rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 4.3 Intermediation Commissions** **_percent_**

|Year|Financial Groups<sup>a</sup>|Total Market<sup>b</sup>|
|---|---|---|
|2005|3.3|7.6|
|2006|2.7|7.2|
|2007|2.6|7.0|
|2008|2.5|6.9|
|2009|1.8|6.2|
|2010|1.9|6.4|
|2011|1.6|6.3|

_Source:_ CNSF.

a. Intermediation commissions/direct premium.

b. Acquisition cost/direct premium.

In sum, insurance companies that belong to financial groups have achieved an important market share in the total insurance market in the last few years, with a considerable premium growth rate. The growth of insurers that belong to financial groups is explained in an important way by the synergies between the bank infrastructure and the insurance undertakings.

# **Bancassurance Participation in the Mexican Market**

As mentioned above, in 2010, 15 insurance companies sold products through banks, seven of which belonged to financial groups. The other eight insurers did not belong to financial groups but sold insurance products through bank branches based on specific commercial agreements.

Total premiums sold through banks in 2010 represented 13.6 percent ($1,826 million) of the in-force premiums of the Mexican insurance market. Most of the Bancassurance operations were concentrated in insurers that belonged to financial groups. The top five companies held 89.7 percent of the in-force premiums (figure 4.1).

62
that are necessary to promote and sustain growth and discusses why growth should be shared. The paper concludes by summarizing Sub-Saharan Africa’s economic prospects.

# **II. The Recent Acceleration of Growth in Africa**

Is the growth failure in Sub-Saharan Africa finally reversing? An upward shift in the recent growth rates suggests that a trend break may have taken place around the mid1990s.<sup>3</sup> Annual GDP growth was a sluggish 2.9 percent in the 1980s and 1.7 percent during 1990–94. Since 1994, however, the pace of economic expansion has approached the threshold of moderate growth of 5 percent a year. Even if the record is measured more conservatively with regard to per capita income, the shift is perceptible: relative to its prolonged stagnation or contraction, per capita income grew by 1.6 percent a year in the late 1990s and by 2.1 percent to 3.0 percent a year since 2000. Despite the recent oil price shock, growth has remained good. From 2004 to 2006, the annual growth in GDP and per capita income, when weighted by each country’s GDP, approaches 6 and 4 percent, respectively. Although improvement in aggregate output does not necessarily indicate broad economic development of the region, the current growth episode has nonetheless lasted 12 years altogether, a period that is neither trivial nor brief.

In fact, average incomes in Africa have been rising in tandem with those in other regions (figure 1). The top performers in Africa are doing very well compared with fastgrowing countries in other regions (figure 2).

<!-- Start of picture text --> Figure 1: African per capita income Figure 2: Africa's five fastest growing economies<br>relative to other developing countries relative to Asia<br>8 Africa average Africa top performers Asia top performers China<br>10<br>6 9<br>Developing countries 8<br>4 7<br>Developing countries,<br>excluding China and India 6<br>2 Sub-Saharan Africa 5<br>4<br>0<br>High-income countries 3<br>2<br>-2<br>1<br>0<br>-4<br>1960s 1970s 1980s 1990s 2000-05<br>Source: World Bank various years g. Source: World Bank various years g.<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br>)<br>A n n u a l c h a n g e in re a l G D P p e r c a p ita (% Average annual GDP growth, by decade (percent)<br><!-- End of picture text -->

## **_A. How widespread is the current growth acceleration?_**

- 3 We test the statistical significance of this hypothesis in the growth analysis of the next section.

2
randomly selected at the baseline) in any treated village may be direct PAF beneficiaries. We follow the approach suggested by Duflo, Glennerster, and Kremer (2007). Within-village<sup>5</sup> spillover effects (on outcomes of interests) are plausible for non-beneficiary households in the treated villages, and thus consider all households (regardless of their benefit status) in the treated village as part of the treatment unit.

## **Table 1: Actual and Planned** **<u>treatment status (number of villages)</u>**

||Randomly assigned<br>control|Randomly assigned treatment|
|---|---|---|
|Actual control|61|28|
|Actual treatment|39|72|
|Total|100|100|

<u>Outcome of interest: The main welfare indicator of PAF impact evaluation is per capita</u> consumption. Consumption is considered to be a better measure of welfare than income since it captures the family’s expectations of a more permanent change in future income than current income alone<sup>6</sup> . Food insecurity is another outcome indicator – given it is one of the key outcome indicators for PAF program- and is measured by number of months of food sufficiency for a household. We also examine effects on child level opportunities in education and health/nutrition: school enrollments rates for 6-15 year olds and malnutrition rates for younger children (Table 2). Our analysis also looks at many other dimensions and indicators, most notably those on social capital and empowerment.

