rafmacalaba/datause-encoder-data huggingface dataset:
# **_The panel analysis_**

The estimator is provided by the Pooled Mean Group Estimator Methodology provided by Pesaran, Shin and Smith (1999). Thus we base our panel analysis on the unrestricted error correction ARDL _(p,q)_ representation: _i =_ 1,2, ..., _N,_ stand for the cross-section units, and _t =_ 1,2, ..., _T_ , indicate time periods. Here _yit_ is a scalar dependent variable, x _it_ ( _k_ x 1) is the vector of (weakly exogenous) regressors for group _i,_ μ _i_ represent the fixed effects, φ i is a scalar coefficient on the lagged dependent variable, β i is the _k_ x 1 vector of coefficients on explanatory variables, λ ij’s are scalar coefficients on lagged first-differences of dependent variables, and δ ij’s are _k_ x 1 coefficient vectors on first-differences of explanatory variables and their lagged values. We assume that the disturbances ε it ‘s are independently distributed across _i_ and _t,_ with 2 zero means and variances σ _i_ > .0 We also make the assumption that σ i<0 for all _i_ and thus there exists a long-run relationship between _yit_ and xit : ' where θ _i_ = −β _i_ / φ _i_ is the _k_ x 1 vector of the long-run coefficient, and η _it’s_ are stationary with possibly non-zero means (including fixed effects). Then, equation (4) can be written as: where<sup>η</sup> _i_ , _t_ − 1 is the error correction term given by (5), and thus φ _i_ is the error correction coefficient measuring the speed of adjustment towards the long-run equilibrium.

We consider the Pooled Mean Group (PMG) estimator advanced by Pesaran, Shin and Smith (1999), which allows the intercepts, short-run coefficients and error variances to differ freely across groups, but the long-run coefficients are constrained to be the same; that is θ _i_ = θ , _i_ = 1,2, K , _N_ . The common long-run coefficients and the group-specific short-run coefficients are then computed by the pooled maximum likelihood (PML) ~ ~ ~ ~ estimation. Denoting the PML estimators by φ _i_ , β _i_ , λ _ij_ , δ _ij and_ θ . , we obtain the PMG 9
<!-- Start of picture text --> Policy Researchl<br>Education and Employment<br><!-- End of picture text --> WPS **1056**

- This papera product of the Technical Department, Latin America and the Caribbean Region is part of a larger effort in the department to document the role of education in the region's development efforts. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Liliana Longo, room 14-187, extension 39244 (December 1992, 93 pages).

Psacharopoulos and Ng use household survey dzta for 18 Latin American countries to assess earnings differentials by level of education, and to assess how these differentials changed in the 1980s.

Introducing the cost of education allows them to estimate private and social rates of return on investments on education across several dimensions: by gender, by level of education, by sector of employment, by nature of the secondary school curriculum, and over time.

The results show that, in most countries, the earnings premium received by graduates of higher education decreased in the 1980s. Investment in primary education shows the highest rate of return among all levels considered - and is still the number one investment priority in most countries.

The Policy Research Working Paper Series disseminates the findings of work under way in the Bank. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

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# **APPENDIX B STATISTICAL TESTS OF RELIABILITY**

Three tests were used to gauge inter-rater reliability:

1. **Percent Agreement.** First, we measured the number of scores that were equal or adjacent (±1) between the two raters. For example, if one rater scored a certain dimension a 4, while their partner rated it a 3, 4, or 5, that would count as being in agreement. The results and analysis are presented below. This is in accordance with CLASS certification, which requires raters to score within one point of a master code to prove proficiency.

2. **Intraclass Correlation Coefficient (ICC).** Using a two-way mixed-agreement ICC we assessed the agreement between raters across subjects. Ratings range from -1 to 1, with higher ratings indicating higher levels of agreement between raters.

3. **Cohen’s Kappa.** Linear and quadratic weighted kappas were used to measure agreement while taking into account the effects of chance. This statistical method evaluates the likelihood that agreement was due to observations of same behavior versus both raters randomly choosing same number. Ratings range from 0 to 1, with higher ratings indicating greater agreement not due to chance.

Three comparator studies are presented in addition to the three pilot counties to provide a reference for other implementations of the CLASS tool in upper elementary and junior secondary (table B.1).

