rafmacalaba/datause-encoder-data huggingface dataset:
**FIGURE 12: INCREASE IN HOUSEHOLD EARNINGS FROM ACCESS TO MARKET THROUGH REHABILITATED RURAL ROADS (IN US$ ANNUAL)**

<!-- Start of picture text --> 500<br>469.7<br>400<br>347.9<br>300<br>200<br>100<br>0<br>Without roads With roads<br>Source: INEI 2004-2006<br>Annual Earnings per Capita<br><!-- End of picture text -->

# **8. Solutions / Recommendations**

In this paper we presented the three areas that have the largest impact on logistics costs and consequently on competitiveness: (i) transport infrastructure, (ii) company logistics organization, and (iii) the organization, rules and regulations that affect trade facilitation.

The recommendations provided follow the sections developed in the paper. Some of the recommendations can be assessed following standard cost/benefit analysis. However, others can not. Such is the case of company logistics organization. A well developed market of business logistics providers can have a tremendous impact on aggregated logistics costs. The problem is that the performance of service logistics providers depends on a wide variety of factors, most of them related to investment climate (taxation, standards and quality regulation, among others). Coming up with a cost/benefit estimate for the measures aimed at improving the productivity of service logistics providers or to facilitate trade is very difficult. This is clearly not the case of infrastructure investments (road, railways, etc) because their costs and benefits can be measured with more accuracy.

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# **Figure 1.   Sales for Constant Returns to Scale Sectors: Determined by Constant  Elasticity of Transformation Production Structure**

<!-- Start of picture text --> Russia sales<br>(not own Regional Market)<br>Rest of world sales<br>σT = 5 = etadx<br>CET<br><!-- End of picture text -->

**Own Regional Market**

**Output (CRTS good g in Regional Market r)**

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|_nitrogen _kg_|Nitrogen<br>equivalent of<br>applied inorganic<br>fertilizer (kg)||ETH: UREA (46 -<br>0 - 0), DAP (18 -<br>46 - 0), and NPS<br>(19 - 46 - 7).<br>NGA: UREA and<br>NPK (20- 10 – 10<br>appears to be the<br>most prevalent<br>blend).<br>Mali: UREA, DAP,<br>NPK and cereal<br>complex ( 15 – 15<br>– 15) are included.<br>A few less<br>prevalent inorganic<br>fertilizers are left<br>out.<br>TZA: CAN and SA<br>|
|---|---|---|---|
||||also used.|
|_inorganic_fertilizer_value_LCU_|Inorganic fertilizer<br>value, in LCU|Inorganic fertilizers valued using<br>median prices per in a defined<br>geographical area. The area in<br>question is the EA (or admin 1 in<br>some surveys) under the<br>condition that there are more than<br>10 observed sales in the area. If<br>there are less than 10 sales, prices<br>are calculated at a higher<br>geographical level. When<br>possible, separate prices were<br>calculated for units that couldn’t<br>be converted into kilograms.|ETH (w1), TZA<br>(all waves): all<br>fertilizers are<br>assumed to be<br>purchased (no<br>question asking for<br>the quantity<br>purchased). This<br>might lead to an<br>underestimation of<br>fertilizer prices as a<br>portion may be<br>obtained for free as<br>gifts or leftover<br>from the previous<br>season. Moreover,<br>purchased<br>fertilizers are at the<br>plot holder level in<br>ETH (wave 1), and<br>purchases are<br>assumed to be<br>equally distributed<br>among plots.|
|_inorganic_fertilizer_value_USD_|Inorganic fertilizer<br>value, in USD|Fertilizer values<br>(_inorganic_fertilizer_value_LCU)_<br>are converted to current USD and<br>deflated to 2020 dollar values,<br>using exchange rates and a<br>deflator extracted from World<br>Bank databases (Azevedo et al.,<br>2011)||
|_total_family_labor_days_|Total family labor<br>days on the plot||UGA: Absent in<br>wave 7.|
|_total_hired_labor_days_|<br>Total hired labor<br>days on the plot||UGA: Absent in<br>wave 7, season 2.|

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prior data-collection efforts. So issues about incentives and financing are crucial to the production of policy-relevant data. Here again, the public good nature of data is an issue. Public access data will almost certainly be undersupplied by individuals working on their own.

