rafmacalaba/datause-encoder-data huggingface dataset:
The notion that opinion leaders, who stand out within the community in terms of their socio-economic and human capital characteristics, are key to information diffusion contrasts with the common observation (verified empirically), that most people tend to have communication networks composed mostly of individuals who are like them in social status and other respects. Sociologists and communications scholars define the tendency for communications to flow mostly among similar individuals as _homophily_ , which is contrasted with _heterophily_ , whereby individuals communicate with those who are different from them. The prevalence of homophily in communication networks leads to an alternative characterization of opinion leaders as being similar in their socio-economic characteristics to the followers. Thus, Weimann’s 1994 comprehensive study of opinion leadership concludes: _“The almost single possible generalization on the socio-demographic level is the tendency towards similarity of the influential-influencee dyad. The trickle-down model, suggesting a vertical flow from upper classes to lower classes, from more educated to less educated, or from higher income groups to lower income groups, was rejected in almost all domains. The more frequent flow was from leader to follower from the same social group. This tendency resulted in a homogeneity of the leader-followers groups in terms of most socio-demographic measures. People turn to seek advice from their peers, from individuals of the same background, interests, and values. The flow of information and influence is likely to be rather horizontal.” (p. 88)_ Rogers (1995) offers a possible middle ground between the two views. He observes that homophily may act as a barrier to the diffusion of new knowledge because “new ideas usually enter a system through higher status and more innovative members” (Rogers, 1995, p. 288). But while he identifies a general tendency for followers to seek information and advice from opinion leaders who are perceived as more technically competent than themselves, he emphasizes that 5
**_Table 1: Labor productivity convergence regressions, Primary sector_**

|||Pri<br>|marysecto<br>|r||||
|---|---|---|---|---|---|---|---|
||Total<br>aggregation|Disaggreg<br>Agriculture +<br>Mini|ated by<br>Fishing &<br>ng|Disaggrega<br>sect|ted by all<br>ors|Agriculture<br>only|Mining<br>only|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Dependent variable:||||||||
|Growth of VAper worker over a decade||||||||
|Log initial VA per worker|‐0.025**|‐0.030***|‐0.050**|‐0.030***|‐0.056***|‐0.008|‐0.061***|
||(0.009)|(0.006)|(0.022)|(0.006)|(0.016)|(0.011)|(0.016)|
|Constant|0.247***|0.296***||0.283***||0.102|0.649***|
||(0.079)|(0.057)||(0.062)||(0.085)|(0.169)|
|Department FE|�|�||�|<br>|�<br>�|�|
|Industry FE|�|�||�|<br>|�<br>�|�|
|Observations|24|46|46|63|63|24|22|
|R‐squared|0.246|0.296|0.714|0.227|0.584|0.025|0.437|
|Stardard errors in parenthesis.||||||||
|*** p<0.01, ** p<0.05, * p<0.1||||||||

**_Table 2: Labor productivity convergence regressions conditioning on initial poverty level, Primary sector_**

|||Primarysector<br>Ditd||||
|---|---|---|---|---|---|
|||saggregae||Ariculture|Minin|
||Total<br>aggregation|by Agriculture<br>+ Fishing &<br>Mining|Disaggregated<br>by all sectors|g<br>only|g<br>only|
||(1)|(2)|(3)|(4)|(5)|
|Dependent variable:||||||
|Growth of VAper worker over a decade||||||
|Log initial VA per worker|‐0.054***|‐0.033***|‐0.032***|‐0.037***|‐0.064***|
||(0.010)|(0.006)|(0.007)|(0.012)|(0.015)|
|Log initial poverty level|‐0.125***|‐0.090**|‐0.036|‐0.097***|‐0.122|
||(0.032)|(0.035)|(0.040)|(0.029)|(0.074)|
|Constant|0.420***|0.274***|0.278***|0.276***|0.614***|
||(0.076)|(0.051)|(0.061)|(0.087)|(0.164)|
|Observations|24|46|63|24|22|
|R‐squared|0.565|0.364|0.236|0.363|0.508|
|Stardard errors in parenthesis.||||||
|*** p<0.01, ** p<0.05, * p<0.1||||||

Upon further investigation, it is apparent that the unconditional convergence in the primary sector is driven by the mining industry. Figure 2 reveals that the relationship between the growth in labor productivity and its initial levels in agriculture has been essentially flat across Peru’s departments.

