rafmacalaba/datause-encoder-data huggingface dataset:
Table A2. 7 Communication and simplification of rules and procedures

||**_Weights by_**<br>**_th b _**|**_Question_**<br>**_weights_**|**Coding of a**|**nswers**||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**_eme ( j )_**|**_(c k )_**|**_Yes_**|**_No_**||**Bulgaria**|**Romania**|**Croatia**|**Albania**|**OECD**|**EU15**|**Poland**|**Slovakia**|
|**Communication**|1/2|||||||||||||
|There are systematic procedures for making regulations<br>known and accessible to affected parties||2/12|0|6|-|yes|yes|yes|yes|n.a.|n.a.|yes|yes|
|There is a general policy requiring "plain language"<br>drafting of regulation||1/12|0|6|-|yes|yes|yes|no|n.a.|n.a.|yes|yes|
|Affected parties have the right to appeal against<br>adverse enforcement decisions in individual cases||4/12|Yes or in all<br>cases<br>In some<br>cases<br>0<br>3|No<br>6|-<br>-|in all cases|in all cases|in all cases|in all cases|n.a.|n.a.|in all cases|in some<br>cases|
|There are inquiry points where affected or interested||||||||||||||
|foreign parties can get information on the operation and<br>enforcement of regulations||3/12|0|6|-|yes|yes|yes|yes|n.a.|n.a.|yes|yes|
|Government policy imposes specific requirements in|||Governmen<br> <br>For some<br>|No|-|government<br>|government<br>|government<br>|government<br>|n.a.|n.a.|government<br>|government<br>|
|relation to transparency/freedom of information||2/12|t wide<br>sectors|||wide|wide|wide|wide|||wide|wide|
|government wide|||0<br>3|6|-|||||||||
|**Simplification**|1/2*(Wi-Min W)|/(Max W98-|Min W)|||||||||||
|National government (all ministries and agencies) keeps<br>a complete count of the number of permits and licenses<br>||1/3|0|6||yes|no|no|no|n.a.|n.a.|yes|no|
|required||||||||||||||
|There is an explicit program to reduce the administrative<br>burdens imposed by government on enterprises and/or||1/3|0|6||no|yes|yes|yes|n.a.|n.a.|yes|yes|
|citizens||||||||||||||
|There is a program underway to review and reduce the<br>number of licenses and permits required by the national<br>government||1/3|0|6||yes|yes|yes|yes|n.a.|n.a.|no|no|
|**Country scores (0-6)**|||jbj kckanswerjk|||**0.3**|**0.1**|**0.5**|**0.3**|**0.5**|**0.5**|**0.8**|**1.4**|

|**Weight for the simplification eleme**|**_Weights_**<br>**_(d k )_**<br>**nt Wi **|
|---|---|
|Administrative burdens for corporation|1/4|
|Administrative burdens for sole proprietor firms|1/4|
|Sector specific administrative burdens|1/4|
|Communication|1/4|
|**Country weight (0-1)**|kdkscorek|

46
The education situation of Pakistan is generally poor in absolute terms, relative to other countries in its region, and relative to developing countries at its level of per capita income (see, e.g., United Nations Educational, Scientific and Cultural Organization 2007 for a recent comparative picture). Given the present trend, Pakistan is unlikely to meet the United Nations’ Millennium Development Goal of universal primary education by 2015. The education situation of Punjab—Pakistan’s most prosperous and populous province and the site for the FAS program—is by and large comparable to the rest of the country. As Panel A in Table 1 shows, estimates using household sample survey data from 2004/05, which is just before the FAS program was initiated, show that the participation rate in formal school (grade 1+) of children ages 6–15 years was 65.7%, with a significantly lower share for children from the poorest (bottom expenditure quintile) households.