**Table 2:** **<u>Outcome Indicators for PAF Impact Evaluation</u>**

|**_Dimension_**|**_Indicators_**|**_Sample size_**|
|---|---|---|
|Income/Consumption|Real Per capita Household Consumption|3,000 HHs (2,774 panel households)|
|Food insecurity|Proportion of households with food sufficiency of<br>6 months or less|3,000 HHs (2,774 panel households)|
|Education|School enrollment rate of 6-15 year olds|5,500 children|
|Health/Nutrition|Child Malnutrition (underweight) among children<br>under 5years|2,300 children|

# **3. Empirical Strategy**

We start with the “intent-to-treat” estimation specification using the following regression:

> 5 Cross-village spillovers are less likely given villages are far from each other.

> 6 The PAF monitoring system also collects data on income and other socio-economic characteristics at the household level to report on income changes for households assisted by the fund.

6
time, in weather, soil fertility, or technology, would likely also have an effect on tobacco adoption. Exploring these relationships in depth would require data of a higher spatial resolution and greater frequency than is currently widely available.

However, despite the constraints associated with spatial and temporal resolution, the tobaccomaize relative land suitability index is a composite variable that is envisioned to capture the local knowledge acquired by farmers through years of experience working their land. We expect this understanding of the potential and limitations of the physical environment to be an important factor in tobacco adoption. The variation in the tobacco-maize relative land suitability index is, therefore, expected to positively predict household tobacco adoption, fulfilling in principle the requirement of instrumental relevance. Moreover, the tobacco-maize relative land suitability index is defined based on average climate conditions for the period of 1960-1990, avoiding direct links to children’s nutritional outcomes obtained during the IHS2. Since this IV is also uniformly dependent on exogenous factors, and does not necessarily reflect differences in genetic factors, skills or other unobserved child-/household-level attributes, we claim that it does not influence the outcome variable beyond its impact through tobacco adoption, and thus satisfies the exclusion restriction.

Our second IV, the IHS1 district-level estimate of tobacco farmers, is based on the premise that farmers are likely to grow commercialized crops after seeing others in their neighborhood adopt. The estimate is predetermined so it will not be correlated with time-variant unobserved variables. In this respect, the variable is expected to predict household tobacco adoption in a statistically significantly fashion, fulfilling the requirement of instrument relevance. Since the IHS1 information precedes the births of the IHS2 sample children that underlie our analysis, the IV avoids any direct links to children’s current nutritional health and also satisfies the instrumental exogeneity requirement.

# **4.2 Identification through Staple Food Price Shocks**

As argued above, our instruments are defined at a higher level than the household as well as the community, and should not be related to the outcome of interest beyond their association with the household tobacco adoption. We address concerns regarding any remaining omitted variable bias linked to within-district unobserved heterogeneity by identifying the causal impact of interest as the difference across the tobacco adoption effects informed by two unique samples of children that 14
7 takes at least 3 years, we include data from 2006 and 2011 when the number of utilities in the database is relatively stable.<sup>3</sup> The median NRW (as measured by the volume lost in percentage of water production) was 26 percent in 2006 and 27 percent five years later in 2011 (Table 1), while the standard deviation remains high.

# **Insert Table 1: Non-Revenue Water (as percentage of water production)**

The median NRW (as measured by the volume lost in cubic meters per km per day) has decreased from 20 cubic meters per km in 2006 to 16 in 2010. Yet, this indicator shows wide variations by year and between utilities (Table 2). In 2010, the best performing quartile of utilities had a median NRW in cubic meters per km of 5 or less. The worst performing quartile of utilities were those where this median indicator was 35 or higher.

