046||
|Union member|**0.892**|-0.011|0.504|**1.423**|-0.088|0.091|**0.595**|0.054|0.006|
||(0.144)|(0.038)||(0.304)|(0.059)||0.167|0.045||
|Age (32+)|**0.641**|0.02|0.007|**0.802**|0.051|0.278|**0.552**|-0.013|0.017|
||(0.137)|(0.026)||(0.229)|(0.044)||0.18|0.031||
|Manager|**0.944**|**0.708**|0.031|**0.545**|**1.047**|0.000|**1.409**|**0.514**|0.893|
||(0.330)|(0.078)||(0.301)|(0.146)||0.775|0.079||
|Professional|**2.072**|**0.468**|0.439|**1.541**|**0.626**|0.920|**1.555**|**0.383**|0.881|
||(0.777)|(0.059)||(0.824)|(0.129)||1.137|0.064||
|Skilled production|**0.902**|**0.196**|0.117|**0.855**|**0.266**|0.051|**1.768**|**0.158**|0.237|
||(0.185)|(0.036)||(0.207)|(0.054)||0.513|0.044||
|Vocational degree|**1.332**|**0.167**|0.620|**3.37**|**0.171**|0.045|0.311|**0.216**|0.066|
||(0.330)|(0.058)||(1.106)|(0.085)||0.487|0.067||
|Junior/senior sec.|**0.841**|**0.126**|0.133|**1.613**|**0.221**|0.426|**0.872**|0.062|0.479|
||(0.190)|(0.040)||(0.498)|(0.066)||0.269|0.05||
|University|**0.631**|**0.519**|0.000|-0.382|**0.468**|0.000|**0.994**|**0.505**|0.155|
||(0.224)|(0.056)||(0.249)|(0.094)||0.359|0.068||
|H0: Vocational coeff=1|||0.314|||0.032|||0.157|
|H0: Secondary coeff =1|||0.402|||0.219|||0.635|
|H0: University coeff =1|||0.099|||0.000|||0.986|
|H0: Manager coeff=1|||0.866|||0.131|||0.598|
|H0: Professional coeff =1|||0.168|||0.512|||0.625|
|H0: Skilled production coeff =1|||0.594|||0.482|||0.135|
|<br>H0: Unionized worker coeff=1|||0.452|||0.164|||0.015|
|Joint test: occupation coefficients =1|||0.402|||0.387|||0.480|
|<br>Joint test: occupation premia are equal|||0.065|||0.000|||0.648|
|Joint test: education coefficients =1|||0.118|||0.000|||0.524|
|Joint test: education premia are equal|||0.000|||0.000|||0.159|

Notes: Shares of uinon members, older workers, managers, professionals, skilled production workers, holders of vocational, secondary, and upper-level degrees are calculated as the mean values at the country, industry, size class, and firm age class levels. The estimation of the production function and the wage equation is done jointly, clustering the errors at the firm level. Standard errors in parentheses below coefficients. **Bold** coefficients indicate significance at the 10% level or better. The production function coefficients represent one plus the productivity premium; the wage regression coefficients are semi-elasticities. The equality of premia test is a Wald test of the null hypothesis that the wage regression coefficient is equal to the production function coefficient minus one for the corresponding category.

47
Yitzhaki, S. (1979) Relative Deprivation and the Gini Coefficient, Quarterly Journal of Economics, XCIII: 321-324.

Wang, P. and Vander Weele, T.J. (2011) Empirical Research on Factors Related to the Subjective Well-being of Chinese Urban Residents, Social Indicators Research, 101, 447-459 Winkelmann, L. and Winkelmann, R. (1998) Why are the unemployed so unhappy? Evidence from panel data, Economica 65, 1-15 32
section, we investigate this issue further using models that aim at identifying the determinants of TFP differences across firms. We start by estimating convergence equations for TFP to assess whether the observed dynamics in productivity lead to corrections in the observed differences in TFP. In particular, we estimate a model given by where a negative estimate of  indicates convergence. The presence of convergence dynamics would point to the fact that, on average, firms with low productivity levels tend to increase their productivity more than their high-productivity counterparts. We include a dummy variable for state-owned enterprises in the specification, whose parameter estimate informs us about whether state-owned firms converge in the long-run to higher, lower or similar productivity levels as compared to privately- and mixed-owned firms.<sup>13</sup> Since we are interested in unveiling convergence dynamics between firms, we do not include firm-specific fixed effects in the specification at this point.<sup>14</sup> The results of the estimation of (6) are presented in Table 7 for the different TFP data obtained using the methods described above.

