rafmacalaba/datause-encoder-data huggingface dataset:
local migration and income responses may be small, but empirically we do find effects. Second, because night lights data are only available after 1991, the period of analysis is shorter and starts later. We can thus define a better proxy for whether cities are industrial: the national agriculture share in GDP data for 1989-1991. We can use a lower threshold of 30% of GDP in agriculture, and still define more industrialized countries so that they contain 25% of this larger sample.<sup>24</sup> We also again try to proxy the likelihood of a city having industry by its distance to the coast. We use the same moist/arid cutoff of 1.0 as in Section 5. Here, as suggested by the theory, we will find that the impact of rainfall in the current year and in some cases the previous year has a very different impact on the city income proxy depending on a city’s likely dependence on agriculture, and also on whether the country is arid or moist as in Section 5.

Our main results are in Table 6. Column 1 shows a zero average effect, while column 2 highlights the differential between towns that are more and less likely to have an industrial base. In column 3, the elasticity of city lights with respect to rainfall is -0.163 for the base case of an arid country where cities are more likely to have industry. Applying the lights-GDP elasticity of about 0.3 from Henderson, Storeygard and Weil (2012), this implies a rainfall-city product elasticity of about -0.05 for more industrialized countries. If agricultural productivity increases with increased rainfall, local city industry suffers in the competition for the local district labor force and lights decrease. In arid countries where cities and towns are more likely to just serve local agriculture, there is a positive net elasticity, again as predicted by theory, but it is much smaller in absolute value, and only significantly different from zero in column 2 (p=0.038), not column 3 (p=0.11). The net effects for moist countries are not precisely estimated and the distinction here is not as important as in the previous section. Point estimates suggest that the effect in moist countries where cities are more likely to have industry is zero.

In column 4, distance to the coast replaces national agricultural share as a proxy for city manufacturing potential. The results are very strong. In arid countries, for cities on the coast and hence most likely to be engaged in export activity, the elasticity of light with respect to rainfall is now -0.40. The elasticity declines with distance from the coast (with very significant effects), reaching zero at about the sample mean log distance to the coast (12.5, or log of approximately 270,000 meters). Net coefficients for moist countries are again insignificant.

In column 5 of Table 6 we look for lagged effects. For the base case of arid countries where cities are likely to have some industry, there is a significant negative lagged effect of rainfall on city incomes,

> 24 We assume that Nigeria’s agricultural share (net of resource rents) is higher than 30% based on the earliest available data, from the 2000s, when it is above 50%.

22
# **IV. Border Clearance Time, Delays, and Costs**

In this section we focus on the frequency, source, and magnitude of delays at the border, as well as freight forwarders’ response to delays and the consequences of delays for freight forwarders’ business.

## **_Reported border clearance times exhibit considerable heterogeneity_**

Since a key objective of our survey is to understand the effects of delays in the process of importing we inquired with freight forwarders about border-related clearance procedures for a typical import shipment of the most traded product, referring to all agencies involved in border/terminal clearance (not differentiating between customs and technical agencies). For small freight forwarders, a typical import shipment is expected to be cleared in 1 to 5 hours, as seen in Figure 6, and only 4% of them expect to clear their import shipments in less than an hour. In contrast, 17% of medium/large freight forwarders expect to clear their import shipments in less than an hour and 25% in 1 to 2 hours. But at the same time, overnight stays at the border or terminal and at least 24 hours for clearance of import shipments are substantially more likely for medium/large freight forwarders (34%) than for small freight forwarders (20%). Yet no medium/large freight forwarder expects to spend more than 2 days to clear import shipments while 8% of small freight forwarders expect so.

**Figure 6. Time Spent at Border/Terminal for Clearance by Freight Forwarders**

<!-- Start of picture text --> Share of freight forwarders<br>0% 5% 10% 15% 20% 25% 30% 35%<br>Less than one hour 4% 17%<br>5%<br>1‐2 hours 22% 25%<br>22%<br>3‐5 hours 17% 29%<br>28%<br>5‐10 hours 8% 18%<br>17%<br>9%<br>Overnight 17%<br>9%<br>At least 24 hrs. 11% 17%<br>12%<br>6%<br> 2‐5 days 0%<br>5%<br>2%<br>More than 5 days 0%<br>2%<br>Small Medium/Large Total<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders. Note: the statistics shown are based on responses by all 153 freight forwarders.

