rafmacalaba/datause-encoder-data huggingface dataset:
- 4 -

Given the prevalence of schooling to childhood and the centrality of the curriculum to schooling, comparative research about the composition of school curricula is long overdue. In this paper, we report data on official curricular policies regarding elementary schooling for a wide range of less-developed countries in the mid-1980s and, to a lesser extent, in the 1960s. Our basic aim is to describe and compare cross-nationally three aspects of primary school curricula: 1) what subjects are taught during the primary school cycle; 2) what percentage of instructional time is allocated to different subjects; and 3) and how much instructional time is available overall in primary education. We also explore the issue of whether national variations in curricular offerings and emphases are related to general world trends, global regional cultures and such national characteristics as econornic development and educational expansion.

It is easy to imagine many properties of societies or time periods that might affect official curricular policies. It is important to keep in mind, however, that the rapid expansion of the institution of mass education itself has a worldwide character, and often is not predicted by immediate factors of time and place. World educational expansion has clearly outpaced most national-level indicators of political development and practically all national-level measures of economic modemization and growth. The very processes that have been involved in the spread of mass education may also generate a good deal of homogeneity in content. The taken-for-granted cultural value of mass education, thus, may carry with it taken-for-granted content too. This theme, as we discuss below, is useful for understanding both the character and stability of curricuiai policies on primary education in the contemporary period. Some of the most important observations we report concem, not the variation among the world's elementary school curricula, but its absence.

# II. Background and Issuei

In earlier periods, the substantive content of the school curriculum seems to have been a central cultural issue. Classic social theorists such as Durkheim discussed the curriculum
Reference List Ahlin, C. and R. M. Townsend. 2007. "Using Repayment Data to Test across Models of Joint Liability Lending." _Economic Journal 117_ (2): F11-F51.

Armendariz de Aghion, B. 1999. "On the Design of a Credit Agreement with Peer Monitoring." _Journal of Development Economics 60_ (1): 79-104.

Banerjee, A. V., T. Besley and T. W. Guinnane. 1994. "Thy Neighbor's Keeper: The Design of a Credit Cooperative with Theory and a Test." _Quarterly Journal of Economics 109_ (3): 491-515.

Besley, T. and S. Coate. 1995. "Group Lending, Repayment Incentives and Social Collateral." _Journal of Development Economics 46_ (1): 1-18.

Conning, J. and C. Udry. 2005. "Rural Financial Markets in Developing Countries." Working Papers. New Haven, CT: Economic Growth Center, Yale University.

Cull, R., A. Demirguc-Kunt and J. Morduch. 2007. "Financial Performance and Outreach: A Global Analysis of Leading Microbanks." _Economic Journal 117_ (2): F107-F133.

Godquin, M. 2004. "Microfinance Repayment Performance in Bangladesh: How to Improve the Allocation of Loans by MFIs." _World Development 32_ (11): 1909-26.

Guttman, J. M. 2006. "Repayment performance in group lending programs: A survey." Working Paper 2006-01. Indianapolis, IN: Network Financial Insitute.

Luoto, J., C. McIntosh and B. Wydick. 2007. "Credit Information Systems in Less Developed Countries: A Test with Microfinance in Guatemala." _Economic Development and Cultural Change 55_ (2): 313-34.

Stiglitz, J. E. 1990. "Peer Monitoring and Credit Markets." _World Bank Economic Review 4_ (2): 351-66.

Zeller, M. 1998. "Determinants of Repayment Performance in Credit Groups: The Role of Program Design, Intragroup Risk Pooling, and Social Cohesion." _Economic Development and Cultural Change 46_ (3): 599-620.

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“Consider the most important item which you [sell]. If you buy Rs. 1000 worth of raw materials how much revenue will you receive from the final products that you [sell] with these raw materials on average?”<sup>6</sup> We then asked the percentage of sales this item represents. The average markup on the most important item sold for all firms in the sample is 66 percent. Manufacturers report somewhat higher markups, 83 percent compared with 47 percent for retailers. We can use the reported markup for each firm to estimate the purchases of raw materials which correspond to the revenues in the given month. Specifically, [revenues / (1 + markup)] yields the inputs associated the reference month’s revenues. We calculate adjusted revenues minus expenses by replacing the inputs purchased during the reference month with the inputs used to produce the revenues reported in the reference month. Table 4A shows the results of this exercise using the data from round 8 of the SLMS survey, and data from rounds 2 through 8. We show on Table 4A both the reported profits and the adjusted revenues minus expenses, as calculated in Table 3. The data shown on the table are from the sample trimmed at the 1<sup>st</sup> and 99<sup>th</sup> percentiles of adjusted profits and markup-adjusted revenues minus expenses.