# **Insert Table 2: Non-Revenue Water (in cubic meters per km per day)**

The different NRW indicators are not necessarily very strongly correlated. So it can happen that a utility is showing excellent performance in one NRW indicator but is showing a much lesser performance when measured in another NRW indicator. An example is Singapore’s Public Utilities Board whose NRW was only 4 percent of system input in 2007, which is an excellent performance. Yet, when using NRW per km per day, Singapore’s performance is good, but less than excellent – with a loss of about 10 cubic meters per day in the same year – below that of the best performing quartile of utilities (and significantly above the median value for NRW in utilities in high-income countries). The opposite also happens with utilities that show high NRW as measured in for instance percentage of water produced, but where the losses look much less dramatic when using a different loss indicator. Hence, it is important – recommended by IWA - that one not only looks at one NRW indicator as this may give only partial information about the

> 3 The IBNET database has been growing over time.  It started with a small number of utilities in 1996 and has been growing to 1,861 utilities in 2010 .
season October-March.<sup>10</sup> We match these EPA-level estimates to households in our analysis sample and then construct the sample-weighted TA-level median of the EPA long-term average mean maize yields, and percent difference from the long-term mean as explanatory variables.

Furthermore, targeting is based on the 2010 district poverty rates provided by the Malawi National Statistical Office (NSO, 2012). Additional explanatory variables that can explain aid distribution across TAs include the TA population density,<sup>11</sup> the median accessibility index in the TA,<sup>12</sup> median slope, and elevation. While not explicitly used by the MVAC, in view of the political economy considerations that have been shown to impact decisions related to humanitarian aid distribution elsewhere in Africa (Francken et al., 2009), we include information on the political party of the member of the parliament (MP) associated with the communities in our sample. Using the publicly-available data on the election results provided on the Malawi Electoral Commission (MEC) website, we match data on the political party composition of MPs elected to TA’s.<sup>13</sup> In some cases, TAs map to more than one constituency, so we construct a simple mean of constituency-level political alignment dummies at the TA level to capture the proportion of political representation in the TA that is part of the president’s party, in opposition to the president’s party, or independent. The 2009 parliamentary election results are applied to observations in 2013, and 2014 parliamentary election results are applied to observations in 2015 and 2016.

Finally, MVAC has constructed livelihood zone categories that also inform the Household Economic Analysis, to capture the extent to which households in different locations are more vulnerable

> 10 Maize yield estimates data had many fewer missing data points.

> 11 TA population estimates are derived from NSO (2008). TA area is derived from publicly available geospatial data. TA population density is calculated by the authors as population divided by area.

> 12 The accessibility index captures density of road networks and proximity to urban centers.

> 13 - Maps of the constituencies were accessed at <u>https://mec.org.mw/maps district/</u> to allow linking to TAs, since our - data lacks GPS-confirmed constituency information.  Election results were accessed at: https://mec.org.mw/2014 <u>tripartite-elections</u> for the 2014 elections and https://mec.org.mw/2009-general-elections/ for the 2009 elections.

15
Ozano, K., Roby, A., MacDonald, A., Upton, K., Hepworth, N., Gorman, C., Matthews, J.H., Dominique, K., Trabacchi, C., Chijiutomi, C., Tshabalala, Z., Joshi, D., Udalagama, U., Nicol, A., 2022. Groundwater: Making the Invisible Visible: FCDO Briefing Pack on Water Governance, Finance and Climate Change. Report. https://nora.nerc.ac.uk/id/eprint/532312/. Accessed 25 Mar 2023.

- Rodella, A. S., Zaveri, E., Bertone, F., 2023. The Hidden Wealth of Nations: The Economics of Groundwater in Times of Climate Change. Washington, DC: World Bank. https://openknowledge.worldbank.org/server/api/core/bitstreams/5f1877a1-b30f-42c3-8fe29bdd581d915c/content. Accessed 23 Dec 2023.

- Rohde, M.M., Albano, C.M., Huggins, X., Klausmeyer, K.R., Morton, C., Sharman, A., Zaveri, E., Saito, L., Freed, Z., Howard, J.K., Job, N., Richter, H., Toderich, K., Rodella, A.-S., Gleeson, T., Huntington, J., Chandanpurkar, H.A., Purdy, A.J., Famiglietti, J.S., Singer, M.B., Roberts, D.A., Caylor, K., Stella, J.C., 2024. Groundwater-dependent ecosystem map exposes global dryland protection needs. Nature 632, 101–107. https://doi.org/10.1038/s41586-024-07702-8.

- Rohde, M. M., Froend, R., Howard, J., 2017. A global synthesis of managing groundwater dependent ecosystems under sustainable groundwater policy. Groundwater, 55(3), 293-301.

