rafmacalaba/datause-encoder-data huggingface dataset:
based on unit values from consumption purchases in the IOF data and the unit sales value from maize sales in the AIS data, respectively. A caveat to be noted is that the sample of households in the AIS data is skewed towards rural regions (about 16% of the sample is urban). As a result, estimates of production ratios for urban households may be prone to larger sampling error.

We first start with examining the NBR for each of the individual food items within each province. The results are summarized in Table 4. Panel A presents the results for maize, Panel B for rice and Panel C for cassava, respectively. In general, the average NBR is negative for maize, and the magnitude of the welfare impacts is also larger for maize. The last row of Panel A shows that on average, for the whole country, maize forms about 10.7% of the total expenditure and maize production value accounts for 4.6% of the total. This is consistent with the fact that Mozambique is a net-importer of maize and that consumer prices are higher than producer prices. The negative NBR implies a negative effect of maize price increases. A 10% increase in the price of maize would reduce the average welfare of households by 0.61%. The negative effect holds for all provinces except for Zambezia and Maputo City and is higher in the poorest provinces with the highest dependence on maize – Niassa, Manica and Tete. In each of these a 10% increase in prices results in a negative welfare impact of more than 1%.

The PR for rice is nearly the same as it is for maize, on average, while the CR is much smaller (Panel B, last row). The NBR for the whole country, on average, is 0.031 indicating that a 10% increase in rice prices would result in a positive welfare effect of about 0.3%.<sup>9</sup> The welfare implications for rice price increases are generally positive across all provinces with the exception of Cabo Delgado and Inhambane. The magnitude of the positive effect ranges from a high of 0.9% for Sofala to a low of 0.1% in Nampula. An increase in cassava prices, on the other hand, has moderate negative welfare implications on average. Overall, the NBR indicates that a 10% increase in cassava prices implies a negative effect of close to 0.2%. The welfare effect is negative for most provinces, with the highest effect seen in Nampula – a 0.8% reduction in welfare.

> 9 It is worth interpreting this result with caution since the overall net positive NBR for rice may be driven by potential bias in the urban areas. For instance, in the AIS-2015 sample in Maputo City, 96 percent are rice producers. Similarly, a high share of urban households are rice producers in urban areas in other provinces as well. In contrast, in the IOF-2014/15 data less than 1 percent of the households in Maputo city report any income from agricultural sales; and less than 1 percent report any auto-consumption of rice.

18
Panel A Panel B

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Policy ReseaRch WoRking PaPeR 4630

# **Abstract**

Microfinance institutions have proved the possibility of providing reliable banking services to poor customers. Their second aim is to do so in a commercially-viable way. This paper analyzes the tensions and opportunities of microfinance as it embraces the market, drawing on a data set that includes 346 of the world’s leading microfinance institutions and covers nearly 18 million active borrowers. The data show remarkable successes in maintaining high rates of loan repayment, but the data also suggest that profit-maximizing investors would have limited interest in most of the institutions that are focusing on the poorest customers and women. Those institutions, as a group, charge their customers the highest fees in the sample but also face particularly high transaction costs, in part due to small transaction sizes. Innovations to overcome the well-known problems of asymmetric information in financial markets were a triumph, but further innovation is needed to overcome the challenges of high costs.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to study different policies to improve access to financial services. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at rcull@worldbank.org or ademirguckunt@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
**Appendix Table 9: Children in monetary poor households in 2022 at $6.85 (UMIC) lines in 2017 PPP in 2013, 2017 and 2022 by region (%)**

|**Region**|2013|2017|2020|2022|
|---|---|---|---|---|
|**East Asia and Pacific**|60.9|49.3|45.0|42.0|
|**East Asia and Pacific without**<br>**China**|69.4|61.3|58.3|57.7|
|**South Asia**|92.2|89.3|88.7|86.7|
|**South Asia without India**|89.6|85.8|85.0|83.3|
|**Sub-Saharan Africa**|92.5|91.7|91.6|91.1|
|**Sub-Saharan Africa without**<br>**Nigeria**|92.1|91.2|90.7|90.3|
|**Latin America and Caribbean**|47.5|44.7|42.9|39.5|
|**Europe and Central Asia**|12.6|11.4|10.6|10.8|
|**Middle East and North Africa**|52.4|53.9|57.0|55.2|
|**Total**|72.7|69.5|68.8|67.4|

**Appendix Table 10: Children in monetary** **<u>poor households in 2022 at the $2.15 line in 2017 PPP by income group</u>**

|Income group|Extreme poor<br>(millions)|Headcount<br>poverty rate<br>(%)|Share of<br>extreme<br>poor(%)|Share of<br>population<br>(%)|
|---|---|---|---|---|
|**Low income**|153.7|47.1|46.1|15.5|
|**Lower middle income**|166.3|15.3|49.9|51.5|
|**Lower middle income without India**|114.1|18.1|40.6|43.8|
|**Lower middle income without Nigeria**|126.2|12.9|43.0|48.9|
|**Upper middle income**|13.0|2.2|3.9|28.8|
|**Upper middle income without China**|12.8|3.3|4.6|27.3|
|**High-income**|.5|.6|.2|4.2|
|**Total**|333.5|15.8|100.0|100.0|

