rafmacalaba/datause-encoder-data huggingface dataset:
measures that would promote better implementation of programs and more reliable and accountable functioning of public institutions in disadvantaged areas? The Central Government (because CSSs today are shaping the direction of developmental investment in the social sector) has a major responsibility to reorient the approach of the major social sector programs to address the issue of disparity reduction and espouse the cause of a strong and unequivocal equity-centered approach.

# **References**

Filmer D. and L. Pritchett 1998, “Estimating wealth effects without expenditure data – or Tears: An Application to Educational Enrolments in States of India”, _World Bank Policy Research Working Paper_ No. 1994 Washington, DC: World Bank.

Government of India (1999), Educational Development Index 1998-99, Department of Secondary and Higher Education, Ministry of Human Resource Development.

Government of India (2002), National Human Development Report 2001, Planning Commission, New Delhi.

Government of India (2005), Analysis of School Data: 2002-03 to 2004-05(mimeo) Technical Support Group (TSG), Ed.CIL Jhingran, D and D. Sankar (2009): “Measuring Educational Development and Disparities”, in Preet Rustagi (ed) “Concerns, Conflicts and Cohesions” Universalization of Elementary Education in India”, Oxford University Press and Institute of Human Development.

Rao, J.V.R. P (2003), “State Participation in Centrally Sponsored Programs”, May 2003, <u>http://pib.nic.in/feature/feyr2003/fmay2003/f050520031.html</u> Sankar, Deepa (2008): “What is the Progress in Elementary Education participation in India during the last two decades? An Analysis using NSS Education rounds”, Discussion Paper Series Report No.24, South Asia Human Development Sector, The World Bank.

UNDP (2004), Human Development Report, UNDP, New York.

UNESCO (2005), “Education for All: The Quality Imperative”, EFA Global Monitoring Report 2005, UNESCO, Paris.

UNESCO (2009), “Overcoming inequality: why governance matters”, EFA Global Monitoring Report 2009, UNESCO, Paris.

<u>www.dise.in: Website for District Information System for Education, National University for</u> Education Planning and Administration, New Delhi.

<u>www.education.nic.in</u> : Official website of Ministry of Human Resource Development, Government of India.

Yadav, A. K and M. Srivastava (2005), Educational _Development Index in India_ , Institute of Applied Manpower Research, Manak Publications Pvt. Ltd.

27
third principal component (PC3) can best be described as being related to the **environmental and health impacts,** involving such impacts as: **‘** environmental sustainability of the wider community’, ‘environmental sustainability of farming enterprise’, ‘health risks of farm workers’ and ‘participation of women’. Looking across these three principle components, it would appear that respondents judged the impact of participation in terms of the alleviation of constraints (PC1), on the one hand, and the direct (PC2) and indirect (PC3) impacts of participation on the other.<sup>3</sup>

# **_Constraints inhibiting smallholder participation in higher­value supply chains_**

The questionnaire explored constraints to smallholder participation from two perspectives: (i) the ability of smallholders to participate in such supply chains; and (ii) the willingness of buyers to procure from smallholders given prevailing conditions and alternatives.

To capture the _supply side constraints_ , respondents were first presented with a series of 21 potential factors constraining smallholder abilities to participate and asked to score each item on a five‐point Likert scale from ‘very important’ (1) to ‘very unimportant’ (5). Following the scaling question, respondents were able to indicate further potential impacts through an open‐ended question.

The constraints considered most important (Table 3) were all related to infrastructure and logistics (for example ‘weak/inadequate rural transport infrastructure’, ‘non‐ competitive/high cost transport services’, weak/inadequate rural marketing infrastructure’ and ‘lack of scale’) followed closely by a set of factors related to (weak) other support services (for example ‘weak/limited availability of technical advisory services’, ‘lack of access to competitively priced finance’ and ‘lack of pertinent information on prevailing standards’). In contrast, respondents downplayed the constraints typically associated with smallholder households (and asset‐holdings) themselves, including ‘insufficient availability of family labour’, ‘insecure land tenure’, ‘fragmentation of household land holdings’, ‘risk management/food security’ concerns,

> 3 The results of the PCA suggest that there are three broad potential impacts underlying the scaling data presented in Table 1.  In order to derive measures of these three impacts, multi‐item scales were defined on the basis of the critical items loading on to the relevant principle component.  All three of the scales were reliable, with the Cronbach α exceeding 0.81 in all cases (Spector, 1992; Aiken, 1996). According to the mean scores, access to resources and markets (1.853) was considered the most positive potential impact of participation in higher‐value.  Participation in such supply chains was also judged to have a positive economic and social impact (2.110).  The environmental and health impacts, however, were judged to be ambiguous; the mean multi‐item scale score was 2.506, corresponding to ‘neither positive nor negative’.

