rafmacalaba/datause-encoder-data huggingface dataset:
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

# Benchmarking

The main objective of the regression models is to predict expected levels of infrastructure stocks for the purposes of benchmarking. As described in the first section of the paper, benchmarking is done by comparing the infrastructure stock in each country with the value predicted by the regression model and calculating the deviation. The value predicted by the model takes into account the effect of each country’s history and environment in determining the level of infrastructure stocks the country might be expected to have. A negative deviation indicates that the country has performed below the benchmark, while a positive one indicates the opposite. The deviations are averaged across countries to produce unweighted average deviations at the regional and subregional level. For the purposes of sensitivity analysis, deviations are calculated for each of the two model specifications described above. Following Bogetic (2006), a deviation of less than 10% in absolute value is considered not to constitute a major divergence from the benchmark. Hence, attention focuses on larger deviations of more than plus or minus 10% that are taken to be indicative of a substantial degree of over or underachievement.

**Table 3.5 Mean deviations from predicted values, OLS**

|Region|Density of<br>roads|Density of<br>roads (arable)|Density of<br>paved roads|Density of<br>paved roads<br>(arable)|Access to water|Access to<br>sanitation|Access to<br>electricity|Generating<br>capacity|Internet<br>connections|Mobile<br>telephones|Fixed-line<br>telephones|
|---|---|---|---|---|---|---|---|---|---|---|---|
|_Subregions of A_|_frica_|||||||||||
|Central|–6|–6|–12|–13|2|4|–7|–18|–62|–39|–10|
|EAC|–12|–10|–55|–52|5|–13|–26|–11|–117|–57|–72|
|ECOWAS|–7|–14|–13|–20|–0.1|–1|–10|–9|–143|–30|–4|
|SADC|9|12|13|16|0.2|–3|–11|–21|9|7|–17|
|_World regions_||||||||||||
|SSA|–4|–5|–6|–8|1|–1|–11|–17|–78|–25|–15|
|SAS|24|25|13|14|–0.4|16|30|29|–36|35|35|
|EAP|–7|–6|–4|–4|–2|–1|9|–2|–33|–20|11|
|ECA|6|9|–6|–3|0.02|–0.5|N/A|–7|75|95|–18|
|LAC|–7|–7|–0.3|0.4|–0.1|–1|3|16|–11|30|9|
|MNA|0.1|3|1|4|–3|–3|10|4|–2|–2|5|

Note: SSA = Sub-Saharan Africa, SAS = South Asia, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and Caribbean, MNA = Middle East and North Africa.

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# **8.2 Tables**

Table 1: Impact of MNREGA on seasonal depth to groundwater

||Monsoon|Kharif|Rabi|Pre-monsoon|
|---|---|---|---|---|
|Post-MNREGA|-0.07|-0.08|-0.15|-0.11|
||(0.03)|(0.02)|(0.02)|(0.02)|
|95% CI|[-0.13,-0.01]|[-0.13,-0.04]|[-0.20,-0.10]|[-0.15,-0.07]|
|N|6355|6939|6932|6852|
|Pre-MNREGA mean (m)|6.10|6.16|6.81|8.21|
|**Fixed Efects:**|||||
|District|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|

Notes: Our outcome is the IHS transformation of the depth to water in each season specified in the column heading. 100 _×_ the coefficient is the percent change in the depth to water after the implementation of MNREGA. All regressions include controls for monthly temperature, a quadratic control for monthly precipitation and a quadratic state _×_ year trend. Errors are clustered at the district level. Our estimates are based on the approach described in Callaway and Sant’Anna (2021) and implemented using their Stata package `csdid` .

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different assets, or on total amounts held, but simply on whether a participation threshold has been reached.

Whether examined from household survey data, or from inference based on numbers of bank and other financial intermediary accounts, it is clear that the fraction of households using<sup>2</sup> the services of formal<sup>3</sup> financial intermediaries is small in most developing countries. In contrast, access percentages in the 80s and 90s are recorded for households in advanced economies (where the relevant policy issues are best discussed in the language of exclusion rather than of access).