**Appendix Table 11: Children in monetary** **<u>poor households in 2022 at the $3.65 line in 2017 PPP by income group</u>**

|Income group|Children in<br>poverty (millions)|Headcount<br>povertyrate(%)|Share of poor<br>(%)|Share of<br>population(%)|
|---|---|---|---|---|
|**Low income**|241.9|74.2|29.5|15.5|
|**Lower middle income**|517.4|47.7|63.1|51.5|
|**Lower middle income without India**|291.5|46.2|49.6|43.8|
|**Lower middle income without Nigeria**|440.5|45.0|59.3|48.9|
|**Upper middle income**|59.2|9.8|7.2|28.8|
|**Upper middle income without China**|53.9|13.7|9.2|27.3|
|**High-income**|1.0|1.1|0.1|4.2|
|**Total**|819.5|38.9|100.0|100.0|

14
**Table 5: Product-destination spells. Discrete time survival model**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dependent variable:<br>Ongoing spell =0 Completed spell =1|||||||
|Init. Value (log)|-0.0548***|-0.0596***|-0.0568***|-0.0548***|-0.0569***|-0.0548***|
||(0.0129)|(0.0106)|(0.0125)|(0.0129)|(0.0134)|(0.0136)|
|Dest. x firm count (log)|0.146***|0.0846**|0.191***|0.146***|0.153***|0.156***|
||(0.0480)|(0.0397)|(0.0430)|(0.0480)|(0.0520)|(0.0521)|
|Prod. x firm count (log)|-0.130***|-0.122***|-0.164***|-0.130***|-0.131***|-0.146***|
||(0.0409)|(0.0337)|(0.0331)|(0.0409)|(0.0436)|(0.0442)|
|Firms x prod x dest. count (log)|-0.177***|-0.199***|-0.126***|-0.177***|-0.169***|-0.169***|
||(0.0477)|(0.0439)|(0.0433)|(0.0477)|(0.0496)|(0.0495)|
|Employment (log)|0.0168|0.0529|-0.00615|0.0168|0.0324|0.0299|
||(0.0377)|(0.0332)|(0.0361)|(0.0377)|(0.0412)|(0.0415)|
|TFP (log)|-0.128***|-0.0850***|-0.133***|-0.128***|-0.133***|-0.130***|
||(0.0377)|(0.0325)|(0.0345)|(0.0377)|(0.0397)|(0.0400)|
|Foreign owned|-0.233***|-0.184**|-0.240***|-0.233***|-0.209**|-0.225**|
||(0.0780)|(0.0725)|(0.0749)|(0.0780)|(0.0866)|(0.0875)|
|RCA||-0.00284|||||
|||(0.00599)|||||
|FTA|||-0.271***|0.120|||
||||(0.0633)|(0.642)|||
|K/L (log)|||||-0.0365|-0.0297|
||||||(0.0295)|(0.0295)|
|Real Ex. Rate (log)|||||-0.0269||
||||||(0.0239)||
|Rauch referenced|||||0.0655|0.0764|
||||||(0.122)|(0.124)|
|Rauch homogeneous|||||-0.101|-0.107|
||||||(0.213)|(0.216)|
|Real Ex. Rate (growth)||||||-0.0303|
|||||||(0.0280)|
|Duration dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector FE|Yes|No|Yes|Yes|Yes|Yes|
|Destination FE|Yes|Yes|No|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|4,349|4,349|4,349|4,349|4,108|4,024|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 This result confirms the theoretical expectations that higher value transactions signal a lower degree of uncertainty about the trading partner or the demand conditions in the export market and thus have higher survival probabilities. This finding suggests that there is substantial trial and error in exporting. With high costs of learning about foreign demand conditions, tastes, and product specification requirements, many firms opt for directly engaging in international transactions without having a thorough understanding of foreign market conditions, which makes them more vulnerable to failure. The fact that firms with higher initial values survive longer in export markets seems to support this interpretation. Firms that start exporting at a greater scale 15
Policy Research Working Paper 4979

# **Abstract**

Extreme weather events are known to have serious consequences for human health and are predicted to increase in frequency as a result of climate change. Africa is one of the regions that risks being most seriously affected. This paper quantifies the impact of extreme rainfall and temperature events on the incidence of diarrhea, malnutrition and mortality in young children in Sub-Saharan Africa. The panel data set is constructed from Demographic and Health Surveys for 108 regions from 19 Sub-Saharan African countries between 1992 and 2001 and climate data from the Africa Rainfall and Temperature Evaluation System from 1980 to 2001.