Adjusting for the timing of expenses and revenues using the estimated markups significantly increases the correlation between reported profits and revenues-expenses. For the round 8 data, the Pearson (Spearman) correlation between adjusted profits and adjusted revenues-expenses is 0.36 (0.54). The correlation increases to 0.67 (0.72 for Spearman) when we use the markups to adjust revenues-expenses for timing issues. The correlations are slightly lower when we use rounds 2-8, perhaps because the markups have some seasonal component. The percentage of firms with negative profits is also much lower once we account for the mismatch in the timing of purchases and sales. Only 9 percent of firms now have negative profits, compared with 21 percent before adjusting for timing mismatches. Timing mismatches thus appear to be a major reason firms show negative revenues-expenses.

> 6For manufacturers, the question was worded in terms of good manufactured rather than sold.

11
# **Table 1a. The Effect of Informality on Economic Growth, controlling for GDP per capita**

|**_Dependent variable: Per capita GD_**<br>|**_P Growth,_**<br><br>O|**_1985-200_**<br><br>LS|**_5, countr_**<br>|**_y average_**<br>|<br><br>I|V||
|---|---|---|---|---|---|---|---|
|[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|
|Initial GDP per capita<br>-0.1966|-0.3519|-0.3498*|-0.6910*|-0.6976***|-0.7684***|-1.2819***|-1.7200***|
|(2000 US$, 1985, in logs)<br>1.29|1.54|1.88|1.98|3.06|2.83|2.69|2.95|
|Schneider Shadow Economy<br>-0.0747***||||-0.1479***||||
|(% of GDP)<br>3.87||||4.39||||
|Heritage Foundation Informal Market|-0.8009**||||-1.3294***|||
|(ranging 1-5: higher, more informality)|2.41||||4.05|||
|Self Employment||-0.0657***||||-0.1775***||
|(% of total employment)||3.11||||3.21||
|Non-contributor to Pension Scheme|||-0.0423***||||-0.0872***|
|(% of labor force)|||2.80||||3.39|
|Constant<br>5.4231***|6.9131**|6.6475***|9.2161**|11.8634***|11.7604***|17.1971***|19.8890***|
|3.15|2.57|3.35|2.59|4.29|3.80|3.18|3.33|
|No. of observations<br>119|127|72|91|84|87|59|68|
|R-squared<br>0.20|0.08|0.13|0.11|-|-|-|-|
|Hansen J Statistic (P-value)<br>-|-|-|-|0.48|0.21|0.30|0.70|

Notes:

1. Heteroskedasticity-robust t(z)-statistics are presented below the corresponding coefficients. 2. *, ** and *** denote significance at the 10 percent, 5 percent and 1 percent levels, respectively.

3. For IV regressions [5] to [8],

- Endogenous variable: each of four informality measures.

- Instruments: Law and order; Business regulatory freedom; Average Years of Secondary Schooling.

- Sociodemographic factors is not included as an instrument because it does not pass the exogeneity test using the C statistic (Difference-in-Sargan statistic).

4. See Appendix 1 for definitions and sources of variables.

Source: Authors’ estimation 17
(2013) have 421 individuals for their study of China’s one child policy’s effects; Chuang and Schechter have data on between 49 and 140 households for their analyses of how shocks affect time and risk preferences, and their survey of 19 studies of risk preference stability in the literature shows a median sample size of 84; Hao et al. (2014) have 144 individuals for their study on migration and risk preferences in China; and di Tella et al. (2007) have 312 households for their study of how property rights affect pro‐market beliefs.

