rafmacalaba/datause-encoder-data huggingface dataset:
better investment opportunities need to be available to farmers in Uganda, and further research on the interventions needed to ensure this is warranted.

This paper fits into the literature that uses household panel data to understand the drivers of income growth and welfare improvements (as in Deininger and Okidi 2003, Dercon 2004, Gunning et al. 2000, Jalan and Ravallion 1998, 2004). The focus of the analysis is on the exogenously determined factors. These are both the factors we can better identify the impact of, and, as it turns out, the factors that seem to explain much of the income changes observed. The findings are consistent with the results of Dercon (2004) and Gunning et al. (2000), in which weather shocks are found to be important determinants of household income growth.

# II. Data

The analysis in this paper uses data from 2,356 households across Uganda, present in all the waves of the nationally representative Uganda National Panel Survey (UNPS) from 2005/6 to 2011/12. This survey is one of the LSMS-ISA surveys and has data on household characteristics, household consumption and income from a variety of sources. It also contains a rich agricultural module, a module on shocks, and is accompanied by a community survey. Many of the modules in the survey are comparable to the modules used in the nationally representative Uganda National Household Survey (UNHS), which is a cross-section survey and is used to compute the official poverty numbers. The consumption module is identical across the two surveys.

Four rounds of the UNPS are used in this analysis comprising data collected in 2005/6, 2009/10, 2010/11 and 2011/12 (henceforth 2006, 2010, 2011 and 2012). Households can be matched across rounds using a unique household identifier, and, in addition, a sample of households that split from the original household during this period are also followed. The attrition in the UNPS was quite substantial between 2006 and 2010, but it has been moderate in the latter three rounds (see Table 1). For the purposes of this analysis we focus on households that were present in all four rounds of the UNPS and that are engaged in agricultural production: a total of 2,356 households. This is done to ensure the panel is balanced, but it does result in the exclusion of households that have split from original households. Given the majority of this analysis is on agricultural income growth, and given many split-offs represent households that have moved out of the agricultural sector, this is not too much of a concern. Nonetheless, our findings are unchanged when the split-off households are included in the regressions.<sup>3</sup>

**Table 1: Attrition in the UNPS by wave**

||**Sample**<br>|**Original sample**<br>**retention**|**Split-off**<br>**HHs**|**Total**|
|---|---|---|---|---|
|2006|3,123|100|0|3,123|
|2010|2,607|83.5|367|2,974|
|2011|2,564|82.1|305|2,869|
|2012|2,356|75.4|479|2,835|

_Source: Uganda Bureau of Statistics_ (2013)

> 3 Results are available from the authors on request.

4
# **Love the Job... or the Patient?  Task vs. Mission-Based Motivations in Health Care**

Sheheryar Banuri, Philip Keefer, and Damien de Walque<sup>1</sup> University of East Anglia Inter-American Development Bank Development Research Group, The World Bank <u>sbanuri@gmail.com pkeefer@iadb.org ddewalque@worldbank.org</u>

**JEL Codes** : C91; H83; J45

**Keywords** :  public sector reform, civil service, intrinsic motivation, extrinsic motivation, performance

> 1 Banuri: School of Economics, University of East Anglia, Norwich, UK, NR4 7TJ (e-mail: sbanuri@gmail.com); Keefer: Inter-American Development Bank, 1300 New York Ave, NW, Washington, DC (e-mail: pkeefer@iadb.org); de Walque: World Bank, 1818 H St, NW, Washington, DC (e-mail: ddewalque@worldbank.org). The authors have no relevant or material financial interests that relate to the research described in this paper.  The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and do not necessarily represent the views of the World Bank, the Inter-American Development Bank, their respective Boards of Directors, or the countries they represent. The authors are grateful for financial support from the World Bank. In addition, the authors are grateful to Dr. Maurice Ye and Bambio Yiriyibin, Ousmane Haidera, Paul Jacob Robyn, and participants of the CBESS seminar series at the University of East Anglia and the Netherlands Institute of Government annual work conference.
**<u>Table II.3  Shares of World Cocoa Production /1992/93 – 1996/97</u>**