**Table B.1: Three Comparator Studies**

|**Study**|**Number of**<br>**Teachers**|**Grades**|**Raters**|**Supporting**<br>**Organization**|**Subjects**|**Period**|
|---|---|---|---|---|---|---|
|Measures of Effective<br>Teaching (MET)|1,333|4–8|Graduate<br>Students at<br>University of<br>Virginia (UVA)|Gates Foundation|Math and<br>English|2009|
|Understanding Teaching<br>Quality in Algebra Study<br>(UTQ-A)|82|6–12|Trained Raters at<br>Educational<br>Testing Service|UVA, W.T. Grant<br>and Spencer<br>Foundations|Math and<br>English|2009–11|
|Secondary<br>MyTeachingPartner Study<br>(S-MTP)|78|7–12|Graduate<br>Students at UVA|UVA|No subject<br>specified|2007–09|

## _Percent Agreement_

The simplest measure of agreement provides a quick way to gauge where raters had an easier or more difficult time in agreeing on observed behaviors. It is also in line with CLASS protocols, as the same measure is used to evaluate whether a rater has passed CLASS training.

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Policy ReseaRch WoRking PaPeR 4699

# **Abstract**

New Zealand’s new Recognized Seasonal Employer program allows workers from the Pacific Islands to come to New Zealand for up to seven months to work in the horticulture and viticulture industries. One of the explicit objectives of the program is to encourage economic development in the Pacific. This paper reports the results of a baseline survey taken in Vanuatu, which the authors use to examine who wants to participate in the program, and who is selected among those interested. The findings show that the main participants are males in their late 20s to early 40s, and most are married and have children.

Most workers are subsistence farmers in Vanuatu and have not completed more than 10 years of schooling. Such workers would be unlikely to be accepted under existing migration channels. Nevertheless, the program workers from Vanuatu tend to come from wealthier households, and have better English literacy and health than individuals not applying for the program. Lack of knowledge about the policy and the costs of applying appear to be the main barriers preventing poorer individuals applying.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to examine the economic effects of migration and remittances. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmckenzie@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Regression 2 does not confirm Hypotheses 3A and 4A related to short run crises and economic fundamentals. Only the size of the market instead has both a positive and highly significant relationship with the introduction of PPPs.

Hypothesis 5A, on long run environmental factors, is confirmed with countries that have integrated renewable energy in their system more likely to attract PPPs in T&D. This confirms the importance of including considerations related to access to the grid and development of the network so as to integrate renewable-based energy into the power system. Because of the potential concerns with endogeneity due to the fact that private investment in both segments of the power sector can be driven by similar factors, we also used instrumental variables, including the lagged variable of PPP investment renewable-based generation variables. The results are robust to the introduction of instrumental variables.

Regression 3 confirms the robustness of the previous results controlling for regional dummies.

**Table 3 Key Determinants of Introduction of PPP in Distribution**

||**(1)**|**(2)**|**(3)**|
|---|---|---|---|
|**GOVER**<br>**SECTORALG**|**NANCE VARIAB**<br>**OVERNANCE V**<br>|**LES**<br>**ARIABLES**<br>||
|**Introduction of a regulatory agency**|0.297**|0.266*|0.249*|
|_REG_|(0.148)|(0.154)|(0.156)|
|**ECONOMYWIDE**|**GOVERNANCE **<br>|**VARIABLES**<br> <br>||
|**Control of Corruption**_wgi_cc_|0.100<br>(0.135)|-0.043<br>(0.158)<br>|-0.121<br>(0.164)|
|**Degree of democracy****_Polity2_**|0.064***<br>(0.017)|0.053***<br>(0.017)|0.050***<br>(0.017)|
|**LONG RUN ENVIRONM**|<br>**ENTALSUSTAI**|<br>**NABILITY FACT**<br>|<br>**ORS**<br>|
|**PPP Investment in Renewables**||<br>|0.522***<br>0167|
|**MACROE**|**CONOMIC CON**|<br>**TROL**<br>|(.)<br>|
|**GDP**<br>_ln_GDP_||0.241**<br>(0.105)|0.250***<br>(0.109)|
|**Population**<br>_ln_Pop_||-0.086<br>(0.114)<br>|-0.148<br>(0.118)|
|**Constant**|-2.144***<br>(0.160)|-6.752***<br>(1.244)|-6.093***<br>(1.167)|
|**N**|1185|1143|1143|
|**Log likelihood**|-183.66|-174.58|-169.82|
|**χ2**|34.51***|49.12***|58.64***|
|**Pseudo R2**|8.59|12.33|14.72|

_Note:_ *, **, *** indicate significance of the coefficient respectively at 10%, 5% and 1% confidence level.

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checks of this condition before implementing the imputation. Even fewer studies, if any, attempt to standardize the variables in both the survey and the census.

Another interesting and useful area that needs more research is how to produce and interpret the evolution of poverty maps over time. Multiple challenges exist with generating such dynamic poverty maps. One is that we would need survey and census data at two points in time, and both these data sources should be comparable both at each point in time (i.e., the issue discussed immediately above) and over time (i.e., the issue of the constant parameter assumption as discussed with survey-to-survey imputation methods). While some alternative techniques have been proposed,<sup>21</sup> it seems that this topic still needs more development.