Table 7: Convergence equations: TFP

||OLS|FE|OP|LP|
|---|---|---|---|---|
|Lagged TFP|-0.099**|-0.025***|-0.008**|-0.027**|
||(0.042)|(0.009)|(0.003)|(0.011)|
|State-owned|-0.159**|-0.113*|-0.159**|-0.160**|
||(0.068)|(0.063)|(0.072)|(0.070)|
|Intercept|0.377*|0.297|0.552**|0.483**|
||(0.224)|(0.212)|(0.260)|(0.246)|
|R <sup>2</sup>|0.015|0.008|0.023|0.020|
|Obs.|727|727|727|727|

_Notes:_ Each column refers to a different TFP estimate, abbreviations as in preceding section. Robust standard errors in parenthesis. *(*)[***] stands for significance at the 10%(5%)[1%] level.

> 13

> The long-run equilibrium for private firms according to equation (6) is given by  0/  , while state-owned enterprises converge to (  0   1)/  . 14All specifications include year fixed effects.

21
<!-- Start of picture text --> °<br>fo)<br>N<br>2<br>2)<br>><br>o<br>Qe<br>oO<br>s=<br>a<br>®<br>a<br>ro)an<br>fo)<br>ive)<br>co)<br>1970 1980 1990 2000 2010 2020<br>Year<br>——®——_ Average leave days for mothers ——®——._ Average leave days for fathers<br><!-- End of picture text -->
20

**Table 1. Regional distribution of official international remittances, 1998 and 2003** (million US dollars)

|_Region_|_1998_|_2003_|_Annual percent_<br>_change, 1998 to_<br>_2003_|
|---|---|---|---|
|East Asia and Pacific|1,668.2<br>(8)|5,302.1<br>(7)|+23.1|
|Europe and Central Asia|8,630.9<br>(17)|6,507.6<br>(20)|(-5.5)|
|Latin America and Caribbean|13,929.3<br>(21)|31,615.3<br>(22)|+17.8|
|Middle East and North Africa|9,424.4<br>(7)|11,075.9<br>(5)|+3.3|
|South Asia|13,293.7<br>(5)|17,851.9<br>(5)|+6.1|
|Sub-Saharan Africa|2,918.8<br>(19)|2,477.9<br>(17)|(-3.2)|
|**Total**|49,865.2<br>(77)|74,830.9<br>(76)|+8.5|

Notes: Numbers in parentheses are countries reporting receipt of official international remittances. Official international remittances defined as “worker remittances” as listed in IMF Balance <u>of Payments Statistics Yearbook, and do not include “compensation of employees” or</u> “other current transfers.” Source: IMF, Balance of Payments Statistics Yearbook (2004).
_14_ _WATER WHEN IT COUNTS_ this volume is divided by area, our measure of water availability is interpretable as the depth of water that could be applied to a plot if the volume of water is evenly distributed across a plot. This measure allows us to benchmark the degree to which a plot has enough water to meet recommended requirements.

To summarize the degree to which a plot is being allocated sufficient water to meet the cultivated crop’s requirements, we compute the water gap as water availability minus growth stage specific water requirements. Growth stages are defined by the planting dates reported by farmers in the follow-up surveys, and the average length of the growth stages according to the FAO Cropwat 8.0.<sup>10</sup> The gap is defined in mm/day for each crop-specific growth stage. A negative water gap means that there is not enough water available to the plot to meet the recommended watering requirements given its growth stage. A positive gap means that sufficient water is available for the plot to meet its requirements. A plot whose water gap is larger in absolute value is applying a level of water that is further away from the recommendation.

Using the water gap to measure proximity to the water requirements reveals interesting patterns of misallocation. Figure 2 shows the average water available to plots observed with crops in growth stages 1, 2, 3, or 4 (gray bars) along with the recommended requirements for the three most common crops (lines). What emerges is a striking pattern between water availability and requirements across crop growth stage. While water _requirements_ vary greatly by growth stage, water availability does not. When crops are at growth stage 3, the available volume of water to the average plot is close to (just above) the requirement for crops. But in growth stages 1 and 2, we do not observe water quanti-

> 10For the remainder of the paper, we will refer to four main growth stages, labeled 1-4, which correspond to these stages as follows: 1-Initial stage, 2-Vegetative Stage, 3-Reproduction Stage, and 4-Maturation Stage. The implications of water stress on total production vary depending on the timing of the stress in the crop cycle. Water shortages during the initial and reproduction stages typically can lead to irreversible damages (Varzi, 2016).
Rahman, J. and T. Zhao (2013), “Export Performance in Europe: The Role of Vertical Supply Links” IMF Working Paper, No. 62.

Saslavsky, D. and B. Shepherd (2012), “Facilitating International Production Networks: The Role of Trade Logistics”, World Bank Policy Research Working Paper, No. 6224.