16
in 1993 to 12.5 percent in 2016 (Statistics South Africa, 2017). Mining remains a vital sector in terms of job creation, indirect linkages to other industries through upstream procurement and downstream value addition, significant foreign exchange generation and the country’s balance of payments. In 2016, total capital investment declined for the first time since 2010, with large decreases in mining and manufacturing (National Treasury, 2017). Although the agriculture sector accounts directly for only around 2.5 percent of GDP, its linkages to the informal economy, rural areas and other upstream (agricultural inputs) and downstream (food processing) activities, increases its overall value to the economy considerably.

The changing sectoral composition of employment mirrors the structural transformation that has occurred on the production side. Although mining employment recovered somewhat during the commodities boom, employment in the rest of the economy grew substantially faster. Mining’s employment share declined from 6.2 percent in 1995 to 3.2 percent in 2015 and manufacturing’s share of total employment declined gradually from 18.3 percent in 2008 to 14.3 percent in 2016. The services sector’s increased contribution to GDP has been matched by a significant increase in employment, particularly from 2001 onwards. Services are almost completely supplied by the domestic market and used mostly in domestic production and expenditure. Thus, services sectors tend to have higher growth and employment multipliers than goods markets (Gabriel, 2016). Services’ disproportionate absorption of low skilled workers makes the growth of services sub-sectors a potential driver of employment growth (Altman et al., 2005).

Rapid technology change in production is creating an increasing bias towards skills-intensive employment demand (Rodrik, 2008). Historical underinvestment in education for the majority of South Africans have contributed to a lack of adequate skills in the labor force (Faulkner and Loewald, 2008). In a skills constrained economy, skills biased technological change has the unintended consequence of raising wage premiums (see Wittenberg, 2014) and reinforcing wage inequality.

Gabriel (2016) uses a new social accounting matrix (SAM) to look at structural change and productivity growth rates by sector in South Africa. He finds that on aggregate, productivity grew by approximately 1.4 percent per annum from 1993-2013, however, nearly all of that growth occurred prior to 2008 – with significant variation by sector. There are no sectors that exhibit both high productivity growth and high employment growth - mining and electricity both exhibit negative productivity growth. Manufacturing sectors tend to exhibit positive productivity growth but negative employment growth while services sectors tend to show positive employment growth but low rates of productivity growth.

4
This conclusion does not apply to labor earnings in the informal sector though. If data from a representative household survey are used, instead of data from a sample of formal sector firms, a negative association between labor earnings and local unemployment rates can be found. This was shown by Hoddinott (1996) using urban data from the 1985, 1986 and 1987 rounds of the CILSS. Their results also indicate that the negative influence of unemployment on labor earnings is stronger in the case .of young workers, and weaker in the case of public sector employees. Overall, and after controlling for a number of potentially serious econometric problems, most notably unobserved location-specific effects, Hoddinott estimates the wage-unemployment elasticity of Cote d'Ivoire at -0.13. This is similar to the -0.1 elasticity reported by Blanchflower and Oswald (1994) for the US, Britain and other industrial countries.

# b) Nominal rigidity

The case to devalue the CFA Franc rested in part on the premise that formal sector wages were rigid downwards. The conventional wisdom is that the adverse shocks of the late 1970s led to a drop of aggregate demand, hence creating a substantial deflationary pressure. While the labor earnings of those who were not protected by the regulations in force declined, the story goes, workers in the formal sector succeeded in preventing their wages to be cut. Labor costs in formal sector firms thus failed to match the decline in aggregate demand, and this misalignment was at the roots of a deep recession. While this interpretation of the economic developments of the 1980s and early 1990s is plausible, there is little empirical evidence to support it.