Despite the lack of concrete evidence, it is also generally believed that there is also a _direct_ role for the financial system in reducing poverty by reaching more households with microfinance services. But the scale of this impact remains unclear. Access percentages are correlated with poverty rates and national per capita income, but not very closely. However, conditioning on mean national income, attempts (including in this paper) to detect a statistically robust causal relationship on cross-country data between financial _access_ and poverty have not been successful to date. Intriguingly, then, the strongest cross-country econometric evidence for a financial sector impact on poverty relates to financial depth (total value of private credit in relation to GDP), and not financial access (percentage of households with an account). Whether this is a causal effect is unclear.

Microeconomic studies on the impact of microfinance on poverty are also surprisingly inconclusive. Although beneficiaries of microfinance schemes are vocal in their praise and gratitude, a comprehensive assessment needs to consider displacement effects and endogeneity of financial access. Only in a few cases so far has it been possible to devise convincing ways of adjusting for, or excluding, these complicating factors. On balance, most observers regard microfinance interventions as poverty-reducing, while continuing

> 2 There is an obvious and important conceptual distinction between access and usage, and surveys often explore this (for further discussion, see Honohan, 2004b).  In the present paper we treat the terms synonymously, which may not be too bad an approximation when equating access to the use of _any_ financial product.

> 3 The discussion is generally confined to the formal financial system and as such excludes borrowing from informal lenders and participation in, for example. informal rotating savings and credit schemes.

4
## **Data source**

The data used for the construction of this indicator include the same street grid data and transit network data as used for the indicator on accessibility.

## **Methodology**

We first compute for each household the number of public transport ‘runs’, that is the number of vehicle departures at all stops available within a 10 minute walking time over the course of an hour. We use the same 8:00 am to 9:00 am window to be consistent with the accessibility indicator and to account for the peak services during morning commutes. The walking distance are analyzed using the road-grid distance, using 3.6 km/h as walking speed and based on the same network as used for the accessibility analysis. A public transport vehicle that can be stopped continuously, is assumed to stop every 100 meters.

To give an example, a household that is within a 10-minute walk of three different bus stops, which have a bus arriving – to any destination - once an hour, every thirty minutes (i.e. 2 per hour) and every five minutes (i.e. 12 per hour) between 8 am and 9 am will have a value of 1+2+12=15 for this metric.

Once we have this number of runs, we normalize the number to obtain an index between 0 and 100%. In practice, we divide by 60 and truncate the values equal to or over 1 to be equal to 100%. This means that we have an index of an average number of bus reachable each minute between 8 am and 9 am, at a maximum distance of 10-minute walk, with the maximum being 1 bus per minute (all locations that have a higher availability will report an index=100%).

# _c. Measuring safety_

## **Definition**

The third and last indicator of the public transport network looks at a different aspect of transport, that can affect the decision to use public transport – namely the safety of the built environment at the public transit stops. This should be interpreted as a broader neighborhood level measure of safety.

## **Data source**

We rely on the built environment audits conducted in the three cities and described in section 2.1.

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improve internal labor incentives, the government granted managerial wage discretion (called _the flrm-specific wage scheme)_ for managers to base employees' wages on their observed productivity, controlling only either the firm's aggregate wages or their growth rate. That managerial discretion, presumably, could be manifested in determining employee wages based on his output (pay sensitivity) and on his effort and skills (monitoring); yet, in a team production setup, managers could not tell each employee's productivity. Individual-level pay sensitivity is thus precluded from being a component of that discretion. In the rest of the paper, therefore, managerial wage discretion is justifiably viewed as managers' heightened right to monitor as an incentive device for employees.<sup>9</sup> The share of firms with managerial wage discretion increased in the latter 1980s: it was only 0.5% in 1980, 5% in 1984, then jumped to 20% in 1987, and 35% in 1989.

4. Making a firm more self-financing. A SOE could finance its investment through government funds, bank loans, and profit retention. Traditionally the government funds were the major source. Over the decade the share of finance attributable to bank loans and profit retention increased: the figure for by bank loans was 14% in 1980, 22% in 1984, 32% in 1987, and 27% in 1989; for profit retention the figure was 13% in 1980, 15% in 1984, 15% in 1987, and 17% in 1989. In the rest of the paper, direct government finance is regarded as a redistributive mechanism: since its funds are more heavily subsidized than other sources, greater reliance on government funds indicates larger transfer to SOEs.

