rafmacalaba/datause-encoder-data huggingface dataset:
made available free-of-charge by government is effectively inaccessible to the public unless they pay again, and do so illegally. Moreover, if patients believe they will be asked for informal payments at health facilities they may choose to go without care (Lewis 2006).

## **TABLE 14. INCIDENCE AND MAGNITUDE OF INFORMAL PAYMENTS FOR** **<u>HEALTH IN SELECTED COUNTRIES, 2001-2006</u>**

||**Informal payment for general health**<br>**services as % of half-monthly per**<br>**capita**|**Share of households that report**<br>**making informal payments (%)**|
|---|---|---|
|Benin (2006)|53|4|
|Guatemala (2005)|23|16|
|Guinea (2005)|43|22|
|Madagascar (2006)|113|15|
|Paraguay (2006)|15|5|
|Peru(2001)|20|3|

Source: World Bank Country Diagnostic Survey (various years).

A special case of informal payments involves supplementary in-kind medical costs for necessary medical supplies and care that should be provided by the health facility but are not. In Tajikistan, family members bathed, fed, provided medical and non-medical supplies, and even administered injections to their relatives (Falkingham 2004). While part of these costs are incurred as voluntary activities by families to help defray the costs of medical care, some of these also reflect governance failure on the part of health care providers, especially when these activities include responsibilities such as administering medications and injections, which should be carried out by medical professionals.

# **Potential solutions**

Policy options for discouraging informal payments in health systems are not well understood and tend to vary across situations and the existing evidence is inconclusive. The argument that informal payments ensure health service delivery where otherwise only very limited services would be provided, or no services at all, fails to address the problem of corruption.<sup>22</sup> Some suggestive lessons indicate that the introduction of formal user fees may reduce informal payments under certain conditions. The combination of improved provider incentives and mechanisms to hold providers accountable have also been shown effective in some settings.

# _Combining incentives and accountability to discourage informal payments_

In most cases informal payments benefit individual health workers but not the provider institution. By contrast, formal user fee revenues tend to benefit from fiduciary oversight and contribute to health service provision. The introduction of formal user fees that provide supplementary compensation for providers who previously benefitted from informal payments appears to discourage informal payments. But the success of

> 22 For example, in the former Soviet Union informal payments were considered a necessity to compensate health workers given their low wages (Balbanova 2007).

53
Table 6. Fixed costs in $

||Estimate|Std. Error|
|---|---|---|
|f|6,404|476|
|f <sup>EU</sup><br>m|251,250|19,054|
|d<sup>EU</sup>|4,240|317|
|f <sup>US</sup><br>m|67,869|5,237|
|fe|77,348|5,372|

While interpreting our cost estimates it is important to understand that they include all ...xed costs, sunk and not sunk, monetary, opportunity, or psychic that the producer takes into account in making decisions. For example, if getting around corrupt o¢ cials to enter an industry creates costs in terms of bribes, time spent, or headaches, these would show up in industry entry costs.<sup>41</sup> It is worth noting that market entry costs to the EU are quite high. As marginal costs of exporting to the US are higher than those of exporting to the EU (tari¤s are higher and there are quotas), ...xed costs of entering the US market are estimated to be low to help match the relatively large share of ...rms (37%) that export to the US. This makes sense in terms of the institutions as the presence of large US retailers from the US looking for suppliers abroad reduces the ...xed cost of entering the US market. Conversely, for the EU: despite preferences and the absence of quotas, the share of ...rms exporting to the EU is only 60%. As a result, the EU market entry costs come out to be large. Documentation costs are low, which is consistent with a large share, 73%, of the ...rms that export to the EU choosing to meet ROOs. Fixed cost of production are also estimated to be low to account for the presence of small exporters. Finally, industry entry costs come from the free entry condition.

It is also worth noting that such a rich structure of ...xed costs is rarely estimated. Estimates for documentation costs, for example, are almost impossible to ...nd. Our approach allows us to provide such estimates.

> 41According to the World Bank’s Worldwide Governance Indicators, Bangladesh scores poorly on control of corruption. Doing-Business Indicators also consistently place Bangladesh among the countries with the highest costs of doing business. Both facts are consistent with high ...xed entry costs.

27
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18. Urban versus rural access to water supply and sanitation, 2006**

<!-- Start of picture text --> a. Water supply b. Sanitation<br>80 100<br>70 90<br>80<br>60<br>70<br>50 60<br>40 50<br>30 40<br>30<br>20<br>20<br>10 10<br>0 0<br>Piped Standposts Wells and Surface water Flush toilets Improved Traditional Open<br>(public) boreholes latrines latrines defecation<br>Urban Rural Urban Rural<br>% population % population<br><!-- End of picture text --> Source: AICD water supply and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. Access figures calculated by AICD using data the 2006 Multiple Indicators Cluster Surveys published by WHO 2010a and 2010b The effects of low investment in the water supply and sanitation sectors are compounded by low budget-execution ratios. The discrepancy between the budget cycle and the cycle of projects makes it hard for the Ministry of Energy and Water Resources to spend up to 50 percent of its investment budget each year. As of 2010 a trust fund set up to finance sustainable development projects in the water and sanitation sector was not operating (Global Water Partnership 2010).