- Saaty, T.L., 1977. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15, 234–281. https://doi.org/10.1016/00222496(77)90033-5.

- Schmitter, P., Kibret, K.S., Lefore, N., Barron, J., 2018. Suitability mapping framework for solar photovoltaic pumps for smallholder farmers in sub-Saharan Africa. Appl. Geogr. 94, 41–57. https://doi.org/10.1016/j.apgeog.2018.02.008.

- Soenen, C., Reinbold, V., Meunier, S., Cherni, J.A., Darga, A., Dessante, P., Quéval, L., 2021. Comparison of Tank and Battery Storages for Photovoltaic Water Pumping. Energies 14, 2483. https://doi.org/10.3390/en14092483.

- Taylor, C. J., Alley, W. M., 2001. Ground-water-level monitoring and the importance of long-term water-level data. US Geological Survey. https://pubs.usgs.gov/circ/circ1217/pdf/circ1217_final.pdf. Accessed 15 Dec 2022.

- UNICEF, WHO, 2022. Progress on drinking water, sanitation and hygiene in Africa 2000-2020: Five years into SDGs. https://www.unicef.org/wca/reports/progress-drinking-water-sanitation-and-hygiene-africa-2000-2020. Accessed 06 Sept 2024.

- Vezin, T., Meunier, S., Quéval, L., Cherni, J.A., Vido, L., Darga, A., Dessante, P., Kitanidis, P.K., Marchand, C., 2020. Borehole water level model for photovoltaic water pumping systems. Appl. Energy 258, 114080. https://doi.org/10.1016/j.apenergy.2019.114080.

- Wada, Y., Van Beek, L.P., Van Kempen, C.M., Reckman, J.W., Vasak, S., Bierkens, M.F., 2010. Global depletion of groundwater resources. Geophys. Res. Lett. 37.

- Xie, H., Ringler, C., Mondal, Md.A.H., 2021. Solar or Diesel: A Comparison of Costs for Groundwater-Fed Irrigation in Sub-Saharan Africa Under Two Energy Solutions. Earths Future 9, e2020EF001611. https://doi.org/10.1029/2020EF001611.

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# Table D6. Distribution of Manufacturing Establishments Vehicles for Workers by Employment Size, Thailand

<!-- Start of picture text --> VEIICLEI (COA VEHICLES FOR VRERS) FIRMSIZE (EMPLOYEES per FIRM)<br>Freqmwcy<br>Percent<br>Raw Pct<br>Col Pct 11-19 120-49 150-99 1100-199 1200-499 1500-999 11000 & I Total<br>I I I I I I tlover I<br>................ ........ ........ ........ ....... +..... ...... --------<br>YES I 1 0 2 0 8 2 a | 21 29<br>I 0.33 0.00 60.67 2.67 0.67 9.67<br>3 3.45 0.00 6.90 27.59 1 27.59 1 27.59 1 6.90<br>3 003 .00W 2.94 14.81 17.02 I 50.00 1 25.00<br>nO I 321 74 661 461 391 81 61 271<br>1 10.67 1 24.67 22.00 1 15.33 1 13.00 1 2.67 2.00 1 90.33<br>I 11.81 1 27.31 1 24.35 1 16.97 14.39 2.95 2.21 1<br>t 96.97 1 100.00 I 97.06 1 85.19 8 82.98 I 50.00 75.00 I<br>--------------- + .. .. . ... . . .. .. . -- -- - .. .. . ... . . ... ...<br>Total 33 74 60 54 47 16 8 300<br>11.00 24.67 22.67 18.00 15.67 5.33 2.67 100.00<br><!-- End of picture text -->
Policy Research Working Paper 8520

# **Abstract**

This paper explores the effect of market orientation on (known) natural resource wealth using a novel dataset of world-wide major hydrocarbon and mineral discoveries. Consistent with the predictions of a two-region model, the empirical estimates based on a large panel of countries show that increased market orientation causes a significant increase in discoveries of natural resources. In a thought experiment whereby economies in Latin America and sub-Saharan Africa remained closed, they would have only achieved one quarter of the actual increase in discoveries they have experienced since the early 1990s. The results call into question the commonly held view that natural resource endowments are exogenous.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa 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://www.worldbank.org/research. The authors may be contacted at rarezki@worlbank.org, rick.vanderploeg@economics.ox.ac.uk, and FToscani@imf.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
section briefly illustrates the various shocks that affected Uganda during the 1990s and summarizes the main findings of previous studies. It then briefly discusses the issue of passthrough.

