rafmacalaba/datause-encoder-data huggingface dataset:
we use a cutoff distance of 20 km, we assume there is little economic footprint beyond that distance. Of course, any such distance is arbitrarily chosen, which is why we try different specifications to explore the spatial heterogeneity by varying this distance (using 10 km, 20 km, through 50 km) as well as a spatial lag structure (using 0 to 10 km, 10 to 20 km, through 40 to 50 km distance bins).<sup>4</sup> Second, we collapse the DHS mining data at the district level.<sup>5</sup> The number of districts has changed over time in Ghana, because districts with high population growth have been split into smaller districts. To avoid endogeneity concerns, we use the baseline number of districts that existed at the start of our analysis period, which are 137. Eleven of these districts have industrial mining. Because some mines are close to district boundaries, we additionally test whether there is an effect in neighboring districts.

# **3.1 Resource data**

The Raw Materials Data are from InterraRMG (2013). The data set contains information on past or current industrial mines. All mines have information on annual production volumes, ownership structure, and GPS coordinates on location. We complete this data with exact geographic location data from MineAtlas (2013), where satellite imagery shows the actual mine boundaries, which allows us to identify and update the center point of each mine. The production data and ownership information are double-checked against the companies’ annual reports.

For Ghana, this exercise results in 17 industrial mines tracked over time. We have annual production levels from 1990 until 2012. As mentioned, Table 1 shows the mining companies active in Ghana during recent decades, with opening and closing years (although some were closed in between, and are not presented in the table). Figure 2 shows the geographic distribution of these mines.

## **Figure 2 Gold mines and DHS clusters in Ghana**

**Panel A Gold mines and 20 km buffer zones Panel B Gold mines, DHS clusters, and 100 km buffer zones**

> 4 The distances are radii from mine center point, and form concentric circles around the mine. 5 The DHS and the GLSS data are representative at the regional level, and not at the district level. Since the regional level is too aggregated, we do the analysis at the district level, but note that the sample may not be representative.

8
the country should decrease relative to the rest of the world in order to keep labor employed. This will cause the relative prices of non-traded goods in any sector to reduce, thus depreciating the real exchange rate.

In normal circumstances, the lower relative labor cost should increase the export profitability of home firms, thus reducing the profit cut-off point that makes entering the export market convenient for home firms (only the most productive firms export). This is constrained by what determines the fixed cost of entry in the export market: the higher the cost of entry (both of national firms in the export market and foreign firms in the domestic), intended as physical, regulatory and economic (volatility and market structure), the larger the distance between national and international prices, as the economy will have a much larger proportion of less productive non-trading firms.

The logic of Balassa-Samuelson is maintained at the level of each individual sector: assume that labor has the same price across sectors, but the productivity of firms is differentiated: trading firms are more productive as a necessary condition to access the international market as they have to pay the cost of entry in the international market; non-trading firms are less productive but survive because they do not have to pay the fixed cost of entry in the international market. Wages will be higher the larger is the number of high productive firms in the market. The least productive firms will cover the increase in wages, due to the high productive firms, by increasing prices, thus replicating the Balassa-Samuelson inflation effect of productivity catch up.

The most important implication of the Balassa-Samuelson effect in the South African context is that the difference in prices across countries reflects differences in productivity levels, as they are reflected in differences in wage levels. The first observation is therefore that the South African real exchange rate is depreciated because of dismal productivity performances that have characterized this economy over the past 20 years.

In practice, the Balassa-Samuelson reasoning focuses our attention on the role of productivity in the export sectors as the source of productivity growth and wage increases. Either in the traditional perfect competitive framework or in the most recent models of monopolistic competition we need to first consider the sectoral composition of South Africa’s traded sector. As far as trading in goods is concerned, the following graph from the Atlas of Economic Complexity shows the composition 9
|Eastern Europe<br>and Central Asia|1,718,000|1,588,000|567,000|27,000|4,000|2,000|15,000|23,000|
|---|---|---|---|---|---|---|---|---|

## **2.4. Prioritization of rural investments**

We built a model to prioritize rural road investments based on two simple criteria: (i) maximizing the RAI increase per kilometer paved; and (ii) candidate roads for upgrade must be connected to the existing primary and secondary network. The only investment option available in our model is to upgrade existing roads labeled as either ‘tertiary’ or ‘track’ in OSM to an all-season road where those roads are directly adjacent to the network of current primary and secondary roads. The analysis was done in 19 countries in which the potential for increasing the RAI is high, and for which the data were complete enough.<sup>3</sup> The algorithm for investment prioritization takes a two-step approach, which can be described as follows:

**Step 1** : The OSM road data are processed to identify the largest current connected network of primary, secondary, tertiary roads and tracks (the largest connected sub-graph of the network). In many countries, a significant number of road segments are disconnected or misaligned in OSM. A quick fix is to generate a buffer of 200 meters around each road and remove the roads that are not connected to the network outside the buffer. In addition, all roads with a length of less than 500 meters were omitted, as well as the roads serving less than 0.01% of the total rural population.

