rafmacalaba/datause-encoder-data huggingface dataset:
both economics (Olson 1982) and sociology (Banfield 1958), so that one would expect to find a great deal of empirical research on the distinction between bonding and bridging social capital. Instead, there are few studies on this distinction, and they are especially rare in economics. Putnam (2000: 23) frankly admits that _“I have found no reliable, comprehensive, nationwide measures of social capital [for the US] that neatly distinguish ‘bridgingness’ and ‘bondingness’.”_ The few economic studies to have considered the bonding/bridging distinction in the analysis of the positive effects of social capital focus on _economic growth_ . A much-cited study in this regard is Knack and Keefer (1997), which is based on the World Value Survey (WVS). Its main finding is that trust of the survey’s respondents impacts positively and significantly on a country’s economic growth. The subsequent literature has largely confirmed this result (e.g. Zak and Knack 2001; Beugelsdijk et al. 2004; Dearmon and Grier 2009). Knack and Keefer then attempt to apply the bonding/bridging distinction of social capital, though they do not use this terminology. In fact, they distinguish people’s participation in social organizations depending on the kind of organization, i.e. whether it is of the Putnam variety, like cultural organizations which enhance cooperation and solidarity, or of the Olson variety, like political parties, which are rather motivated by rent-seeking behavior. However, although the estimated coefficients in the overall GDP growth equations differ between the two kinds of organization, they are not significant.

Two other very recent studies focus on regional growth in the US and the EU respectively. Knudsen et al. (2010) apply factor analysis to distinguish among the different kinds of participation in social organizations. However, they only find that participation is not of the same kind, so that they are obliged to fix an _a priori_ criterion, and to plead for “experts” for its application. They can thus distinguish organizations into two groups depending on whether these organizations are exclusive or inclusive with respect to new entrants. The authors again apply factor analysis to several indicators of social capital, and find two substantial factors that can be attributed to bridging and bonding social capital respectively. The first factor has positive and substantial loadings on the indicators of ‘diversity of friendships’, ‘inclusive organizations’, and ‘political activity’, while the second factor has positive and substantial loadings on the indicators of ‘exclusive organizations’ and ‘faith-based engagement’. The other loadings in both factors are instead negligible. After the application of other tests, it is finally concluded that 6
# **Tables**

Table 1: Descriptive statistics - Control variables

|||Men<br>(1)|W|omen<br>(2)|t-test<br>diference|||
|---|---|---|---|---|---|---|---|
|Variable|N|Mean/SE|N|Mean/SE|(1)-(2)|Min|Max|
|Dar es Salaam|2231|0.725<br>[0.009]|2228|0.725<br>[0.009]|-0.000|0|1|
|Dodoma|2231|0.200<br>[0.008]|2228|0.200<br>[0.008]|-0.000|0|1|
|Age in years|2231|21.080<br>[0.057]|2228|21.048<br>[0.061]|0.032|16|27|
|Years of education|2231|9.120<br>[0.069]|2228|8.996<br>[0.069]|0.124|0|14|
|Father’s education|2231|8.071<br>[0.056]|2228|7.785<br>[0.050]|0.286***|0|15|
|Mother’s education|2231|7.576<br>[0.054]|2228|7.426<br>[0.049]|0.150**|0|14|
|Cognitive Ability|2231|0.719<br>[0.005]|2228|0.686<br>[0.005]|0.033***|0|1|
|Social desirability index|2231|3.412<br>[0.010]|2228|3.442<br>[0.010]|-0.030**|1.50|5.00|
|Equitable beliefs regarding|2231|2.353|2228|2.997|-0.644***|1.00|5.00|
|PSDM abilities||[0.023]||[0.025]||||

_Notes:_ The values displayed for t-tests are the differences in the means across the groups. PSDM = Problem-solving and decision-making. *** p _≤_ 0.01, ** p _≤_ 0.05, * p _≤_ 0.1.

