rafmacalaba/datause-encoder-data huggingface dataset:
- 10 -

groups define what is and is not legitimate knowledge for the schools (e.g., Whitty and Young, 1976; Goodson, 1983; Kleibard, 1986; Labaree, 1986).

The guiding idea of this new work is that the curriculumn isproduced in particular settings by the interaction of multiple and conflicting forces, rather than arising from or being constrained by functional imperatives or modem cultural rules. This view stresses the diversity of school curricula and the deficiency of general explanations in contrast to local historica. ones. It calls attention to the impact of particular interest groups, professional societies and social institutions in the struggle to establish a school curriculum. Such li-es of thought are at odds with the arguments of the functionalists, but are also inconsiste. with the "world culture" institutionalists. Where the latter vision predicts sweeping worldwide consistencies ov-r time and place, the new conjuncturalist arguments predict great diversity between places, though consistency over time within places. Each nation-state (or family of nation-states descending from a common metropole) is seen as having its own curricular trajectory. Thus, the guiding argument is that: Nation-states tend to have a high degree of consistency in curricular emphases over time, but differ sharply from each other, reflecting unique historical patterns.

With an eye to the general issues outlined above, we turn to the description and analysis of our cross-national study of primary school curricula in less-developed countries.

# Im. Data: Nature and Limitations

Given the paucity of academic research comparing curricula across countries and over time, we began our search for data with limited aspirations. It quickly became clear, however, that the taken-for-granted character of the curriculum around the world generated much more data than we had expected to find. Indeed, the data collection process itself
suggesting intra-group liquidity is an essential link between local affiliates and the EMDE group parent.

Based on the expertise of World Bank staff working with EMDEs, we collected information for 51 EMDE groups in 27 countries as of September 2017 (Annex 2, Panel A). These groups are active in 133 host countries of which 92 are EMDEs. We found evidence that these groups are complex in terms of size; geographical reach; the range of activities in both the financial and real sectors; and group morphology (Table 3):

- (i) <u>Group size and revenue: total group assets comprise $9.7 trillion, of which $6.5 trillion</u> is held by two Chinese banks. Total assets in overseas financial and real sector subsidiaries of these EMDE stood at $823.7 billion (8.4 percent of group total assets), about half located in EMDE hosts. These subsidiaries also account for a sizeable share of total group revenue generated abroad.

- (ii) <u>Group complexity: these groups are complex and span multiple countries and</u> industries. On average, each EMDE group has majority ownership of 30 foreign subsidiaries; 17 are located in EMDE host countries of which 14 reside in the group’s own region. Importantly, on average 13 of these foreign subsidiaries are in the real sector. Subsidiaries are in some cases majority-owned up to 6<sup>th</sup> level down the ownership chain, suggesting groups have complex morphologies as well.

- (iii) <u>Materiality of groups in host countries: the size of foreign subsidiaries of these EMDE</u> group is significant in some host countries. Median assets of these subsidiaries are 4.8 percent of host country GDP. However, their size exceeds 10 percent of GDP in various hosts, particularly in small SSA and LAC countries (see Annex 2, Panel B).

**Table 3: Key statistics EMDE groups (September 2017)**

|**$9.7 trillion**<br>_Total assets_|**4.8%**<br>_Median assets of foreign subs to host GDP_|
|---|---|
|**Number of host countries**||
|**133**|**92**|
|_All hosts_|_EMDE hosts only_|
|**Assets in host countries**||
|**$823.7 billion**<br>_All hosts_|**$401.4 billion**<br>_EMDE hosts only_|
|**Number of foreign subsidiaries in hos**|**t countries**|

14
Policy Research Working Paper 5858

# **Abstract**

Do public sector employees earn less than their counterparts in the private sector? This paper addresses this question in the case of Peru, a country where civil service reform is being debated yet the only available empirical studies on wage differentials date back to the late 1980s. Using data from the 2009 national household survey, the authors perform a multiple step analysis. First, they estimate a single equation with a public sector dummy, which is found to be statistically significant and _positive_ when only monetary wages are taken into account. However, when in-kind payments and bonuses are included to measure compensation, the analysis finds a private sector premium. Second, they estimate for public and formal private employees two distinct wage functions, including the inverse Mills ratio. This takes into account the selection bias resulting from workers self-selecting into the public or private sector. Third, these results are used to decompose wage differentials using the standard Oaxaca-Blinder approach. The results show that the compensation differentials are not significant except for the sub-sample of employees that achieved a postgraduate degree.