||Country|Avg.<br>1992/1993-<br>1996/1997|1997/1998|1998/1999|1999/2000|
|---|---|---|---|---|---|
|1|Cote d’Ivioire|37.2%|41.4%|41.4%|45.9%|
|2|Ghana|12.5%|15.2%|14.1%|14.2%|
|3|Indonesia|10.5%|12.3%|4.9%|13.3%|
|4|Brazil|9.5%|6.3%|4.9%|4.0%|
||**_Subtotal 1_**|**_69.7%_**|**_75.2%_**|**_65.3%_**|**_77.4%_**|
|5|Malaysia|5.9%|2.4%|2.7%|1.5%|
|6|Nigeria|5.7%|6.1%|7.1%|5.4%|
|7|Ecuador|3.3%|1.1%|2.7%|3.1%|
|8|Dominican Republic|2.1%|2.6%|0.9%|1.2%|
|9|Columbia|1.9%|1.4%|1.4%|1.2%|
|10|México|1.7%|1.3%|1.3%|1.2%|
||Subtotal 2|**_20.6%_**|**_14.9%_**|**_16.1%_**|**_13.6%_**|
||**_Subtotal 3 (1+2)_**|**_90.3%_**|**_90.1%_**|**_81.4%_**|**_91.0%_**|
|11|Papau New Guinea|1.3%|1.1%|1.3%|1.5%|
||Quantity Produced in<br>‘000mt|2,579|2,690|2,808|3,073|

Source: ICCO (2001) “ICCO Annual Report for 1999/00”. London.

# **C. Comparing the Situation in Coffee and Cocoa Markets**

Global coffee and cocoa markets are experiencing “crises.” Although coffee prices have sharply fallen and cocoa sharply risen, the future is uncertain for both. Large-scale investments in either sector would be risky. Long-term trends of expanding global production (and declining real prices) need to be juxtaposed against slowly growing demands, and changes in markets. Niche markets are growing and market premiums for certain attributes are more possible now than ever. A major feature of changing demand is the demand for some “differentiable product” and the willingness of consumers to pay a premium. The markets for specialty products are better developed for coffee than for cocoa. Potential for expansion into these “niche markets” exists. But, many coffee and cocoa producers are seeking the same “silver bullet,” and with wellintentioned support from international development agencies, donors, and NGOs, competition for these “niche markets” will increase. This competitions is particularly relevant for the coffee and cocoa sectors of Dominican Republic, where there has been a long-time emphasis on the need to improve quality, to expand certified organic production, and target other specialty and niche markets.

29
- Debt Crisis Occurred (previous year): Equals 1 if a debt crisis occurred in the country during the previous year, 0 otherwise, from Laeven and Valencia (2018).

- Exchange Rate Crisis Occurred (previous year): Equals 1 if an exchange rate crisis occurred in the country during the previous year, 0 otherwise, from Laeven and Valencia (2018).

Institutional variables from the Polity IV Project:

- Executive Recruitment Concept: Index of openness of executive recruitment. Ranges from 1=Succession by birthright to 8=Competitive election. Values for interruption, interregnum and transition periods have been recalculated using interpolation.

- Executive Constraints Concept: Index of the degree of constraints on the executive. Ranges from 1=Unlimited authority to 7=Executive parity or subordination. Values for interruption, interregnum and transition periods have been recalculated using interpolation.

- Political Competition Concept: Index of degree of competition in politics. Ranges from 1=Suppressed to 10=Institutional electoral. Values for interruption, interregnum and transition periods have been recalculated using interpolation.

34
The FE estimate of the privatization effects on productivity is located at the high end of the spectrum of the privatization effects in the literature. For instance, Brown, Earle and Telegdy (2006) find the productivity effects to be around 15-50 percent in Romania, 8-28 percent in Hungary, 2-16 percent in Ukraine, and -5 to 14 percent in Russia. Estrin et al. (2009) similarly would place our FE estimate of around 15 percent at the high-end of the estimates in Eastern European and the CIS countries. In addition, privatization is positively associated with ROA by 1.2 percentage points, or an increase of 0.1 standard deviation (11.2 percent).

