rafmacalaba/datause-encoder-data huggingface dataset:
fixed effects, which control for all time-invariant market specific determinants of prices, βt are year fixed effects, and εit are other time-varying, location-specific shocks to prices. Coefficients γm capture the monthly impact of disaster on prices, interpreted as the 100*γm percent change in prices associated with a standard deviation of the weather index.

# **2.2 Data**

# _Household-level data_

Household-level information is used to construct both outcome variables, such as household per capita consumption, children’s school attendance, among others, as well as control variables, including demographic structure, family composition, school attainment, gender and age of household heads, and area of location, etc. These data come from the national household survey of living conditions ( _Inquérito aos Orçamentos Familiares_ , known as IOF for its acronym in Portuguese), collected over a 12-month survey period by the National Statistics Office of Mozambique (Instituto Nacional de Estatistica, INE). We use three waves of the IOF survey: the IOF 2002/03 (collected between July 2002 and June 2003), the IOF 2008/09 (collected between September 2008 and August 2009) and the IOF 2014/15 (collected between August-2014 and July2015). The 2002/03 and 2008/09 IOFs are cross-sectional surveys whereas the latest one, the IOF2014/15, was designed and implemented as a panel survey but used as pooled cross section for the purposes of this analysis.<sup>1</sup> All IOF surveys are representative at the national, rural-urban, and provincial levels. The information collected by these surveys captures the main variables included in the regression analysis, such as household welfare (total, food and non-food consumption, and poverty status), education, labor outcomes, health, morbidity, food security and possession of assets. The IOF 2002/03 interviewed 8,700 households and provides the baseline information for shocks that occurred between 2003 and 2007. The IOF 2008/09 visited 10,832 households and provides the baseline information for the shocks that took place between 2009 and 2013. Finally, the IOF 2014/15 provides the follow-up data for 10,369 households.

> 1 The 2014/15 survey was originally designed to interview over 11,000 households four times (once in each quarter) during a 12month survey period starting in August 2014 and ending in July 2015. Yet, due to logistical and budget constraints, the survey was carried out only during three quarters: Q1 (August-October), Q2 (November-January) and Q4 (May-July).

7
Policy Research Working Paper 7377

# **Abstract**

This study examines the effects of the extension of compulsory schooling from 5 to 8 years in Turkey in 1997—which involved substantial investment in school infrastructure— on schooling outcomes and, in particular, on the equality of these outcomes between men and women, and urban and rural residents using the Turkish Demographic and Health Surveys. This policy is peculiar because it also changes the sheepskin effects (signaling effects) of schooling, through its redefinition of the schooling tiers. The policy is also interesting due to its large spillover effects on post-compulsory schooling as well as its remarkable overall effect; for instance, we find that the completed years of schooling by age 17 increases by 1.5 years for rural women. The policy equalizes the educational attainment of urban and rural children substantially. The urban-rural gap in the completed years of schooling at age 17 falls by 0.5 years for men and by 0.7 to 0.8 years for women. However, there is no evidence of a narrowing gender gap with the policy. On the contrary, the gender gap in urban areas in post-compulsory schooling widens.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at murat.kirdar@boun.edu.tr, dmeltem@metu.edu.tr, and iskoc@hacettepe.edu.tr.

_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
_Appendix Table 6: Food intake patterns of young children in control villages_

|Food item|Percentage of children who<br>consumed the food item in the<br>past week|
|---|---|
||Children aged<br>Children aged|
||0-5 years<br>0-3 years|
|Fish|83.58<br>82.37|
|Green Vegetables|86.86<br>85.02|
|Banana|81.75<br>81.12|
|Eggs|73.11<br>71.92|
|Meat|56.45<br>53.98|
|Sweet potatoes|49.15<br>47.27|
|Papaya|36.74<br>36.35|
|Mango|20.32<br>19.34|
|Dairy|27.86<br>28.55|
|Carrot|13.5<br>13.1|

Source: 2011 _Pantawid_ impact evaluation survey 55
_Africa’s Macroeconomic Story_ of 45 countries. In a few of the 8 exceptions (notably Sierra Leone and Liberia), higher volatility coincided with a marked increase in growth.<sup>9</sup>

# **Figure 11: Volatility in Real GDP per Capita by Region, 1960-2012**

# _Standard Deviations_

<!-- Start of picture text --> Growth rates Output gaps<br>(percentage point deviation from trend) (percent deviation from trend)<br>2<br>1.0<br>1 0.5<br>0 0.0<br>EAP ECA LAC MNA SSA SAS EAP ECA LAC MNA SSA SAS<br>1960-1974 1975-1994 1995-2012 1960-1974 1975-1994 1995-2012<br><!-- End of picture text --> Source: World Bank _Global Economic Prospects_ database.