11
**Table 8:** Probability of a short birth interval (shorter than 24 or 15 months), conditional on the first two born girls

||_<_24 m|onths|_<_15 m|onths|
|---|---|---|---|---|
|Subsample|All<br>children|frst 4<br>children|All<br>children|frst 4<br>children|
||(1)|(2)|(3)|(4)|
|girl|0.021|0.036*|0.015*|0.018*|
||(0.015)|(0.022)|(0.008)|(0.011)|
|girl<sup>_∗_</sup>dead child|-0.046|-0.069|-0.015|-0.015|
||(0.033)|(0.048)|(0.023)|(0.032)|
|dead child|0.184***|0.193***|0.117***|0.109***|
||(0.028)|(0.044)|(0.020)|(0.029)|
|Birth order dummies|Yes|Yes|Yes|Yes|
|Mother FEs|Yes|Yes|Yes|Yes|
|Observations|11086|8149|11086|8149|
|R-squared|0.244|0.319|0.276|0.373|

_Notes._ OLS estimates, linear probability model. Robust standard errors adjusted for clustering at the household level in parentheses. Using survey weights.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at 1%, 5% and 10% levels, respectively.

child. The continuous (dotted) line represents the distribution after the birth of a girl (boy). Given that the length of the interval increases with birth order, I consider the first four births for each woman.<sup>31</sup> to avoid confounding effects. As expected, the distribution of the length of the interval after the birth of a girl lies to the left of the distribution of the interval after a boy. The two distributions are statistically different from each other, as confirmed by the rejection of the null hypothesis of equality of distributions (Kolmogorov-Smirnov test, with a p-value of 0.004). Appendix figure A.1 shows that the distributions in the subsample of women who had two boys or mixed gender composition are not significantly different from each other. Consistently, figure 6 uses the subsample of women with three girls as the first three born children and shows that the whole distribution of the interval after each successive girl lies to the left of the distribution after each boy (and the two distributions are statistically different from each other as shown by the Kolmogorov-Smirnov test with p-value 0.047). This evidence confirms the idea that the pressure to have a son increases with the number of girls born, and leads to a significant reduction in spacing between births, with implications for the health status of mothers and their children.

# **6 Son preference and family structure**

## **6.1 Marital outcomes**

The anthropological and demographic evidence introduced in section 2 suggests that infertility and sex composition of earlier-born children are among the factors affecting the husband’s decision to

> 31This also allows me to exclude the hypothesis that the effect only comes from women who had many children.

24
through the same extent to deposit and lending rates, or adjustment occurs at different speed. The real Treasury bill rate proxies for the marginal cost of funds, and this benchmarks for interest rate decisions by banks. The real T-bill rate could also proxy for alternative investment opportunities of banks. Finally, banks’ balance sheets are affected by movements in the exchange rates. The appendix provides an overview of all the main variables in the panel regressions.

The balance sheet and income statement data of the Nigerian banks in the sample are subject to a wide dispersion of observations among certain variables. Therefore, similar to Beck et al (2005), we remove these potential outliers from the summary statistics, correlation analysis and subsequent panel estimations. Upon inspection of the data, the 1<sup>st</sup> and 99<sup>th</sup> percentiles of the distribution of both the narrow and wide spread are excluded, the 99<sup>th</sup> percentile of the distribution of liquidity, loan loss provisions and interest income share is excluded as well as the negative observations for the equity variable are excluded.

Table 2 gives some summary and correlation statistics of the main variables in the panel estimations used. From panel A, we observe a higher mean and volatility of the narrow spread versus the wide spread. This also mirrors the graphical inspection of the spreads in figure 1. The ratio of overhead costs to assets averages 6% throughout the sample period. Also, despite truncating the distribution of both liquidity and loan loss provisions at the 99<sup>th</sup> percentile, the variables still exhibit some substantial volatility at the top end of their observations. The average market share in the deposit segment is negligible at 1% with the highest market share being 20%. This is not surprising given the high degree of fragmentation in the Nigerian banking market with a total of 89 banks. Annualized quarterly inflation averages 14.4%, the average real Treasury bill rate is close to zero but experienced some substantial variation throughout the sample period with a range of -18.4% to 18.9%. Also, industrial production averages 0.9% per quarter on average, whereas the exchange rate was subject to a 1.2% depreciation per quarter on average.