There are other reasons why we under-invest in the tools of evaluative research. Tool development often entails bridging gaps between theory and practice, including developing tools appropriate to practice. Such work is not typically valued highly in academia, where greater emphasis is given to theoretical and deeper methodological developments. At the same time, while tool development has received support from development aid agencies, higher priority tends to be given to efforts with more immediate bearing on specific policy issues. Efforts focused directly on tool development for practitioners are not academic enough by some perceptions, but too academic in the eyes of others, including those who provide funding.

While development data still remain weak, we can point to progress in some areas. Much of the data now used routinely are the products of past research projects, and it would seem very unlikely that any of these data would exist without strong initial public support, given the extent of the externalities problem. A number of the World Bank’s most successful data initiatives started as research projects.<sup>4</sup> The _Living Standards Measurement Study_ (LSMS) is one example (see <u>http://www.worldbank.org/lsms/). This started in the Bank as a research project around</u> 1980, with the aim of greatly improving household survey data in developing countries; the LSMS has now done over 80 large multi-purpose household surveys in 50 countries and has set the standard for survey data collection in developing countries. At a more macro level, international trade data and methods provide an important class of examples of global public goods in which tool development would be unlikely to happen if one relied solely on development practitioners at country level (both individual governments and the “country teams” of international agencies). Other examples include the various cross-country data bases that have been developed out of research projects aiming to assess country performance in both economic, political and social domains. The Bank’s global poverty monitoring effort that started as a small research project for a background paper to the 1990 _World Development Report_ .<sup>5</sup> The Bank’s _Enterprise Surveys_ (formally known as _Investment Climate Surveys_ ) started in its research

> 4 I focus here on World Bank examples; one could also mention other important example such as USAID’s _Demographic and Health Surveys_ <u>(http://www.measuredhs.com/) and RAND’s</u> _Family Life Surveys_ .

> 5 See World Bank (1990); the background paper was Ravallion et al. (1991).

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points, and then recovered slightly in 2010. The drop in deposit growth was much more apparent among the countries with high growth of deposits prior to the 2008 crisis (13 percentage points; the 3<sup>rd</sup> quartile) than for countries with lower pre-crisis deposit growth (9 percentage points; the 1<sup>st</sup> quartile). In addition, some countries, such as Azerbaijan, Botswana, Iceland, Moldova, and Montenegro transitioned from the 75<sup>th</sup> percentile of countries with the fastest growing deposits to the 25<sup>th</sup> percentile of countries with the slowest growing (dropping) deposits, generally experiencing deposit withdrawals in 2009 and thus much larger declines in deposit growth than other countries (60 p.p., 31 p.p., 59 p.p., 45 p.p., and 102 p.p., respectively).

The timing of the peak and trough of deposit growth around the 2008 financial crisis varies notably across countries. While the global average suggests that the peak of global deposit growth was in 2007, and the trough of global deposit growth was in 2009, there is a significant heterogeneity across countries regarding the year in which the peak and trough actually occurred. Figure 3 shows that although 67 countries experienced the peak of deposit growth in 2007, 55 countries experienced the peak in 2006, and 43 experienced it in 2008. Similarly, 2009 was the year of the lowest deposit growth for 83 countries, but other 52 countries experienced the lowest growth only in 2010. These observations lead us to concur that we need to allow for a country specific timing in calculation of the drop in deposit growth experienced by a given country around the 2008 global crisis. This variable will be our first main variable of interest and also our dependent variable in the regression analysis, later in the paper.

