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Its primary education sector has experienced significant growth in participation: from 2008 to 2012, during which time its universal basic implementation plan was implemented, primary enrollment increased from 600,000 to 1.4 million students. The primary gross enrollment ratio increased from 60.5 to 114.7, and the primary net enrollment rate was 86 percent according to the latest available data from 2012 (World Bank 2016b). Children begin school with three years of elementary school followed by six years of primary school.<sup>3</sup> The language of instruction in elementary school is a local language while English is the official language of instruction for primary schools.

Despite significant increase in enrollment, learning outcomes remain poor. Eighth grade exam results reveal poor outcomes for literacy and numeracy (World Bank 2011). Early grade reading assessments conducted in the National Capital District, Madang Province, Western Highlands Province and East New Britain from 2011 to 2013 found that children’s crucial pre-reading skills including “alphabetic principle and phonetics” were two years behind the curriculum target. They also found that students took five years to attain some reading skill objectives required by the firstgrade curriculum (World Bank 2014a, 2014b, 2014c, 2014d).

The Reader Booster Programme was designed as a remedial course to improve early literacy skills for children in the first two years of primary school, grades 3 and 4 (see Government of Papua New Guinea 2015; 2016). Kindergarten and grades 1 and 2 are typically taught at elementary schools. The program was implemented in Madang Province in 2013 and Western Highlands Province in 2014. The intervention developed highly scripted lesson plans for teachers to follow for one lesson per week and provided teachers with training to implement follow-up lesson plans. Time for these lessons was scheduled during the curriculum time allocated for language instruction; the national curriculum allows teachers to have flexibility in which materials they use and how they teach. In addition to training, teachers were also provided with mentoring and coaching.

> 3 Until 2017, the general education system in PNG includes 12 years of schooling divided by elementary schools offering a three-year program consisting of a preparatory year and grades 1 and 2, and primary schools offering a six-year program consisting of grades 3-8.

4
found both for all teaching practices as well as all sub-domains of teaching. In most domains, effect sizes tended to be higher in the 2016 classroom observations than the 2017 classroom observations. The proportion of teachers in control schools fully implementing the teaching techniques increased between 2016 and 2017.

Table 8. Effect of CLRW on the proportion of teachers fully implementing all teaching practices targeted by the intervention

||Treatment|Control|Difference|
|---|---|---|---|
|School year 2016|0.926|0.518|0.407***|
||(0.006)|(0.019)|(0.02)|
|School year 2017|0.89|0.623|0.267***|
||(0.007)|(0.017)|(0.019)|

Standard errors included in parentheses. For differences, statistical significance at the 1, 5 and 10 percent levels are denoted by ***, **, and *, respectively.

Table 9. Effect of CLRW on average TEGRA reading scores (standard deviations)

||Effect on s<br> <br>|tudents' aver<br>reading score<br>|age TEGRA<br>s<br>|
|---|---|---|---|
||1 year<br>exposure|2 years'<br>exposure|1 year after<br>2 years of<br>exposure|
|all students|0.19***|0.33***|‐0.09**|
||(0.04)|(0.06)|(0.04)|
|girls|0.18***|0.34***|‐0.11***|
||(0.05)|(0.06)|(0.04)|
|boys|0.21***|0.38***|‐0.04|
||(0.04)|(0.08)|(0.05)|
|difference in girls' and boys' effects|‐0.03|‐0.04|‐0.07|
||(0.04)|(0.08)|(0.05)|
|sample size|2199|818|862|

Standard errors included in parentheses. Statistical significance at the 1, 5 and 10 percent levels denoted are by ***, **, and *, respectively. Effects are estimated by equations (4) and (5), presented in Annex Table 7.

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Table 2 Division of Labor by Gender among Smallholders (Percent of those above 15 years of age who work regulasly on task)

||||Males|||F|emales||
|---|---|---|---|---|---|---|---|---|
||Plant|Weed|Harvest|Market|Plant|Weed|Harvest|Market|
|CROP|||||||||
|Maize|54|55|54|24|87|87|89|52|
|Potatoes|8|8|8|4|13|13|14|7|
|Coffee|13|12|11|10|13|16|16|13|
|Tea|5|5|5|4|5|6|6|6|
|Pyrethrum|5|5|5|4|7|7|6|5|
|Cotton|6|6|6|5|8|8|8|5|
|LIVESTOCK||Work|regularl|y||Work|regular|ly|
|Poultry care|||3||||9||
|Stall feed livest|ock||8||||12||
|Graze cattle|||25||||24||
|Milk cattle|||12||||37||
|Graze sheep/goat|s||21||||27||
|Carry water|||5||||89||
|Carry wood|||5||||89||
|Prepare food|||5||||90||
|Clean house|||5||||90||
|Care for children|||1||||63||
|Buy food|||23||||71||