If the conventional wisdom was right, over the 1980s and early 1990s there should have been a decline of labor earnings in the informal sector and a relative stability of wages in the formal sector. Moreover, if the downward rigidity resulted, as it is claimed, from labor market policies and institutions, trends in formal sector wages should have reproduced those of the SMIG. This is because the SMIG not 24
<!-- Start of picture text --> Latin America & Carib. Mid. East & North Afr. East Asia & Pac.<br>RCPS.5 2080 RCP€.5 2080 RCPS.5 2080<br>m SSP4<br>20 mTM SSP5 20 20<br>FE: FE: 5<br><!-- End of picture text --> <!-- Start of picture text --> =e = iad<br>DYeov2o<br>a<br>-20 a -20 a 20<br>-40 -40 -40<br><!-- End of picture text --> <!-- Start of picture text --> toa 2 & irs Bg > toa 2 & re se. taa 2 & res<br>3 Fo 3 FoO 8 io<br>Sub-Saharan Africa South Asia Europe & Cent. Asia<br>RCPS.5 2080 RCPS.5 2080 RCPS.5 2080<br><!-- End of picture text --> <!-- Start of picture text --> 20 20 20<br>2a= i=ova o=)=<br>£££<br>o0 3 it] 5 iv]<br>= < =e<br>CT2ov2o2<br>a<br>-20 a -20 a -20<br><!-- End of picture text --> <!-- Start of picture text -->
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Using the criterion of two-thirds of household expenditure per equivalent adult (OECD equivalence scale), poverty in Eastern Europe was found to be significantly lower than in FSU countries. Hungary and Poland have the lowest poverty incidence (21-23 percent) and a poverty gap less than 15 percent of the poverty line. Bulgaria is slightly worse-off with a poverty rate of 26 percent and a poverty gap of 20 percent. Each of the FSU countries exceeds those statistics by far. Estonia has a poverty incidence of 30 percent and a poverty gap of 20 percent. In Russia and Kyrgyz Republic, the poverty rate is around 40 percent and the poverty gap is in the 25-30 percent range.

The profile of poverty shows some common aspects for Eastern Europe and the Former Soviet Union as well as pronounced differences:

- _ rural poverty is higher than urban poverty; within urban areas, the capital city has the lowest poverty (except in Hungary); however, in the East European capitals, the poverty gap was higher than elsewhere in the country.

- in Eastern Europe, there is a very strong correlation between poverty incidence and the number of children in the household; in the FSU this is less pronounced, except in Russia;

- single person household, especially elderly females, have very high poverty rates (except in Poland); their poverty is also more severe;

- = consistent with this, pensioner households have above average poverty incidence and gap (except in Poland);

101
**Figure 10. The bottom 40 percent benefitted more from growth than the overall population, 20062011**

<!-- Start of picture text --> 16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>-2%<br>-4%<br>Growth rate of the bottom 40% Growth rate of the overall population<br>Source: ECSTSD’s preliminary calculations based on ECAPOV data. Welfare aggregate is consumption plus durables and<br>health. Geometric mean is used to calculate average growth rates.<br>Figure 11. Inequality in Moldova also declined<br>Gini coefficient, 1999-2004 and 2006-2011<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Total Urban Rural Total Urban Rural<br>Serbia 2007-2010 Croatia 2004-2008 Macedonia, FYR 2003-2008 Albania 2008-2012 Georgia 2006-2011 Armenia 2007-2011 Montenegro 2006-2011 Slovenia 2005-2010 Czech Republic 2005-2010 Kosovo 2006-2011 Hungary 2005-2010 Bulgaria 2007-2010 Turkey 2006-2011 Ukraine 2005-2010 Lithuania 2005-2010 Estonia 2005-2010 Moldova 2006-2011 Kyrgyz Republic 2006-2011 Latvia 2005-2010 Kazakhstan 2006-2010 Tajikistan 2004-2009 Romania 2006-2010 Belarus 2006-2011 Russian Federation 2004-2009 Poland 2005-2010 Slovak Republic 2005-2010<br>1999 2004 1999 2004 1999 2004 2006 2011 2006 2011 2006 2011<br><!-- End of picture text --> Source: ECSTSD’s preliminary calculations based on ECAPOV data. Welfare aggregate is consumption plus durables and health. Geometric mean is used to calculate average growth rates.

