rafmacalaba/datause-encoder-data huggingface dataset:
The increase in total years of schooling of China’s working population hinges on the education levels of younger cohorts (Figure 1.3). For example, for the age cohort of 20–24 years, there is a 0.3 year increase in the average years of schooling between two consecutive years, reflecting the rapid expansion of education coverage and participation in recent years. However, looking ahead, with the demographic change, the age composition of the labor force will be changing with lower shares of younger cohorts. To counter the negative effects of the population decline, an increase in education attainment for the younger population is crucial.

Figure 1.3 Average years of schooling of employed labor force by age, 2014 and 2015 <!-- Start of picture text --> 14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>总计 16-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+<br>Total<br>2014 2015<br>years<br><!-- End of picture text --> _Source:_ National Bureau of Statistics of China 2015b, 2016.

Whether and how fast the 13th FYP’s goal can be reached largely depends on younger cohorts staying in school longer. As education for the first nine years is compulsory and nearly universal, reaching the 13th FYP’s goal then hinges more and more on access to and retention at the postcompulsory education level. Currently only 77 percent of 34 million Chinese youth at age 20 have completed high school, and for rural youth this ratio is only 47 percent, as shown in Figure 1.4. China’s upper secondary education or post-compulsory secondary education reform has been increasingly attracting attention in recent years, particularly following the country’s pledges to universalize (Pu Ji) this level of education. Post-compulsory secondary education is a special and important stage for most Chinese students, which links the nine-year compulsory education either to advanced learning in college before they enter the labor market or to a job directly upon graduation. Post-compulsory secondary education therefore has been considered a key period to improve the quality of the nation’s human resources.

5
<!-- Start of picture text --> 25-<br>2.0-<br>1.5-<br>°gi=<br>Fi$B 10-<br>0.5-<br>ppp‘<br>° epPgown Mittary Nauelees<br>Coefficient<br><!-- End of picture text --> <!-- Start of picture text --> . v<br>so -° Oyo i<br>ce .<br>0.0- .<br>:<br>5<br>0<br>-0.2- ¢<br>2000 2005 2010 2015 202<br><!-- End of picture text -->
Lewis and Lockheed

# **<u>Table 2: Variable Description and Data Source</u>**

|_Variable Name_|_Description_|_Source_|
|---|---|---|
|Female primary completion rate|The percentage of girls in the last year of<br>primary school, minus the number of<br>repeaters, divided by the total number of<br>girls of official graduation age.|World Bank (2005a)|
|Difference in the male and female primary<br>completion rate|Male primary completion rate minus the<br>female primary completion rate|World Bank (2005a)|
|Learning score (imputed)|Scores from various sources equated to<br>TIMSS 1999, supplemented with TIMSS<br>|Crouch and Fasih<br>(2004)|
||2003 scores||
|GDP per capita (log)|Atlas method|World Bank (2005a)|
|Ethnolinguistic fractionalization|The probability that two randomly<br>selected individuals in a country belong to<br>different groups|Alesina and others<br>(2003)|
|Ethnolinguistic fractionalization squared||Alesina and others<br>(2003)|
|Average years of schooling, female (age||<br>Barro and Lee (2000)|
|25+)|||
|Education expenditure (percentage of<br>GDP)|Percentage of GNP accounted for by<br>public spending on public education plus<br>subsidies to private education at the<br>primary, secondary and tertiary levels.|World Bank (2005a)|
|Female labor force participation rate|ILO database estimates of the extent to<br>which women are active in the labor force|World Bank (2005a)|
|Socialist dummy|1 = socialist history; 0 = other|Authors|
|Road density|Total road network/land area|World Bank (2005a)|
|Rural population|Percentage of total population living in<br>ruralarea|World Bank (2005a)|

# **<u>Table 3 Summary statistics for regression variables</u>**

|_Variable_|_Mean_|_Standard_<br>_deviation_|_Number of_<br>_cases_|
|---|---|---|---|
|Female primary completion rate|78.91|26.43|129|
|Difference in the male and female primary completion rate<br>(percentage of relevant age group)|3.67|8.73|129|
|Learning score|383.43|96.49|56|
|GDP per capita (log)|8.10|0.90|130|
|Ethnolinguistic fractionalization|0.46|0.25|136|
|Ethnolinguistic fractionalization squared|0.27|0.24|136|
|Average years of schooling, female (age 25+)|2.98|1.62|73|
|Education expenditure (percentage of GDP)|4.44|2.21|124|
|Female labor force participation rate|37.34|10.28|133|
|Socialist dummy|0.22|0.42|150|
|Road density (total network/land area)|38.74|57.22|145|
|Rural population (percentage of total population)|51.62|20.43|146|