10
of protection. The first stage _F_ -statistics (see Table A2) lie between 4 and 5 for the imports sample, so that LIML proves to be the more robust estimator for this sample. The last two columns indicate that the preference parameter _a_ lies between 20.8 (Overall protection model) and 62.5 (All tariff model) across the three measures and the variety of _I_ measures – a relatively smaller range of estimates than with the full sample, but not significantly so.

Table 6 presents LIML estimates from the exports sample, where the dependent variables are the two subsidy measures. The first stage _F_ -statistics (see Table A3) range from a low of 0.54 (model with _I_ 80) to a high of 5.27 (model with _I_ 60). The LIML estimator is therefore robust to the weak instrument problem for only models with intermediate measures of political organization. Therefore the lower limit of the _a_ estimates (which come from the model with _I_ 80) are quite unreliable. It would be safest to infer that _a_ exceeds 100 (and is less than 250) from the subsidy data. The results are therefore quite robust to differential rates of tradeoff between lobbying contributions and welfare that may characterize U.S. government preferences across policy instruments.

The main implication from the estimates of the preference parameter _a_ in Tables 2-6 is that the observed values of _z_ / _e_ and their interactions with _I_ can only lead to the observed levels of NTB protection and subsidies if government puts great weight on welfare. That is, the U.S. government is approximately a welfare maximizer. But welfare maximization would lead to free trade, and trade in agriculture is by no means free! The deadweight losses from protection are considerable. Table 7 compiled from Hufbauer, Elliott, and Schott, indicates the extent of those losses from protection in U.S. agricultural products.<sup>47</sup> They estimate the annual deadweight losses from protection due to sugar quotas during 1977-84 to be $540 mn., due to Section 22 protection of dairy products in 1983 to be $1.6 bn., due to peanut quotas in 1983 to be $14 mn., due to voluntary export restraints of meat in 1983 to be $280 mn., and due to a policy to extend the fish conservation zone further out to sea to be $185 mn. On the subsidy side, Friedman estimates the deadweight losses from grain crop subsidies in 2002 to be between $2 to $6 billion

> 47 Hufbauer, Elliott, and Schott (1986).

21
# **6. Economic Performance Before and After Lib(eralization**

How did aggregate economic performance change following liberalization? Of course a full causal analysis of the impact of liberalization goes well beyond the before and after experiment, and requires controlling for many other factors. Nevertheless, it is worth reporting the results of a simple before-and-after comparison, using the dates from Approach (b) above. The comparion does not reveal a clearcut change in mean rates of inflation, monetary depth or GDP growth. If anything there is a small average improvement in inflation, but a disirnprovement in monetary depth and economic growth, relative to industrial country trends. Table 9 shows mean pre- and post-liberalization mean values of these variables for the set of countries for which liberalization dates for the Treasury Bill rate have been identified above. The data are also shown normalized as the difference between the subject countries and the mean for seven large industrial countries taken as a control group. Inflation-much higher than in the control group fell somewhat more after liberalization, but the difference of 1.4 per cent is not statistically significant. The modest increase in monetary depth (liquidity ratio and in annual GNP growth is in both cases less than occurred simultaneously in the control group, though again the difference is insignificant.

Table 9: _Macroeconomic performance before and after interest liberalization_

||Before|After|Before|After|Differ|ence|Better<br>|
|---|---|---|---|---|---|---|---|
||||||||?|
||Libera|lization|(relative<br>gro|control<br>up)||(t-stat)||
|Inflation (CPI growth %)|27.7|21.8|+19.9|+18.5|-1.4|(0.2)|Yes|
|Liqudity ratio (M2/GDP %)|35.3|37.0|-23.9|-27.2|-3.4|_(0.5)_|No|
|GNP growth (%)|2.3|2.6|+0.6|+0.2|-0.4|(0.6)|No|

Note: Source: _International Financial Statistics._ Annual data l975-97. The liquidity ratio takes the ratio of end-year M2 to the mean GDP of same and following year. 19 liberalizing coimtries including Portugal as in Table 1. Control group: US, UK, Germany, Italy, Sweden, Switzerland and Canada. Date of liberalization is that method B above.

