rafmacalaba/datause-encoder-data huggingface dataset:
Given that digitization of administrative boundaries is at the district level, computing the distance between places requires additional assumptions. For places belonging to different population size groups but within the same district, the distance is assumed to be zero. For places in different districts, regardless of their population size group, we use the pairwise distance between the corresponding districts. For any two districts, the pairwise distance is computed as the length of the shortest surface-level curve between their centroids, based on the Haversine formula.

We also generate information on the location characteristics of individual places, using to that effect the Spatial Database for South Asia (Li et al., 2015). This platform combines data from the Census of India, the Household Consumer Expenditure and the Employment and Unemployment modules of the NSS, the Economic Census, administrative records, remote sensing data and crowdsourced data. The Spatial Database for South Asia provides information on a range of socioeconomic indicators, including the urban extent, demographics, jobs, economic activity, infrastructure, ICT, finance, business, living standards, education, health, and environment (Table 1).

Table 1. Summary statistics, by type of location

||**Small**|**rural**|**Large**|**rural**|**Small**|**urban**|**Large**|**urban**|**To**|**tal**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Places**|57|9|22|1|58|1|2|5|14|06|
|**Observations at household level**|458|73|107|85|336|51|59|18|96|227|
||Mean|Std|Mean|Std|Mean|Std|Mean|Std|Mean|Std|
|**Household expenditure per capita**<br>**(current India Rupees per month)**|1527|1133|2063|2736|2466|2246|3590|3277|2042|2052|
|**Demographics**|||||||||||
|Household size|4.94|2.23|4.70|2.16|4.41|2.16|4.15|2.20|4.68|2.21|
|Children under 6|0.07|0.10|0.07|0.10|0.06|0.10|0.05|0.10|0.06|0.10|
|Children above 6|0.10|0.11|0.10|0.11|0.09|0.11|0.08|0.11|0.10|0.11|
|Female adults|0.23|0.13|0.25|0.14|0.24|0.15|0.23|0.16|0.24|0.14|
|Female dependents|0.03|0.08|0.04|0.09|0.03|0.08|0.03|0.08|0.03|0.08|
|Male dependents|0.04|0.09|0.04|0.10|0.03|0.09|0.03|0.09|0.04|0.09|
|Female household head|0.09|0.29|0.13|0.34|0.12|0.33|0.10|0.30|0.11|0.31|
|**Skills**|||||||||||
|Maximum education of adults (years)|8.40|4.46|9.02|4.41|10.45|4.46|11.06|4.44|9.35|4.57|
|**Assets**|||||||||||
|Land (0.000 hectares)|0.96|2.14|0.53|1.41|0.19|1.03|0.06|0.64|0.59|1.72|
|Dwelling|||||||||||
|Own|0.95|0.21|0.93|0.26|0.69|0.46|0.58|0.49|0.84|0.37|
|Rent|0.03|0.17|0.06|0.23|0.27|0.44|0.37|0.48|0.14|0.34|
|Other|0.02|0.13|0.02|0.13|0.04|0.19|0.05|0.21|0.03|0.16|
|No|0.00|0.03|0.00|0.02|0.00|0.03|0.00|0.05|0.00|0.03|

(Continued) 9
- Jones, D., D. Molitor, and J. Reif (2018): “What Do Workplace Wellness Programs Do? Evidence from the Illinois Workplace Wellness Study,” Working Paper 24229, NBER.

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29
21 modest but significant change in estimates when crossing coder's OPE and OBP ranings if the "halo effect' was strong. The lack of a consistent downward movement of the estimates (in the multivariate case they actually rise) suggest at least that the 'halo effect" is not dominating the results.

Table **_5:_** Results of Monte Carlo simulation of the combined **effects of measurement error and "halo** effects" **using cross coder** information.

||||Degree o|f measure|ment error|||
|---|---|---|---|---|---|---|---|
|||_0|'|.25||.5||
|Degree<br>of|1|2<br>AonA|3<br>AonB|4<br>AonA|_5_<br>AonB|_6_<br>AonA|7<br>AonB|
|EffectH|0|_.5_<br>(true)|_.5_<br>(true)|.40|.40|.33|.33|
||.25|.75-|_.75_|.65|.60|_.58_|_.50_|
||.5|1|1|.90|.80|.83|.67|
||1|1.5|_1.5_|1.4|1.2|1.3|1.0|