The preferred specification in two key papers of the literature on privatization effects (Brown, Earle and Telegdy 2006; Brown, Earle, and Gehlbach 2009), likely an important improvement over the firm fixed effects model, is to allow for firm-specific random growth rates, as in the impact evaluation literature (Heckman and Hotz 1989). In particular, besides firm fixed effects, this fixed-effects and fixed-trend (FE-FT) specification allows for firmspecific fixed effects and growth rates, as follows: i i 1 r r r i i )<sup>′</sup> i (5) ii ii ii ii ii In practice, the FE-FT model is estimated in two steps: first detrending all variables for each YY = ββ PPr rr + δδZZ + (XX ∗λλ θθ + γγ + rrττ + δδ + DD firm separately, and then re-estimating the model with these detrended variables. The results are shown in Panel C of Table 6.

The effects based on the FE-FT specification are qualitatively similar to the FE estimation, but significantly smaller. For instance, the effect of privatization based on TFP_IN is 2.6 log points, roughly 20 percent of the FE estimate. Similarly, the FE-FT estimate of the effect of privatization on labor productivity is 5.7 log points, or about 30 percent of the FE estimate. These FE-FT estimates would place the productivity effects of privatization in the middle of the range found in the literature (see Estrin et al. 2009).

Based on the OLS, the FE, and the FE-FT estimations, privatizations in China had positive associations with productivity, and the magnitude ranges from moderate (such as 3 log points based on the FE-FT specification) to large (such as 16 log points based on the FE specification).

# **Instrumental Variable Estimation**

As shown before, the privatization of SOEs is strongly related to time-varying firm characteristics. To deal with the endogeneity issue, we use the following two-stage, least ``` 19 ```
_Table 3: Subjective assessment of own economic condition and food security_

|||**All Iraqi**|**residence**|||**Displaced**||
|---|---|---|---|---|---|---|---|
||||(3) Non-|(4) Difference|||(7) Difference|
||(1) All|(2) Displaced|<br>Displaced|<br>(2 - 3)|(5) PDS|(6) Non-PDS|<br>(5 - 6)|
|Variables|Mean [SD]|Mean [SD]|Mean [SD]|b (se)|Mean [SD]|Mean [SD]|b (se)|
|A. Assessment of own economic condition||||||||
|1. Current condition (good/very good =1)|0.13|0.06|0.14|-0.08***|0.06|0.05|0.01|
||[0.33]|[0.25]|[0.34]|(0.01)|[0.23]|[0.34]|(0.02)|
|2. Current condition compared to before Jan.||||||||
|2014 (slightly/much better = 1)|0.16|0.11|0.17|-0.06***|0.10|0.16|-0.06*|
||[0.37]|[0.34]|[0.37]|(0.02)|[0.29]|[0.58]|(0.04)|
|3. Economic outlook for the next two years||||||||
|(slightly/much better = 1)|0.18|0.30|0.16|0.14***|0.31|0.24|0.07*|
||[0.39]|[0.49]|[0.36]|(0.02)|[0.44]|[0.66]|(0.04)|
|_Subjective wellbeing index (A1 + A2 + A3)_|0.47|0.47|0.47|0.00|0.47|0.45|0.02|
||[0.72]|[0.68]|[0.73]|(0.03)|[0.62]|[0.97]|(0.07)|
|B. Household food security in last 30 days||||||||
|1. Member(s) had to eat fewer meals (yes =1)|0.12|0.23|0.09|0.14***|0.23|0.25|-0.02|
||[0.33]|[0.45]|[0.29]|(0.02)|[0.40]|[0.68]|(0.04)|
|2. No food of any kind to eat at least once||||||||
|(yes =1)|0.09|0.14|0.07|0.07***|0.15|0.10|0.05**|
||[0.28]|[0.37]|[0.25]|(0.02)|[0.34]|[0.47]|(0.02)|
|3. Member(s) went to sleep at night hungry||||||||
|(yes =1)|0.05|0.09|0.05|0.04***|0.09|0.10|-0.01|
||[0.23]|[0.30]|[0.21]|(0.01)|[0.27]|[0.47]|(0.02)|
|4. Member(s) went whole day and night||||||||
|without eating (yes =1)|0.04|0.06|0.03|0.02*|0.06|0.05|0.00|
||[0.19]|[0.25]|[0.18]|(0.01)|[0.22]|[0.35]|(0.02)|
|_Household hunger score - HHS (B2 + B3 + B4)_|0.18|0.29|0.15|0.14***|0.29|0.25|0.04|
||[0.61]|[0.80]|[0.56]|(0.04)|[0.73]|[1.07]|(0.05)|
|_Observations_|8615|1849|6766|8615|1282|567|1849|
|_Sample = "Short-form"_||||||||