Source: GOK Integrated Rural Surveys 1976-79 Basic Report, cited in GOK and UNICEF, 1984.

i. Marketing. Related to their role as farmers is women's involvement in marketing agricultural produce, particularly at the local level. As providers of food for the family, they participate in the market as both buyers and sellers. Women may sell varying amounts of their vegetables from their kitchen gardens, staple food crops from their main fields, as well as nonfood crops. However, their access to and control over income derived from these sales may differ depending on the crop. Gene-ally, women do control income derived from the sale of their surplus food ciL ,--S Food crops are marketed through formal and informal channels. Officially, maize is marketed under the control of the National Cereals Produce Board (NCPB), but informal maize markets (small private traders) handle the bulk 6
The conceptual framework has been used to investigate the effects of land registration on financial development and economic growth using time series data from Thailand (Byamugisha, 1999). The estimation results indicate that land titling has positive and significant long run effects on financial development while the quality of land registration services, measured by the volume of public expenditure on land registration, has positive and strongly significant effects on economic growth. Land titling was found to have negative and significant effects on economic growth in the current period but positive and significant effects in the long run.

The above empirical findings are country-specific and therefore cannot be automatically generalized to apply to other countries. Further country case studies and cross-country ones are required to generate globally applicable findings and conclusions about the economic effects of land registration on financial development and economic growth.

# **VI. Conclusions**

Economic historians have long documented the role private property rights played in the development of the Western world. Political economists have empirically established the positive relationship between private property rights and economic growth but have done so using proxies as measures of property rights. And agricultural economists and urban real estate specialists have also empirically determined the link between landownership security and land productivity in the sectors of their interest. It should be noted that the use of proxies by political scientists to measure property rights was a source of weakness in their investigations. Similarly, the studies of the agricultural economists and real estate specialists had some particular weakness in so far as they did not account for the land registration impacts that accrue across sectors and those that affect the economy as a whole. This paper has attempted to address this weakness by developing a theoretical and conceptual framework to guide the empirical analysis of the effects of land registration on the economy as a whole. Furthermore, by using a direct measure of private property rights (land registration), our study aims to overcome the weakness of the earlier property rights studies which used proxies to measure private property rights. Empirical results from the application of our economy-wide land registration framework indicate that it is sound.

12
tion we observe with the ACT subsidy.

Changes in mismatch rates relative to the Control need to be interpreted with caution, given that more patients visit clinics on _PV_ and _DV_ days. More generally, we cannot extrapolate our findings in the voucher treatments to mismatch rates we would observe in a long-term equilibrium with subsidized ACTs, since the composition of patients visiting the clinic would likely change. However, we have no evidence of differential selection in _PV_ and _DV_ , and the analysis indicates that patient-driven demand in our sample significantly worsens the treatment-illness match.

One weakness of our expected match measure is that it assumes no private information on the part of doctors. Doctors may have access to private signals ̃ – – in particular malaria tests that improve upon _π_ when making prescription decisions.<sup>33</sup> This would bias our expected match measures downwards, because private signals would improve the match of treatment to illness _con-_ ̃ _ditional_ on _π_ . Assessing the consequences for estimated treatment effects is more difficult, since this depends on what drives “marginal” prescriptions in the different experimental conditions. For example, suppose all marginal prescriptions in _PV_ were driven by malaria negative patients. In this case, we would falsely estimate a positive effect on _mp_ (since marginal patients ̃ will have _π >_ 0) and underestimate the negative effect on _mn_ .

Appendix Table B16 uses the home-tested RDT subsample to get an idea of how much private information skews our expected match estimates. We find strikingly similar treatment effects on the expected and actual match for the home-tested subsample.Control group means are also similar, at 0.48 for the expected match and 0.51 for the actual match. Analogous numbers for purchases are 0.56 and 0.61. While these numbers are consistent with some private information, the impacts on match estimates are very modest, likely because providers often do not use, or do not comply with malaria tests.

# **5 Conclusion**

A critical challenge for developing country health systems is to craft policy that simultaneously avoids undertreatment and overtreatment. Undertreatment remains a major problem; for example, treatable conditions like pneumonia, diarrhea, and malaria account for 48 percent of global postnatal child mortality (WHO, 2018). Overtreatment is problematic for a range of reasons:

> 33This is captured by the private signal _η_ in our model in the Appendix.