# Power

## **Achievements**

Access to power has steadily improved in Cameroon. National access to electricity increased from 37 percent in 1996 to 46 percent in 2002, and to 48 percent in 2007 (Helio International 2009; World Bank and IFC 2010), above the average for Africa’s resource-rich countries (table 10). Estimates of urban access suggest that between 65 percent and 88 percent of the urban population has access to electricity (USAID 2010; Nkama 2007). At 88 percent, access to power in urban areas is greater than in most low-, middle-, and resource-rich countries in Africa. But these positive trends do not extend to rural areas: only about 14 percent of rural dwellers benefit from access to electricity, half the level incomparable countries.

Several regulatory measures have been adopted to attract private sector participation in the power sector. In 1998, the government initiated a series of reforms to develop the sector. In 2000 a sector regulator (ARSEL) and a rural electrification agency were established. The reforms provided for private sector participation in transmission, generation, and distribution. In 2001, the state-owned, vertically integrated utility was privatized, becoming AES Sonel, and granted a monopoly over transmission and distribution in the concession area, as well as the right to own up to 1,000 MW of installed generation capacity. In 2006 the Electricity Development Corporation (EDC) was established as an asset holder; EDC currently supports the preparations for the Lom Pangar Hydropower project. In 2009 a rural energy fund was created to establish a transparent and private sector–based approach to rural energy.

30
# **3. Growth and Structural Transformation**

<mark>Although many African countries have been recognized as among the best-performing growth countries, with GDP growing by roughly 10% annually during the last decade (Stiglitz, 2013),</mark><sup>2</sup> <mark>the popular narrative is that they are “growing rapidly but transforming slowly”. As mentioned earlier, in many African economies, manufacturing—the sector that has led rapid development in East Asia—is declining as a share of GDP. The worry is that without a major transformation, Africa’s recent growth spurt may soon run out of steam.</mark> Figure 4 compares economic growth rates by sectors in 100 countries during the last two decades. It shows that non-agricultural sectors have grown much faster in low income countries. Both manufacturing and services sectors have experienced faster growth rate than agriculture. Service has experienced the fastest growth rate. This is true for all low income countries, from Ethiopia to Kenya to Zambia. The exception to this trend is China where manufactures have grown much faster than services.

**Figure 4: Service has experienced higher growth rates than manufacturing**

<!-- Start of picture text --> 14.0%<br>GDP Growth Rate by Sector, 1990-2012 12.4%<br>12.0%<br>10.9%<br>10.0%<br>8.3% 8.3%<br>8.0%<br>6.9%<br>6.7%<br>6.0%<br>5.0%<br>4.6% 4.6%<br>4.2% 4.0%<br>3.7%<br>4.0% 2.5% 3.2% 2.5% 3.2% 3.2% 2.5% 2.8% 2.9% 3.0%<br>2.1% 2.2%<br>1.7%<br>2.0%<br>0.0%<br>Low income Lower High Sub-Saharan Sub-Saharan Ethiopia India China<br>middle income: Africa - Africa - Oil<br>income OECD Non-oil Producing<br>Producing<br>Agriculture Manufacturing Services<br>Source: Authors' calculations using World Development Indicators. Ethiopia figures from national accounts. Countries<br>grouped based on World Bank definitions. "Growth rate" calculated as compound annual growth rate based on constant<br>GDP at PPP.<br><!-- End of picture text -->

> 2 <u>http://www.theguardian.com/global-development/2013/apr/17/imf-africa-economic-growth-surging; http://www.mckinsey.com/insights/economic_studies/whats_driving_africas_growth; Radelet (2010) http://www.cgdev.org/files/1424419_file_EmergingAfrica_FINAL.pdf; Leipziger and Yusuf (2012)</u> - <u>http://policydialogue.org/files/events/TICAD_Africa_revised 1_Yusufliepziger.pdf; http://www.imf.org/external/pubs/ft/survey/so/2010/BOK111610A.htm; http://www.imf.org/external/pubs/ft/survey/so/2010/BOK111610A.htm</u>

9
is the variation in infrastructure development. However, only a few studies have examined the underlying factors responsible for the differential development of infrastructure across the Indian states. Using the panel data from 18 Indian states between 2004 and 2020, this study empirically analyzes the factors influencing infrastructure development across the states. The results show that although the inter-state difference in infrastructure levels has been narrowing over time, it is still high in all three types of infrastructure considered in the study – physical, social, and financial-. Kerela, Himachal Pradesh, and Karnataka have performed well in all three infrastructure indices, while Bihar, Uttar Pradesh, and Jharkhand have been lagging behind other states.