18
**Table 7**

_Individual Linear Regression Results of Parenting Stress, Depression, and Anxiety on ECD (0-35 =_ _<u>months) (n 1397)</u>_

|Outcome|Predictor|Beta<br>Coefficient|Linearized<br>Standard<br>Error|t-value|p-value|R-<br>Squared|
|---|---|---|---|---|---|---|
|ECD Unadjusted|Parenting Stress|.005|.007|.800|.427|.001|
||Depression|.004|.007|.630|.527|.000|
||Anxiety|-.016|.009|-1.910|.057|.004|
|ECD Adjusted|Parenting Stress|.008|.007|1.210|.225|.071|
||Depression|.010|.007|1.380|.168|.072|
||Anxiety|-.006|.009|-.670|.505|.070|

_Note._ Anxiety is a continuous score, 0= least, 21= most anxious Parenting Stress is a continuous score, 0=least, 25= most stressed Depression is a continuous score, 0=least, 20= most depressed (+) controlled for food insecurity (continuous 0-8; higher indicates more insecurity, flood impact (impacted in 2022 or 2023), fear of husband (never afraid=0, sometimes afraid=1, always afraid=2), asset index (1= poorest, 5= wealthiest), maternal education (1=less than primary up to 8= graduate degree), feeling safe while home (1= very safe up to 4= very unsafe), child gender (0=male, 1=female), refugee status and child age (continuous in months) Individual linear regression analyses were run with each exposure variable (depression, anxiety, parenting stress) on the AIM-ECD sum score, measured as percentage correct (Table 8). The unadjusted model regressed the exposure on ECD for 3- to 6-year-olds controlling only for child age, while the adjusted regression analysis regressed the exposure on ECD for 3- to 6-year-olds controlling for food insecurity, flooding impact, fear of husband, asset index, maternal education, feeling safe when home after dark, child gender, refugee status, child age, and enrollment in early childhood education. A significant positive association (p<.05) of parenting stress on ECD for children aged 36-72 months was found in the unadjusted and adjusted models. A significant negative association was seen between maternal anxiety and ECD for 3- to 6-year-olds in the adjusted model, but not the unadjusted model. No association was seen between maternal depression and ECD in either the unadjusted or adjusted model. However, the coefficient in both instances is negatively signed.

**Table 8** _Individual Linear Regression Results of Parenting Stress, Depression, and Anxiety on ECD (36=_ _<u>72 months) Direct Assessment (n 635)</u>_

|Outcome|Predictor|Beta<br>Coefficient|Linearized<br>Standard Error|t-value|p-value|R-<br>Squared|
|---|---|---|---|---|---|---|
|ECD Unadjusted|Parenting Stress|0.007|0.002|3.560|<.001|.092|
||Depression|-0.001|0.002|-0.480|.629|.063|
||Anxiety|-0.004|0.003|-1.510|.133|.068|
|ECD Adjusted|Parenting Stress|0.004|0.002|1.980|.048|.278|
||Depression|-0.003|0.002|-1.46|.144|.273|
||Anxiety|-0.006|0.003|-2.250|.025|.278|

24
- 25 -

Aggregate 1988 Commodity Volumes versus Handling Capacities<sup>5 4</sup> in India's Major Ports

|Commodity|Volume<br>Handli<br>....... (in million|ng<br>Capacity<br>tons).|
|---|---|---|
|Various (Containerized)|5.300|4.800|
|Various (Breakbulk)|22.350|21.650|
|Petroleum Products|55.250|67.350|
|Fertilizers|3.900|12.180|
|Iron Ore|41.500|35.000|
|Coal|6.250|10.500|

# **Source: All India Shippers Council**

58. What becomes readily apparent is the inescapable fact that the national port system is likely to collapse under the load of growirng traffic volumes before long. This is a high price the economy will face for the years of voluntary neglect. Looking at the container trades which have the potential of reaching 25 million tons by 1995, even the addition of Nhava Sheva in 1989 will not provide the necessary capacity to handle these volumes efficiently.<sup>5 5</sup> The 70 percent containerization target that the Government contemplates for the 8th Five-year Plan, has therefore no real basis. The consequences for national trade development become painfully obvious.