# _Cross-Country Growth Regressions_

In an effort to bring together some of the issues outlined above, we examined a few simple crosscountry regressions to determine which of the factors can best explain the recent growth acceleration in the mid-1990s. The regressions have of the general form: where the dependent variable τ i is the average per capita growth rate over the period 1995-2012 for country “i”, Xi is a vector of explanatory variables, Zi is a vector of binary variables corresponding to various country groupings and ε i is a random error term. Explanatory variables included growth and level measures of initial conditions and volatility:

- initial conditions:

9 Countries with greater growth accelerations did not exhibit either more or less output volatility, on the whole. The cross-country correlation between the change in trend growth between 1994 and 2012 and the change in volatility over the two sub-periods is essentially zero.

18
WPS3739

# **Patenting and Research and Development: A Global View***

Mariano Bosch Daniel Lederman William F. Maloney _The World Bank_

**Abstract:** Using a new global data base on patents and innovation inputs, this paper employs several recently developed econometric techniques to examine the dynamic relationship between research and development and U.S. patents granted. We confirm at the country level the recurrent micro-level finding of a strong relationship between the two and estimate the OECD elasticity to be effectively unity. This conflicts with the frequent micro level finding of strong diminishing returns in knowledge generation and suggests the importance of spillover effects measurable only at the aggregate level. Developing countries, however, do show diminishing returns and implicit rates of return to innovative effort in these countries appear to be a fraction of those found in the OECD. We then seek to explain the variance in elasticities by introducing proxies for the functioning of the national innovation system, the set of institutions and agents that create and diffuse knowledge. Across the entire sample, several variables, in particular education, security of intellectual property rights, and in some specifications the quality of research institutions and their interaction with the private sector impact the elasticity of transformation of R&D into patents.

World Bank Policy Research Working Paper 3739, October 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* We are grateful for helpful comments from Eli Berman, Bronwyn Hall, Andrés Rodriguez Clare and especially Frank Windmeijer. This research was funded by the regional studies program of the Office of the Chief Economist for Latin America and the Caribbean at the World Bank.
modes of information transmission are likely to succeed or fail in getting people to retain and actually use information to achieve specific objectives.<sup>11</sup> In addition to the local generation of information about local agency that intervention 1 tries to accomplish, intervention 2 tries to do the same for learning outcomes. During field experimentation it was found that the issue people raised most frequently in response to the leading questions on education issues, and about which they were most animated, was a government scholarship program intended to provide cash assistance to students from “backward” castes. Parents complained that they were not getting these scholarships, while teachers responded that many were not getting it because they were not satisfying other eligibility criteria such as having a child of school age regularly attending school. Teachers complained that parents inappropriately enroll underage children who can’t and don’t attend school, to lay claim to the scholarships. The second issue that attracted attention was the new government mid-day meal program. Actual learning levels attracted the least attention, and the facilitators had a difficult time steering the conversation away from scholarships and school meals to the broader issue of learning.

Introducing the testing tool appeared to make a difference. When some Pratham workers broke away from those that were beginning to discuss education with the people, and began asking children who had collected around the meeting to read, village attention quickly shifted to the children attempting to read. Mothers would begin to push forward their children to see if they could read; when children couldn’t read, there would be a sense of collective agitation and concern, and questions would begin to be raised. In intervention 2, therefore, during small meetings around the village, the intervention teams invited local volunteers to use the testing tool themselves to gather data on children’s learning, and thus develop their own hamlet-level report card on children’s learning status, including their school enrollment status. At the big village meeting, the aim of the intervention was to have these local volunteers share the information they had themselves collected about children’s enrollment and learning status.

> 11 Lupia, Arthur. 2003. “Necessary conditions for improving civic competence.” University of Michigan, processed. [Retrieved on July 12, 2006 from http://www-personal.umich.edu/~lupia/necessary.pdf.]

25
<!-- Start of picture text --> 18<br>16<br>14<br>12<br>8c 10<br>ic<br>$8<br>6<br>4<br>2<br>0<br>Advanced economies EMDEs<br><!-- End of picture text --> <!-- Start of picture text --> 0.6<br>[-_]Long-run<br>0.5 (Business-cycle<br>8 0.4<br>Cc<br>éoO<br>< 0.3<br>@<br>@<br><=<br>” 0.2<br>0.1<br>0<br>Advanced economies EMDEs<br><!-- End of picture text -->
Table A2 Comparison of empirical data and simulated data