**Step 2** : The network of connected roads is then overlaid with the population layer, and the number of people living within 2km of each tertiary road or track is calculated. Tertiary roads and tracks are then ranked according to the number of people per kilometer of road.

The first road from this list is recorded and marked as upgraded to all-season, the new RAI is calculated, and the algorithm starts at Step 2 again, until no more improvement to accessibility can be achieved given the restrictions outlined in Step 1.

# **3. Results and discussion**

## **3.1. Global Rural Accessibility Index estimates**

Results for rural accessibility are displayed in Figure 3. While most European countries have an RAI close to 100 percent, most developing countries are below 60 percent, all Sub-Saharan countries are below 51 percent, and 24 countries are below 20 percent.

> 3 Data were considered good enough if the completeness score on Open Street Map was higher than 75 percent.

7
- [48] Parry, I., D. Heine, S. Li, and E. Lis (2014): Getting Energy Prices Right: From Principle to Practice. Washington, DC: International Monetary Fund.

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- [53] Reboredo, J. C. (2018): Green bond and financial markets: Co-movement, diversification and price spillover effects. Energy Economics, 74, 38-50

- [54] Sachs, J. (2015): Climate Change and Intergenerational Well-Being, in Bernard, L. & W. Semmler (Ed.).The Oxford Handbook of the Macroeconomics of Global Warming, . Oxford: Oxford University Press, 248-259.

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- [56] Stiglitz, J., N. Stern, M. Duan., O.Edenhofer, G. Giraud, G. Heal, E. Lèbre la Rovere, A. Morris, E. Moyer, M/ Pangestu, P. Shukla, Y. Sokona, and H. Winkler (2017): Report of the High-Level Commission on Carbon Prices. Washington DC: World Bank Group.

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16
floor. This minimum varies by country: it is around $5,000 in Ahmedabad and about $30,000 in Hong Kong SAR, China. Poorer households that cannot afford this cost have to live in dwellings whose areas are often even smaller than the plot of land they can afford.

Various government regulations fix minimum land consumption per households, either directly by fixing a minimum plot size or indirectly by fixing a maximum floor-area ratio (the proportion between the land area and the floor area that can be built on it).

Arbitrary regulations fixing minimum land consumption affect the poor differently than the rich. Poor households that cannot afford the minimum land consumption fixed by regulations are forced to live in informal housing. More affluent households are often forced by regulations to consume more land than they would in the absence of regulations.

Evaluating current household land and floor space consumption standards and the prices corresponding to these standards in different locations is the only way to evaluate housing demand and current affordability. The search for affordable housing should include measures to improve the existing low-income housing stock and to increase the supply of new low-cost housing. The flow of new low-cost housing might come from developing new greenfields, recycling older housing stock, or densifying existing residential areas.

Housing is a continuum: different income groups with different mobility compete for the same land. So it is necessary to have a complete view of the current consumption characteristics of all income groups. Detailed housing consumption surveys by income group should be conducted yearly to provide an updated overall view of the market. On the supply side, the regulatory constraints on the richer households, obliging them to consume more land than they demand, have a negative effect on the poorest.

Over the years, the income distribution of households will be constantly changing. Some who were poor will become middle class, but they might be partially replaced at the bottom of the income scale by poor people moving into the city.

The stock of housing units will also change each year because of new additions (often at the top of the income scale), while some of the older stock will be demolished. The inability to increase the supply of new units, usually because of land supply bottlenecks, will raise housing prices and reduce housing consumption for all, with more dramatic consequences for the lowest income groups. Due to new land and housing flows, it is essential as part of the land supply quantification to project the change in housing stock and to compare it with the changes in the number of households in each income group. Cities usually monitor the number of new formal units being built each year, but they often fail to monitor the drop in the existing housing stock due to demolitions or land use changes and the growth or contraction of the informal sector.