The findings from the correlation analysis in panel B of table 2 might anticipate some of the signs and significance levels in the subsequent panel estimations. While overhead costs are significantly positively correlated with the narrow and wide spreads, 13
# **Figure B28: Medium-term cycles—Energy and base metals**

<!-- Start of picture text --> Log scale<br>1.00<br>Coal Crude oil Natural gas<br>0.50<br>0.00<br>-0.50<br>-1.00<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>0.60<br>Aluminum Copper Lead Nickel Tin Zinc<br>0.30<br>0.00<br>-0.30<br>-0.60<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 69 —_
- 21 -

The 1987 Trade Policy Adjustment loan recommended removal of QRs and their replacement by tariffs, the relaxation of import licensing restrictions on 110 items and<sup>their removal</sup> from 165 items. Tariffs on 152 items not produced locally were to be reduced to the 0-5 percent range and the maximum tariff was to be lowered to 60 percent. This TPA loan also included<sup>a recom-</sup> mendation to replace the sales tax with a VAT.

More recently, the Bank reviewed the reforms in tariff levels and reduction of QRs and recommended that an across-the-board tariff reduction would clarify the government's<sup>intention of</sup> achieving a low and narrow tariff structure. The Bank also noted that the<sup>primary objective of tariff</sup> policy should be to provide relatively low and consistent levels of protection across activities, rather than to generate revenues.

MOROCCO: The main recommendations in Morocco related to QR reduction in the 1979-82 period. A 1984 report recommended phasing out of the Special Import Tax (SIT) and achieving a<sup>lower</sup> maximum tariff. A ceiling ERP of 25 percent was also an object of this ITPA loan.<sup>The</sup> comprehensive trade study of 1984 recommended a phased program of QR<sup>elimination and reduction</sup> of tariffs with the intention of achieving a maximum ERP of 25 percent<sup>in 5 years. rhe study</sup> suggested that a higher level of tariff protection be allowed for new industries<sup>on a temporary basis.</sup> ZAIRE: Reduction of the level and dispersion of tariffs was sought to<sup>be achieved in Zaire by the</sup> introduction of a minimum and a maximum tariff of 10 and 60 percent respectively with a timetable to reduce the maximum to 30 percent in 3 years. While the object of this exercise at one point was to achieve a uniform nominal tariff of 30 percent this was later amended to achieve a maximum<sup>tariff</sup> of 30 percent.
Policy Research Working Paper 5538

# **Abstract**

In the aftermath of the global financial crisis of 2008–2009, there has been an increased interest in the role of small and medium enterprises in job creation and economic growth. However the lack of consistent indicators at the country level restricts extensive crosscountry analyses of lending to small and medium enterprises. This paper introduces a new dataset to fill this gap in the small and medium enterprise data landscape. In addition, it provides the first set of results of analyses with this new dataset, predicting the global small and medium enterprise lending volume to be $10 trillion. The bulk of this volume, 70 percent, is in high-income countries. On average, small and medium enterprise loans constitute 13 percent of gross domestic product in developed countries and 3 percent in developing countries. Note that although a unique small and medium enterprise definition does not exist, differences in definitions across countries are not statistically significant in explaining the differences in small and medium enterprise lending volumes.

This paper is a product of the Financial Access Team in Consultative Group to Assist the Poor, Financial and Private Sector Development. 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:// econ.worldbank.org. The author may be contacted at nmylenko@worldbank.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
|Cotton textile apparel|Cotton|2003|Malawi’s cotton was not competitive due to|
|---|---|---|---|
|value chain report|||the low ginning out-turn (GOT) estimated<br>at<br>33-35%;<br>and<br>high<br>polypropylene<br>contamination which reduced Malawi’s<br>cotton lint ontheinternational market|

Source: Ministry of Agriculture and Food Security, various reports.