The results show that both excess rainfall and extreme temperatures significantly raise the incidence of diarrhea and weight-for-height malnutrition among children under the age of three, but have little impact on the long-term health indicators, including height-for-age malnutrition and the under-five mortality rate. The authors use the results to simulate the additional health cost as a proportion of gross domestic product caused by increased climate variability. The projected health cost of increased diarrhea attributable to climate change in 2020 is in the range of 0.2 to 0.5 percent of gross domestic product in Africa.

This paper—a product of the Environment Department, Sustainable Development Network—is part of a larger effort in the department to analyze the health impact of extreme weather events associated with climate change in sub-Saharan Africa. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at lwang1@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
# **Table 17: Financial inclusion reforms**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|-0.119*|-0.014|-0.018|-0.061|-0.005|-0.011||||||
||(0.067)|(0.059)|(0.055)|(0.058)|(0.040)|(0.040)||||||
|Population density (log)|0.006|0.033|0.037**|0.021|0.036*|0.040**||||||
||(0.031)|(0.021)|(0.018)|(0.029)|(0.018)|(0.016)||||||
|Expected GDP (log)|0.059*<br>(0.031)|0.008<br>(0.029)|0.006<br>(0.027)|||||||||
|# deposit accounts/1,000 adults (log,<br>|0.003|0.005|0.024|-0.012|0.003|0.022|-0.032|0.012|0.035|||
|lagged)|(0.056)|(0.046)|(0.046)|(0.058)|(0.046)|(0.046)|(0.057)|(0.050)|(0.049)|||
|# loans/1000 adults (log lagged)|0.005|-0.068|-0.041|-0.016|-0.072|-0.044|-0.011|-0.035|-0.027|||
|, ,|(0.067)|(0.056)|(0.054)|(0.069)|(0.050)|(0.050)|(0.049)|(0.038)|(0.038)|||
|Deposit insurance|0.020|0.134|0.151*|0.114|0.147**|0.163**||||||
||(0.105)|(0.095)|(0.088)|(0.087)|(0.072)|(0.065)||||||
|Credit rank|-0.001|-0.001|-0.000|-0.001|-0.001|-0.000||||||
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)||||||
|FI effective index||0.560***|||0.573***|||0.554***||0.548***||
|||(0.119)|||(0.107)|||(0.106)||(0.083)||
|FI index|||0.559***|||0.569***|||0.554***||0.523***|
||||(0.100)|||(0.092)|||(0.093)||(0.071)|
|Constant|1.030***|0.382|0.030|0.857***|0.347|-0.003|0.552**|0.220|-0.020|0.092**|0.037|
||(0.306)|(0.307)|(0.301)|(0.302)|(0.274)|(0.257)|(0.229)|(0.215)|(0.208)|(0.039)|(0.041)|
|σ|0.294***|0.242***|0.227***|0.305***|0.243***|0.228***|0.322***|0.261***|0.246***|0.287***|0.277***|
||(0.034)|(0.027)|(0.024)|(0.036)|(0.028)|(0.024)|(0.038)|(0.028)|(0.026)|(0.019)|(0.019)|
|Number of observations|61|61|61|61|61|61|62|62|62|146|146|
|Log-likelihood|-22.14|-11.55|-7.960|-24.19|-11.60|-7.996|-28.10|-16.88|-13.25|-52.05|-46.83|
|Pseudo R<sup>2</sup>|0.184|0.574|0.706|0.108|0.572|0.705|0.0271|0.415|0.541|0.300|0.370|
|Robust standard errors in parentheses,|*** p<0.01,|** p<0.05, *|p<0.1|||||||||
|Dependent variable: Reform index. Re|sults of Tobit|estimation,|dependent va|riable is betw|een 0 and 1.|||||||