The public school system has been hampered in its ability to improve education outcomes due to, in large part, the lack of effective accountability and incentive systems which promote the legitimate and efficient use of allocated resources (Social Policy and Development Center 2003).<sup>3</sup> While the public school system has struggled to enroll children and educate them, the private school system has grown dramatically as reflected by the number of institutions and share of enrollment. In addition, responding to the broad demand for greater access and better quality, the system has evolved in character, becoming less elite and more egalitarian, increasingly

> 3 Recent evidence on relative teacher performance in private and public schools in Punjab suggests this. Andrabi et al. (2008a) find that, while public school teachers are on average significantly better paid and have higher levels of education, training, and experience than their counterparts in private schools, the teacher absenteeism rate is 15% in public schools compared to 8% in private schools. They also find that, whereas private school teacher salaries are increasing in teacher competency (as measured by teacher test scores) and decreasing in teacher absenteeism, public school teacher salaries do not exhibit similar correlation patterns and instead appear to be largely determined by teacher credentials (education, training, and experience). Looking at child education outcomes, Andrabi et al. find that private school teacher salaries are increasing in student test scores whereas public school teacher salaries are not. The responsiveness of private school teacher salaries to these key behaviors and outcomes suggests the influence of market discipline which the public school system is not subject to, at least directly.

7
Source: Estimates based on the _Enquête Nationale sur l’Emploi_ (ENE; Labor Force Survey) Source: Estimates based on the _Enquête Nationale sur l’Emploi_ (ENE; Labor Force Survey) Labor market variables differ depending on geography. Employment distribution varies across regions. For example, 58 percent of employment in Drâa-Tafilalet was in agricultural jobs in 2014, while in the Oriental region this share was just 30 percent while 45 percent was in services (Figure 2.13a).

**Figure 2.13a Employment distribution varies across regions (2014).**

Percentage of total employment <!-- Start of picture text --> 100%<br>90% 21%<br>27% 30% 32% 33% 35% 31%<br>Services 80% 42% 45% 41%<br>Public administration 70% 11%<br>9%<br>Construction 60% 6% 4% 10% 10% 10% 20% 9%<br>50% 11% 7% 8% 11%<br>8%<br>Utilities 12% 9%<br>40% 17% 12%<br>Manufacturing 8% 10%<br>30% 55% 58% 5%<br>Extractive industries 20% 45% 45% 44% 45%<br>Agriculture, forestry 10% 30% 30% 30% 28%<br>and fishing<br>0%<br>Oriental<br>Khénifra<br>Béni Mellal- Drâa-Tafilalet Fès-Meknès Settat Souss-Massa Hoceima<br>Grand Casablanca- Marrakech-Safi Rabat-Salé-Kenitra Southern Regions Tanger-Tetouan-Al<br><!-- End of picture text --> At the same time, those regions with the highest shares in agricultural jobs (Béni MellalKhénifra, Drâa-Tafilalet, and Marrakech-Safi) have the lowest unemployment rates. These shares are stable over time, compared to the distribution of 2000 (Figure 2.13b).

**Figure 2.13b Employment distribution varies across regions (2000).** Percentage of total employment

9 | P a g e
school at age 18. There are also likely to be effects at the intensive margin, whereby household resources and effort devoted to schooling during primary school affect the ability of children to continue schooling in later years. In addition to these direct effects through prior schooling, migration by household members six or more years ago may still result in higher household wealth today, influencing the ability to pay for schooling later on. Furthermore, schooling decisions may depend on the expectation of migration in the future. This expectation will depend in part on previous household migration experience, whether or not the migration episodes occurred within the last five years. For these reasons we prefer the ENADID to the Census for examining the effects of migration on education.

The ENADID asks migrants who have ever been to the U.S. for work a set of additional questions about their migrant experience, including the number of trips they have ever made, and whether they had legal documentation to work. Approximately 50 percent of all migrants have made more than one trip, with a mean of 2.8 trips per migrant. The vast majority of migrants in our sample had no legal documentation to work, especially on their first trip. Over 91 percent of first-time migrants who went to work in the U.S. had no legal documentation to do so. This is important to note, as it indicates that the majority of Mexicans in our sample contemplating migration are likely to end up working without documentation in the United States. Kossoudji and Cobb-Clark (2002) find evidence from an amnesty program that the returns to human capital are higher for legal workers than for illegal workers in the United States. This corroborates our conjecture that migration lowers the incentives to acquire education for prospective Mexican immigrants.<sup>8</sup>

> 8 See also Rivera-Batiz (1999).