Note: Standard deviations are in brackets and standard errors are in parentheses. Significance levels are denoted as follows: * p<0.10 ** p<0.05 *** p<0.01.

32
importance to policy makers to understand the determinants of migration flows into and out of the Northeast states as well as rural-urban migration within a state.

Why has migration failed to equalize real regional incomes? At least four plausible explanations for this failure emerge. First, all the migration prospects have, in fact, migrated and that differences in standard of living are due to differences in the human capital bases of the remaining population. That is, because of low levels of education, old age, or poor health status, the remaining population in regions such as the Northeast would be poor no matter where it resided. The second explanation relates to the first, the disparities in regional levels of well-being are due to differences in the distribution of. occupations due to long-term investments in business capital. That is, there may be no difference in remuneration for the same job across the regions, but one region has more well-paying jobs because private industry has traditionally invested there. Third, migration has run its course and regional differences in levels of living are due to differences in costs of living. Finally, standards of living have not equalized due to market failures and constraints (perhaps discrimination) faced by migrants into areas such as the Southeast.

The main purpose of this paper is to shed light on how migration flows between Northeast and Southeast Brazil have affected well-being in the Northeast. More specifically, the direction of migration flows, the characteristics of -migrants and their household, and some of the determinants of migration. The paper is organized in six sections. Section 2 contains an overview of migration dynamics in Brazil. Section 3 provides information on socioeconomnic indicators for migrants and non-migrants in receiving and sending areas. Section 4 assesses the human capital approach to migration. Section 5 focuses on migration and schooling of children. Finally, section 6 concludes.Additionally, this paper has two appendices. Appendix A contains population figures by state level for 1999. Appendix B contains information on the labeling of the variables.

# **2. Migration patterns within Brazil**

This section of the paper describes broad patterns of migration within Brazil using the 1999 PNAD data and the 2000 Census. A migrant, for the purposes of this study, is defined.as a person who changed state of residence over a defined period of time. Interregional migration over the entire lifetime of the migrant and migration over the past ten years are examined, sending and receiving regions are identified and flows between these regions are documented. Since the largest flows of migration historically occurred between the Northeast (NE) and Southeast (SE) regions, these inter-regional flows are analyzed in more detail.

# _Data_

The PNAD is an annual national household survey conducted and performed by IBGE, the Brazilian Census Bureau, in the third quarter of each year. The data are derived from interviews of approximately 100,000 households. The survey began at national level in 2
education sector according to the Presidential Economic Reforms Program for 2010-2014 „Prosperous society, competitive economy, effective government‟<sup>4</sup> . Also the „launch of optimization‟ of the school network is envisaged by the Ukraine‟s Budget Declaration for 2011 which was approved on April 19, 2010. Schools are planned to be closed if they:

- have less than 10 students and only offer grades 1-4 (level I schools);

- have less than 40 students and only offer grades 1-9 (level I-II schools);

- have less than 100 students and offer all grades (1-11/12, level I-III schools)<sup>5</sup> .

School size is thus used as the key decision criterion to close schools. Given that pupils of closed schools will be reallocated to other schools, the remaining schools will see an increase in size and most likely will have the size of their classes increase. Also the Budget Declaration 2011 set to revise the school norms that influence the amount and type of staff to be hired.

In this paper, we attempt to study what the impact of school size and class size are on the performance of schools in Ukraine, using data on educational performance from Ukraine‟s Independent External Test and data on educational inputs from the Ukrainian Ministry of Education, Youth, Sports and Science. The paper is intended as a key input in the discussions around the optimization or consolidation of the network of schools in Ukraine that has been ongoing for some time now.