<!-- Start of picture text --> Source: Calculations based on HBS data.<br><!-- End of picture text --> 11
Policy Research Working Paper 5030

# **Abstract**

The study addresses governance challenges in public service delivery in China. It builds on the citizen scorecard survey conducted in five Chinese cities in 2006 to gauge citizens’ experience with public services, and demonstrates the usefulness of citizens’ feedback for policy development and implementation. The survey found that citizens were generally pleased with urban public services, but worried about the associated fees. Compared with the official urban residents, the urban poor and rural migrants in cities reported sharper utilization constraints, lower readiness to complain or pay informal fees, and a much larger income share spent on public services. The reported citizens’ perceptions sometimes diverged from the evidence and pointed to significant information asymmetries. Explaining the survey results, the study reveals problems of inadequacy, inequality and misaligned incentives in public resource allocation. The study presents several successful experiments reducing the dependence on user fees in basic education and primary healthcare. It recognizes that China has been undertaking comprehensive reforms to enhance equity and quality in public service delivery. Such reforms have included measures to strengthen the regulatory, monitoring, and enforcement systems and accountability relationships. In the context of the ongoing reforms, this study highlights the need to: a) hold the provincial governments accountable for public service delivery performance; b) develop effective mechanisms to align public resources and incentives at each level of government with the national priorities; and c) develop proper means to empower the citizens. In this context, the study affirms that the Chinese government is rightly placing reforms in the intergovernmental, administrative, and public finance systems at the top of its agenda.

This paper—a product of the Poverty Reduction and Economic Management Depatment, East Asia Region—is part of a larger effort in the department to promote good public sector governance. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at hbrixi@worldbank.org or hanabrixi@hotmail.com.

_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
**_Energy demand models for policy_** **_<u>formulation</u>_**

being based on extrapolation or arbitrary assumption, their attractiveness for any longterm work is rather low.

Despite their weaknesses, this approach is used for its simplicity. Westoby and Pearce (1984) note that most of the earlier work on energy forecasting in the UK used the “energy ratio” (which is popularly known as energy intensity”) and the “energy coefficient” (i.e. the elasticity of energy demand with respect to national income or GDP). This practice was discontinued only in the early 1980s when the reliance on sophisticated models started to rise. Similarly, Codoni et al (1985) reported the use of income elasticity of demand for an energy assessment study of Korea. Grover and Chandra (2006) report that Indian state agencies rely on income elasticities for forecasting primary energy and electricity demand<sup>7</sup> . In two recent reports on energy policies of India and China<sup>8</sup> , simple measures of GDP-elasticity and energy intensities have been used for demand forecasting for ten or more years. Some studies [e.g. in Armstrong (2001), Craig et al (2002), and Westoby and Pearce (1984)] argue that simple models can sometimes produce accurate results similar to those obtained from sophisticated ones. Many sophisticated models also retain simple techniques in some of their sub-components. For example, intensities or gdp-elasticities are commonly used in engineering-economic models while growth rates and elasticities are often used for forecasting independent variables in econometric approaches. In addition, these techniques can be used both at the aggregated and disaggregated levels. The virtue of simple models is that the skill and data requirement is low and such models are more tractable rather than the hidden assumptions of complex models (Brown, 1984). This is further supported by Craig et al (2002) who found that many long-term forecasts using sophisticated models for the USA produced inaccurate forecasts in the past. Armstrong (2001) echoes the same view and states that “simple models can sometimes yield results as accurate as more complicated techniques.”

> 7 Grover and Chandra (2006) also made long-term electricity demand forecasts (up to 2052) for India based on certain assumptions about GDP-elasticity of electricity demand.

> 8 See GOI (2006) and Berrah et al. (2007).

26
O.A.M.D.G.

governmental protection against armed groups such as guerrillas and paramilitaries (compare Vargas, Restrepo and Spagat (2004) and Reynal-Querol (2005)). Access in Colombia can be difficult through dispersion of settlement (rurality), rainforest/mangrove swamps (Pacific region), or mountainous terrain (intermediate to high altitudes). Second, coca plants, a major source of income for the armed groups as well as common delinquents, are best planted in certain areas. While the actual plantations are endogenous to the protection by armed groups, the geography apt to their cultivation, i.e. Andean altitudes between 1,000 and 2,000m, is exogenous. Regarding judicial efficiency, our municipal database contains the arrests under the Drug Act per million inhabitants. This is related to both the incidence of drug crime and police efficiency. Previous studies, e.g. Martinez, Medina and Steiner (2001) and Martinez and Medina (2003) use arrests under the Drug Act as a proxy of local law and order enforcement. Regarding welfare and wealth, the database provides the official Indicator for Quality of Life. This indicator combines different variables about access to services. The dataset further covers the sum of assets held by banks in the municipality, as recorded by Financial Regulation. Finally, we can approximate inequality through the percentage of people in the two lowest income strata, in line with Bourguignon (2003).