28
|Constant|(0.097)<br>0.298***|(0.158)<br>0.330|(0.470)<br>-2.032***|(0.550)<br>-2.791***|(0.622)<br>-2.144***|(0.718)<br>-1.588***|(0.646)<br>-3.303***|(0.639)<br>-3.341***|
|---|---|---|---|---|---|---|---|---|
||(0.098)|(0.241)|(0.494)|(0.463)|(0.352)|(0.384)|(0.484)|(0.538)|
|Number of observations|618|468|629|472|678|521|553|426|
|r2_w|0.293|0.420|||||||
|r2_b|0.079|0.069|||||||
|r2_o|0.072|0.064|||||||
|rho|0.678|0.718|||||||

Notes: This table reports panel regressions with individual firm fixed effects for the Georgia COV-ES data. Size information used as the interaction terms is taken from the total number of full-time equivalent workers in the baseline ES. Columns 3 through 8 report coefficients from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

60
non-tradable sector--meaning it has zero GVC participation--doubling the FDI leads to an increase in the labor productivity by 0.7 percenr; for a sector with average GVC participation, i.e., GVC being equal to the coefficient value of 0.24, doubling the FDI will increase the average labor productivity of firms in that sector by 0.2 percent. In columns (3) and (4), we further investigate how forward GVC participation and backward GVC participation influence the effects of FDI on labor productivity, and the results are qualitatively similar. As a variant, we use median GVC participation in the data sample as a cut-off by dividing the full sample into two samples: a sample with low GVC participation sectors and high GVC participation sectors. Column (5) shows that-for the sectors with low GVC participation--the overall effect of FDI on labor productivity is positive and statistically significant, while doubling the FDI gives rise to a productivity growth by 0.4 percent. For the sectors with high GVC participation there is no effect: the correlation between the FDI and the labor productivity is negative but not significant.

**Table 3:** FDI has Higher Spillovers on Labor Productivity for Sectors with Lower GVC Participation

|Dependent. Variable: ln<br>||Full sa|mple||Low GVC|High GVC|
|---|---|---|---|---|---|---|
|labor productivity (**_ln Y_**)|||||||
||(1)|(2)|(3)|(4)|(5)|(6)|
|_ln FDI_|0.001|0.007***|0.006**|0.005|0.004**|-0.005|
||-0.493|-2.693|-2.329|-1.517|-2.32|(-1.594)|
|_ln FDI × GVC_||-0.021**|||||
|||(-2.215)|||||
|_GVC_||-2.089***|||||
|||(-6.490)|||||
|_ln FDI × GVC_forward_|||-0.017**||||
||||(-2.056)||||
|_GVC_forward_|||-0.559**||||
||||(-2.127)||||
|_ln FDI × GVC_backward_||||-0.014|||
|||||(-1.363)|||
|_GVC_backward_||||-1.934***|||
|||||(-7.009)|||
|_Constant_|3.697***|4.198***|3.831***|4.155***|3.508***|3.902***|
||-421.651|-53.448|-57.435|-63.315|-356.135|-341.925|
|Country-sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of country-sector|1,719|1,719|1,719|1,719|929|918|
|Observations|7,848|7,848|7,848|7,848|3,948|3,900|
|R-squared|0.513|0.56|0.523|0.569|0.629|0.529|
# **_Natural gas_**

Unlike oil and even more so than LPG, natural gas is difficult to store and transport, making it more difficult to trade than oil. In 2013, less than one third of natural gas consumed globally crossed national borders, about two thirds by pipeline and one third as liquefied natural gas (LNG) (BP 2014). Although spot prices are the most readily available data on natural gas prices, only a fraction of the internationally-traded gas is sold on the spot market. The considerable upfront costs associated with setting up LNG terminals have meant that LNG projects are seldom, if ever, undertaken without longterm contracts in place.

As a result, there are three major and distinct natural gas markets: North America, Europe, and Asia. The degree of divergence in the prices in the three markets (Figure 1) signals the impact of high transportation costs on the global gas market. Oil, too, used to have distinct regional markets, each with its own prices, but the commercialization of very large crude carriers beginning in the late 1960s reduced transport costs. After 1973 the share of transport in the landed cost of imported oil declined precipitously (even across long distances, such as from Saudi Arabia to New York), helping to further equalize prices across different regional markets. Such a dramatic cost reduction is unlikely for natural gas in the foreseeable future.