This paper is a product of the Economic Policy Unit, Latin America and the Caribbean Region. 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 acoppola@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
# **1. Introduction**

While earlier theories of development focused on accumulation of capital, macro-economic policies, or natural endowments as key determinants of subsequent performance, an influential current of literature has recently postulated a central role of institutions as preconditions of economic growth. In this tradition, broadly accessible and secure property rights that allow holders to enjoy the benefits from investment without being challenged by outsiders or state agents are viewed as an important element of an environment conducive to growth. As a consequence, measures of the security of such rights are now routinely included in assessments of the “investment climate” used by multi- and bilateral donors to determine countries’ progress on policies or eligibility for external funding (World Bank 2006).

However, despite a near-consensus on the importance of property rights, the literature offers little guidance for policy-makers interested in increasing the security of such rights to key economic assets, including land, especially against the state. This is important in view of evidence that state representatives who may be unable to credibly commit to abstaining from use of their power for private objectives (Hoff and Stiglitz 2004) can be the most important threat to property rights, especially in transition countries and environments characterized by rapid urbanization and associated demands for land acquisition. While this implies that policies to counter institutional bias in favor of the wealthy and prevent state representatives from using their power for personal gain could greatly strengthen tenure security, evidence on changes on the ground remains scant and most of the debate has focused on individual property rights security.

In this paper we use the case of rural property rights reform in China, a country where property rights to land have traditionally been insecure and where increased pressure on land in peri-urban areas has given rise to a series of well-publicized conflicts. We build on the vast US literature on land takings under eminent domain to lay out key arguments relating to justification and limits of such takings and the need and desirability of a full compensation requirement. This helps to understand not only how the magnitude and distribution of benefits and costs of land acquisition in China differs from what is found in other settings but also how the 2003 rural land contracting law (RLCL) changes these and thus should affect leaders’ behavior and overall tenure security. Higher population density, rapid recent economic growth and -most importantly- legal constraints on acquisition of rural land for non-agricultural purposes, have immensely increased the benefits from land taking, as reflected in escalating land prices. Officials’ ability to apply eminent domain with little public scrutiny and to rely on readjustment (land reallocation) to acquire large tracts of land without any cash outlay further imply extensive use of land taking not only as a source of income by local government but reportedly also for individual enrichment and corruption.

1
# **Table 1: Summary of Search 2 literature search elements**

|**P**|**Population**|**General population**grouped by age and physiological status:<br>• Infants and young children aged zero to 59 months<br>• Children and adolescents five years or older<br>• Women of reproductive age, including pregnant and lactating women<br>• Adults*|
|---|---|---|
|**I**|**Intervention/**<br>**Exposure**|**Any measure of ‘diet’ or ‘food’ intake:**<br>• Intake of food items or food groups/categories (breastmilk†, animal source foods, dairy<br>products, ultra-processed food, unhealthy food, sugar-sweetened beverages, salt‡)<br>• Measures of dietqualityandpatterns(diet diversity,healthy/unhealthydietpattern)|
|**C**|**Comparison**|Anyor no comparison|
|**O**<br>Note:|**Outcome**<br>|**Anthropometric measures of nutritional status**: incidence and/or prevalence of low<br>birthweight, small-for-gestational age,child stunting (height-for-age), child wasting<br>(weight-for-height, MUAC), thinness (BMI, MUAC), overweight and obesity (weight-for-<br>height, BMI), body fat percentage, waist circumference<br>**Biochemical measures of nutritional status**: incidence and/or prevalence of specific<br>micronutrient deficiencies (e.g. serum retinol or serum ferritin), anemia (hemoglobin)<br>**Biological measures of NCD risk factors and outcomes**: gestational diabetes, gestational<br>hypertensive disorder, pre-eclampsia, raised blood glucose/type 2 diabetes mellitus, raised<br>bloodpressure/hypertension|

*Older adults were not separated given the literature gap in LMICs.

†Breastmilk was one of the food groups considered as part of the diet for infants and young children but was not included as a specific search term.