# **VI. Dynamic Poverty Estimates**

# _Motivation_

Different poverty situations are best addressed with different policy responses. In particular, transitory and chronic poverty typically require different policy instruments and no single policy may successfully address both. For example, while transitory poverty can be alleviated with safety net programs, chronic poverty would need to be tackled with structural and longer-term interventions such as investment in human capital and building infrastructure.<sup>22</sup> However, poverty estimates based on cross-sectional data provide only static snapshots of poverty rates, rather than the dynamics of poverty transitions over time. Absent a clear understanding of poverty dynamics, a seemingly unchanged poverty rate of, say, 15 percent in two periods could conceal dynamic processes ranging from zero mobility (i.e., where all households see no change in their welfare) to perfect mobility (i.e., where all poor households in the first

> 21 For example, Nguyen (2011) offers an innovative study that uses panel data from household surveys to estimate the relation between expenditure in the second period and household characteristics in the first period. The estimated parameters are then applied to a census in the second period to predict expenditure and poverty measures in a future third period. This approach may address, partially but not completely, the issues raised above.

> 22 See, e.g., Barret (2005) and Ravallion (2016) for more discussion on different policy approaches to reduce poverty.

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63 izer exposure measure (see equation 42). Sri Lanka’s NFS data also includes estimates of fertilizer prices and of each crop’s total cultivated area, which we combine with fertilizer requirements data to compute country-level fertilizer expenditure for each crop in 2022. Section VI.A.3 discusses how such fertilizer expenditures are used to estimate technological parameters.

NFS data on soybeans’ fertilizer intensity was deemed unreliable by agriculture specialists, so we complement the NFS data with data from the International Fertilizer Development Center and the International Fertilizer Association. Specifically, we use the ‘Asia’ entries in these databases to impute the fertilizer intensity of soybeans that appears in Appendix Table A2. Appendix Figure A2 shows that this alternative fertilizer intensity metric has a close linear relationship with our original metric from the NFS (with the exception of soybeans, which are a clear outlier in the graph), thus also serving as a validation for the latter.

Figure A2: Fertilizer Intensity: Sri Lanka vs Asia <!-- Start of picture text --> 0 200 400 600<br>Fertilizer intensity (kg/ha): Asia<br>3000<br>2000<br>1000<br>Fertilizer intensity (kg/ha): Sri Lanka<br>0<br><!-- End of picture text --> _Notes_ : The figure shows the fertilizer intensity of 22 crops (see Appendix Table A2 for the full list), measured in kg/ha, from two alternative data sources: Sri Lanka’s NFS (on the vertical axis) and the databases of the International Fertilizer Development Center and the International Fertilizer Association (on the horizontal axis). For the latter source, we use the estimates that correspond to Asia. The fertilizer intensity metrics add up the use of urea, TSP, and MOP. The outlier point on the top left corresponds to soybeans. The red line is the regression line when we exclude the outlier point from the sample, with a corresponding correlation coefficient of 0.677.

We use FAO data on the use of manure and chemical fertilizers in Sri Lankan agriculture in the period 2001-2021, namely the total nitrogen content (by weight) of manure applied to the soil and the total use (by weight) of 23 different types of chemical fertilizer. The data also includes the amount of manure that is lost through leaching or volatilization, allowing us to compute the net amount of manure that is applied to the soil.

We obtain data on fertilizer subsidies provided to farmers by Sri Lanka’s government in the 2013-2022 period from the Cost of Cultivation Reports produced by the Department of Agriculture’s Socio Economics & Planning Centre. For each crop, the fertilizer subsidy rate (in %) is
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE According to the UNICEF-WHO Joint Monitoring Program, Côte d’Ivoire is not on track to meet the MDG for sanitation. Based on household surveys, access to improved sanitation has been stagnant, increasing only 3 percentage points from 20 percent in 1990 to 23 percent in 2008 (table 7). Although approximately 18 percent of the population has shared access to improved sanitation facilities, this does not count towards the achievement of the MDG.A large share of the population—30 percent—still practices open defecation, although this is significantly lower than in the low-income benchmark group (table 11). Furthermore, access to flush toilets and improved latrines are four times and two as high as in the low-income group, respectively. On the other hand, use of traditional latrines is about half that found in the low-income peer group. Overall, a relatively large share of Côte d’Ivoire’s population has access to higher-end sanitation solutions, albeit in many cases on a shared basis. Looking ahead, an important area for action will be to move the large share of the population that continues to practice open defecation onto the first rung of the sanitation ladder.