**Figure 3: Histogram of the Timing of the Peak (left) and Trough (right) in Deposit Growth in Individual Countries**

<!-- Start of picture text --> Number of countries with MAXIMUM deposit Number of countries with MINIMUM<br>growth in a given year deposit growth in a given year<br>80<br>80<br>70<br>70<br>60<br>60<br>50<br>50<br>40<br>40<br>30 30<br>20 20<br>10 10<br>0 0<br>2006 2007 2008 2009 2007 2008 2009 2010<br><!-- End of picture text --> Source: Authors’ calculation based on deposits data from IMF’s FAS database.

An interesting stylized fact to inspect at this point is the maximum drop in deposit growth across countries that have experienced and those that have not experienced a banking crisis around 2008. Figure 4 shows the calculated drops in deposit growth for different country groups (the details of this calculation are provided in section 4). When looking across all countries (the 6
such as surveying owners and real estate experts, likely due to poor and biased valuation practices. Furthermore, cadastral information on plots in the tax database is not linked to address information that is visible and known by taxpayers. This lack of data harmonization makes it impossible to send tax notifications to most property owners. As a final challenge, manual practices in collecting new information on properties imply a cumbersome, timeconsuming and error-prone data collection process by tax agents. Ongoing research examines the impact of a new mobile application that helps to determine property values and process billing and collection on tax collection. Box 2 presents a case study from Liberia on how using information from a newly developed electronic tax database increased tax collection.

Second, technology is important for facilitating and monitoring compliance with real estate taxes. The ideal technology system will not only support property identification using a geospatial database and valuation, but will also integrate billing, payments, appeals and enforcement. Dzansi et al. (2022) report from a census of local governments in Ghana that only 17 percent of them have a digital database of properties or a revenue software of any kind to assist with billing, and find that use of technology is associated with higher revenue collection. In addition, using a randomized experiment in a large municipal government, they find that revenue collectors using electronic tablets with geospatial data and navigational directions to more easily locate properties collected twice as much revenue as control group collectors.

Technology solutions can also be used to reduce compliance costs and revenue leakage. Weigel (2020) and Bal ́an et al. (2022) describe the use of a payment technology in door-todoor property tax campaigns in Kananga, Democratic Republic of Congo. Taxpayers made cash payments to tax collectors instead of traveling to the tax office, thereby reducing their compliance costs. The tax collectors used handheld printers to issue receipts to taxpayers and each transaction was recorded in the device’s memory and downloaded to the government database on a regular basis. To monitor leakages, supervisors confirmed that the revenues deposited were equal to the amounts on the receipts that were issued.

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impact human development, such as reducing the utilization of education and health services, depleting the productive assets of the poor and, in particular, worsening nutrition, as food prices are higher (World Bank, 2008).

The welfare impact of a rise in food prices depends on the distribution of net sellers and net buyers of food in an economy, and can be represented by the concept of Net Benefit Ratio (NBR) of a food price shock, introduced by Deaton (1989). The NBR is the difference between the ratio of food sales over total income and the ratio of food purchases over total expenditures for a household. If the NBR of a household is positive, a rise in food prices will increase the NBR and thus have a positive impact on its welfare. On the contrary, a net consumer (NBR < 0) will suffer from such a shock. In urban areas, where most households are net consumers, a rise in food prices is likely to have a negative impact on welfare, whereas it is more contrasted in rural areas. Poor rural households are often unable to produce a marketable surplus that exceeds their food expenditures, and if such is the case their welfare will worsen as a result of the food crisis.<sup>2</sup> Arndt et al. (2008) assess the impact of higher fuel and food prices in Mozambique at the households and macroeconomic level. As this country is a net importer of food and imports all its fuel, the crisis is a negative terms-of-trade shock. Rice and maize prices increased by 68.8% and 57.3% in Maputo respectively, which is slower than international prices, but wheat prices increased faster (107.6%). While rural Mozambican households are fairly well insulated from a variation in market prices because of the large part of food consumption that is home produced, poor households living in Maputo city will suffer the most, because consumption of own-produced food is non-existent. Arndt et al. (2008) predict that the poorest households in Maputo lose the most while middle-income groups in the rural north and center gain the most.