8
9 rare case,, (a female heavy smoker over age 35), however, the mortality risks of contraception are s:bstantially lower than those of childbearing in developed and especially in developing countries. Moreover, the risks associated with contraceptive use can be further reduced by judicious choice of, and switching between, contraceptive methods.

# III. IMPACT 3F ORGANIZED FAMILY PLANNING PROGRAMS ON MATERNAL HEALTH

Here we attempt to summarize what is known about the actual and potential impact on maternal health of organized family planning programs, and the implications of that knowledge for policy and for programs.

If all women saying they want no more children and currently not using effective contraception, were able with family planning to avoid all future births, about a quarter of all maternal deaths would be prevented (Maine et al, 1987). For every death prevented, at least ten women would avoid serious health impairment from pregnancy-related illness and complications (Harrison, 1987). But what do we know of the actual impact of family planning use on maternal health? And what are the program implications of what we know, in other words, how can we narrow the gap between the potential and actual contribution of family planning? Data from the 61 countries which have conducted World Fertility Surveys (WFS) and Contraceptive Prevalence Surveys (CPS) in the last decade or so provide important clues to these questions (Population Reports, 1985). We turn now to a consideration of the main characteristics affecting use of contraception, focusing mainly on the woman, and the extent to which users belong to those groups most at risk from pregnancy.

# Accegtor Characteristics

There are three main groups of clients for family planning services: those seeking to delay the first pregnancy; those wishing to space children ("spacers"); and those who wish to prevent additional pregnancies ("limiters"). Overall, there are more "limiters" than "spacers" in developing countries. WFS/CPS data demonstrate that about one quarter of contraceptive users in Asia we--e "spacers" versus about one-third in Latin America and the Middle East. In kfrica, however, three-quarters of the relatively small number of users were "spacers" (Population Reports, 1985).

Ag. Current use of contraception is highest among women aged 30-39 (Population Reports, 1985) and is lower among women in their forties and twenties. The differentials are relatively small, however. In 16 selected developing countries in the mid- to late-seventies, prevalence rates were 37, 47, 51, 48 and 48 for the 5-year age groups between 20 and 44 years of age (Population Encyclopedia, 1982). (No African country was included in this analysis). Use is least common among the youngest women, increases durinLg the middle to late childbearing years, and then declines among the oldest women (Population Reports, 1985). Women aged 40-44 are less likely to use contraception than younger women, especially in Latin America. On the positive side, family planning programs are reaching many women in their thirties, who generally speaking, are at greater risk than those in their
Table 11: Survival Analysis (probability of not having had a child) - 1955-1980, Cox PH Model