||W|ages and em|ployment t|ransition p|robabilitie|s||||
|---|---|---|---|---|---|---|---|---|---|
||Wages|Food &<br>beverages|Textiles|Minerals|Metals|Other<br>manuf.|Non<br>traded|Unemp.||
|A) Simulated data||||||||||
|Food & Beverages|0.958|0.866|0.023|0.030|0.023|0.029|0.029|0.001||
|Textiles|0.955|0.023|0.864|0.031|0.023|0.029|0.029|0.001||
|Minerals|1.039|0.018|0.018|0.902|0.018|0.022|0.023|0.001||
|Metals|0.959|0.023|0.023|0.030|0.867|0.028|0.029|0.001||
|Other Manuf.|1.012|0.020|0.020|0.026|0.020|0.891|0.024|0.001||
|Non-Traded|1.016|0.019|0.019|0.026|0.020|0.023|0.893|0.001||
|Unemployment||0.086|0.086|0.115|0.087|0.106|0.108|0.413||
|B) Empirical data||||||||||
|Food & Beverages|0.943|0.717|0.002|0.001|0.005|0.015|0.205|0.059||
|Textiles|0.953|0.005|0.748|0.000|0.011|0.029|0.123|0.093||
|Minerals|1.169|0.018|0.025|0.692|0.046|0.087|0.136|0.076||
|Metals|0.876|0.021|0.024|0.016|0.615|0.170|0.122|0.064||
|Other Manuf.|1.039|0.007|0.009|0.005|0.041|0.713|0.166|0.062||
|Non-Traded|1.020|0.008|0.004|0.001|0.003|0.015|0.908|0.061||
|Unemployment||0.014|0.018|0.002|0.008|0.029|0.374|0.556||
||||Firm Inve|stment||||||
|||||Percenta|ge of Fir|ms||||
|||All rms||Lo|w-_K_ rm|s|H|igh-_K_ rm|s|
||_I_<0|_I_=0|_I_>0|_I_<0|_I_=0|_I_>0|_I_<0|_I_=0|_I_>0|
|Data|3.87|35.63|60.49|3.85|41.84|54.31|3.90|29.42|66.68|
|Simulated data|8.45|43.72|47.83|3.25|31.21|65.54|13.65|56.23|30.12|

Notes: Comparison of data from EPH (Encuesta Permanente de Hogares, Permanent Household Survey) and from EIA (Encuesta Industrial Anual, Annual Industrial Survey), and simulated data based on parameter estimates.

51
Fewer Questions, More Answers: Truncated Early Stopping for Proxy Means Testing Tim Ohlenburg<sup>∗1</sup> , Juul Pinxten<sup>2</sup> , Daniel Fricke<sup>1</sup> , and Fabio Caccioli<sup>1</sup>

> 1University College London (UCL)

> 2World Bank

**JEL** I32, C83, O22 **Keywords** proxy means testing, adaptive survey design, policy simulation

> ∗This paper has benefitted from the patient guidance and comments of Utz Pape, Virgi Agita Sari, Gracia Hadiwijaja, and Sailesh Tiwari. We are also grateful to reviewers Takaaki Masaki, Matthew Wai-Poi, and Phillippe Leite, whose constructive feedback improved contents and presentation.

1
6 Africa received preferences, i.e., between a less and more favorable tariff environment.

# III. Maior Factors Influencing Africa's 'irade Performance

Table 1 identifies the major products sub-Saharan Africa exported to OECD markets, ranked in terms of their 1962-64 values, it reports the value of shipments of these items, and also shows how the region's competitive position changed over the last three decades. The products listed were the 30 largest three-digit export items i- tLe 1962-64 base period when they accounted for 86 percent of all African shipments (their corresponding share stood at 63 percent in 1991-93). The table also reports Africa's initial share of OECD imports of each good along with the 1962-64 to 1991-93 share change. Finally, the table shows compound annual growth rates for OECD imports of each product, both globally and from siib-Saharan Africa, along with similar information for broad groups like foodstuffs, agricultural materi. Ls, or manufactures.

The most striking point evident in Table 1 concerns the general deterioration in Africa's competitive position for these key export items (as reflected ip -hanges u- market shar s. and its implications. For the 30 products combined, Africa's market l Ia'-:; UCLLh-Cd bv over I i percentage points (from 20.8 percent to 9.7 percent) which implies annual trauc ios.:cs ior the region of jus: under $11 billion. (To put this figure in proper perspective UNCTAD (1993) reports 1991 OECD official development assistance to Sub-Saharan Africa totalled $10.9 billion). Africa's competitive position for oilseeds and vegetable oils experienced major negative changes as the region's market shares for groundnuts, palm nuts and kernels, palm oil and other fixed vegetable oils all fell by between 47 to almost 80 percentage points. Table 1 also shows that the declining African market shares cut across all major product groups, the decline was -3.1 percentage points overall, but larger reductions occurred for
21

- 21/ See Jacobson (1978). To be conservative, we measure total compensation losses, which exceed earnings losses by the amount of fringe benefits. Morkre and Tarr (1980, Chapter 3) discuss the merits of this measure and the alternative unemployment cost measure.