The increase in interest rates and their volatility have not been the only factor influencing economic development over the past two decades. Fuwthermore, financial liberalization has normally been a complex, long-drawn out, and sometimes reversed process. It would be surprising if a before and after comparison based on our single date (method JR) were to show a significant impact.<sup>33</sup> 28
**Table A.5:** Correlation between Corporate Governance indexes and performance – Water Sectors (in levels)

||Non Revenue<br>Water|Continuity of<br>the Service|Potability|Water<br>Coverage|Sewerage<br>Coverage|Res. Water<br>Tariffs|Res. Sewerage<br>Tariffs|Labor<br>Productivity|Metering|
|---|---|---|---|---|---|---|---|---|---|
|Legal Soundness|**-0.33**|**0.34**|-0.05|-0.08|0.09|**0.29**|-0.01|**0.54**|**-0.48**|
|CEO Competitiveness|-0.02|**-0.52**|-0.12|-0.13|**0.26**|0.23|-0.23|0.07|-0.02|
|Board Competitiveness|-0.23|-0.12|**0.31**|**0.29**|-0.04|0.01|0.12|0.15|0.03|
|Professional Management|**-0.27**|-0.13|0.24|0.23|-0.07|**0.31**|0.11|**0.53**|-0.09|
|Transparency & Disclosure|**-0.29**|0.09|**0.31**|**0.39**|0.17|-0.11|**0.32**|0.26|**0.26**|
|Performance Orientation|**-0.37**|-0.23|**0.62**|**0.35**|0.18|0.17|0.03|**0.46**|0.21|
|Corporate Governance|**-0.42**|-0.14|**0.41**|**0.32**|0.17|**0.30**|0.10|**0.59**|-0.04|

**Table A.6:** Correlation between Corporate Governance indexes and performance – Water Sectors (in growth rates)

||Non Revenue<br>Water|Continuity of<br>the Service|Potability|Water<br>Coverage|Sewerage<br>Coverage|Res. Water<br>Tariffs|Res. Sewerage<br>Tariffs|Labor<br>Productivity|Metering|
|---|---|---|---|---|---|---|---|---|---|
|Legal Soundness|0.11|**-0.37**|**0.25**|-0.24|0.13|-0.04|0.17|-0.05|-0.03|
|CEO Competitiveness|-0.04|**0.70**|0.17|0.24|**0.33**|**-0.52**|**-0.38**|0.01|-0.17|
|Board Competitiveness|-0.10|**0.36**|0.22|0.02|-0.21|-0.03|**-0.32**|**0.32**|**0.28**|
|Professional Management|-0.21|**0.27**|0.16|-0.23|**0.25**|-0.23|-0.20|**0.29**|**0.36**|
|Transparency & Disclosure|0.09|0.32|-0.01|**0.28**|0.20|-0.13|-0.09|0.08|**0.32**|
|Performance Orientation|-0.05|**0.42**|**-0.73**|-0.11|**0.55**|0.13|**0.37**|**0.41**|**0.51**|
|Corporate Governance|-0.06|**0.48**|0.05|-0.04|**0.39**|**-0.25**|-0.13|**0.30**|**0.41**|

26
|(8)|-0.0224_∗∗_<br>(0.0104)|0.000782<br>(0.00509)|0.0213_∗∗∗_<br>(0.00811)<br>Yes|Yes|Yes|Yes|Restricted<br>127737||
|---|---|---|---|---|---|---|---|---|
|(7)|-0.0205_∗∗∗_<br>(0.00685)|0.00164<br>(0.00265)|0.0278_∗∗∗_<br>(0.00623)<br>Yes|Yes|Yes|No|Main<br>696135||
|(6)|||0.0225_∗∗∗_<br>(0.00843)<br>Yes|Yes|Yes|Yes|Restricted<br>127737||
|(5)|||0.0294_∗∗∗_<br>(0.00645)<br>Yes|Yes|Yes|No|Main<br>696135||
|(4)||0.000174<br>(0.00513)|Yes|Yes|Yes|Yes|Restricted<br>127737||
|(3)||0.00108<br>(0.00268)|Yes|Yes|Yes|No|Main<br>696135|ival.|
|(2)|-0.0239_∗∗_<br>(0.0103)||Yes|Yes|Yes|Yes|Restricted<br>127737|status.<br>l _×_ Year of arr|
|(1)|-0.0245_∗∗∗_<br>(0.00677)||Yes|Yes|Yes|No|Main<br>696135|gender, marital<br>Canton of arriva|
||Unemployment rate|Network|Attitudes<br>Country of birth FE|Arrival year FE|Canton of arrival X Current year FE|Education FE|Sample<br>Observations|Dependent variable is Employment dummy.<br>All regressions also include age, age squared,<br>Standard errors are clustered at the level of <br>* p < 0.10, ** p < 0.05, *** p < 0.01|