Note: The results are the average estimates from 1,000 replications of 120 observations each of the model: _y=, *x_ + e f **_=.5,_** _x,_ **E** N(0,1) , A(B)'s observations on the x variable are subject to measurement error of the form: **_xA>_** = _x_ + **_k*qA,_** where A(B) indicates that A and B have different random measurement error of proportion k. The observations on the dependent variable y are determined by

**_yAW)_** = **_y_** + 6 **_*xA(m_** so that the measurement error of A or B influences the

measurement of y by common 'halo' factor of a

# Results by performance indicator
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 1. Infrastructure has contributed much to economic growth—but could contribute much more**

<!-- Start of picture text --> a. Infrastructure’s contribution to annual per capita economic b. Potential contributions of infrastructure to annual per capita<br>growth in West African countries, in percentage points, 2001-05 economic growth in West African countries, in percentage points<br>5<br>5<br>4<br>4<br>3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>Roads Electricity Telecom Roads Electricity Telecom<br>Percentage Points Percentage Points<br>n a go r e i us<br>Beni Cote d'Ivoire Mauritius Guinea Burkina Faso Senegal Ghan Mauritania T o Nigeria South Africa Gambia Nig Sierra Leone Guinea-Bissau Niger Togo Nigeria Burkina Faso Guinea-Bissau Sierra Leone Benin Mauritania Gambia Senegal Ghana Guinea Cote d'Ivoire South Africa Maurit<br><!-- End of picture text --> a. Infrastructure’s contribution to annual per capita economic growth in West African countries, in percentage points, 2001-05 _Source_ : Calderon 2009.

Evidence from enterprise surveys suggests that infrastructure constraints were responsible for about 60 percent of the productivity handicap faced by Beninese firms (figure 2a), with the remainder traceable to poor governance, red tape, and financing constraints. Of the countries of the region, infrastructure constraints were highest in Benin. Customs clearance before export was the constraint that weighed most heavily on the country’s firms. The time waiting for phone connections was the second-most-important constraint for increasing productivity (figure 2b). Transport is also a major issue hindering firms’ productivity. The average output loss owing to electricity supply problems is about 7.5 percent, a number higher than in most countries in Sub Saharan Africa (World Bank 2005a).

**Figure 2. Infrastructure deficits hold back firms’ productivity**

<!-- Start of picture text --> b. Degree to which infrastructure is perceived by firms as an obstacle to<br>growth, by sub-sector<br><!-- End of picture text --> a. Degree to which infrastructure is perceived by firms as an obstacle to growth <!-- Start of picture text --> Mali<br>Senegal<br>Mauritania<br>Niger<br>Burkina Faso<br>Benin<br>0% 20% 40% 60% 80% 100%<br>percent of productivity deficit attributable to sectors<br>Electricity Customs clearance Transportation ICT Water<br><!-- End of picture text --> <!-- Start of picture text --> Benin<br>Senegal<br>Mali<br>Mauritania<br>Burkina Faso<br>Niger<br>0% 50% 100%<br>percent of productivity deficit attributable to sectors<br>Infrastructure Others<br><!-- End of picture text --> _Source_ : Escribano and others 2009.

3
19 with that of the sovereign. Dependence on ECB funding—particularly by Greek and Portuguese banks, and by smaller banks in Italy and Spain—may, in effect, create unintended consequences down the road.

**23. Moreover, there are signs that additional risks may be emerging, partly in response to the tightening of regulatory requirements for banks and reduced bank credit.** Signs of bank disintermediation in the Euro Area have become evident, with large European companies faced with markedly higher fees, and margins on bank loans increasing their reliance on bond and capital markets since the crisis.<sup>12</sup> There is also anecdotal evidence of risk being squeezed out of banks into the ―shadow‖ banking system as banks are discouraged from engaging in certain (riskier) activities. Banks have also been engaging in deals with private equity and hedge funds to preserve bank capital, including by slicing various exposures, repackaging them, and then shifting them from their balance sheets. Despite its growing importance—the sector reached 25– 30 percent of the total financial system in 2010 (FSB, 2011a,b)—the shadow banking system is not regulated to the same degree as traditional banking, and can thus be a more fertile breeding ground for risks.