If it is not economic to export domestic natural gas—for example, if the amount of economically recoverable natural gas reserves is too small or the reserves are too distant from major foreign consumption centers—export-parity equivalent prices cannot be used as the reference price. In such cases, market prices will be above the cost-recovery level at a minimum, and, at the high end of the price spectrum, at parity with petroleum products for which that natural gas might substitute.

Natural gas is valued for its energy content. In markets where unit prices are based on volume, the energy content of different gas streams needs to be examined to assess if the price level is appropriate. If pipeline operators set strict limits on the gas composition, such specifications can be used to estimate the energy content. Calculating the reference price for compressed natural gas (CNG) for automotive use involves estimating the cost of establishing and operating a CNG distribution network.

**Figure 1: Natural gas prices in three markets**

<!-- Start of picture text --> 20<br>United States Europe Japan<br>15<br>10<br>5<br>0<br>Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15<br>unit<br>US$/million British thermal<br><!-- End of picture text --> _Source_ : World Bank data.

20
However, it is true that the observed deterioration in Argentina's fiscal balance in the final years of the decade reflects in part the effects of declining growth on tax collection and those of rising interest rates on debt service. To assess the fiscal stance adopted by the authorities, we need to gauge the effects of these factors beyond their immediate control.

Consider first the effects of cyclical variation in output on the fiscal balance. Unfortunately, we do not have sufficient data to correct for this factor at the level of the consolidated public sector., but only for the Federal government. Nevertheless, Figure 4.1 shows that the time profile of the Federal and consolidated deficits is roughly similar, so the cyclically-adjusted fiscal stance should also be fairly similar for both government definitions.

**Figure 4.3. GDP and Potential GDP (Hodrick-Prescott Trend and Linear Trend)**

<!-- Start of picture text --> 330000.<br>310000-<br>~-TREND GD)P(HPfllter)<br>- Linear trenid,'<br>290000 - Preferred Estimate<br>270000-<br>250000- in n<br>230000 -<br>w t w0n L = o DZD0b rXe o :0 0 o 000 0 0g_ 0 _<br>a z X -) z X z E z : z X- z z :E z<br><!-- End of picture text -->

**Source: Ministerio de Economia de la Republica Argentina and Ministerio de Obras y Servicios Piblicos.**

Construction of cyclically-adjusted fiscal figures typically requires estimates of potential output, whose calculation near sample endpoints is subject to considerable inaccuracy due to the difficulty in distinguishing between temporary output movements and changes in trend at the beginning and end of the sample period.<sup>31</sup> To partially remedy this problem, we construct two potential output estimates - one through a Hodrick-Prescott (HP) filter, and another using linear extrapolation. Figure 4.3 shows that the HP method appears to umderestimate potential output at the end of the period -- as it projects zero or even negative potential growth -- while the linear extrapolation probably

> 31 See Orphanides and van Norden (2002) for a discussion.

31
# **1 Introduction**

Access to jobs, particularly to better jobs, is crucial for economic development. Proximity to urban areas has always been considered an important aspect for rural households for accessing jobs, goods, and services, and it is widely studied in the literature. The studies show that living closer to urban areas increases non-farm employment and market oriented activities (Sheng et al., 2022; Deichmann et al., 2009; Fafchamps and Shilpi, 2005, 2003), improves the economic well-being and nutrition of rural households (Stifel and Minten, 2017; Sharma, 2016), and overall, positively affects spatial dimensions of development (Sharma, 2016). However, proximity alone cannot be a good measure of accessibility because it does not take into account road condition - which can have a large impact on travel time and costs to urban centers.

Poor transportation infrastructure restricts accessibility to markets and jobs for the rural population in low- and middle-income countries (World Bank, 2009). Improved roads tend to decrease travel time and travel costs, and promote mobility. Better transportation infrastructure might stimulate mobility of goods and labor, connecting people to jobs.