30
**<u>Table 2: Factor Ratios before and after Land Market Participation</u>**

|||**By**<br>|**type or r**<br>|**ental market**<br>|**particip**|**ation**<br>|
|---|---|---|---|---|---|---|
||**Total**|**Lease-out**||**Autarkic**||**Lease-in**|
|**Before land market participation**|||||||
|Household size per ha of owned land|7.01|5.46|***|7.38||8.16|
|No. of adult labor per ha of owned land|3.88|2.96|***|4.34||4.34|
|No. of male adult labor per ha of owned land|1.89|1.23|***|2.13||2.30|
|Number of oxen per ha of owned land|1.35|0.28|***|1.32|***|2.41|
|No. of observations|2,434|813||770||851|
|**After land market participation**|||||||
|Household size per ha of operated land|9.22|20.61|***|7.38|***|4.30|
|No. of adult labor per ha of operated land|5.11|11.16|***|4.34|***|2.34|
|No. of male adult labor per ha of operated land|2.26|4.27|***|2.13|***|1.23|
|Number of oxen per ha of operated land|1.22|1.09||1.32|*|1.20|
|No. of observations|2,188|514||770||904|

_Source:_ Own computation from AU/WB/UG land tenure survey. **Note:** Asterisks indicate the difference in significance between means for landlord and autarky groups as compared to tenants with *, **, and *** denoting significance at 10%; 5%; and 1%. The top panel excludes 53 landless tenants while 299 pure landlords had to be dropped in the bottom panel.

18
(Figure 1). The urban poverty line varies from a minimum of 1061 Yuan (per person per year) to a maximum of 1358 Yuan across China’s provinces. The food component of the poverty line varies across provinces from 784 Yuan to 1229 Yuan while amongst the 11 cities it varies from 800 Yuan to 1229 Yuan. The cost of living facing the poor in the 11 cities comes reasonably close to matching that found in urban China as a whole.

The second observation from Figure 1 is that the 11 cities have a far higher implied cost of living for the poor than found in rural areas. The (population-weighted) mean of the urban poverty lines for the provinces containing the 11 cities is 1243 Yuan, as compared to 1195 Yuan for all urban areas; by contrast the mean for the rural lines is 849 Yuan.<sup>12</sup> These tests cannot be considered conclusive, given that the 11 cities may well have higher prices than other urban areas, in the same province. Non-negligible price differentials between large and small cities are known to exist elsewhere.

However, from these observations, it is reasonably clear that the prices obtained in the 2005 ICP survey are un-representative of China’s rural areas, where prices are appreciably lower for many goods, especially food for which the poor tend to have the highest budget share. And it is plain that there is no way one could credibly correct this problem by re-weighting the data, given the narrow region of common support evident in Figure 1. The direction of bias is clear: the new consumption PPP overstates the cost-of-living in China, and this bias is likely to be larger for the poor, who naturally have a high budget share for food. As we will see in the next set of calculations, it remains true that China is poorer than we all thought, but not as much so as the uncorrected 2005 consumption PPP suggests.

# **International poverty lines**

Chen and Ravallion (2004, 2007) used a line of $32.74/month at 1993 PPP, or $1.08 per day. This was chosen as a deliberately conservative line, whereby the amount of absolute poverty in the world was judged by the standards of what “poverty” means in the poorest countries of the world; naturally, better off countries tend to have higher poverty lines, as shown in Ravallion et al. (1991). The precise line used was set at the median of the lowest 10 lines in a sample of 33 national poverty lines, though this was shown to be virtually the same poverty line if instead one estimated the expected value of the poverty line in the poorest country, which gave

> 12 Note that these numbers imply a 41% urban-rural differential in 2002; allowing for the different inflation rates in urban and rural areas gives a differential of 37% in 2005.