||||Dependent varia|ble: Age at first bi|rth||
|---|---|---|---|---|---|---|
||Whol|e Sample|At le|ast KCPE|At least S|econdary|
||Cox PH|Cox PH,I|V<br>Cox PH|Cox PH,IV|Cox PH|Cox PH,IV|
|First-stage(al|l Second-stage|RHS variable|s included,but no|t reported)|||
|Reform||-0.100||0.278**||0.510***|
|||(0.13)||(0.12)||-0.16|
|Second-stage|||||||
|Education|-0.076***|-0.049**|-0.099***|-0.073**|-0.074***|-0.056*|
||(0.00)|(0.02)|(0.00)|(0.03)|(0.01)|(0.03)|
|Year<sup>2</sup>|-0.000***|-0.000***|-0.000***|-0.000***|-0.000**|-0.000*|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Muslim|0.003|0.044|-0.001|0.052|-0.076|-0.025|
||(0.05)|(0.06)|(0.06)|(0.06)|(0.07)|(0.07)|
|City|-0.017|0.034|0.059|0.051|0.052|0.022|
||(0.07)|(0.06)|(0.07)|(0.07)|(0.08)|(0.08)|
|Town|0.036|0.042|-0.006|0.014|-0.061|-0.055|
||(0.06)|(0.06)|(0.07)|(0.07)|(0.09)|(0.08)|
|Countryside|0.077|0.108|0.028|0.072|0.040|0.053|
||(0.06)|(0.07)|(0.06)|(0.08)|(0.07)|(0.07)|
|Abroad|-0.200|-0.177*|-0.074|-0.085|-0.199|-0.184|
||(0.12)|(0.11)|(0.11)|(0.12)|(0.16)|(0.15)|
|Embu|0.380***|0.304***|0.288***|0.296***|0.294***|0.304***|
||(0.06)|(0.06)|(0.06)|(0.08)|(0.10)|(0.11)|
|Kalenjin|0.328***|0.245***|0.239***|0.283***|0.139|0.187*|
||(0.06)|(0.08)|(0.06)|(0.07)|(0.10)|(0.10)|
|Kamba|0.297***|0.205**|0.238***|0.246***|0.238***|0.231**|
||(0.06)|(0.09)|(0.05)|(0.07)|(0.09)|(0.10)|
|Kikuyu|0.309***|0.218**|0.189***|0.217***|0.216***|0.233**|
||(0.06)|(0.09)|(0.06)|(0.07)|(0.08)|(0.10)|
|Kisii|0.375***|0.293***|0.327***|0.345***|0.325***|0.334***|
||(0.06)|(0.07)|(0.06)|(0.07)|(0.09)|(0.10)|
|Luhya|0.485***|0.397***|0.368***|0.406***|0.284***|0.334***|
||(0.06)|(0.06)|(0.06)|(0.08)|(0.09)|(0.10)|
|Luo|0.462***|0.371***|0.352***|0.367***|0.247**|0.253**|
||(0.07)|(0.07)|(0.07)|(0.09)|(0.10)|(0.12)|
|Masai|0.080|0.095*|0.007|0.075|0.024|0.089|
||(0.06)|(0.05)|(0.06)|(0.09)|(0.09)|(0.13)|
|Meru|0.070|0.045|0.039|0.081|0.107|0.141|
||(0.06)|(0.07)|(0.07)|(0.08)|(0.10)|(0.11)|
|Mijikenda|0.073|0.051|-0.017|0.072|0.036|0.101|
|/Swahili|(0.07)|(0.06)|(0.09)|(0.10)|(0.12)|(0.13)|
|Recall|-0.081***|-0.077***|-0.116***|-0.107***|-0.107***|-0.100***|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.03)|(0.04)|
|SurveyFE|Yes|Yes|Yes|Yes|Yes|Yes|
|#obs.|18139|18152|11752|11763|6127|6138|

Significance level, *: p<0.10, **: p<0.05, ***: p<0.01.

42
we report the results for the PPML-N model, which are qualitatively similar.

Since the Services Trade Restrictions Database also describes in detail how policy restrictiveness varies across sectors within any given country, we go one step further and exploit the cross sectoral variation in the STRI (Tables 8 and 9). Importantly, this approach employs sectoral fixed effects obtained from the first stage which are then pooled across services sectors. For our policy measure, we use a pooled vector of disaggregated country-by-sector STRI scores, which allows a considerable increase in the number of observations. We follow the structure of tables 6 and 7 but in addition include sector fixed effects in every specification. Table 8 reporxts the result for the Probit model. The results are broadly consistent with those reported in Tables 6 and 7. In this case, however, the interaction term between the STRI and GDP also appears to be positive and highly statistically significant when inserted alone in the regression (column 2).<sup>29</sup> The results are also qualitatively unchanged when additional control variables are included (column 6) or when China, India, the U.S. and the UK are excluded (column 7).