A large majority of households in low- and middle-income countries are still mainly employed in agriculture, often in self-subsistence farming. According to the accounts data, the average agricultural productivity gap is 3.5, meaning that value added per worker in agriculture is more than three times lower than the one of the non-agricultural sector. There are higher disparities in different income levels of the countries. In low-income countries labor productivity in non-agricultural employment is 4.5 times higher than in agricultural employment, it is 3.2 times higher in middle-income countries, and 2.2 higher in high-income countries (Gollin, 2023; Gollin et al., 2014). Structural transformation has been a key policy issue in low- and middle-income countries in recent decades. One of the ways to address this problem is to give rural households opportunities to be employed in more productive sectors. Proximity to cities and access to adequate road infrastructure could play an important role in increasing non-agricultural employment among rural people (Aggarwal, 2018; Asher and Novosad, 2020).

This paper analyzes the impact of road quality on agricultural and non-agricultural employment, studying the evidence from Armenia. Empirical studies on the relationship 2
9

# **5. Estimation of the gravity equation of trade: what defines the potential need for aid for trade?**

The results of estimating (13) reported in table 1 show that the coefficients for all variables are significant at the 1% and have the expected signs.. Also as predicted, the coefficient for the GDP is close to one.

|**Table 1. Gra**<br>**Dependent variable:**<br>**Ln(average export)**|**vity equation o**<br>Average<br>2005-2007|**f trade-Cross-**<br>Average<br>2006-2008|**section and p**<br>Average<br>2007-2009|**ooled regress**<br>Average<br>2008-2009|**ions**<br>All periods|All periods<br>controlling<br>also for<br>bilateral<br>tariff|
|---|---|---|---|---|---|---|
|Ln(average gdp)|1.067***|1.092***|1.089***|1.064***|1.078***|1.139***|
|Average Limao and Venable<br>indicator standardized|0.240***|0.268***|0.225***|0.210***|0.235***|0.298***|
|Ln (Average TRI MFN)<br>standardized|-0.096***|-0.061***|-0.091***|-0.093***|-0.085***|-0.090***|
|Average LPI custom standardized|0.323***|0.322***|0.303***|0.278***|0.306***|0.277***|
|Landlocked|-0.340***|-0.405***|-0.437***|-0.414***|-0.399***|-0.262***|
|Colony|0.645***|0.623***|0.656***|0.627***|0.637***|0.320***|
|comlang_off|0.479***|0.437***|0.409***|0.437***|0.439***|0.610***|
|Contig|1.126***|1.118***|1.064***|1.007***|1.079***|1.315***|
|Lndist|-1.436***|-1.441***|-1.457***|-1.450***|-1.446***|-1.323***|
|Average remoteness|8.164***|9.228***|8.755***|7.473***|8.395***|8.652***|
|Ln (Average weighted average bilat|eral tariffs)|||||-0.690***|
|N|12386|12532|12649|12703|50270|23932|
|Importer dummies|YES|YES|YES|YES|YES|YES|
|Year dummies|||||YES|YES|

Standardizing the variables permits drawing comparisons among the three key dimensions of the exporter trade-related capacity index. The results suggest that the three dimensions have a similar impact on trade volume but not exactly equal: the quality of customs appears a major determinant, followed by the quality of infrastructure, and finally the trade restrictiveness index facing the exporting country. In sum, these results indicate that, taken together,
<!-- Start of picture text --> nN E-Commerce nN Fintech i} IT<br>_t!<br>1<br>e « !<br>i]<br>1<br>eT 4<br>©o\<br>a \<br>)@ \<br>3 “7<br>5] y \<br>2 . \<br>|! \<br>eo}6\\a\\<br>\<br>iN a ac-------- ‘x<br>s, N<br>nN SS ~ ——$Ss$sl___ ° ‘s x<br>-~e N s<br>= = — x ~s *<br>nN<br>0 5 10, 15 20 25 30 0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Age in Years Age in Years Age in Years<br>== == Fronticese Africa LAC == == Frontice Africa = LAC == == Fonte Africa = LAC<br><!-- End of picture text -->