While a rural/urban HIV prevalence breakdown is unavailable for Mozambique, data from other African countries show that most people living with HIV/AIDS in Africa live in urban areas

> 2 The NBR does not allow for behavioral changes on the part of producers or consumers and measures only first order impacts. As households and firms modify the structure of their consumption and production, second order adjustments should dampen any negative first-order impacts.

3
Market house price data can often be obtained more easily and, most importantly, tend to be available in the public domain, in contrast to tax administration data which are subject to important confidentiality concerns and require cooperation from governments. Also, house sellers have no incentive to understate the value of their homes, in contrast to the income they report on their tax returns.

Using house prices as an alternative to income tax records demands two methodological innovations to the study of top incomes. Firstly, we will not be observing actual household income or expenditure (as is the case with tax record data), but rather a predictor of income. Secondly, a database with house price listings is generally not obtained using a particular sampling design. Therefore, the data are not guaranteed to provide a nationally representative sample, they will arguably be biased towards large urban centers. We will propose workable solutions to both these challenges that will hopefully contribute to a wider use of this approach. Note that the methodology is not restricted to the use of house prices, it can be applied to any database containing predictors of top incomes.

We illustrate our approach with an empirical application to Egypt which provides a good testing ground for our method. In addition to being a major Arab country, inequality in Egypt is of considerable interest not least because it has been cited as one of the factors behind the Egyptian revolution (Hlasny and Verme, 2013). Estimates of inequality based on household surveys suggest that inequality is low in Egypt and that it has declined in the last decade to a Gini of around 31 in 2009. Using house prices to capture top incomes we find that inequality may be significantly underestimated in Egypt. The Gini for urban Egypt in 2009 is estimated at 47 _._ 0 compared to a survey-only estimate of 36 _._ 4. Our results are in contrast with other studies using different methods of adjusting for top incomes in Egypt (Hlasny and Verme, 2013), which report a more modest effect.<sup>2</sup> Their correction however does not consult a second source of data. If the main problem is that high income earners are simply missing from the survey, then no adjustment that relies soley on the survey will resolve the downward bias in estimates of inequality. The only way to obtain a meaningful correction is to bring in a second source of data that carries the necessary information on top incomes and hence will permit for the consistent estimation of income inequality. This reasoning is shared by Alvaredo and Piketty (2014) who similarly argue that the household survey data by themselves are insufficient to estimate top incomes in Egypt. While they make an appeal for making data on income tax records available, we propose to work with house price data instead. It should be noted that relying on predictors of top incomes rather than actual incomes derived from

> 2The Gini coefficient of household expenditure per capita in 2009 increases from 30 _._ 5 to 31 _._ 8 which is found to be statistically significant, but not economically significant.

3
roads and market infrastructure also receive more credit while rural electrification appears to reduce credit availability. Credit expansion was particularly rapid in areas with rapid increase in the number of commercial banks and also expanded with the number of cooperative banks. Areas where the consolidation of primary cooperative credit societies reduced, the number of PACs received more credit, while increases in the number of land development banks have negative effect on credit use. The price variables have no significant effect on credit use and neither does rainfall received in the year of observation. Based on the estimates of Table 4, we predict the amount of credit supplied to each district by formal lending agencies each year.

Table _5_ presents the estimates of the aggregate supply equation. The first column uses data from 21 years, covering both the 1960s and 1970s. The variable "commercial bank branches" is used as a farmer-exogenous variable to instrument credit supply. Complete credit data are only available for the 1970s. The equations in the second and third columns, therefore, used only data from nine years. In the second equation, the predicted total rural credit from Table 4 is used. In the third equation, we focus on cooperative agricultural credit and use predicted cooperative credit from an equation similar to that in Table 4. Cooperative credit includes credit from both PACs and land development banks.