- Shiritori, M., B. Tumurchudur and O. Cadot (2010), “Revealed Factor Intensity Indices at the Product Level”, UNCTAD Policy Issues in International Trade and Commodities Study Series, No. 44.

- Sturgeon, T. and O. Memedovic (2010), “Mapping Global Value Chains: Intermediate Goods Trade and Structural Change in the World Economy”, UNIDO Development Policy and Strategic Research Branch Working Paper, No. 5.

Taglioni, D. and D. Winkler (2014), “Making Global Value Chains Work for Development”, The World Bank.

Wignaraja, G., J. Kruger and A.M. Tuazon (2013), “Production Networks, Profits, and Innovative Activity: Evidence from Malaysia and Thailand”, Asian Development Bank Institute Working Paper Series, No. 406.

World Bank (2014). “South Africa Economic Update: Focus on Export Competitiveness.” The World Bank.

World Bank (2016), “Factory Southern Africa? SACU in Global Value Chains.” The World Bank.

# VIII. Data Sources

- Anderson, K. and E. Valenzuela (2013), "Updated National and Global Estimates of Distortions to Agricultural Incentives, 1955 to 2011", World Bank. Available at www.worldbank.org/agdistortions.

Barro, R. and J. Lee (2013), "A New Data Set of Educational Attainment in the World, 1950‐2010." Journal of Development Economics, vol 104, pp.184‐198. Version 2.0. Available at <u>www.barrolee.com</u> CEPII, GeoDist database. Available at <u>www.cepii.fr/CEPII/en/bdd_modele/presentation.asp?id=6</u> Kauffman, Kraay and Mastruzzi (2014), Worldwide Governance Indicators. Available at <u>www.govindicators.org</u>

- Kee, H.L, A. Nicita and M. Olarreaga (2009). "Estimating Trade Restrictiveness Indices", Economic Journal (119). Available at go.worldbank.org/FG1KHXSP30

UN Comtrade (2014), International Trade Statistics.

World Bank (2014), Wealth of Nations. Downloaded from data.worldbank.org World Bank (2014), Doing Business. Available at <u>www.doingbusiness.org</u> 23
<!-- Start of picture text --> T1A T1A<br>T1B T1B<br>T1C T1C<br>T2A T2A<br>T2B T2B<br>T2C T2C<br>−2.5 0.0 2.5 5.0 7.5 −2.5 0.0 2.5 5.0 7.5<br>(a) Balanced panel (b) Unbalanced panel<br><!-- End of picture text --> Notes: the figures present the posterior distribution of treatment effects after wave 2. The vertical bar marks the median of each posterior distribution. The shaded areas indicate the 95% credible intervals. A total of 8,000 posterior draws sampled from 4 independent Markov chains were used.

Figure 4: Posterior distributions of treatment effects for ORF scores.

to the short exposure and the one-off measurement of ORF immediately after treatment. That said, the estimates suggest that an IVR intervention for parental engagement in their children’s reading will have a positive impact on children’s reading skills. This is an encouraging finding given the relatively “light touch” of this intervention. Based on the estimates from the unbalanced panel, it is more than 95% likely that implementing the arm with the highest engagement, T1B, which asks parents to carry out a few simple reading exercises sequenced the same for all children, will lead to positive reading fluency gains. While the effects of the 4.5-week intervention tested here were moderate, it stands to reason that exposure for the full term or even the full school year will generate larger effects. The program may also lead to continued joint reading between parents and children after the calls end.

A remaining question is how the treatment effects on fluency compare between the different arms and whether call exposure and efficacy vary significantly strongly so that one of the arms with lower call engagement could be more effective for reading outcomes. Unfortunately, the answer is hampered by the quality of the data and the relatively small effect sizes. From Figure 4, there is significant overlap in the credible intervals of all arms, even for the treatment arms with a large share of observations. To get a sense of the uncertainty, Table A.4 in Appendix C shows the “probability highest” and the expected regret for each arm based on the posterior distributions _of the ORF model_ . The probability that T1B leads to the highest possible reading gains among the six arms lies between 12% and 20% according to these estimates. T1B generates a posterior regret of 0.94 cwpm in the balanced panel and 1.14 in in the unbalanced panel. In the balanced panel, T1B is the arm with the lowest posterior regret. In the unbalanced panel, arm T2B has the lowest posterior regret, with 0.92 cwpm. While the probability optimal is higher than for T1B for three arms (T2A, T2B, and T2C), it is below 26.3% for all of them, and the difference in expected regret is less than 0.24 cwpm.

The low probability optimal for the arms with lowest regret reflects the noise in these estimates. Note also 32