59. In many respects, the ports have been caught by surprise. Containerization, increasing ship sizes with complex navigation and deep draft requirements, growing pressures by the trading community but also by ship operators for faster and reliable services are all phenomena for which most of the national ports were totally unprepared. These ports are stuck with configurations of sea- and land-side infrastructure designed for ship and cargo movements, typical for seatrade decades ago. Unfortunately, also the regulatory provisions for port services, the organizational arrangements and -quite frequentlythe management attitudes reflect practices which had their validity in the past but are hopelessly inadequate for meeting the requirements of today's trade and transport markets.

60. Thus much of the misery is self-inflicted. As a conservative estimate: actual port capacities could be boosted by at least 35 percent without any major investment, if prevailing procedures and practices were

- **54/ The capacity figures represent a** composite rating, which **takes** into account installations for ship-shore cargo moves, available yard and warehousing space, port internal transport facilities, as well **as** interfaces with inland transport systems.

- **S5/** The **new** installations at **Nhava** Sheva, together with the expandec container facilities at Madras (which **are** presently under construction) will increase the total container handling capacity of the national port system to somewhere between 12 **and** 15 -lion tons.
# **Part I – Framework**

# **Introduction**

The objective of this work is to analyze the distributional implications of subsidies and to simulate the impact of subsidies reforms on household welfare and on the government budget. Our intention is not to provide all possible means of analysis and simulation but to restrict choice to a selected number of tools that require a minimum amount of data and are relatively simple to understand and to implement. The manual wants to serve policy analysts who, as often is the case, need to react quickly to governments‟ requests. Time and quality of outputs are two dimensions that are normally inversely related. Given that the time given by politicians to analysts is typically short, we try to improve on the quality of outputs by designing standard and data parsimonious routines.<sup>4</sup>

# **Definitions**

Different texts often refer to the same concepts with different names. Here we clarify the jargon that will be used throughout the manual.

It is important to distinguish first the category of subsidized good. We distinguish between **primary, intermediate and final subsidized goods** depending on where the product enters the production chain. This distinction is instrumental in understanding from the start whether we need to look into effects that reach households through other agents. For example, fuel may be considered as a primary product given that is an input for most production processes, although it is also a product that can be consumed by households. Electricity is typically an intermediate product as it needs some form of energy to be produced and is also a production input for other productions, although is also a final product consumed by households. A product like bread instead is a final product as it does not usually enters other processes of production and is directly consumed by households. The categorization in these three types of products helps us to immediately identify some of the boundaries of the analysis as it points to the number of intermediaries and the price-shift mechanisms that we need to consider.

We distinguish between **direct and indirect subsidies** with respect to households. In tax theory, direct taxes are taxes that are paid directly to the government by the tax payer such as the Personal Income Tax (PIT) while indirect taxes are taxes that are first collected by intermediaries such as the Value Added Tax (VAT). Analogously, we can define direct subsidies as subsidies that are provided directly to the beneficiaries such as subsidies on bread or electricity and as indirect subsidies as subsidies that are provided to intermediaries such as enterprises.

Following from the above definition, we can distinguish between **direct and indirect effects** of subsidies on household welfare.

> 4 The manual was inspired by an analysis of electricity subsidies conducted in Jordan in November 2011: Verme, P. (2012) “Electricity Subsidies and Household Welfare: Can Household Afford to pay for the Budget Crisis?” mimeo, World Bank.

6
This paper focuses on the economic welfare of households living inside GMAs. It tries to answer the question, do the households in GMA enjoy higher levels of welfare relative to the conditions they would have been in had the area not been designated as a GMA? Within the GMA, the paper tries to determine the factors that influence household participation in CRB/VAG activities, and whether the participating households get any extra benefits. Also of interest is whether such benefits of living in the GMAs, and, once in the GMA, of participating in CRB/VAG activities (if they exist) accrue more to the poorer segments of the communities<sup>1</sup> .