# **Measuring Violence in Colombia**

As the introduction showed, the country is suffering from various kinds of violence. These can roughly be divided into first, violence that explicitly challenges the state, such as that proceeding from the guerrillas and paramilitaries, and second, common delinquency, proceeding from organized small gangs and unorganized crime. (A third force, drug cartels, in their original form belong largely to the past, since the Cali and Medellin cartels have been dismantled and much of the drug business has been picked up by both guerrillas and paramilitaries, and smaller fractionalized gangs.)
# **Table 6 –** Cross-country covariates of SME loan volume/GDP. Dependent variable: SME loans/GDP (log)

<u>(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19)</u> GNI per capita (2007, log) 0.482*** 0.439* 0.309 0.438** 0.505*** -0.048 0.442*** 0.487*** 0.409** 0.464*** 0.313** 0.444*** 0.561*** 0.534*** 0.509*** 0.452*** 0.388*** 0.311** 0.540*** (0.162) (0.236) (0.207) (0.176) (0.152) (0.173) (0.133) (0.131) (0.171) (0.130) (0.134) (0.162) (0.188) (0.195) (0.152) (0.157) (0.129) (0.136) (0.141) Offshore financial center dummy -1.098* -0.995 -1.009 -0.925 -0.909 -1.050 -1.120* -1.010 -1.064* -1.026* -1.016 -0.983 -0.483* -1.020 -0.992 -1.003 -1.248* -1.099 -1.427** (0.621) (0.654) (0.640) (0.619) (0.611) (0.720) (0.620) (0.602) (0.604) (0.594) (0.617) (0.593) (0.252) (0.619) (0.618) (0.603) (0.655) (0.659) (0.688) Maximum inflation (1997-2007) -0.012* (0.006) Interest spread (2007) -0.057* (0.031) Lending rate (2007) -0.043 (0.026) GDP growth ( average 2002 -2007) -0.034 (0.086) Dom.Credit/GDP,growth (avg 2002-2007) 1.331 (1.700) Domestic credit/ GDP (2007) 0.016*** (0.005) Legal rights index (2007) 0.067 (0.081) Credit information index (2007) -0.021 (0.153) Closing business - days (2007) -0.202 (0.360) Contract enforcement - time (2007) -0.082 (0.361) Starting a business - time (2007) -0.465** (0.173) Fuels and mining/ GDP (2007) 0.504 (2.292) Foreign owned banks, share of assets -0.658 (0.951) Government owned banks , share of asse 0.376 (0.914) Banking concentration -0.302 (0.995) Branches per 100,000 adults (log) 0.061 (0.216) Overhead costs/total assets (avg, 2002-2 -22.983*** (5.936) Net interest margin (avg, 2002-2007) -21.433*** (6.473) Bank Cost/Income (avg 2002-2007) -2.580** (1.259) Constant -6.747*** -6.168** -4.908** -6.367*** -7.284*** -3.411** -6.956*** -6.883*** -6.077*** -6.258** -4.104*** -6.625*** -7.446*** -7.486*** -7.002*** -6.834*** -5.097*** -4.385*** -5.696*** <u>(1.546) (2.239) (2.058) (1.974) (1.481) (1.419) (1.148) (1.154) (1.782) (2.694) (1.346) (1.514) (1.842) (1.880) (1.585) (1.199) (1.337) (1.428) (1.479)</u> N 43 29 32 45 43 40 45 45 43 45 45 41 35 36 44 45 44 44 44 <u>Adjusted R2</u> 0.224 0.144 0.117 0.190 0.184 0.326 0.200 0.188 0.197 0.188 0.289 0.120 0.223 0.160 0.186 0.188 0.363 0.347 0.256 Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 27