**IV. TRANSMISSION CHANNELS TO EMERGING MARKET AND DEVELOPING COUNTRIES**

**24. Deleveraging pressures on European banks and tight credit conditions have important implications for emerging markets and developing economies in a highly integrated global financial system.** The impact of deleveraging and tighter credit conditions is being transmitted to the rest of the world through various channels, including: (i) reduced crossborder claims of European banks on the public, private, and banking sectors of emerging market and developing economies; (ii) sales or downscaling of noncore, nondomestic businesses in host economies; (iii) deleveraging by subsidiaries and branches of foreign banks faced with reduced funding flows from parents, or parent attempts to transfer dividends, capital, or liquidity to headquarters; and (iv) increased cost of borrowing for subsidiaries, either as a result of a general worsening of funding conditions or as investor concerns about parents generate anxiety about the overall health of banking groups.

**25. Accordingly, a country’s vulnerability to European bank deleveraging depends on a combination of factors.** These include: (i) the size of cross-border claims of European banks relative to the recipient‘s economy, particularly where local affiliates play a key role in the

> 12 Based on an analysis of 161 large corporates, a Fitch report found that European companies increased their reliance on bond markets since the crisis, as European banks faced a multitude of market and regulatory pressures, resulting in a reluctance to lend to companies without markedly higher fees and margins charged on loans. A more difficult banking environment, coupled with a growing investor appetite for bonds, have, in turn, made bond issuance more attractive, with the median share of bonds in total debt rising from 51 percent in 2008 to 69 percent in 2010.
Figure 3: Monthly Nominal Price Returns <!-- Start of picture text --> Crude oil Coal Natural gas<br>Soybean oil Barley Maize<br>Sorghum Rice Wheat<br>1970m1 1980m1 1990m1 2000m1 2010m1<br>Sugar Coffee<br>1970m1 1980m1 1990m1 2000m1 2010m1 1970m1 1980m1 1990m1 2000m1 2010m1<br>1.0<br>0.5<br>0.0<br>-0.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>1.0<br>0.5<br>0.0<br>-0.5<br><!-- End of picture text --> Note. The vertical lines represent the structural breaks identified with the Qu and Perron (2007)’s methodology: 1979M11, 1987M07 and 2007M05.

20
- **, , , , and** , “Tax audits as scarecrows: Evidence from a large-scale field experiment,” _American Economic Journal: Economic Policy_ , 2021, _forthcoming._

- **, Rodrigo Cenia, and Maria Sauval** , “Factura Electronica y Cumplimiento Tributario: Evidencia a Partir de un Enfoque Cuasi-Experimental,” IADB Working Paper IDB-DP-561, Inter-American Development Bank 2017.

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- lands,” _Journal of Banking and Finance_ , 2010, _34_ (8), 1738 1744.

- **Burgess, Robin and Rohini Pande** , “Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment,” _American Economic Review_ , June 2005, _95_ (3), 780–795.

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- **Davidson, R. and J. G. MacKinnon** , Oxford University Press, 1993.

- **Direcci ́on General Impositiva** , “Estimaci ́on de la evasin en el Impuesto al Valor Agregado mediante el M ́etodo del consumo, 2000-2016,” Technical Report, Direcci ́on General Impositiva 2019.

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23
Table 4 ( _Continued_ )