Third, we can ask whether large impacts would be consistent with the results we obtain. Considering first the estimates one by one, the standard errors provided in each table allow one to determine the confidence intervals for the different impacts. These confidence intervals typically do not contain the large effect sizes of many of the studies in the literature, but we cannot rule out moderate effects. For example, from Table 3, a 95% confidence interval for the impact on the 11‐point risk‐seeking scale is (‐0.27, +0.30), and for the likelihood of choosing a risky job is (‐0.14, +0.14). Any impact on risk attitudes is therefore relatively modest, and less than the 20 percentage point change seen in Cameron et al. (2013), or the 20 percentage point increase in the likelihood of choosing a risky activity for Hurricane Katrina evacuees (Eckel et al, 2009). For the impact on pro‐market beliefs, the 0.53 estimate of di Tella et al. (2007) lies right at the boundary of a 90 percent confidence interval for our LATE impact (‐0.18, 0.53). A 95 percent confidence interval for the impact on household efficiency in game A is (‐0.03, +0.09), which is less than the 16 percentage point change in the amount sent by player 1 in the trust game of Cameron et al. (2013).

Finally, we follow Young (2016) in conducting a randomization‐inference based omnibus test of overall statistical significance of the 20 outcomes tested in Tables 2, 3 and 6.<sup>9</sup> His preferred randomization‐t method yields a p‐value of 0.738. As a result, we cannot reject that there is no treatment effect whatsoever. In contrast, if we add 0.1 of a standard deviation of the outcomes (typically considered a small effect) to the ballot winner sample, the same test would have a p‐value of 0. Likewise if we were to subtract 0.1 of a standard deviation from the ballot winner sample, we would again get an omnibus test p‐value of 0. This shows that the omnibus test would reject the null of no treatment effects even if relatively small treatment effect sizes truly were present.

# **6.2 Are Tongan Migrants Similar to New Zealanders?**

Another potential explanation for why we do not find an impact of migration on preferences and beliefs among Tongan migrants to New Zealand could be if these migrants are already quite similar to New

> 9 We thank Alwyn Young for sharing code to conduct this test. This procedure also provides randomization‐inference p‐values for each coefficient. None of these are statistically significant, with the smallest p‐value being 0.201 using his randomization‐t (or randomization‐p) values.

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# **Caught in a Productivity Trap: A Distributional Perspective on Gender Differences in Malawian Agriculture**<sup>**1**</sup>

Talip Kilic<sup>2</sup> , Amparo Palacios-Lopez<sup>3</sup> , and Markus Goldstein<sup>4</sup>

**Keywords:** Gender, Agriculture, Productivity, Decomposition Methods, sub-Saharan Africa, Malawi.

**JEL Classification:** C21, J16, Q12.

**Sector Boards:** Agriculture and Rural Development (ARD), Gender and Development (GEN).

1 This study is the first output of a research program on Gender and Agriculture in sub-Saharan Africa that is led by the World Bank Living Standards Measurement Study (LSMS) team, and is funded by the International Fund for Agricultural Development (IFAD) and the World Bank Research Support Budget. The authors would like to thank Solomon Asfaw, Hans Binswanger, Calogero Carletto, Asif Islam, Donald Larson, Ramón López, Paul Winters, Benjamin Wood, the seminar participants at the American University (Washington, DC), and the participants at the Fourth Wye City Group Conference on Statistics on Rural Development and Agriculture Household Income (Rio de Janerio, Brazil) for their comments on earlier versions of the paper. The Appendix including the results from the sensitivity analysis referenced in Sections 5.3 and 7.2 are available upon request from the authors or could be downloaded at http://tinyurl.com/mwgenderandagriculture.

2 Development Research Group, The World Bank. Corresponding author: tkilic@worldbank.org.

3 PhD Candidate, Department of Agricultural and Resource Economics, The University of Maryland, College Park.

4 Development Research Group and the Africa Region, The World Bank.
than zero during the crisis years for countries in all income groups, except for _CIF_ in highincome countries. Table 5 also presents Wald statistics that test if the behavior of flows during the crisis years or in the immediate aftermath was significantly different from the one observed in the run-up. The Wald tests show that the decline in capital inflows by foreigners and capital outflows by domestic agents during the crisis years (in comparison to the average flow in the previous two years) is statistically significant for all income levels, including _CIF_ in high-income countries. Furthermore, the Wald tests show that gross capital flows remain at depressed levels, or decline even further, during the two-year period after the onset of the crisis.

Figure 4 shows that the median retrenchment in gross capital flows around crises is rather large. For instance, _CIF_ in high-income countries on average declines from inflows of 5.5 percent of trend GDP during the pre-crisis year to outflows of 4.3 percent during the first postcrisis year. In middle-income countries, these flows reverse from 0.4 to -2.5 percent of trend GDP over the same period. In low-income countries, _CIF_ declines from around 0.2 percent of trend GDP during the two years preceding the turmoil period to around -1.7 percent of trend GDP during the year following the onset of the crisis. Similar numbers are estimated for _COD_ .