_5. Management (or Contract) Responsibility System_ (MIRS) and firm wage elasticity. '<sup>0</sup> The counterpart to the Household Responsibility System of the Chinese 9In this paper, monitoring refers to that performed by managers on employees. We do not discuss the monitoring the government conducted on the firms, for we do not have a good measure of government monitoring on firms. While production autonomy may sound like lessened government monitoring on firms, it is not clear that was the case. '<sup>0</sup> AII information about MRS is from _MRS in Practice._ 6
agricultural PAC money were members of the influential House Agricultural committee. They included the committee chair (de la Garza ) and three subcommittee chairs (Stenholm, Rose, Volkmer). Figure 2 bears out much the same story for the 1997-98 election cycle. The pattern of giving in these figures clearly indicates that agricultural PACs targeted politicians with influence over agriculture policy. The amounts are not inconsequential. For example, in the 1993-94 cycle, agricultural PACs delivered between 8% (Fazio) and 60% (de la Garza) of the total PAC money received by the top twenty “Ag PAC” recipients.<sup>41</sup>

# _Instrumental Variables_

Since the regressor is composed of the endogenous variables _I_ and _z_ we construct two exogenous variables from the agriculture census of 1997 that identify each of the endogenous variables. The first instrument, termed PaymentConcentration, is a measure of geographic concentration and pressure. It is constructed using data on total government payments available for each state. Total government payment to a state is then distributed across the top four commodities produced in that state.<sup>42</sup> This approximates how much each commodity gets by virtue of its being a visible top four commodity in the state. Summing across the states gives us variable PaymentConcentration which measures government payments to each visible commodity. The second instrumental variable termed Farmland is simply the acres of land in farms for each crop. The first variable is constructed in order to identify political organization and the second to identify the output-to-imports and output-to-exports ratio _z_ . Since the regressor in model (4) is a composite of the two, for identification we use linear, quadratic, and cross-product of these two variables as our set of instruments.

files, and then merged with the relevant cycle’s CN files. Agriculture committee and sub-committee assignments for each Congress were obtained from Congressional Quarterly (1996-1999).

41 Top 20 list-makers who were not on the Agricultural committee represented districts with influential agricultural constituents (CA, OK, VA, MI). For example, Fazio’s district (CA, district 3) was among the 30 leading districts by market value of agricultural products (1997 Census of Agriculture).

> 42 These include payments for acreage reductions, commodity credit loans, payments for meeting environmental targets satchel wetlands, etc.

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|Croatia|1|8|3|3|15|
|---|---|---|---|---|---|
|Ethiopia|2|1|0|1|4|
|Georgia|1|0|0|1|2|
|Ghana|3|1|0|0|4|
|Indonesia|1|3|1|1|6|
|Kenya|4|1|0|1|6|
|Kosovo|2|0|0|2|4|
|Peru|15|1|0|0|16|
|Romania|4|2|1|3|10|
|Solomon Islands|1|1|0|0|2|
|South Africa|1|3|1|2|7|
|Ukraine|16|4|0|1|21|
|Uruguay|1|0|1|3|5|
|Total|72|32|7|24|135|

_Source_ : World Bank Infrastructure SOE Database.

# **4 Fiscal Injections to SOEs**

# **_4.1 A Typology of Fiscal Injections to SOEs_**

We created the following measures of fiscal injections to support infrastructure SOEs:

- **Operational subsidies:** Operational subsidies are the fiscal transfers governments send to SOEs to cover shortfalls for the year and, often, to compensate the SOE for specific QFOs. They also include extraordinary transfers to cover unexpected losses, shortfalls in capital investments, etc. These subsidies can account for a large share of assets, especially when they cover equity shortfalls (that is, are used to recapitalize an SOE). In some countries, they are made regularly, especially when governments compensate SOEs ex ante for specific QFOs and mandates.

- **Government injections of equity:** Governments can bail out and recapitalize SOEs by injecting equity. These equity injections increase the equity owned by the government (in SOEs that have other shareholders). We track them using a variable called Proceeds from Equity Injection from Government, which adds up the additions to equity from government either directly (by a domestic government entity, such as a ministry or a government agency) or indirectly (by a domestic government entity through its shareholding in another company, such as another SOE, or any other enterprise with government participation).

- **Credit from the government:** This variable is created by adding up long-term debt or loans from the government or other government-affiliated creditors except SOEs and stateowned banks. Credit usually has a direct impact on the budget and on the support governments report for their SOEs.