||1999|2000|W<br>2001|orking<br>2002|2005|2006|2007|
|---|---|---|---|---|---|---|---|
|Age|.118**<br>(2.36)|.234***<br>(4.57)|.138***<br>(11.93)|.158***<br>(13.84)|.145***<br>(3.05)|.151***<br>(7.97)|.141***<br>(7.00)|
|Male|-.052<br>(-0.47)|.286***<br>(2.94)|.238***<br>(4.29)|.254***<br>(4.59)|.350***<br>(3.36)|.123<br>(1.37)|.214**<br>(2.52)|
|Indigenous|.280**<br>(2.23)|.164***<br>(1.52)|.160***<br>(2.71)|.174***<br>(3.07)|.431***<br>(4.03)|.087<br>(1.05)|.010<br>(0.12)|
|Urban|-1.105**<br>(-9.32)|*-.808**<br>(-8.12)|*-1.003*<br>(-17.07)|**-.986*<br>(-16.18)|**-.964*<br>(-8.73)|**-1.083<br>(-11.11)|***-1.175***<br>(-12.91)|
|Spanish|-.773*<br>(-1.65)|-.799***<br>(-3.22)|-.367***<br>(-2.95)|-.610***<br>(-6.83)|-.398<br>(-1.14)|-.534**<br>(-2.41)|-.154<br>(-0.92)|
|EdMHead|dropped|dropped|-.074<br>(-1.17)|-.157*<br>(-1.76)|-.277**<br>(-2.03)|-.111<br>(-0.78)|-.231**<br>(-2.28)|
|EdFHead|dropped|dropped|-.159<br>(-1.04)|-.043<br>(-0.31)|-.522*<br>(-1.82)|-.604<br>(-1.61)|-.274<br>(-1.11)|
|Extr. poor|.148<br>(1.07)|.117<br>(1.22)|.147**<br>(2.54)|.159***<br>(2.60)|.145<br>(1.40)|.472***<br>(5.26)|.239***<br>(2.54)|
|BJP|.|.|.|.|.|.|.104<br>(1.08)|
|Constant|.289<br>(0.55)|-.560*<br>(1.64)|-.491***<br>(-3.63)|-.262**<br>(-2.01)|-.319<br>(-0.87)|-.324<br>(-1.34)|-1.990***<br>(-6.73)|
|Log ps. Likelihood|-8.2e+5|-7.5e+5|-1.7e+6|-1.8 e+6|-8.0 e+5|-1.6 e+6|-1.6 e+6|
|Wald Test (ρ=0)<br>(Prob> Chi2)|280.91<br>(.000)|269.60<br>(.000)|783.83<br>(.000)|950.09<br>(.000)|3831.58<br>(.000)|501.22<br>(.000)|608.30<br>(.000)|
|Observations|935|1416|4517|4459|1186|2421|2487|
|ρ 21|.677***<br>(13.33)|.808***<br>(26.59)|.467***<br>(14.18)|.580***<br>(19.88)|.493***<br>(7.17)|.517***<br>(10.45)|.419***<br>(7.65)|
|ρ 31|-.382***<br>(-6.21)|-.450***<br>(-8.08)|-.145**<br>(-3.54)|* -.241***<br>(-6.09)|-.253***<br>(-3.66)|-.266**<br>(-4.12)|* -.051<br>(-0.61)|
|ρ 32|-.265***<br>(-4.35)|-.405***<br>(-6.92)|-.082**<br>(-2.44)|-.132***<br>(-4.20)|-.014<br>(-0.22)|-.049<br>(-0.95)|.042<br>(0.86)|
|LR test|1.6e+6|1.5e+6|3.5e+6|3.7e+6|1.6e+6|3.4e+6|3.2e+6|
|(Prob> Chi2)|(.000)|(.000)|(.000)|(.000)|(.000)|(.000)|(.000)|

Source: Authors’ estimations based on MECOVI. In 1999 and 2000 the variables EdFhead and EdMhead have been dropped because there are very few educated heads. Notes: The number in parenthesis report z-statistics. Estimations performed using the expansion factor. *** Significant at 1%, **significant at 5%, * significant at 10%.

24
|**Table 1. Infrastructure at landing sites on Kenyan sh**<br>|**ores of Lake Victoria:**<br>|
|---|---|
|**Facility**|**Number**<br>**(Percent)**|
|Banda|80<br>(26.9%)|
|All-weather road to beach|60<br>(20.2%)|
|Electricity|27<br>(9.1%)|
|Fish store|16<br>(5.4%)|
|Pontoon/Jetty|11<br>(3.7%)|
|Cold room|1<br>(0.3%)|

_Source: Fisheries Department (2000)._ Abila (1995; 2002) has identified three main supply routes between fishers and the industrial fish processing plants as part of the export supply chain:

- **Sales by fishers to agents of industrial fish processing plants:** On many beaches, for example where the cooperative society is dysfunctional and/or fisher associations have not been established, fishers sell their fish directly to agents.

- **Sales by fisher associations to agents of industrial fish processing plants** .  For

- example, on Sori-Karungu beach three associations have close links with agents, bypassing the cooperative.  The agents provide fishing gear to the groups in return.

- **Sales by cooperative societies to agents of industrial fish processing plants:** For example, on Mugabo beach all fish is delivered to the cooperative, including from fisher associations.  The cooperative is the sole seller of fish on the beach to the agents.