To answer these questions, we use household and community level survey data from GMAs and other areas near national parks (non-GMAs). The rest of the paper is organized as follows: Section 2 focuses on methods and procedure. Section 3 presents the estimation results. Section 4 summarizes and concludes.

# _1.1 Background_

Zambia’s national parks and Controlled Hunting Areas (CHAs) were reorganized into national parks and GMAs in 1972 when the National Parks and Wildlife Act of 1968 came into force. Subsequently the National Parks and Wildlife Act of 1991 replaced the 1968 version and in turn it was replaced by the Zambia Wildlife Act of 1998. There were no substantial changes in the subsequent Acts in terms of national parks. The national parks are almost exclusively reserved for conservation and enhancement of wildlife, ecosystem, biodiversity and natural beauty. All forms of land use in GMAs are subject to provisions of management plans developed by CRBs (Zambia Wildlife Act, 1998, Section 7).

The CBNRM program in Zambia evolved out of the ADMADE program financed by USAID in the 1990s. By 1997 the current community institutional structures were established. In this system, VAGs are composed of five to ten elected representatives from a cluster of villages of populations of 500-1,000. Each VAG elects its representative to sit on the CRB. Each CRB consists of 9-10 members and elects a chairperson. An officer of ZAWA is assigned to the area under a CRB, as its unit leader.

The revenue stream to CRB originates from ZAWA and is called “Wildlife Conservation Revolving Funds (WCRF).” (ZAWA, 2007) Fifty percent of all ZAWA revenue is allocated to CRBs for community development out of which 5 percent goes directly to the chiefs. Each CRB may employ one or more local game scouts to assist ZAWA in enforcing anti-poaching regulations from the 45 percent allocation. CRBs enjoy wide latitude in deciding how the remaining WCRF is used.

The early days of the CBNRM program witnessed widespread misappropriation of funds. (Astle, 1999, Grant Thornton Associates et al, 2005). In light of such criticisms of the CBNRM program, this study seeks to differentiate between the poor and the non-poor

> 1 In line with the primary justification for the establishment of the GMA institution.

3
# **Table 3: Accounting decomposition of net interest income to total assets, 2005-14, continued**

|||As a<br>|percentage of n<br>|et interest inco<br>|me<br>||As a per<br>|centage of tota<br>|l assets<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|[1] Group|[2]<br>Net interest<br>income to<br>total assets|[3]<br>Overhead -<br>noninterest<br>income|[4]<br>Tax<br>expenditure|[5]<br>Loan loss<br>provisions|[6]<br>After-tax<br>profit|[7]<br>Noninterest<br>income|[8]<br>Overhead|[9]<br>Tax<br>expenditure|[10]<br>Loan loss<br>provisions|[11]<br>After-tax<br>profit|
|East Asia & Pacific|3.081|11.0|17.3|17.3|54.5|2.475|2.813|0.532|0.532|1.678|
|Europe & Central Asia|3.478|32.0|7.6|31.2|29.3|2.868|3.980|0.263|1.084|1.019|
|Latin America & Caribbean|4.333|36.8|10.6|16.2|36.4|2.342|3.936|0.461|0.703|1.575|
|Middle East & North Africa|2.640|14.9|12.4|19.9|52.8|1.314|1.707|0.327|0.524|1.395|
|South Asia|3.601|25.8|18.4|20.4|35.3|1.605|2.533|0.664|0.736|1.273|
|Sub-Saharan Africa|4.475|28.4|16.0|19.4|36.2|4.319|5.591|0.718|0.867|1.619|
|OECD high income|1.745|32.4|10.7|28.6|28.4|1.101|1.666|0.186|0.498|0.495|
|High income|2.031|25.7|10.8|23.7|39.8|1.285|1.808|0.219|0.481|0.807|
|Upper middle income|4.000|32.1|11.1|20.6|36.2|2.705|3.991|0.443|0.825|1.446|
|Lower middle income|4.148|30.3|13.4|20.2|36.0|2.493|3.751|0.557|0.838|1.495|
|Low income|5.034|31.1|15.5|17.9|35.5|4.258|5.826|0.782|0.899|1.785|

_Note_ : The component variables, as percentages of total assets, are pooled weighted averages over the 2005-14 period. Net interest income to total assets is reported as the countrylevel sum of overhead, tax expenditure, loan loss provision, after-tax profit, less noninterest income, all in percentage of total assets. Region and income level aggregates are based on simple averages of relevant countries.