The analysis so far has included the global financial crisis that hit countries in 2008. However, the empirical evidence in Milesi-Ferretti and Tille (2010) suggests that this latest crisis has been marked by a significant decline in capital flows around the world. A re-estimation of equation (4) around this episode, reported in the top panel of Table 6, reproduces their findings. The Wald tests suggest a significant retrenchment in capital flows during in 2008 and the following year in comparison to the pre-crisis period for all income groups.

To test if the 2008 global financial crisis is driving our results and as a robustness exercise, we re-estimate our event study analysis excluding this episode. The results are reported 20
attainment of her parents (three levels: up to elementary school, lower secondary, and higher), the highest occupational status of her parents (two levels: not in the labor force/blue collar and white collar) and the geographical area of birth (three areas: North, Centre, and South). Note, however, that those households for which the identification of the type is not possible because of missing information about one or more circumstances are excluded. The sample sizes of each wave considered are 6,428 in 2002, 6,354 in 2006, and 6,579 in 2010.

The list of types with their respective opportunity profiles<sup>23</sup> is reported in Table 1 for each wave. Types are ranked according to their average income. Rankings are clearly driven by the regional origin of the household head. In particular, although some reranking takes place for types of other regions, five of the six types from the South of Italy are the lowest-ranked at all times.

To analyze growth, we consider two four-year periods: 2002–06 and 2006–10. The exercise is appealing because it compares two periods during which Italy faced two different economic slowdowns. The former was characterized by the almost total absence of growth in 2002 and 2003. The latter, triggered by the 2008 financial crisis, was characterized by a deep fall in the GDP growth rate in 2008 and, after a slight respite between 2009 and 2010, is ongoing.

# **_Opportunity and Growth in Italy: The Results_**

The GICs for the two periods are reported in Figure 1. These curves are obtained by partitioning the distribution into percentiles and by plotting against each percentile its specific growth rate, expressed in yearly percentage points.

> 23All standard errors are obtained using the sample weights according to the suggestion in Banca d’Italia (2012).

19
Table 13: Output Response to Shocks

|OLS - Robust Standard Errors||||
|---|---|---|---|
|Changes from Mean||||
|Dependent Variable: Log Output (Quantity)||||
|Shocks: Log Output Price||||
|Internal<br>|Finance<br>|Acc<br>|ess<br>|
|(1)|(2)|(3)|(4)|
|Constant<br>0.010|0.400|-0.408|-0.535|
|(0.03)|(1.67)*|(0.76)|(1.00)|
|Always Cnstr<br>-0.854|-1.155|-0.137|-0.150|
|(1.81)*|(3.01)***|(0.17)|(0.20)|
|Sometimes Cnstr<br>-0.946|-1.279|-0.620|-0.403|
|(2.58)**|(4.29)***|(1.10)|(0.74)|
|Positive Shock<br>2.109|1.476|-1.215|-0.542|
|(1.59)|(4.20)***|(0.94)|(0.38)|
|Always Cnstr*Positive Shock<br>-2.576|-1.920|3.309|2.856|
|(1.28)|(1.30)|(1.03)|(0.89)|
|Sometimes Cnstr*Positive Shock<br>-2.923|-1.762|0.505|0.272|
|(1.95)*|(2.46)**|(0.35)|(0.18)|
|Negative Shock<br>-5.988|-3.407|0.391|-0.804|
|(2.24)**|(4.92)***|(0.17)|(0.38)|
|Always Cnstr*Negative Shock<br>4.705|1.695|2.826|3.685|
|(1.62)|(1.33)|(0.65)|(0.94)|
|Sometimes Cnstr*Negative Shock<br>4.424|1.323|-2.277|-1.595|
|(1.61)|(1.37)|(0.98)|(0.72)|
|Lagged Log Capital Stock<br>-0.092|-0.113|-0.094|-0.115|
|(4.11)***|(4.60)***|(4.59)***|(5.06)***|
|Lagged Log Revenue<br>0.076|-0.001|0.110|0.078|
|(3.93)***|(0.03)|(2.26)**|(1.76)*|
|Always Cnstr*Lagged Log Revenue<br>0.053|0.088|-0.014|0.007|
|(1.19)|(2.22)**|(0.16)|(0.08)|
|Sometimes Cnstr*Lagged Log Revenue<br>0.072|0.102|0.044|0.026|
|(2.51)**|(3.87)***|(0.86)|(0.53)|
|Time trend<br>0.041|0.042|0.043|0.047|
|(3.13)***|(1.90)*|(3.29)***|(2.20)**|
|Control for Firm Characteristics<br>no|yes|no|yes|
|Observations<br>954|953|954|953|
|R-squared<br>0.16|0.21|0.17|0.22|
|N.frms<br>200.00|200.00|200.00|200.00|
|Robust t statistics in parentheses||||
|* signifcant at 10%; ** signifcant at 5%; *** signifca|nt at 1%|||