The determinants for physical, social, and financial infrastructure development are different. While economic factors are relatively more influential for physical and financial infrastructure, fiscal and demographic factors are more relevant for social and financial infrastructure. There is also complementarity between these three types of infrastructures. An increase in physical infrastructural assets is found to promote both social and financial infrastructures. Financial development also positively impacts both physical and social infrastructure development, as the availability of credit and financial instruments at a reasonable cost is important for public and private infrastructure investment.

The level of economic development is the primary driver for the endowment of all three types of infrastructures as it contributes to infrastructure development through both demand and supply pulls. Similarly, an increase in investment leads to infrastructure development both directly and indirectly. The latter occurs through its multiplier effect on the economy. Fiscal factors such as government expenditure (capital and social expenditure) and debt have differential effects on the different types of infrastructures. Contrary to expectations, an increase in debt seemed to promote infrastructure growth for all three kinds of infrastructure.

The study also confirms that demographic factors such as urbanization, population density, and proportion of SC/ST population have affected infrastructure development. Intuitively, a rise in urbanization increases the demand for physical and social, leading to an increase in the supply of such provisions. As expected, the higher share of the SC/ST population in a state negatively affects social and financial infrastructure because people belonging to SC/ST communities are more likely to experience economic deprivation and social exclusion. Political factors such as having both state and central government from the 30
# **_2._** _The Experiences of Post-1980 Globalizers_

The objective in this section is to identify developing countries that have significantly opened up to foreign trade in the past 20 years and to compare their experience to those of other developing countries that have remained more closed. We identify these post- 1980 globalizers based on their growth in trade relative to GDP in constant prices, and based on their reductions in average tariff rates. Both measures have strengths and weaknesses. Trade volumes are clearly endogenous variables that reflect a wide range of factors other than trade policy. Across countries, a significant share of the variation in trade reflects countries' geographical characteristics. We abstract from these geographical determrinants of trade by focusing on proportional _changes_ in trade volumes relative to GDP, but we recognize that growth in trade volumes may also reflect many factors other than trade liberalization. We therefore also use reductions in average tariff rates to identify globalizers. The average tariff rate is clearly a policy variable, but the relationship between tariff rates and trade volumes is not that strong (Dollar and ZoidoLobat6n, 2001). This reflects both the fact that trade volumes are determined by many factors other than policy, and also the fact that available data on tariffs are a very imperfect measure of trade policy. For example, we use a dataset of unweighted average tariffs (since this gives us the best country and period coverage) which can give disproportionate weight to tariffs on commodities that represent a small fraction of imports. On the other hand, trade-weighted average tariffs give no weight to tariffs on goods that are so high that imports are choked off entirely. Moreover, in many countries non-tariff barriers ranging from explicit quotas and licensing schemes to local content requirements and health and safety standards constitute significant obstacles to trade that are not captured by average tariffs. The advantage of trade volumes is that they in part reflect these non-tariff barriers to trade.

We begin with a group of 101 countries for which we have data on trade as a share of GDP in constant prices beginning in the 1 970s. We begin by separating out the 24 OECD countries (before recent expansions), and add to that group five economies that we think of as early liberalizers (Chile, Hong Kong, Taiwan, Singapore, and South Korea). Their stories are well known, and we: want to focus on the developing countries that have opened up during the recent wave of globalization in the 1 980s and 1990s. This expanded group of rich countries provides a useful benchmark against which to measure the experience of the globalizing and non-globalizing developing countries. With these wealthy countries put aside, we have trade data for 73 developing economies.3

> 3 We do not have constant local currency trade to GDP ratios for Turkey, an OECD member, for the 1970s. This is why the 29 rich countries and the 73 developing countries do not add up to the total of 101 countries.