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rented from relatives, the duration of the contract and whether a written contract is present. Considering the 2002-2012 period, we note an increase in formalization, with longer, written contracts.

In Figure 1 we display maps of selected areas, East, North and West, and South, in India, with district boundaries following the 2001 Census. Panel A displays the maps representing the situation in 2002. Panel B maps represent the situation in 2012. The blue colour represents high levels of fixed rent, while the yellow represents low levels (with the red an in between stage). Note that most of the populous states in the south of India, such as Tamil Nadu, Andhra Pradesh, Telangana and Kerala, have witnessed a substantial increase in fixed rent arrangements.

# **Migration**

We do not have any direct measure of landlord migrants, as there is no information on land ownership in the Census. We proxy for landlord migrants using high-skilled migrants, a group defined by a cut-off of at least 10 years of education.<sup>15</sup> This assumption is supported, at least to some degree, by the data. Using the NSS Land and Livestock Survey round 70 (conducted in 2013), we note a significant, positive correlation between acreage of land owned and the education (in years of education) of the household head (a correlation coefficient of 10%). In Appendix Figure A3 we present the histogram of the education level of the head of the household, by type of landholding. On the left-hand side are the landless, defined as possessing less than 2 hectares, and on the right-hand side, the landed, defined as owning more than 2 hectares. While there is an overlap between the educational distributions of the landless and the landed, we note that very few of the landless have more than ten years of education. We notice a similar pattern using the national ARIS-REDS panel survey of rural households from 2005. Using the sample of all individuals who engaged in migration that year (that is, temporary migrants), we find that

> 15This cut-off follows the structure of education in India: At the end of lower secondary education, in year 10, an exam has to be taken and passed in order to progress, and further education often requires travelling further afield.

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1G until a consumption path was found that left zero wealth at age 75.1 In this manner we simulated the income, consumption and saving of each cohort of households, starting at the beginning of the 20th century and going out to the end of the 22nd century.<sup>2</sup> In order to do the aggregation for any period, say 1950-54 or 2085-89, it was necessary to have the income and consumption paths for every cohort of households that existed in the period.<sup>3</sup> To get the aggregate saving rate of the economy for each 5-year period, we multiplied the income and consumption per household for each cohort by the number of households (women) in the cohort during the period. Then we added up the income and consumption across households.

# Simulation Results

The paths of consumption for households did not change greatly over the transition. Most of the change in the aggregate saving rate resulted from the changes in the relative numbers of different age households.

Figures 3 to 9 show the simulated saving rates with base-case assumptions for Brazil, China, S. Korea, Mexico, Nigeria, Pakistan, and Turkey. The saving rates are all in the range of 3 to 11 percent, which is 1 A point of imprecision in the simulation is that the wealth of households that die early just disappears and is not available for consumption by the survivors. 2 Since UN population data with the requicite breakdowns by age start only in 1950, we had to project the earlier history of all households that were still existing in 1950. This was necessary to have simulations of aggregat2 saving that began in the 19509. Without some back projection we could not have computed any aggregate saving rates for years before 2010. For the pre-1950 projections we assumed the age-specific death and fertility rates that prevailed in 1950. Our results are not sensitive to these assumptions.

3 The unit of income and consumption was the annual earnings of one full adult worker in the base period (1980-84).
Table 9: Larger and poorer households do not substitute away from most important plot in response to sample plot shock