43
```
- 24 -
``` ``` hitherto poorly-performing countries. Thus, both institutional and economic reformers generally were advocates of what were perceived as ‘best practice’, optimal policies.
``` ``` In sharp contrast, recent empirical work on ‘growth ``` ``` accelerations’ has demonstrated that far-reaching reforms – either institutional or economic – need not be necessary to kick-start growth.
Careful empirical analysis by Hausmann, Pritchett and Rodrik (2004) of data worldwide identified over 80 growth accelerations over the 19501990 period that lasted for eight or more years . They found that ``` ``` “The onset of economic growth does not require deep and extensive institutional reform ...Moderate changes in country-specific circumstances (policies and institutional arrangements, often interacting with the external environment), can produce discontinuous changes in economic performance....” (Rodrik, Analytic Growth intro, pp. 8-9, 17)..... Once growth is set into motion, it becomes easier to maintain a virtuous cycle with high growth and institutional transformation feeding on each other.” (Rodrik, Getting Institutions Right, 2004, p.10) ``` ``` Consistent with this pattern, Figure 3 thus delineates a third ``` ``` development sequence – one where the first step is the initiation of growth itself.
```
|Table 2: Main Mode to w|ork byHou<br><5k|sehold Inco<br>5-7.5k|me(%)<br>7.5-10k|10-20k|>20k|All HHs|
|---|---|---|---|---|---|---|
|On foot|61|50|41|30|15|44|
|Bicycle|6|4|2|1|0|3|
|Train|16|23|26|26|21|23|
|Public Bus|15|17|18|16|13|16|
|Auto-Rickshaw|1|1|1|3|3|2|
|Taxi|0|0|0|0|1|0|
|Own Two-Wheeler|1|4|10|18|21|9|
|Own Car|0|0|0|4|24|3|
|Other’s car|0|0|0|0|1|0|
|Other|0|1|1|1|3|1|
|Total|100|100|100|100|100|100|

24
18 In the past, trade and welfare reduction indicators have not been reported as part of the OECD regular monitoring activities. This may have been because it was thought that economic models and elasticity data would need to be agreed upon, which would raise technical and political problems. The measures we estimate in this paper are such that one can avoid the need to select a pair of price elasticity estimates for each product of each country. As such they could provide an attractive and politically uncontroversial supplement to the current policy monitoring indicators generated by the OECD, and by other multilateral institutions such as the FAO, UNCTAD, World Bank and the WTO.

The importance of TRIs and WRIs will also be relevant for a new FAO/OECD project, funded by the Bill and Melinda Gates Foundation and getting under way in 2010, which aims to estimate agricultural policy indicators for a sample of African countries over the next few years. In African countries, different policy instruments operate such that the TRI could have a different sign in some years to the NRA aggregate (because of, for example, export taxes). Furthermore, if there is no economy-wide model for some of the African countries in the FAO/OECD project sample, the TRI and WRI can provide at least partial equilibrium indicators of the effect of national policies in reducing agricultural trade and national economic welfare.

# **References**

Anderson, J.E. and J.P. Neary (2005), _Measuring the Restrictiveness of International Trade Policy_ , Cambridge MA: MIT Press.

Anderson, K and J.L. Croser, (2009) _National and Global Agricultural Trade and Welfare Reduction Indexes, 1955 to 2007_ , World Bank, Washington DC, database available at <u>www.worldbank.org/agdistortions</u> Anderson, K. (2009), ‘Five Decades of Distortions to Agricultural Incentives’, Ch. 1 in K. Anderson (ed.), _Distortions to Agricultural Incentives: A Global Perspective, 1955-2007,_ London: Palgrave Macmillan and Washington DC: World Bank.
to vary across indicators k, the fact that P(k) multiplies the error term is an innocuous rescaling which slightly simplifies some of the expressions which follow.

We use this unobserved components model, which treats unobserved govemance as a random variable rather than as a fixed parameter to be estimated, for a pragmatic reason. We will shortly also assume that the variance of the disturbance term e(j,k) may differ across indicators. In this case, we cannot treat the ga)s as fixed parameters to be estimated for each country, since individual effects are not identified in a fixed effects model with heteroskedastic disturbances. 4 Moreover, it should be clear from Equation (1) that naive aggregates such as a simple average of rescaled indicators for each country will not result in sensible estimates of govemance, as long as the parameters a(k) and ,3(k) differ across indicators and different countries appear in different sets of indicators. It is also not possible to remove the dependence of the observed data on these nuisance! parameters by standardizing (i.e. by removing the sample mean from each indicator, and dividing by the sample standard deviation). This is because if indicator k is non-representative, the sample mean will reflect not only a(k), but also the mean of go) in the sample of countries covered by indicator k. Even if an indicator is representative in the sense that the standard deviation of unobserved governance is equal to one in the sample of countries it covers, the sample standard deviation of observed scores will reflect not only ,8(k), but also the standard deviation of the disturbances.

The disturbance term s(j,k) captures two sources of uncertainty in the relationship between true governance and the observed indicators. First, the particular aspect of governance covered by indicator k is imperfectly measured in each country, reflecting either perception errors on the part of experts (in the case of polls of experts), or sampling variation (in the case of surveys of citizens or entrepreneurs). Second, the relationship between the particular concept measured by indicator k and the corresponding broader aspect olF governance may be imperfect. For example, even if the particular aspect of graft covered by some indicator k, (such as the prevalence of 4 To see this, consider the special case! where a(k)=O and 13(k)=1 for all indicators. We can make the likelihood function of the observe **d** ata arbitrarily large simply by estimatin **g** o) as the observed score on a particular indicator, for example ga)=y(j.K), for every country j, and setting cr(K)=0. Kiefer (1980) provides a detailed discussion of this point.

9