- **Sales by cooperative society direct to industrial fish processing plants** :  This route by-passes the traders/agents altogether.   For example, on Wichlum beach all fish is handled by the cooperative.  The cooperative is active in the marketing of the fish, some of which is delivered directly to the industrial fish processors.

Within Kenya as a whole, the first two of these routes is most common, indicating the important role played by agents within the export supply chain. However, alongside these agents operate a range of local traders and artisanal processors that purchase fish to supply to local markets in a fresh or processed form. Many of these are from the local community or hinterland.

The supply chain for fresh fish normally involves the direct supply of fish to agents from fishers (or through their cooperative society or fisher association). There is very limited use of wholesalers (Figure 7). Thus, the supply chain is shorter and more amenable to coordination and control. This contrasts, for example, to the more traditional supply chain for locally processed fish which can involve both direct sales of fresh fish to the processor or the use of wholesale markets.

21
enterprises’ energy intensity between 1987 and 1992. Fan, Liao, and Wei (2007), using a similar decomposition analysis employing sub-sector data at the two-digit level, find that the efficiency effect contributed more than structural change to the decline in China’s energy intensity. Ma and Stern (2008) also conduct a similar analysis but look at the role of inter-fuel substitution in subsector energy intensity decline. Like the earlier studies, they find technological change to be the most important factor explaining the reduction in sub-sector energy intensity from 1980–2003 but also find that inter-fuel substitution had no effect on this decline. He and Wang (2007) also show that foreign direct investment induced reductions in provincial energy intensity between 1996 and 2007.

Rising energy costs throughout China have also induced energy savings. By 1999, the allocation of energy through the state plan was almost totally eliminated (Fisher-Vanden et al. 2004), causing state-owned enterprises to face world prices for energy at the margin. This shift from plan-market allocation to market-oriented allocation has led to an increase in energy prices, especially for state-owned enterprises. Fisher-Vanden et al. (2004) find that rising energy prices contributed significantly to the decline of firm-level energy intensity, with 54.4% of the decline in aggregate energy-use explained by rising energy costs. Hang and Tu (2007) find that higher energy prices helped to decrease the intensity of aggregate energy up until 1995; after 1995, however, the effects were negligible or even non-existent.

In this paper, we investigate the factors explaining the decline in energy intensity in four Chinese industries: Pulp and Paper; Cement; Iron and Steel; and Aluminum. There are many studies specifically on Chinese industry; e.g., Wei, Liao, and Fan (2007), Garbaccio, Ho, and Jorgenson (1999), Ma and Stern (2008), Zheng, Qi, and Chen (2011). Wei, Liao, and Fan (2007) show that 4
21 expenditure levels. By far the most any group pays is for school fees and PTA (Badan Pembantu Penyelenggaraan Pendioikan or BP3) dues. And the urban rural differential here is high. The expenditure jump between the ninth and tenth deciles is always the largest and often considerable. The elasticities of expenditure on each category of goods and services with respect to total per capi_a consumption expenditures have been computed using an OLS regression against the decile means, with both variables in logs. They are recorded in the seccnd to last column (with t-ratios on the estimated elasticities in the last column).

With the exception of stationary, spending on all categories rises more than proportionately with total expenditure level as evidenced by elasticities greater than one. The latter will only equal the quantity elasticities with respect to total expenditures if prices for the goods do not vary by expenditure group. For example, if the government is able to subsidize consumption of a good by poorer school going kids there will be a bias in the elasticity for that category. However, it is doubtful how well the government is able to target the poor by way of any of these categories.

There may be other reasons for which prices vary. For example, other school contributions, school fees and PTA dues, and construction contributions are usually set by teachers and parents and can, in principle, be deferred for children from poorer households. However, there is evidence that parents consider these to be compulsory (World Bank, 1991b). It seems reasonable to assume that non payment is strongly associated with stigma in the community which in turn, effectively constrains parents' choice to removal of the child from school or payment of the various contributions. Thus, it can be argued that the elasticities in Table 10 are likely to be reasonably indicative of the underlying expenditure elasticities of demand.

Table 10 gives an indication of the extent of quality differentials which are likely to exist between schools and to be correlated with household living standards. The Indonesian education system largely depends on parents' contributions for all non salary or basic infrastructure
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