6
2

# **1 Introduction**

Infant and under-5 mortality has declined markedly in Sub-Saharan Africa during the last decade. Among the 20 Sub-Saharan African countries that have conducted Demographic and Health Surveys (DHS) since 2005, 18 countries showed improvements in infant survival since the country‘s previous survey, and broad improvements have taken place in under-5 mortality as well. The Millennium Development Goal for under-5 mortality is a reduction of two-thirds over the 25 year period 1990-2015, which implies an annual rate of decline of 4.4 percent; twelve of the 20 recent DHS countries have rates of decline that exceed this rate. For infant mortality, Kenya stands out among the success stories, with an annual rate of decline of 7.6 percent, the highest among the 20 countries.

The gains in infant mortality are just one element of a wide set of improvements in human welfare in Kenya and many countries in Sub-Saharan Africa. In one review of these changes, Radelet (2010) argues that five fundamental changes have driven the broader turnaround in the ―emerging African countries‖ that he profiles: 1) more democratic and accountable governments, 2) more sensible economic policies, 3) the end of the debt crisis and major changes in relationships with the international community, 4) new technologies that are creating new opportunities for business and political accountability, and 5) a new generation of policymakers, activists, and business leaders. Accompanying these changes has been a new push by governments and the international community to implement public health initiatives, including those advanced by the Gates Foundation, the Global Fund to Fight AIDS, Tuberculosis and Malaria, as well as other organizations.

Millions of dollars have been poured into these initiatives, many of which have been shown to be effective on a small scale in randomized controlled trials. However, given the challenges of governance and large-scale implementation in many countries, the effectiveness of such initiatives on a larger scale is uncertain. The fact that these initiatives were implemented at the same time that broader institutional changes were taking place makes it difficult to draw conclusions as to the source of particular outcomes.
second most concentrated sector in Yaoundé which is the country’s political capital. However, it is important to note that seven out of the ten most concentrated industries in Yaoundé are related to manufacturing activities (Manufacture of dairy products, pulp, paper, paperboard and paper products, cereal flour, soap, perfume, detergents and cleaning products, footwear, and other non-metallic products). The most concentrated industries in Kribi are related to the retail trade services, hotels and camps, restaurants and bars. This result is not surprising given that Kribi is one of the country’s loveliest cities endowed with natural touristic attribute.<sup>3</sup>

# **2.2.2 Regional Specialization in Cameroon**

The previous analysis helps understand whether economic activities are localized or clustered in particular places in Cameroon. In this section, we ask a different but related question, which is whether cities or arrondissements specialized in particular activities. To answer this question, we need a relative specialization index that will help understand whether specific industries tend to account for a larger share in the economic activity of a city relative to their average share in all other cities. As its well known in the urban economic literature, cities manufacture different types of goods and, as such, production patterns differ across the urban hierarchy. Specialization and diversification are important for different types of entrepreneurial activities, and diversification is linked to more early-stage entrepreneurial activities (Fritsch and Storey, 2014). Using data on 23 urban areas in 12 European Union countries, they show that urban regions with high levels of economic growth and diversity of economic activities exhibit higher levels of opportunity-motivated entrepreneurial activity than their counterparts. Henderson, Lee and Lee (2001) showed that larger cities tend to have a more diverse industrial base, with providers of niche products and services who can find a market in a large agglomeration. On the other hand, smaller and medium size cities tend to be fairly specialized in food and beverage production, textiles, shoes, or pulp and paper products.

To get an accurate picture of the extent and the strength of cities specialization in Cameroon, we computed the Krugman’s specialization index at the 4-digit nace level (see Appendix A.1 for details). A high value of the index (higher than 0.75) represents a highly specialized city, and a lower value of the index (below 0.35) represents a diverse one. As can be seen from Table 3 and Figure 4, localities are specialized on average (37 percent of cities are specialized in 2009). In other words, compared

> 3Kribi is a town located at the coastal lowlands of the southern region of Cameroon. In addition to its various touristic services such as hotels, bars, sporting complex, and cultural evenings for entertainment, the Chad-Cameroon pipeline project along with the seaport construction are expected to boost the industrial sector there in the coming years.

12