In Table 9 we report the corresponding results with respect to the incoming number of M&A deals. Results are analogous to the ones presented in Table 8. However, unlike in the case of the Probit model, now the coefficient on the interaction term between the STRI and the manufacturing share is appreciably larger than the coefficient on the interaction term between the STRI and the services share. Thus manufacturing and services shares exert a roughly similar effect in terms of attenuating policy restrictiveness as far as the overall probability to attract foreign investment is concerned. However, when the number of deals as a metric of the “intensive margin” is considered, the manufacturing share is relatively more important in enhancing attractiveness for FDI.

While we are aware of the limitations of a cross-section approach, dictated by the crosssectional nature of the policy information available, the results presented in Tables 6 to 9 into manufacturing production.

> 29Again, we also considered a specification in which the STRI was interacted with logged GDP per capita (rather than the shares of manufacturing and services in GDP). The interaction term turns out to be positive and statistically significant in both the pooled probit and the pooled PPML-N specification, respectively. These results would suggest that restrictive policies have a larger impact in poorer countries.

23
# **5. Interpretation and Caveats**

If using these estimates in a simulation exercise of policy changes to facilitate trade, we believe that it is prudent to simulate modest percentage cuts, rather than cuts of 50 to 100 percent. In our policy scenarios, the maximum cuts in the time in trade costs that we implement are 20 percent. The reasons are as follows.

- (i) The time in trade can’t be cut to zero. The world average for shipping a container for exporting or importing is about 23 days, down about two days compared with 2009.<sup>35</sup> However, the most efficient country in the world in the Doing Business data is Singapore. Based in the 2014 _Doing Business_ data, it takes six days to export a shipment on average and four days to import a shipment in Singapore. This is likely a lower bound for most countries to achieve.

- (ii) Policies can’t change infrastructure. Many of the changes responsible for the global decline in the time in trade to ship a container in the past few years are policies such as: improved customs administration; introduction or improvement in electronic submission and processing; introduction of the electronic single window; introduction or improvement in risk management procedures. But poor roads, ports, rail facilities, airports and pipelines also significantly contribute to delays. If polices are being simulated, they can’t be expected to improve infrastructure.

- (iii)Potential double counting.  In a Small Open Economy model, we clearly need to impose the distortion on both imports and exports. But there may be components of double counting if we follow the tradition in this field and impose trade facilitation distortions on both imports and exports for both countries in a bilateral relationship in a multi-country model.  Take the example of exports of food products from Kenya to Tanzania. We have an export distortion that we impose in Kenya that reflects the AVE of the time of exporting from Kenya to Tanzania. In addition, we impose an import distortion in Tanzania on food products from Kenya. For there to be no double counting, the distortions should be independent. One could argue the distortions are independent. That is, the exporting time lost for Kenyan food to Tanzania comes from factors under the control of Kenya (like Kenyan roads, ports, customs procedures);

> 35 In 2009 the world average to export a standard containerized cargo by sea transport was 23.5 days, and 25.9 days to import. Today it takes 21.8 days on average to export and 24.2 days to import (World Bank, 2014, p.107).

99
# SECTION I: INTRODUCTION

# Economic Role of Natural Resources

1. Natural resources play a critical role in Indonesia's development. In addition to crude oil and natural gas derivatives, the country enjoys substantial reserves of coal, tin, nickel, gold and other minerals. The world's richest commercial forest is found here, as well as very diverse fishery resources. Although agricultural diversity is also substantial, rice and tree crops (rubber, palm oil, coconut, etc.) dominate total production, benefitting greatly from the abundant water resources and fertile soil in many areas of the country.