The first supply equation with the longer time series was extensively discussed in Binswanger, Klandker and Rosenzweig (1987), who highlighted the strong positive effects 23
SECTOR 1=LIGHT MANUFACTURING SECTOR 2=HEAVY INDUSTRY SECTOR 3=ELECTRICITY,WATER, SEWAGE SECTOR 4=TRANSPORT SECTOR 5=HOTELS, HOUSING, HEALTH SERVICES An input-output matrix, _At_ , is used to determine intermediate and final production in period _t_ . The matrix is 50 x 50, and is taken from _Samwalk_ 2010, the most recent social accounting matrix for Pakistan. The first 50 rows and columns of the input-output matrix correspond to domestic production. The final row and column represents imports of intermediate and final goods, which are treated as a single aggregate commodity. Corresponding to each sector in the input-output matrix, sector-specific value added is produced using capital and urban labor for the nonagricultural sectors, and land and rural labor in agriculture.

_j j_ The specific formulation of the firm's problem is as follows. Let _yKi_ , _yLi_ be the inputs of capital and urban labor to the _j_ th nonagricultural sector in period _i_ . Let _YGi_ be the outstanding stock of government infrastructure in period _i_ . The production of value added in sector _j_ in period _i_ is then given by: where we suppose that public infrastructure may act as a productivity increment to private production.

Sector _j_ pays income taxes on inputs of capital and labor, given by _tKij_ , _tLij_ respectively, in period _i_ . The interpretation of these taxes is that the capital tax is a tax on firm profits, while the labor tax is a personal income tax that is withheld at source. There are no pure profits here, since production functions are constant returns to scale, and hence the corporate income tax is treated as a tax on returns to capital.

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10 plants over the next **_ten_** years. In many of these nations, existing power plant capacity utilization is less than **S0** percent due to poor maintenance and pricing problems. The overall return on power plant physical assets in a sample of 57 developing countries has been estimated at an average of less than 4 percent over the last 3 years and less than 6 percent over the last decade, returns which can not even compare with those of providing education for females.

No doubt efficiency in the power sectors of developing countries can and will be improved. And I have probably understated somewhat the difficulty of raising enrollment rates by neglecting capital costs and not taking explicit account of the special costs that must be incurred in targeting girls. Nonetheless, it is hard to believe that building 19 out of every 20 planned powe- plants and u, ng the savings to finance world equal educational opportunity for girls would not be desirable.

# What Could Be Accomplished?

Letting girls go to school, learn to read, and experience more of the world beyond their homes makes them better off immediately and enriches their families. Over time, getting girls into school can transform societies as their sons and daughters and grandsons and granddaughters reap the benefits. Contemplate a very crude estimate of how much better off the world would be today if major investments in increasing female education had been made a generation ago.

There are a number of different strategies to approaching this counter factual. One method would be to simply extrapolate the calculations based on microeccnometric estimates based on surveys of women within individual countries. Instead, as a kin(d of check on those calculations, I examine the relationship between national rates of infant mortality and female education a generation ago (Table 8 and Figures _5_ and 6), holding constant a variety of country characteristics. The estimates are derived from a sample of the 45 developing countries for which all the needed data are available. As the evidence I have presented so far might lead one to expect, the relationship is both stdstically significant and implies large effects of education on health and fertility. Female education rates are the most potent variable in these equations, dwarfing the effects of male education or the availability of medical care.

These relationships can be used to simulate the impact of an increase in the female secondary school enrollment rate from its average in 1965 to 30 percent (Table 9). For comparison, the male secondary enrollment rate in this sample of countries averaged 22 percent in 1965. The results are striking. In Pakistan alone raising the female secondary enrollment rate from 6 to 30 percent in 1965 would have averted 1.2 million births per year and 297 thousand infant deaths. For a sample of 45 countries that includes about 71% percent of the low income world's population (excluding China), the result would be 9.1 million births per year averted and 3.0 million fewer infant deaths.