In Section II, we first review the academic literature on the determinants of educational performance with a particular emphasis on the evidence that links class size and school size to educational outcomes. Section III portrays a picture of Ukraine‟s educational system, again with an emphasis on size of schools and classes. Section IV provides a detailed description of the data used in this study and how the database was assembled. It is the core of the paper and presents our estimates of class size and school size effects within the „educational production functions‟ of Ukraine. Section V, finally, summarizes the main messages and policy conclusions from this empirical study.

> and stimulated optimization discussions and work in non-pilots. While such activities created conditions for modernization of the education, the larger, more comprehensive reform and completion of the consolidation exercise remains to be implemented.

> 4 However, there are still signs of a push-back from the Government‟s original initiative due to fierce local opposition to school closures.

> 5 The system was undergoing transformation to 12-years of schooling (with experiment already in place in some schools) but in mid-2010 the decision was taken to return to the system of 11-years of full secondary education.

4
Kretlow, A. G., and C. C. Bartholomew. 2010. “Using Coaching to Improve the Fidelity of Evidence-Based Practices: A Review of Studies.” Teacher Education and Special Education: The Journal of the Teacher Education Division of the Council for Exceptional Children 33 (4): 279–99.

Krueger, A., and D. Whitmore. 2002. “Would Smaller Classes Help Close the Black-White Achievement Gap?” In Bridging the Achievement Gap, edited by J. E. Chubb and T. Loveless. Pp 1-35. Washington, DC: Brookings Institution Press.

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36
23 had inordinately high rates of absenteeism. For example, about 85% of Tunisian teachers report few if any absences, but about 15% have missed much of the school year (reportedly due to illnesses and pregnancies.) Data from Pernambuco showed different absence rates according to the day of the week. There was 55% more absenteeism on Monday (4.79% of teachers) than on Thursday (2.69% of teachers). Also afternoon and evening shifts showed greater losses than the morning shift.

**Table 3-1. Instructional Time Use in the Four Countries of the Study**

||**Pernambuco**<br>**(Brazil)**|**Ghana**|**Morocco**|**Tunisia**|
|---|---|---|---|---|
|**School Year(Days)**|200|197|204|190|
|School Closures(Days)|4.79|3.17|1.38|5.15|
|**Days after Closures**|**195.21**|**193.83**|**202.62 **|**184.85**|
|**Teacher Absence** (Days)|12.76|43.01|13.36|11.55|
|Teacher Delays|5.50|39.75|6.94|1.27|
|EarlyClass Dismissals|2.31|2.43|6.68|1.22|
|**No. days schools operated**|**174.65**|**108.6**|**175.6**|**170.8**|
|% Year available for teaching|87.3%|55.1%|86.1%|89.9%|
|**Engagement Rate in Interactive**<br>**or Passive Classroom Tasks**|72.1%|70.2%|82.6%|86.7%|
|**School Days Devoted to Learning**|**125.9**|**76.3**|**145.1 **|**148.1 **|
|**School Year % Spent Engaged**<br>**in Learning Tasks**|**63.0%**|**38.7%**|**71.1%**|**77.9%**|
|**Student off Task Rate**|19.3%|21.1%|9.2%|9.9%|
|Instructional EfficiencyGiven Off-Task Rate|50.8%|30.5%|64.6%|70.2%|
|**Student Absence (Days)**|7.82|9.04|4.30|3.35|
|**Student Delays (Number of Times)**|5.64|10.61|5.187|2.63|

_How satisfactory are these results?_ Comparable indicators for this level of detail are almost nonexistent. US indicators ranged from 39% to 96% in the 1980s and to 85% in 2003 (Chapter 2). However, they mainly measure time use in the classroom and are not really comparable. As more data are collected in other countries, the range of the time loss parameters will be understood better.

# **HOW OFTEN WERE TEACHERS ABSENT OR LATE?**

Teacher delays and absences (legitimate or otherwise) resulted in about 11.5-43 days lost during the school year. The number of days schools were closed for various reasons during the school year was reported as low, from 1.4 day in Morocco to 4.8 days in Pernambuco. But no details were asked about the timing of closures or about partial cancellations of classes, so the figures may be underestimates.