‡Although salt is considered a condiment/seasoning, it was included as part of the diet due to its diet-related NCD policy relevance and direct contribution to NCD outcomes of interest to our search (i.e. hypertension) 19
# **I. INTRODUCTION: WHAT IS FINANCIAL LITERACY?**

Policymakers in both developed and developing countries are increasingly recognizing the importance of financial literacy and of investing resources in financial education programs. Comprehensive national initiatives and programs funded by the World Bank and other donors have sprung up around the world. Given the corresponding increase in the volume of research on the financial literacy and financial education, there is no better time to take stock of the evidence than now. In this paper, we provide a practitioner-oriented overview of the recent research, drawing mainly on what we’ve learned from surveys, impact evaluations, and other empirical work.

But what is ―financial literacy‖? The term can encompass concepts ranging from financial awareness and knowledge, including of financial products, institutions, and concepts; financial skills, such as the ability to calculate compound interest payments; and financial capability more generally, in terms of money management and financial planning. In practice, however, these notions frequently overlap.

They may also have different implications depending on the income level of the country. In high-income countries, for instance, financial literacy is often viewed as a complement to consumer protection. One of the primary goals of financial education is therefore to equip individuals with the capability to navigate a complex array of financial products, including pensions and mortgages, and to make sound financial decisions. The importance of doing so has been further underscored in recent years by the financial crisis and the continued shifting of retirement planning responsibilities from the public sector to individuals.

In low-income countries, however, financial outreach is much more limited, and more sophisticated consumer products are typically accessible only to a small percentage of the population. The role of financial literacy in increasing access to and take-up of financial services therefore receives more focus. Another important distinction is that people in low-income countries rely to a much greater extent on microenterprise for their livelihood. Acquiring ―managerial capital‖, or business skills and knowledge, is thus a more relevant component of financial capability than for the typical wage-earning worker in a developed country. For this reason, we devote separate sections to financial education for consumers (in both developed and 2
transport price differential between Nouakchott and Dakar, exports are processed through the Dakar port. Despite border-crossings costs and a longer distance, they gain from this shift. Maritime transports differential may be very high: in West Africa, shipping lines charge 1,650 USD for a 40’ container from Northern Europe to Douala, but 3,450 USD to Malabo, which is located only 100 kilometers offshore from Douala<sup>50</sup> .

# **Section 7 A Quantitative Model of the Transit Supply Chain**

# 7.1 **The Model**

This section presents a quantitative supply chain model identifying the impact of cost, delays and uncertainty in lead time. The shipper in the landlocked country bears the transit cost of inland logistics operations from/to port and to/from warehouse or factory (of a 40’ container). The supply chain breakdown of the logistics costs is (see section 3):

|C|=|Freight Transportation<br>Costs|_Fees paid for actual transit transportation_<br>_services to truckers or rail operators_|
|---|---|---|---|
||+|Other Logistics Costs|_transit overheads +administrative costs per_<br>_shipment_|
||+|Delays Hedging Costs|_Translated in equivalent inventory cost_|

# **_Notations_**

The transit chain is broken into steps. Some are transport-related (moving goods between borders), many are not (container storage in the port terminal, transit documents, customs processing/ warehousing at the operator’s facilities). For simplicity, we consider one mode of transport in transit:

> 50 Data from the Europe West Africa Trade Agreement is available at <u>www.ewata.org.</u>

44
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## **Appendix 2: Technical Appendix on the Model, Closure and Experiments**

# **Features of MyGTAP:**

MyGTAP has several new characteristics that are helpful in the multiregional context of the standard GTAP model. These features include 1) more flexibility in the treatment of government savings and spending. This is achieved by removing the regional household of the standard GTAP model and replacing it with a separate government and private household; 2) the inclusion of transfers between government and households and among household groups, remittances and foreign capital incomes; 3) allowing the assessment of a policy impact on different household groups and production factors within an economy of interest (e.g. Nigeria), should additional data such as a SAM be available (Minor and Walmsley, 2012); and 4) allowing the allocation of private household expenditure across commodities using either the Constant Difference Elasticity (CDE) or Linear expenditure system (LES) specifications depending on the studied situation (Minor and Walmsley, 2012).