22
# **_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

**Table 6** China’s Mobile Phone Handset Production, Export, and Consumption, 1998 and 2005

|**Indicator**|**1998**|**2005**|**_CAGR_** <br>**(percent)**|
|---|---|---|---|
|**Production**||||
|World handsetproduction (millions of units)|174|816|25|
|China handsetproduction (millions of units)|4|304|86|
|China share of worldproduction (percent)|2.3|37.2||
|**Exports**||||
|China mobilephone exports (millions of units)|2|228|94|
|Export share ofproduction (percent)|55.0|75.2||
|**Consumption**||||
|China mobile phone subscriptions (millions of<br>units)|25|400|49|
|China handset sales<sup>a</sup>(millions of units)|3|88|59|
|China handset imports (millions of units)|2|13|35|
|Import share of sales (percent)|47.1|14.5||

_Source:_ Imai and Shiu 2007. _Note:_ CAGR = compound annual growth rate.

a.. Sales figures for 2003 and 2004 were 109 million and 100 million, respectively. While annual sales may have leveled off or even declined since the 2003 peak, because of saturation in urban markets, the figures for handset sales in domestic consumption probably underestimate the real total due to undercounted sales of illegal and quasi-legal handsets.

Handset sales in China’s domestic market rose from 3.4 million units in 1998 to an astonishing 109 million units in 2003. The most successful multinational brands in China— market leaders Nokia (Finland), Motorola (Unites States), and Samsung (Korea)—account for 95 percent of exports and quickly came to dominate domestic sales as well. Local Chinese brands held just 5 percent market share in 1999, while together Nokia and Motorola controlled 70 percent of the market. As the domestic market took off, however, dozens of local handset brands appeared and were able to capture 55 percent of the market by 2003.

As Imai and Shiu (forthcoming; 2007) argue, the success of local firms can be explained by a combination of market opportunity and government policy. Multinational handset producers could not simply divert a share of export production to the local market. The feature-rich phones produced for export were too expensive for Chinese consumers, and those produced for export to the United States are incompatible with China’s dominant mobile phone standard (GSM) in any case. While even the older GSM phone models sold by multinational firms in China were too expensive for most of the Chinese market, the more fundamental problem for multinational firms was the lack of variety in their lower end product lines. High costs and low product variety created a market opportunity for local cell phone handset companies, and local brands did well by selling simpler, less expensive handsets with a wide variety of exterior designs and other technologically superficial features.

35
# must be negative. This implies that

6 > [1(a + EaMt ) **)** + **(a** + EtfJ, i.e., parents value consumption more than investment in their offspring.

This does not, however, mean that parents must invest equally in male and female children. If parents value their son's human capital more than their daughter's, then the demand for education and health of boys will be less own-price elastic than that for girls. This would mean that

| 5 **8mJ** | < JUL S8ap t and |P.dH | |P.03:11 | % H _ape_ f P

However, computing these own-price elasticlties with the functional forms assumed above yields P _.aS*_ P _.aS*_ **S aPS** S < **£** -1, and _so apD_ Bsf Ps P .01 H _ae_ 3 P .8H H ' H OP HOP8 That is, the demand for education and health are equally price elastic for boys and for girls.11 Given the homothetic preference structure and return functions, parental investment in male and female children are equally price elastic. No gender bias exists as long as preferences are homothetic.

11The cross-pric **e** lasticities all equal 0: _PS. a*_ P.aH* _P.as* P.Oas pS_ Hf **_apS_** _s_ **H** f **H** 14
this structure should at least cover function, organization, and spending unit, and object of expenditure (economic classifications). Function codes and economic classification codes should follow internationally accepted classification schemes (Diamond 2013). Furthermore, codes such as source of funds and program and project codes could be added to track expenditures by fund source and for specific programs and projects. It would then be possible to break down the organization codes to a level where a specific unit is performing a specific subfunction.

3.13 **It is fundamental that the economic classification codes are a subset of the account codes in the chart of accounts** to ensure integration of budgeting and accounting. If expenditures are booked under line items in the chart of accounts (which is different from those in the budget classification system), then reports produced from the FMIS would not provide expenditure incurred by budget appropriation line items.

3.14 **An overly complex budget classification structure and chart of accounts could impede implementation.** A complex budget classification has frequently been justified to allow allocation of every transaction to the lowest level. However, the complexity needs to be balanced with coding requirements (the complete budget classification structure and chart of accounts need to be coded on every transaction). This is cumbersome and can lead to allocation errors. Therefore, there may be a trade-off between the level of detail required for the transaction and the quality of the data entry. Furthermore, a chart of accounts that is too complex could also increase transaction processing times on the server, especially when the number of active users is large, as was the case in Vietnam.

# **CONTROL PROTOCOLS**

# **_Finding 3: A review of existing control protocols is essential. Setting up an automated FMIS without the necessary control functionalities could speed the hemorrhage of resources instead of controlling it._**

3.15 **A review of control protocols is important for a comprehensive diagnostic.** An automated system may increase the speed of disbursements. For example, the Philippines implemented reasonably effective automated systems to distribute budget allocations to spending ministries, but did not implement a system to monitor budget execution. In this situation, the government has an efficient mechanism in place to distribute its financial resources to the line agencies, but it has little control over whether the agencies spend resources as prescribed in the budget.<sup>**21**</sup>

> 21 See project documentation for FMIS operations in Ghana, Philippines, and Zambia.