33
**Figure 2.C. Evolution of International Investment Positions and Share of Active Links: Foreign Direct Investment**

<!-- Start of picture text --> Panel A. North-North Panel B. North-South<br>25 100% 6 100%<br>90% 90%<br>5<br>20 80% 80%<br>70% 70%<br>4<br>15 60% 60%<br>50% 3 50%<br>10 40% 40%<br>2<br>30% 30%<br>5 20% 20%<br>1<br>10% 10%<br>0 0% 0 0%<br>Panel C. South-North Panel D. South-South<br>6 100% 6 100%<br>90% 90%<br>5 5<br>80% 80%<br>70% 70%<br>4 4<br>60% 60%<br>3 50% 3 50%<br>40% 40%<br>2 2<br>30% 30%<br>20% 20%<br>1 1<br>10% 10%<br>0 0% 0 0%<br>New links Old links Share of active links<br>USD, trillions USD, trillions<br>Share of active link Share of active links<br>2001 2003 2005 2007 2009 2011 2013 2015 2017 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>USD, trillions USD, trillions<br>Share of active links Share of active l:inks<br>2001 2003 2005 2007 2009 2011 2013 2015 2017 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Note: This figure shows the trends in the value of international investment positions and share of active links by block for foreign direct investment (FDI). The value of positions distinguishes old and new links. Old links are those that have a positive value in 2001. New links are those that have a zero or missing value in 2001. By definition, there are no new links in 2001. Values are expressed in trillions of 2011 U.S. dollars (USD). The share of active links is the number of country pairs that have a positive value in each year over the number of country pairs with a positive or zero value in the same year. Country pairs with a missing value are not considered. Bilateral data are aggregated for all countries within a source region to all countries within a destination region.
(15) where κ is a constant level parameter and b<sup>�</sup> is the steady state level of bonds-to-GDP ratio. The bond holdings are not uniquely determined in the steady state. So, we set them to replicate the average net foreign asset position of the OECS countries equal to 12.6% of GDP. Parameter κ is set to a very small value sufficient to guarantee the stationarity of bond holdings in the linearized version of the model, but small enough to not have any significant effect on the equilibrium dynamics.

We also ensure that government spending along the balanced growth path is equal to 30% of GDP – its average value in the OECS data. Lastly, following Neumeyer and Perri (2005) we assume that φ is equal to 1. The resulting parameter values are summarized in Table 5.

Once the shock processes and other parameter values are set, we solve the model by linearizing the equations characterizing equilibrium around the steady state and solving the resulting system of linear difference equations.

# **5. Results**

In order to understand the effects of financial frictions, pro-cyclical fiscal policy and various shocks on the economy, we now study the properties of the model economy under several scenarios. First, we consider the case of an independent fiscal rule, where government expenditures are independent of the fundamental state of the economy (productivity). In this case we allow for both independent country risk and induced countries risk, i.e. country risk that arises endogenously in response to changes in fundamentals (productivity). In the second case we study the effects of the fiscal policy that is pro-cyclical to the economic conditions. Here we are again interested in the possible interaction between fiscal policy and country risk, so we consider both independent and induced country risk scenarios.

**<u>Table 5. Parameter values</u>**

||**Parameter**|**Value**|
|---|---|---|
|**Preferences**|||
|**discount factor**|�|0.9608|
|**risk-aversion**|�|5|
|**labor curvature**|�|1.6|
|**labor weight**|�|4.932|
|**Technology parameters**|||
|**productivity growth**|�|3%|
|**capital exponent**|�|0.3568|
|**depreciation rate**|�|0.0912|
|**wage-in-advance**|�|1|
|**bond holding cost**|�|10<sup>��</sup>|

16