# **The applied closure:**

MyGTAP model is a large system of equations. The closure rules defines certain assumptions by specifying which variables are exogenous and which are endogenous. The detailed applied closure is shown hereafter: ! A closure for MyGTAP that fix government savings (qgsave) exogenous poph qoh_s pfactwld psaveslack profitslack incomeslack endwslack cgdslack tradslack gincomeslack hincomeslack ams atm atf ats atd aosec aoreg avasec avareg afcom afsec afreg afecom afesec afereg aoall afall afeall dppriv dpgov dpgsave dphsave dphsave_all to tp tm tms tx txs tpdh tpmh toh atall avaall tf tfd tfm tgd tgm tpd tpm emplh empl sremoh sremih remavo sfyoh sfyih fyavo saidout saidin aidavo realTRNH realTRNG s_sub ; Rest Endogenous ; swap qgsave("Nigeria")=dpgsave("Nigeria"); 29
INFES has (mainly) built or renovated secondary schools in urban areas, while FONCODES has (mainly) built or renovated primary schools in rural areas. In Peru, as in many other countries, the poor are less likely to send their children to secondary school and more likely to live in rural areas. Second, the nonparametric regressions show that the shape of the FON CODES distribution slopes _upwards_ at very low levels of (log) per capita income: the poorest 7% of households are less likely to benefit from FONCODES investments in education infrastructure than their (slightly) better-off counterparts.

Figure 5.9 compares the household-level incidence of FONCODES' school breakfa it program with similar programs executed through PRONAA, the Ministry of Education, andi municipal governments. The results suggest that the FONCODES programs tend to be more propoor than those executed by other public sector agencies. Indeed, the probability of receiving a school breakfast provided by FONCODES decreases (almost) monotonically with income, and is highest for children in the lowest quartile of the distribution. Unlike FONCODES projects to improve educational infrastructure, the school breakfast program is one of the centrallyadministered "special" projects. It is therefore much less dependent on the ability of communities to organize, prepare proposals, liaise with FONCODES officials, and administer funds than the community-based projects in education. It may be harder to target community-based projects than special projects if poor households in poor communities have less institutional capacity.

FONCODES has placed a great deal of importance on geographic targeting, and les5 oil other forns of targeting, such as means testing (World Bank, 1996).12 A comparison of the results on geographic- and household-level targeting suggests that FONCODES has been ni ore able to reach poor districts than poor households in these districts. To explore this issue fuither, we graph the estimated probability of benefiting from investments made by INFES, PRONAA, and FONCODES on the number of standard deviations that the income of household i in di ,trict j is above or below the mean income in district j when both household and district incomes are calculated from the 1996 INEI survey. Figure 5.10 shows that the nonparametric regressior lines for FONCODES school infrastructure and school breakfast programs are "humped", peakir gat about one standard deviation above mean district income. Within a given district, househo **Js** that are somewhat better-off than their counterparts are more likely to benefit from FONCODE', investments. This suggests that there was essentially no (positive) intra-district targeting o- FONCODES resources in 1996.

> 12 The amount of targetinrg may be limited by the nature of FONCODES projects, especially those which involve the construction of infrastructure. It may be possible, however, to distinguish between communities of different characteristics within a district--in particular in the larger districts, which also tend to be less homogeneous. Means testing may also be possible for some of the "special" projects.