# **X. KEY FACTORS AFFECTING AGRICULTURAL COMPETITIVENESS**

98. There are four main factors that seem to explain Malawi’s narrow range of competitiveness against other countries: These include: (i) low productivity, (ii) high cost of agricultural inputs, (iii) high cost of transport and (iv) high trader margins at the assembly level. In the following sub-sections, we provide detailed evidence from the analysis on each of these factors.

# **Low productivity**

99. In general, crop yields in Malawi are lower than in other comparator countries. The yield gap against other countries is more significant in maize, rice and tobacco as can be seen in Figure 26. For example, Malawi’s maize yield at smallholder level is only comparable to Mozambique, Nigeria and Cameroon, but is only half as much compared to Zambia, Brazil and Thailand. In cotton, available data indicates that Malawi’s yield is quite comparable against other countries, with the exception of Cameroon and Brazil, where the yield is higher. Similarly, rice yield is only comparable to Nigeria and to some limited extent Zambia, but in general lower than all the other countries including Mozambique. Smallholder burley tobacco yield for Malawi is only better than that of Nigeria. Malawi’s agro-climatic conditions are less similar with countries such as Brazil, Cameroon, Thailand and Nigeria. The analysis assumes similar levels of crop management and agro-climatic conditions, more especially for Zambia and Mozambique where agro-climatic conditions are not markedly different from those of Malawi.

**Figure 26: Comparison of crop yields across countries (t/ha), 2007/08**

<!-- Start of picture text --> 5<br>4<br>3<br>ton/ha<br>2<br>1<br>0<br>Malawi Mozambique Zambia Nigeria Cameroon Brazil Thailand<br>Maize Cotton Rice Tobacco<br><!-- End of picture text --> Source: FAOSTAT 49
the other three crops, the gains in productivity are significant and all lead to approximately 50 percent increase in yields. Better irrigation can also help agrarian households in times of drought and flooding, which affected 8 percent and 15 percent,<sup>7</sup> respectively, of agrarian households in 2014/15. Irrigation can help mitigate damages to crop production during these times of adverse shocks and thus improve the households’ short- and long-run welfare.

Figure 37: Yield of Major Crops for Irrigated versus Rain-fed Cultivation, 2014/15 <!-- Start of picture text --> Rainfed Irrigated<br>6<br>4<br>2<br>0<br>groundnut sorghum millet seasam<br>Yield (100 kg/acre)<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

As Sudan has allowed for large-scale foreign ownership of fertile lands near the Nile, efficient and equitable distribution of Sudan’s water resources will need to be a policy priority. As seen in Figure 38, the previous Sudanese government leased out lands to foreign entities for agricultural operations. Irrigation on these foreign owned operations near Sudan’s major waterways need to be carefully regulated so that communities that rely on those waters for their own livelihoods are not adversely affected by the operations of these foreign agricultural enterprises. Some operations have already had detrimental effects on local communities (Schwartzstein 2019). As these operations also have the potential to employ large numbers of Sudanese agrarian workers and provide foreign investment into the country, ensuring a balanced approach in regulating the operations of these entities is vital for ensuring prosperity of the communities in and around these operations.

> 7 _Source_ : Authors’ calculations using NHBPS 2014/15.

39
the DM, there was a significant reduction in carbon monoxide at a major traffic intersection in central Delhi. There is also suggestive evidence that the introduction of the Blue Line led to a decline in nitrogen dioxide at the same intersection. Although we find a favorable impact of the DM on Delhi’s pollution soon after some of the larger metro expansions, the overall impact of the metro system on air pollution crucially depends on how the electricity needed to drive the metro is generated. If it is not cleanly generated, then some or all of the benefits from reduced pollution in Delhi may be offset by increased pollution elsewhere.<sup>47</sup> Also, our impact estimates are for the short run, and quantifying the net social benefit of the metro network in the long run is beyond the scope of our analysis.

Much of our analysis was restricted by the poor quality of data on pollution. A longer time series, with fewer missing observations, would have allowed us to draw more definitive conclusions. Given the severity of Delhi’s pollution problem, we would urge the environment ministries at the state and the center to invest in better technology and equipment in order to record pollution levels more accurately and completely. As our analysis reveals, it is difficult to conduct rigorous impact assessments without good quality data.