**Table 11 Benchmarking sanitation access**

||Unit|Low-income<br>countries||**Côte d**|**’Ivoire**||Middle-income<br>countries|
|---|---|---|---|---|---|---|---|
|||Mid 2000s|**LSM88**|**DHS94**|**MICS00**|**MICS06**|Mid 2000s|
|Access to flush toilets|% pop|4.9|16.5|13.4|25.4|23.8|40.8|
|Access to improved latrines|% pop|9.9|13.3|21.7|15.2|17.8|1.4|
|Access to traditional latrines|% pop|50.1|24.0|19.5|26.3|26.4|30.4|
|Open defecation|% pop|40.3|46.2|45.3|31.4|32.3|14.3|

Source: Banerjee and others 2009; Morella and others 2009, derived from AICD water and sanitation utilities database downloadable from <u>http://www.infrastructureafrica.org/aicd/tools/data</u> **.** 20
For the case of manufacturing, the ASI covers all registered factories, and uses a sampling frame that is stratified at the state and the four-digit National Industry Classification (NIC) level. We complement these data by the NSSO which covers all unorganized manufacturing enterprises. In the case of the NSSO the sample stratification is more sophisticated and includes both the district level and the two-digit NIC sectors. For the case of services, the NSSO follows a similar stratification, including the district and the two-digit NIC sectors. Note, however, that some service subsectors, such as retail, wholesale and financial services, are excluded in at least one of the two available years.<sup>1</sup> Furthermore, as the NIC definitions have changed over time, we make them consistent using concordances that come with the data.<sup>2</sup> Sampling weights provided by the separate survey datasets are then applied to create population-level estimates of total employment by district and sector. One obvious issue regards the reliability of this procedure and possible measurement error. To address this issue, we do a number of robustness checks. In particular, we complement our estimation of district-level sectoral employment from firm surveys by an alternative measure using the Employment-Unemployment Survey, frequently referred to as the Labor Force Survey, carried out by the NSSO in fiscal years 1999-2000 and 2004-05. This survey collects individual-level information on location, occupational status and industry of occupation detailed enough to allow an estimation of employment by NIC industry and district. The sample stratification is similar to that of the NSSO. We run robustness checks using both major sources of data, the one based on firm-level surveys and the other based on individual-level surveys.

A last concern is that sometimes districts have been redefined, combined, or split. For these types of changes we follow a simple strategy for assuring consistent district definitions over time. In the case of a single district being divided into two or more “new” districts, we recreate the original district by combining the new districts (backward-compatibility). When two or more previous districts are combined, we recreate the new combined districts in the earlier years (forward-compatibility). In the case of transfers of land between districts we combine the districts involved in all periods.

# **3. The Spatial Evolution of India**

> 1 When compairing the results with the U.S., we will make the definition of services in both countries comparable.

> 2 Nataraj (2009), Kathuria et al. (2010), Hasan and Jandoc (2010) and Dehejia and Panagariya (2010) provide detailed overviews of similarly constructed databases. See also Fernandes and Pakes (2010) for a description of the Indian manufacturing sector.

4
Table 4. Logistic regression of project playgroup sustainability on quality characteristics

|<br>|(1)<br>Playgroup is open in<br>2016(1=Yes)|(2)<br>Playgroup is open & two<br>trained teachers(1 = Yes)|
|---|---|---|
|Change in prop. of teachers with teaching|0.00|0.06|
|experience from 2010-2013|(0.04)|(0.08)|
|Log-likelihood|-65.97|-156.86|
|Observations|245|245|

* p<0.10,  ** p<0.05,  *** p<0.01

Note: Robust standard errors clustered by playgroup. Coefficients are average marginal effects.

Figure 3 plots the relationship between observed quality and sustainability of project playgroups. The mean quality score in our sample was 3.09 (see Table 2).<sup>19</sup> A project playgroup that received a quality rating of minimal (quality score = 3) during the project period has a predicted probability of 92% that it will remain open and a predicted probability of 34% that it will remain open with two trained teachers after the project ends (holding all other variables at their mean). The rate of sustainability increases as playgroup quality increases. A project playgroup that received a rating of good (quality score = 5) during the project period has a predicted probability of 97% that it will remain open and a predicted probability of 42% that it will remain open with two trained teachers.

Figure 3. Probability of sustainability and observed playgroup quality Note: Plot of predicted probability that a playgroup remained open (solid line) and remained open with 2 trained teachers (dashed line). Variable on x-axis was measured during the project period (2010 and 2013) and variable on y-axis was measured after the project period (2016). The model holds constant the following variables at its mean level: proportion of teacher with post-secondary education in 2010, change in proportion of teacher with postsecondary education between 2010-2013, proportion of teachers with teaching experience in 2010, change in proportion of teachers with teaching experience between 2010-2013.

19 Recall that we apply the ECERS-R methodology but only focus on the items that are common between the national standard and the ECERS-R. We refer to this as the quality score.

16