11
Table 7: _Estimate of dynamic model of bank interest rate convergence - Equations (2), (29, (3)_

||_A Dep_<br>_Develo_|_osit rate_<br>_ping (2)_|_B Dep_<br>_Indus_|_osit rate_<br>_trial (2)_|_C Depo_<br>_Country_|_sit rate (2)_<br>_dynamics_|_D Lend_<br> <br>_Develo_|_ing rate_<br>_ping (2)_|_E Lendi_<br>_Spre_|_ng-Deposit_<br>_ad (3)_|
|---|---|---|---|---|---|---|---|---|---|---|
||Estimate|(t-stat)|Estimate|(t-stat)|Estimate|(t-stat)|Estimate|(t-stat)|Estimate|(t-stat)|
|_a_|0.018|_(0.5)_|-0.081|(2.9)|-0.044|(1.9)|0.254|(5.5)|country||
|_bl_|0.361|(30.7)|0.402|(33.3)|country||0.361|(29.2)|0.0318|(6.2)|
|_b2_|0.140|(10.4)|0.086|(7.4)|0.079|(9.7)|0.104|(8.0)|country||
|_b3_|0.027|(24.6)|0.037|(3.3)|0.019|(2.5)|0.028|(2.2)|||
|c|-0.006|(1.0)|-0l.010|(1.7)|0.002|(0.4)|-0.033|(3.5)|0.095|(10.1)|
|_d_|-0.042|(4.6)|-0.049|(6.5)|-0.031|(4.3)|-0.013|(1.6)|0.216|(12.8)|
|Sample|21 devel|oping|19 indust|rial|21 devel|oping|21 devel|oping|21 devel|oping|
||80:Q4-98|:Q2|80:Q4-98|:Q2|80:Q4-9|8:Q2|80:Q4-98|:Q2|80:Q2-9|8:Q2|
|Method|SUR||SlJR||SUR||SUR||SUR- A|R(1) spec|
|RSQ/DW|0.530|1.96|0.451|2.09|0.660|1.84|0.481|2.03|0.241|1.96|

Note: Dependent variables are first differences in the interest rates and spread as indicated; 'Country' means country-specifi **c** oefficients estimated (not reported except for Regression C - Table 8); Method: SUR is Seemingly unrelated regressions system estimate of pool coefficients; 'AR(I) spec' means country specific first order autocorrelation coefficient estimated.

Table 7a: _Estimate of dynamic model of bank interest rate convergence - Equations (2), (29, (3)_

||_A Dep_<br>_Develo_|_osit rate_<br>_ping (2)_|_C Depo_<br>_Country_|_sit rate (2)_<br>_dynamics_|_D Lend_<br> <br>_Develo_|_ing rate_<br>_ping (2)_|_E Lendi_<br>_Spre_|_ng-Deposit_<br>_ad (3)_|
|---|---|---|---|---|---|---|---|---|
||Estimate|(t-stat)|Estimate|(t-stat)|Estimate|(t-stat)|Estimate|(t-stat)|
|_a_|-0.069|(1.7)|-0.078|(3.0)|0.109|(3.1)|country||
|_bl_|0.504|(16.3)|country||0.246|(9.5)|0.024|(2.8)|
|_b2_|0.040|(1.2)|0.045|(2.1)|0.036|(1.3)|country||
|_b3_|0.034|(1.0)|0.003|(0.1)|0.068|(2.4)|||
|_c_|0.007|(1.0)|||-0.024|(2.0)|0.088|(6.6)|
|_d_|-0.056|(3.8)|-0.068|(5.3)|0.011|(1.1)|0.216|(10.7)|
|_dum*a_|0.094|(1.3)|0.031|(0.1)|0.298|(4.0)|0.578|(3.1)|
|_dum*bl_|-0.154|(4.7)|-0.063|(2.0)|0.144|(5.0)|0.099|(2.9)|
|_dum*b2_|0.111|(3.1)|0.028|(1.2)|0.061|(2.0)|||
|_dum*b3_|-0.012|(0.3)|0.040|(1.7)|-0.052|(1.7)|-0.0001|(0.0)|
|_dum *c_|-0.023|(1.8)|||-0.004|(0.2)|0.004|(0.2)|
|_dum*d_|0.010|(0.5)|0.042|(2.9)|-0.056|(3.5)|0.050|(2.6)|
|Sample|21 devel<br>80:Q4-9|oping<br>8:Q2|21 devel<br>80:Q4-9|oping<br>8:Q2|21 develo<br>80:Q4-98|ping<br>:Q2|21 devel<br>80:Q2-9|oping<br>8:Q2|
|Method|SUR||SUR||SUR||SUR-AR|(1) spec|
|RSQ/DW|0.530|1.96|0.780|1.98|0.489|2.01|0.252|2.00|
||Note:|Dependent varia<br>'Country' means|bles are first differences i<br>country-specifi**c**oeffic|n the intere<br>ients estimat|st rates and s<br>ed;|pread as in|dicated;||

Method: SUR is Seemingly unrelated regressions system estimate of pool coefficients; 'AR(1) spec' means country specific first order autocorrelation coefficient estimated.

25