2. The direct contribution of renewable and exhaustible resources to economic development in Indonesia can be approximated by the share of primary producing sectors--agriculture, forestry, fishing and mining--in total value added, exports and employment. As shown in Table 1, nearly 40 percent of GDP is contributed by the direct exploitation and primary processing of natural

# Table 1: DIRECT CONTRIBUTION OF PRIMARY PRODUCTION (X)

||1983|-89|Share of||
|---|---|---|---|---|
||Share of<br>GDP|Growth<br>rate<br>(p.a.)|merchandise<br>e'ports<br> <br>1989/90|Share of<br>employment<br>1988|
|Renewable Resources|21.8|3.7|22.1|55.6|
|Agriculture|19.2|3.7|11.3||
|- Foodcrops|13.5|3.2|0.8||
|- Othercrops|3.4|5.1|9.8||
|-<br>Livestock|2.3|4.8|0.7||
|Fishing|1.6|4.9|2.6||
|Forestry /a|1.0|1.4|8.2||
|Exhaustible Resources /b|17.9|0.6|61.4|0.8|
|Oil and natural gas|16.7|0.3|56.6||
|Other mining|1.2|4.8|4.8||
|Total Primary Sectors|39.7|2.3|83.5|56.4|

/a Logs, sawn timber and plywood.

/b Includes crude oil and condensates, natural gas, LNG and LPG, but excludes other oil products.

Source: Central Bureau of Statistics and Bank Indonesia.
**Table 6: Average temperature (oC) and precipitation (mm) for 1960-90, by station in Chile**

||**Ar**|**ica**|**Antofa**|**gasta**|**La S**|**erena**|**Isla**<br>**Ferna**|**Juan**<br>**ndez**|**Vald**|**ivia**|**Puerto**|**Montt**|**Punta**|**Arenas**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Temp.**|**Prec.**|**Temp.**|**Prec.**|**Temp. **|**Prec.**|**Temp. **|**Prec.**|**Temp. **|**Prec.**|**Temp.**|**Prec.**|**Temp. **|**Prec.**|
|January|22.1|0.0|20.2|0.0|17.1|0.0|18.7|32.4|16.3|57.6|14.4|88.2|10.5|36.5|
|February|22.2|0.2|20.1|0.0|16.9|0.0|18.7|30.8|15.5|51.4|14.2|82.2|10.2|28.1|
|March|21.5|0.0|18.9|0.0|15.6|0.3|17.9|61.9|13.6|70.1|12.2|97.5|8.4|29.7|
|April|19.5|0.0|16.9|0.0|13.6|1.1|16.7|84.5|10.8|136.4|10.1|134.2|6.0|42.7|
|May|17.7|0.0|15.4|0.0|12.3|6.7|15.6|147.4|9.2|296.9|8.5|210.5|3.8|41.9|
|June|16.5|0.2|13.9|0.2|11.1|17.3|13.7|168.0|7.3|266.5|6.8|206.0|1.8|26.9|
|July|15.7|0.3|13.4|0.3|10.7|35.8|13.0|178.6|7.2|316.0|6.7|238.8|1.3|35.1|
|August|15.6|0.1|13.7|0.9|10.9|17.5|12.5|106.2|7.7|250.8|6.7|200.8|2.2|36.1|
|September|16.3|2.2|14.6|0.2|11.6|6.1|12.5|79.4|8.7|154.8|8.0|148.8|4.0|23.8|
|October|17.5|0.0|15.6|0.0|12.8|1.6|13.4|47.3|10.9|122.4|9.5|151.8|6.4|24.5|
|November|18.9|0.1|17.1|0.0|14.0|0.3|15.0|38.8|13.1|69.6|11.6|104.1|8.2|30.9|
|December|20.7|0.0|18.6|0.0|16.2|0.0|16.9|35.7|15.2|69.3|13.4|100.9|9.8|30.1|

_Source:_ Authors’ estimation based on data from the NCDC’s Monthly Climatic Data for the World.

# 4.1. Temperature trends

Using the “normal” values for each station and each month, we calculate monthly anomalies for each station for the whole period (actual temperature minus normal temperature for that month). Anomalies are easier to analyze than the raw temperature and precipitation data, since the seasonal variation is eliminated through the subtraction of normal monthly temperatures. Figure 2 plots the temperature anomalies for each station.

11