**25**

**25**
|PPR<br>Tltle|Working Paper Series<br>Author|Date|Contact|
|---|---|---|---|
|WPS74 Fiscal Stabilization<br>and Exchange Rate<br>Instability: A Theoretical Approach<br>and Some Policy Conclusions Using<br>Mexican Data|Andrew Feltenstein<br>Stephen Morris|August 1988|A. Bhalla<br>60359|
|WPS75 Welfare Dominance and the Design of<br>Excise Taxation in the Cote d'lvoire|Shlomo Yitzhaki<br>Wayne Thirsk|August 1988|A. Bhalia<br>60359|
|WPS76 On the Shadow Price of a Tax<br>Inspector|Shlomo Yitzhaki<br>Yitzhak Vakneen|||
|WPS77 Incentive Policies and Agricultural<br>Performance in Sub-Saharan<br>Africa|Bela Balassa|August 1988|N. Campbell<br>33769|
|WPS78 Economists, Institutions<br>and Trade<br>Restrictions: A Review Article|J. Michael Finger|||
|WPS79 Quantitative<br>Appraisal of<br>Adjustment Landing|Bela Balassa|August 1988|N. Campbell<br>33769|
|WPS80 Emerging Issues of Privatization<br>and the Public Sector|Samuel Paul|September 1988|E. Madrona<br>61711|
|WPS81 Reaching People at the Periphery:<br>Can the World Bank's Population,<br>Health, and Nutrition Operations<br>Do Better?|Richard Heaver|September 1988|S. Ainsworth<br>31091|
|WPS82 Microeconomic<br>Theory of the House-<br>hold and Nutrition Programs|Dov Chernichovsky<br>Linda Zangwill|September 1988|S. Ainsworth<br>31091|
|WPS83 Welfare Costs of U.S. Quotas in<br>Textiles, Steel, and Autos|Jaime de Melo<br>David Tarr|September 1988|C. Cabana<br>61539|
|WPS84 Black Markets for Foreign Exchange,<br>Real Exchange Rates and Inflation:<br>Overnight vs. Gradual Reform In<br>Sub-Saharan<br>Africa|Brian Pinto|September 1988|R. Blade-Charest<br>33754|
given by gross enrollment figures for the Philippines: Between 1980 and 1997, gross enrollment ratios increased in primary school (by 4.4%, from 113 to 118) and secondary school (by 15.4%, from 65 to 75), and decreased at the tertiary level (by 19.2%, from 26 to 21) (Asian Development Bank and World Bank 1999, p. 149).16 Moreover, these changes in supply took place at a time when, if anything, there appears to have been an increase in the demand for more skilled workers in the Philippines-for example, in the booming electronics sector.'<sup>7</sup> Finally, there may be large differences in quality across education levels in the Philippines, with higher quality education being provided in university (for a general discussion of this issue see Strauss and Thomas 1995, pp. 1971-72, as well as Behrman and Birdsall 1983). If there are indeed quality differentials, these could be related to a quality-diluting expansion in primary and secondary enrollments. Whether the fact that a higher fraction of university education than primary or secondary education is provided

**16** However, gross enrollment rates at the tertiary level in the Philippines were well above those of its neighbors throughout the 1990's, including Thailand, China, Indonesia, and Malaysia (but not Korea) (World Development Indicators database, The World Bank). There is also some discrepancy between these figures and those in the World Development Indicators database, which actually show an _increasing_ enrollment ratio for tertiary education in the Philippines over the 1990's. The difference between these estimates appears to be that the World Development Indicators include enrollment in non-university tertiary institutions, whereas the Asian Development Bank and World Bank (1999) report do not. Alternatively, I could have used data on educational attainment (rather than enrollment), such as those found in Barro and Lee (1993) or Nehru, Swanson and Dubey (1994), for the comparisons in this paper. These data sets have been criticized on numerous grounds (Behrman and Rosenzweig 1994). For the purposes of my analysis, the main disadvantage of these data sets is that they do not provide post-1987 data. See also Pritchett (1996).

17 Disaggregated data on sector of employment, which was collected in the APIS but was not available for this paper, might help clarify this point by showing whether there were large differences in wage premia across sectors. Pritchett (1996), for example, argues that the rate of return to education is generally highest in sectors in which there is exogenous technological progress. Amongst other evidence, he cites work by Foster and Rosenzweig (1996) on India, which suggests that rates of return to education went up in villages where the Green Revolution brought agricultural innovation, but not in technologically-stable villages.

18