- 22/ The estimate is conservative since earnings losses are zero after six years, while yearly benefits do not decay.

- 23/ In that experiment, we are increasing the variance of nominal protection by 3.8 and 6.9 times the variance prevailing in 1984. Since the distortionary costs of tariff protection is positively related to the average level of protection and to the variance of tariffs [see Johnson (1962)], another hypothetical calculation would consist of computing the average tariff under the assumption of no tariff dispersion across sectors. In that case, starting from a uniform tariff protection (3.5Z) equivalent to the existing tariff protection in 1984, the distortionary cost element of QRs would require an average uniform protection of 24Z. Adding the rent transfer loss would raise the average uniform protection to 48Z. With linear, rather than constant elasticity demand and supply curves, welfare calculations would yield lower estimates as the corresponding elasticities would increase as one moves up the demand and supply curves.
22 empirical studies. This could **be** caused by inappropriate measures of savings, or by aggregation bias (in time series studies). My own opinion is that the simulation studies neglect issues of uncertainty. If, for example, there is uncertainty about future interest rates and wages (Skinner, 1988), or about what future taxes will be (Skinner, 1989), fluctuations in current net-of-tax interest rates are unlikely to signal a large change in future interest rates. This year's jump in the tax rate provides only a small amount of "news", relative to "noise" about the net interest rate 10 years hence. The role of uncertainty is likely to be even more important in developing countries, where regime shifts and fluctuating commodity prices are the norm.

Econometric studies of savings in developing countries are difficult, owing to problems of measurement. First, gross domestic saving is measured as a residual by subtracting the current account deficit from gross capital formation. Because there are usually no independent mea&'ures of national income, it is often difficult to check the accuracy of the savings estimates (Mikesell and Zinser, 1973). In addition, there is no single net return to saving or user cost of capital; different investment projects receive different tax treatment, while some savings accounts may enjoy preferential treatment over others. In light of these measurement problems, it is perhaps not surprising that evidence from developing countries about savings and taxation is mixed.

A more general model of savings and investment allows for the two to differ by including an international sector. In such a
years of experience in external markets (4.9 years of experienced by individual destination).

# **3 Stylized facts**

In this section we provide a comprehensive analysis of the evolution of firm performance over the life cycle. While the key novel stylized facts we document refer to the joint evolution of performance and prices in international markets, we place these patterns in the context of broader evidence on the dynamics of firm size. This makes it possible to compare key patterns in our data with those reported in the existing empirical literature, and thereby provide an integrated analysis of firm dynamics in both the domestic and external markets.

## **3.1 Firm performance across cohorts**

We begin by examining the relationship between firm performance and age in the crosssection. When examining measures of firm size, we consider the age of the firm. When analyzing export revenue, we consider export-age, defined as the number of consecutive years the firm has been an exporter.

Figure 1 depicts the firm size and export revenue distributions of various cohorts of firms and exporters, based on data for 2005.<sup>8</sup> As in Cabral and Mata (2003) and Angelini and Generale (2008), we use nonparametric estimation methods, notably a kernel density smoother. These methods offer a convenient way of estimating the density of the distribution without imposing much structure on the data.<sup>9</sup> The patterns displayed in the top and middle panels of Figure 1 are well in line with those reported by Cabral and Mata (2003): as firms get older, the size distribution shifts progressively to the right. The diagram reported in the lower panel of this figure focuses solely on exporting firms, and shows that the distribution of export revenue by export-age. As we look at more experienced exporters, the distribution of export revenue shifts systematically to the right.

Inspection of Figure 1 points to a clear relationship between firm performance and age, and between export revenue and export-age. We examine this issue further by estimating an equation of the form: where _yit_ is a measure of size or export performance of firm _i_ in year _t_ , _ageit_ is either the number of years passed since birth of the firm or the number of consecutive years the firm has been an exporter in year _t_ , _λkt_ is an industry-year fixed-effect, _ηr_ is a region

> 8A cohort is defined as the group of firms for which the corresponding age measure in 2005 lied between between 1-5 years, 6-10 years, and so on. Results for each of the other years in the sample are similar, and are available upon request.

> 9As in Angelini and Generale (2008) we use a bandwidth of 0.7. Estimation with alternative bandwidths leads to qualitatively similar results.

6