_7_
module in terms of definitions/thresholds, sequencing of questions, and the training of statistical agents.<sup>23</sup> Our paper has presented a number of potential measures for local level inequality, diversity, and group inequality. Future qualitative work might gain a better understanding of group dynamics at the local level, and how these could be mapped in local indicators (for example from the Population Census), and then mapped across datasets. Similar efforts should be made in devising simple but effective proxies for the effectiveness of local institutions that might help mediate conflict. We have argued that quantitative work is at this stage only effective in identifying underlying associations with conflict. Qualitative work can help in identifying pathways and directions of causality. In a mixed quantitative-qualitative framework (Rao and Woolcock 2003), future refinements of the quantitative evidence and methodological frameworks could lead to better hypothesis testing and the derivation of potential policy findings. This paper is but an initial presentation of the results and implications of the 2003 PODES on conflict. We hope that it will stimulate further debates and consideration, both of the causes of 23 First, the definitions – of conflict and its types – should be clearer in the instrument. At present, it is unclear what exactly is meant by conflict. For example rephrasing the guidance for the question about whether conflict existed in the village, refining its parameters, perhaps by setting a minimum threshold for incidents that should be included, would better ensure consistency of reporting. For example, respondents could be asked “has there been a conflict in the past year in this village in which at least one person was killed or injured, or in which there was damage of property or goods?” This would help ensure that there is a common understanding of conflict amongst respondents, and would hence limit biased under-reporting, giving us more confidence in the resulting data. More accurate descriptors for conflict and crime types would also help in this respect. Second, the ordering of questions might help with the incentives question. If questions are carefully ordered, with ‘gentler’ less sensitive questions coming first, at least some level of trust can be built between interviewer and interviewee, perhaps resulting in more open responses. Other techniques such as using enumerators who speak local languages, and explaining in more detail what the survey will be used for (and what it will not), can also help. A third recommendation, which would show more clearly trends over time, as well as capture more of the social tensions that exist in villages, would be to ask the respondents not just to recall conflicts that took place in the last year, but also those that occurred in the two years before that.

34
of trade for each HS 6-digit product, _n,_ in year _t,_ from exporting country, _d,_ In other words, _Dnd_<sup>_t_=1ifcountry</sup><sup>_d_exportsproduct</sup><sup>_n_totheEU;and</sup><sup>_D_</sup> _nd_<sup>_t_=0ifcountry</sup> _d_ does not export product _n_ to the EU.<sup>16</sup> We then apply the following procedure: First, for each HS 6-digit product _n_ and each year _t_ , we run a cross sectional probit regression: where the dependent variable equals one if there is non-zero trade between the EU and the partner country in that year, and zero otherwise. The right-hand-side variables of this probit regression include _znd_<sup>_t,_and</sup><sup>_z_ ̄</sup><sup>_nd._Matrix</sup><sup>_z_</sup> _nd_<sup>_t_represents all the exogenous right-hand-side</sup> variables and instruments, which are the tariff-inclusive relative price of the product in the US and the relative endowments. The variable _z_ ̄ _nd_ represents the cross years averages of the tariff-inclusive relative price of the US for product _n_ coming from partner country _d,_ and the cross years average relative endowments of the EU. The resulting estimates are used to obtain the inverse Mills ratio, _λ_<sup>ˆ</sup><sup>_t_</sup> _nd_<sup>=</sup><sup>_λ_</sup> � _znd_<sup>_tδ_ˆ</sup><sup>_t_+ ̄</sup><sup>_zndζ_ˆ</sup><sup>_t_�</sup> = _φ_ � _znd_<sup>_tδ_ˆ</sup><sup>_t_+ ̄</sup><sup>_zndζ_ˆ</sup><sup>_t_�</sup> _/_ Φ � _znd_<sup>_tδ_ˆ</sup><sup>_t_+ ̄</sup><sup>_zndζ_ˆ</sup><sup>_t_�</sup> _,_ with _φ_ ( _._ ) and Φ ( _._ ) denoting the pdf and cdf of a normal distribution.

Second, for _Dnd_<sup>_t_=1</sup><sup>_,_thetariff-inclusiverelativepriceoftheproductintheUSandthe</sup> inverse Mills ratio are then used as instruments to estimate the following FE-2SLS regression 16Please refer to Section A5 of the Appendix on how relative prices could determine zero trade.

10
||(8)<br>High-skill|4992|Yes|yes|Heckman|1.11|(8.75)***||
|---|---|---|---|---|---|---|---|---|
|level :|(7)<br>High-skill|5760|Yes|yes|Heckman|1.43|(8.90)***|t 1%|
|s by skill <br> (2/2)|(6)<br>Low-skill|4992|Yes|No|Heckman|1.01|(10.6)***<br>arentheses|igni...cant a|
|ation ‡ow<br>gressions|(5)<br>Low-skill|5760|Yes|yes|Heckman|2.09|(6.70)***<br>atistics in p|at 5%; *** s|
|s of migr<br>ckman re|(4)<br>Total|4992|Yes|No|Heckman|1.92|(7.65)***<br> robust t st|signi...cant|
|terminant<br>LS and He|(3)<br>Total|5760|Yes|yes|Heckman|1.19|(9.35)***<br>ute values of|at 10%; **|
|2. De<br>O|(2)<br>Total|3091|Yes|No|OLS|-|0.76<br>Absol|ni...cant|
|Table|(1)<br>Total|3608|Yes|yes|OLS|-|0.89|* sig|
|||Observations|Dest dum|Orig dum|Method|Mills ratio|R-squared||

24