33
the peak quarterly increase in the unemployment rate was considerably later in 2001:Q4. Second, the model also predicts _low_ levels of TTBs over the period 2004:Q2 through 2007:Q4; this compares well with the realized data on new TTBs.

On the other hand, the model cannot match the magnitude of the spike in realized US TTBs in 2001:Q2. These were almost entirely associated with one single policy decision, i.e., the US steel safeguard initiation in June 2001. However, the US steel safeguard had a number of unique properties, from the perspective of this modeling exercise. First, it was a single action covering hundreds of products. Furthermore, and despite the exclusions and exemptions which pierced the full nondiscriminatory nature of the policy (Bown, 2004), the import restriction still covered many trading partners, a number of which may not have been substantial suppliers of US steel import products. Finally, in the quarterly data, the singular timing of this policy action also ends up being recorded as quite different from a set of similar (in the aggregate) antidumping actions in 1991-92, for example. Because those many separate antidumping actions fell over a couple of different quarters (e.g., 1992:Q2, 1992:Q3) there was not as large a spike in any one quarter as was recorded during the 2001 recession.

# **5 Conclusion**

This paper uses quarterly data for the United States, European Union, and three other industrialized economies to estimate the impact of macroeconomic shocks on import protection over 1988:Q12010:Q4. Estimates from a data sample taken before the Great Recession exhibit evidence of three key relationships. First, there is strong evidence of a countercyclical relationship between domestic macroeconomic shocks – increases in domestic unemployment rates or reductions in domestic real GDP growth – and governments initiating new temporary trade barrier investigations over significantly more imported products per trading partner per quarter. Second, we find these economies used such bilateral import restrictions against trading partners that were going through their own periods of weak economic growth. Third, a real appreciation of the bilateral exchange rate defined in terms of the domestic currency is associated with increases to these forms of import protection. Overall, we find this evidence is especially strong for import protection by the United States and European Union, and the evidence is weaker for smaller economies such as Australia, Canada, and South Korea.

25
32 Annex Table 3. Pollution Indicators per Unit of GDP, In 1970 and 1989, at 1985 prices and exchange rates

||Sulfur (S02) Emissions (k2/1000 USS)||
|---|---|---|
||1970<br>1989|A|
|Funland|15.5<br>4.2|-69|
|Gerwany|8.3<br>2.2|-73|
|Japan|7.2<br>0.5|-93|
|Norway|5.5<br>1.0|-82|
|Sweden|12.2<br>1.8|-85|
|U.S.|10.8<br>4.6|-57|
|OECD|11.4<br>4.0|-65|
||Nitrogen (NO2) Emissions (ke/1000 USS)||
|Finland|4.8<br>4.2|-13|
|Gaenmny|5.3<br>4.3|-19|
|Japan|2.4<br>0.8|-67|
|<br>Norway|_5.1_<br>3.7|-27|
|Sweden|4.0<br>2.9|-28|
|U.S.|7.0<br>4.4|-37|
|OECD|5.7<br>3.6|-37|
||Carbondioxide (CO,<br>Emissions from Energy Use (|kg/USS)|
|Finland|0.45<br>0.28|-38|
|Gerwany|0.46<br>0.29|-37|
|Japan|0.31<br>0.18|42|
|Norway|0.22<br>0.14|-36|
|Sweden|0.36<br>0.19|-47|
|U.S.|0.46<br>0.32|-30|
|OECD|0.43<br>0.29|-33|
||Ener-v<br>Intensitv*||
|Fmnlnd|0.58<br>0.49|-16|
|Germany|0.53<br>0.41|-22|
|Japan|0.38<br>0.27|-30|
|Norway|0.57<br>0.44|-22|
|Sweden|0.58<br>0.52|-10|
|U.S.|0.60<br>0.44|-27|
|OECD|0.54<br>0.41|-24|

* TOE per 1000 USS, pri.asry energy requirements per unit of GDP.

Source: OECD (1991)
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# **Rigidities in Employment**

# **Protection and Exporting**<sup>*****</sup>

## **Murat Şeker**<sup>**†**</sup>

**Keywords:** Exports, firm heterogeneity, employment protection legislations, developing countries **JEL Classification:** F12, F14, F16, J23

> * I would thank to Pierella Paci and to the participants of 2010 World Bank Economists’ Forum for their helpful suggestions.

> † Contact: Enterprise Analysis Unit, World Bank; 1818 H Street NW. Washington DC MSN F4P-400, 20433 Email: mseker@worldbank.org.
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