||Culti-|Irri-|Horti-|Banana|HH|Input|Hired|
|---|---|---|---|---|---|---|---|
||vated|gated|culture||labor/<br>ha|exp./ha|labor<br>exp./ha|
||(1)<br>|(2)<br>|(3)|(4)|(5)|(6)|(7)|
|RDD (Site-by-season FE,|Specifca|tion 5)||||||
|CA|-0.069|-0.097|-0.107|0.052|-82.7|-9.1|-4.5|
||(0.086)|(0.048)|(0.046)|(0.065)|(34.8)|(4.2)|(3.6)|
||[0.424]|[0.046]|[0.020]|[0.418]|[0.018]|[0.031]|[0.216]|
|CA * # of HH members|0.021|0.011|0.014|0.008|10.1|0.6|0.5|
||(0.013)|(0.007)|(0.007)|(0.011)|(4.5)|(0.5)|(0.5)|
||[0.112]|[0.155]|[0.061]|[0.485]|[0.025]|[0.237]|[0.273]|
|CA * Asset index|-0.013|-0.018|-0.015|-0.003|-12.4|-2.5|0.3|
||(0.027)|(0.016)|(0.017)|(0.023)|(10.2)|(1.6)|(1.4)|
||[0.620]|[0.277]|[0.384]|[0.900]|[0.226]|[0.117]|[0.856]|
|Joint F-stat [p]|1.5|1.5|1.8|3.1|2.0|1.9|0.6|
||[0.213]|[0.214]|[0.147]|[0.026]|[0.122]|[0.128]|[0.592]|
|Average efect|0.038|-0.044|-0.038|0.092|-32.2|-6.0|-1.8|
|SFE (Spatial FE, Specifc|ation 5)|||||||
|CA|-0.188|-0.121|-0.129|-0.052|-94.6|-10.3|-2.1|
||(0.098)|(0.051)|(0.047)|(0.083)|(38.9)|(4.1)|(3.5)|
||[0.056]|[0.017]|[0.006]|[0.532]|[0.015]|[0.013]|[0.551]|
|CA * # of HH members|0.039|0.017|0.019|0.023|12.5|0.7|0.3|
||(0.014)|(0.008)|(0.007)|(0.015)|(4.5)|(0.5)|(0.5)|
||[0.007]|[0.030]|[0.012]|[0.110]|[0.006]|[0.185]|[0.539]|
|CA * Asset index|-0.043|-0.035|-0.030|-0.012|-24.0|-3.9|-0.3|
||(0.032)|(0.020)|(0.021)|(0.026)|(12.7)|(1.7)|(1.4)|
||[0.181]|[0.077]|[0.156]|[0.661]|[0.060]|[0.025]|[0.813]|
|Joint F-stat [p]|3.5|2.3|2.6|2.1|2.9|2.6|0.1|
||[0.015]|[0.079]|[0.050]|[0.094]|[0.033]|[0.051]|[0.937]|
|Average efect|0.004|-0.036|-0.037|0.065|-33.2|-6.7|-0.5|
|# of observations|2,176|2,176|2,176|2,176|2,163|2,166|2,166|
|# of clusters|182|182|182|182|182|182|182|
|Control mean|0.368|0.114|0.109|0.199|66.8|5.6|3.9|

_Notes:_ Regression coefficients on a command area indicator for the sample plot (“CA”) and its interaction with W (“CA * W”) are presented above. Specifications control for distance to the command area boundary, its interaction with CA, log area of the sample plot and the most important plot, a command area indicator for the most important plot (“MIP CA”), and all W. RDD specification includes site-by-season fixed effects, and SFE specification includes spatial fixed effects. Standard errors are in parentheses, and p-values in brackets. “Average effect” estimates are from Table 8.

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||||problems in wave<br>3.<br>UGA: only for<br>plots with erosion<br>problems from<br>wave 4 onwards|
|---|---|---|---|
|_livestock_|Is the respondent<br>engaged in<br>livestock<br>activities?|||
|_ag_asset_index_|Agricultural assets<br>index|Index based on a PCA of all<br>agricultural assets owned by the<br>household|UGA: missing in<br>wave 1|
|_perennial_crops_hh_|Does this<br>household grow<br>perennial crops?|||
|_nb_fallow_plots_|Number of fallow<br>plots under<br>household<br>management|||
|_nb_plots_|Number of plots<br>under household<br>management|||
|_share_kg_sold_|<br>Share of harvest<br>output (in kg) sold|Set to missing if reported share<br>above 100% percent. Set to<br>missing if household does not<br>harvest any crops.||
|_agro_ecological_zone_|Agro-ecological<br>Zones||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_dist_popcenter_|HH Distance in<br>(KMs) to Nearest<br>Population Center<br>with +20,000||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above|
|_dist_market_|HH Distance in<br>(KMs) to Nearest<br>Market||From the<br>geovariables files,<br>which are missing<br>in NER w2, TZA<br>w4 and w5, UGA<br>wave 4 and above<br>Also missing in<br>MWI, MLI, NER,<br>and all of TZA and<br>UGA|
|_elevation_|Elevation (m)||From the<br>geovariables files,<br>which are missing<br>inNERw2,TZA|

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