Coffee price changes: What is their role in explaining Uganda’s performance during the 1990s?

Towards the end of the 1980s, the government of Uganda embarked on a series of reforms commonly regarded as necessary for sustained economic growth (Okidi et al. 2004): in 1987 it launched the Economic Recovery Program (ERP), which was later supported by a sequence of additional structural adjustment programs. Inflation decreased to single-digit levels in 1993 and remained stable thereafter. Policy changes in the coffee sector are particularly relevant because they have almost certainly intensified the degree of interdependency between international and domestic coffee prices. The domestic market for coffee was liberalized in the early 1990s. This liberalization entailed the complete withdrawal of the state from marketing, the abolishment of minimum prices, and the removal of the export tax.<sup>2</sup> Not all government interventions in the coffee market ceased completely after the liberalization. To preserve macroeconomic stability during the boom phase, the Ugandan government introduced a coffee stabilization tax, which came into force in late 1994 (Henstridge and Kasakende 2001).<sup>3</sup> Besides liberalizing the domestic coffee market, the government implemented a thorough trade liberalization, which included an extensive reduction of non-tariff barriers, competitive tendering for government purchases, and a switch from export taxation to import taxation. Finally, the government reversed the investment incentive system that was biased in favor of domestic firms so as to attract foreign direct investment.

Two different approaches have been used to assess the relative importance of coffee price shocks and other shocks on poverty and growth in Uganda. The first relies on ex-post econometric analysis. Household surveys are available before and after the change in coffee prices (while other shocks were also operating) and researchers attempt to measure their

> 2 For the institutional details of the coffee market liberalization, see Akiyama (2001). Prior to market liberalization, the coffee sector had been taxed both explicitly and implicitly. Explicitly, the government levied an export tax at a rate ranging between 40 and 100 percent. An additional implicit tax burden resulted from fixing producer prices at 20 percent of the export price (Fafchamps et al. 2003).

> 3 The tax was set at  20 percent on coffee export earnings above a threshold of  U Sh 1100 per kilogram, and 40 percent on receipts above U Sh 2200 per kilogram.

5
|**Table 2: Attrition, OLS estimates**||Outcome: Track|ed at Follow-Up||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Treatment|-.042*|-.038|-.036|.288|
||(.023)|(.024)|(.024)|(.203)|
|Age (in years)|||.001|.007|
||||(.003)|(.005)|
|Married (yes=1)|||-.010|-.023|
||||(.029)|(.052)|
|Has any child(ren) (yes=1)|||.051|.050|
||||(.033)|(.058)|
|Migrated to Addis Ababa (yes=1)|||-.010|.005|
||||(.024)|(.045)|
|Educational education completed (in<br>|||.006|.016|
|years of schooling)|||(.007)|(.012)|
|Treatment x Age (in years)||||-.008|
|||||(.006)|
|Treatment x Married (yes=1)||||.019|
|||||(.062)|
|Treatment x Has any child(ren) (yes=1)||||.0005|
|||||(.069)|
|Treatment x Migrated to Addis Ababa<br>||||-.020|
|(yes=1)||||(.053)|
|Treatment x Educational level completed<br>(in years of schooling)||||<br>-.014<br>(0.15)|
|Constant|.915***|.940***|.857***|.621***|
||(.020)|(.098)|(.132)|(.198)|
|Mean of Dependent Variable:|.884|.884|.884|.884|
|Woreda Dummies|No|Yes|Yes|Yes|
|Observations|935|935|935|935|
|F-statistic (joint significance of all<br>interaction variables)/p-value<br>Notes: The table presents the likelihood of b|eing surveye|d in the follow-up|using a linear pr|0.56/0.73<br>obability model.|

Notes: The table presents the likelihood of being surveyed in the follow-up using a linear probability model. The dependent variable is a dummy that is equal to one if the respondent is tracked between the baseline survey and the follow-up survey, and zero otherwise. The large _p_ value from the F-test on the coefficients of the interaction between the baseline covariates and the treatment dummy indicate that the sample composition did not change due to attrition. Standard errors are in parenthesis. *** denotes significance at 1%, ** at 5%, and * at 10%.

15