Our paper also highlighted the nature of pollution in Delhi by citing several inventory studies. We believe that a multipronged strategy needs to be adopted if Delhi wants to shed its distinction of being the most polluted city in the world (based on WHO’s Pollution Database: Ambient Air Pollution 2014). As potential pollution abatement measures, Delhi’s planners should consider the following: increasing accessibility to metro stations by improving feeder systems, promoting

> 47. The DM has a regenerative braking system on its rolling stock, which generates electricity when brakes are applied and then feeds it back into the system. This way, almost 35 percent of the electricity consumed is regenerated by the system (Sreedharan 2009). Doll and Balaban (2013) provide an excellent analysis of the overall

> carbon footprint of the DM. For the year 2011, they estimate that the DM saved 232,162 tons of _CO_ 2 because of its regenerative braking technology.

30
# **Banking in Africa**

## **Thorsten Beck and Robert Cull***

_This paper was prepared for the Revised/Second Edition of the Oxford Handbook of Banking_

### **JEL Codes:** G2; G3

**Keywords:** Sub-Saharan Africa; Banking; Financial Inclusion; Financial Innovation; Financial Stability

*Beck: Cass Business School, Tilburg University, and CEPR, Cull: World Bank.  We would like to thank Blaine Stephens, Scott Gaul, and the MIX for access to the microfinance data and Ippei Nishida for excellent research assistance. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
# Taxation, Accountability, and Cash Transfers: Breaking the Resource Curse<sup>_∗_</sup>

Shantayanan Devarajan and Quy-Toan Do<sup>_†_</sup> _JEL Classification:_ D73, H27 _Keywords:_ Natural resource curse, taxation, accountability, credible commitment.

> _∗_ We are grateful to Pierre Bachas, Katy Bergstrom, Anne Brockmeyer, Michael Clemens, Jishnu Das, Marcelo Giugale, Stephen O’Connell, Oyebola Okunogbe, Berk Ozler<sup> ̈</sup> and Martin Ravallion for helpful comments and discussions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the governments they represent.

> _†_ Devarajan: Georgetown University (sd294@georgetown.edu); Do: World Bank (qdo@worldbank.org).
Where RCTs could not be used, quasi‐experimental research methods were applied, in this case, typically for studies involving multiple interventions and sanitation programs. Of these, 45 percent used matching methods to build a counterfactual and nine studies used matching to pair subsamples. Likewise, matching and double‐differences methods were applied for large‐scale programs pertaining to infrastructure construction or rehabilitation, and water access uptake. Instrumental variables were applied in only three studies in Asia, while regression discontinuity studies were thoroughly absent. Figure 7 presents the distribution of study research methods by intervention.

**Figure 7. Distribution of Research Methods by Intervention Subgroup (%)**

<!-- Start of picture text --> RCT Quasi‐Experimental<br>120<br>98<br>100 87<br>82<br>80<br>56<br>60 49 51<br>44<br>40<br>18<br>20 13<br>2<br>0<br>Hygiene Sanitation Water Water Multiple<br>Quality Supply<br><!-- End of picture text --> _Source:_ World Bank GWSP‐SIEF WASH Impact Evaluation Database. _Note:_ The total number of papers listed here is greater than 136, given that some papers included more than one intervention.

# **_Outcome Indicators_**

Twenty‐two unique outcomes were identified.<sup>4</sup> When an evaluation presented more than one outcome for each intervention, the outcome with the most significant effect was used in the meta‐analysis. The prevalent unit of analysis (and unit of sampling) was children or households with children. Not surprisingly, the most frequently measured outcome was diarrhea (prevalence or incidence). This was used in more than half of all evaluations (Table 3), either as a single outcome or in combination with another indicator. The second‐most frequently measured outcome was water quality (accounting for 13 percent of the total), which was classified as a direct output of water purification (or disinfection) interventions. Most of these studies used chemical tests at the household level as a proxy for water quality, for example, particle suspension.

> 4 Studies evaluating the effects on enteric disease reduction (5) were classified separately from studies evaluating the effects on diarrhea reduction (72), giving rise to 22 unique outcomes. However, for the purposes of the meta‐analysis, both outcomes were combined into a single group.

8