30
|East and North Asia|17.2|
|---|---|
|Gulf and Red Sea|26.8|
|Mediterranean|20.3|
|Northern Europe|29.5|
|North America – East Coast|21.1|
|North America – West Coast|46.2|
|South Africa|64.6|

Finally, processing time when crossing an international border is also an important part of overall shipment time for international trade. To account for border delays in our computations, we include importing and exporting time from the “trading across borders” section in the World Bank’s Doing Business Database.<sup>10</sup> The data is available for each country considered in our analysis and Table 3 presents the average values aggregated by region. To get bilateral values for border delays for each directed country‐pair, we compute the total border delay as the sum of importing and exporting time for each origin and destination respectively. Whenever a border is crossed along the optimal route, the “border penalty” is incurred.

_Table 3: Export and Import processing time at the border (from the Doing Business Database)_

|**Region**|**Time to export: Border compliance**<br>**(hours)**|**Time to import: Border compliance**<br>**(hours)**|
|---|---|---|
|**East Asia & Pacific**|55.9|70.5|
|**Europe & Central Asia**|28|25.9|
|**Latin America & Caribbean**|62.5|64.4|
|**Middle East & North Africa**|62.6|112.3|
|**South Asia**|59.4|113.8|
|**Sub‐Saharan Africa**|100.1|136.4|

# **d. Reality‐check: How do our pre‐BRI estimates compare with other sources?**

In the previous subsections, we presented the method and assumptions we used to construct estimates of shipping time between city‐pairs across the world. We now ask the following question: how do our estimates before the BRI compare with other sources of information regarding shipping time? This allows us to verify the adequacy of our working hypothesis, in particular regarding the speed along different network elements, the border delays and the port processing time. While those estimates cannot be

> 10 For any border, we use the data on “Border Compliance” and the total delay is assumed to be the sum of export time from the exporting country and the import time from the importing country. We do not include documentary compliance as it does not relate to travel time. All data are available at: <u>http://www.doingbusiness.org/data/exploretopics/trading‐across‐borders</u>

9
# Specifically, it is assumed that

where _β_ is a vector of parameters. From this structure of individual hazard, the likelihood of each observed adoption time can be derived as a function of the adoption time _t_<sup>_∗_</sup> _i_<sup>,</sup><sup>**x**</sup><sup>_i_and</sup> _β_ . An expression for the likelihood can be obtained regardless of whether or not adoption occurred before the date of the interview. When the latter is true, the observation is known as “censored”. Using Cox’s (1975) semiparametric method of partial likelihood, maximum likelihood estimates of _β_ can be obtained numerically without making any functional form assumptions about the shape of _λ_ 0( _t_ ).

An individual with characteristics **x** _i_ has a hazard higher than the sample average if she is more likely to adopt earlier than the average of the sample because she faces a higher probability of switching at any time _t_<sup>_′_</sup> after date zero, conditional on not having switched already before _t_<sup>_′_</sup> . The sign of the relationship between an explanatory variable, _xik_ say, and the outcome variable _t_<sup>_∗_</sup> _i_<sup>thusgoestheoppositewayfromanOLSmodelin</sup> which adoption time is regressed on **x** _i_ : in the proportional hazard model, a positive value of _βk_ implies that an individual with a higher value of _xik_ faces a higher probability of making the transition at any given point in time, and thus reduces the expected value of his adoption time, _t_<sup>_∗_</sup> _i_<sup>.IntheOLSmodel,incontrast,apositivevalueof</sup><sup>_βk_impliesthat</sup> an individual with a higher value of _xik_ adopts later in expectation.

From the model of the previous section, one key explanatory variable of interest is the income gain that an individual expects from the transition. Recall that, in our simple model, an individual starts saving to finance the new technology as quickly as possible only if the expected net gain from adoption is positive. Unfortunately, the researcher does not observe individual _expected_ net gain but only a measure of _realized_ net gain, which � � can be retrieved from _μFi_ and _μCi_ . We interpret realized net gain as a proxy for expected net gain. More specifically, when individual expectations are unbiased, realized net gain equals expected net gain plus a random error term which has expectation zero. Define 19