<!-- Start of picture text --> nN E-Commerce nN Fintech i} IT<br>x41<br>1<br>©©l<br>1<br>o+ |<br>i]<br>© © i]<br>2 \<br>D@® \<br>3°32 \<br>S~<br>e3 1 \\<br>°‘*\<br>\N<br>NN<br>Nn xs ° x x<br>sO nN ee eee ee NS<br>em x<br>—. SS<br>- x,<br>_—<_—_—_——_ :<br>0 5 10 15 20 25 30 0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Age in Years Age in Years Age in Years<br>== == Fronticese Africa LAC === Frontice Africa = LAC == = Frontice Africa = LAC<br><!-- End of picture text -->
Figure B3: Poverty rates across urban versus rural areas <!-- Start of picture text --> (A) $2.15 poverty line<br>Official definition DOU DB<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>(B) $3.65 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Poverty rate (%)<br>Poverty rate (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Each boxplot shows the distributions of poverty rates over different geographic areas in 16 SSA countries. GHSPOP 1km is used for the DOU and DB methods. The dashed lines represent the average national poverty rate in the sample.

Figure B4: Comparison of urban poverty rates between official and DOU/DB urban definitions <!-- Start of picture text --> (A) DOU vs. official definition (B) DB vs. official definition (C) DB vs. DOU<br>60 MWI 60 60<br>50 50 50<br>40 40 MWI 40 MWI<br>TZA NER NER<br>UGA TZA TZA<br>NER<br>30 30 30<br>ETH ETH ETH<br>20 20 20<br>AGO AGO<br>10 BFATCD LSOAGO 10 BFATCD LSOUGA 10 TCDLSOBFA UGA<br>CIVGNB MRTGNBGHA GHAMRTGNB<br>G SENMRTIN GHA GINSENCIV SENGINCIV<br>GAB GAB GAB<br>10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60<br>Urban poverty rate (%) Urban poverty rate (%) Urban poverty rate (%)<br>Official definition Official definition DOU<br>DOU DB DB<br>Urban poverty rate (%) Urban poverty rate (%) Urban poverty rate (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: GHSPOP 1km is used for the DOU and DB methods. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. Dashed lines are 45-degree lines. Poverty is measured using the $2.15 poverty line.

48
<!-- Start of picture text --> Normalized second-stage coefficient<br><!-- End of picture text --> Figure 1: Boxplot of normalized second-stage coefficients by an energy policy package _Notes: The unit of lags are years; different lags are denoted by the colors in the legend. LF=Legal framework; PE=Planning for expansion; IR=Incentives and regulatory support; AI=Attributes of financial and regulatory incentives; NC=Network connection and use; CR=Counterparty risk. Regression specification: RISE index, UNGA affinity IV with five years moving averages. In the boxplot, there is a box from the first quartile to the third quartile, with the 2_<sup>_nd_</sup> _quarter (50% percentile) marked by the internal line of the box. The whiskers extending from the boxes go from each quartile to the minimum or maximum, excluding outliers._ Table 8: Average of normalized second-stage coefficients by energy policy packages and yearly lags

||**La**<br>|**gs inye **<br>|**ars**<br>|
|---|---|---|---|
|**Energy policy package **|3|5|7|
|**Legal Framework**|-0.66|-0.25|0.90|
|**Planning for expansion**|-2.20|0.48|3.02|
|**Incentives and regulatory support**|-0.30|-0.92|2.04|
|**Attributes of financial and regulatory incentives**|-5.35|5.48|19.17|
|**Network connection and use**|-0.08|-0.43|1.48|
|**Counterparty Risk**|0.38|-2.24|-0.67|

_Notes: Regression specification: RISE index, UNGA affinity IV with five years moving averages._ 25
HOUSING MARKETS AND RENT CONTROL IN GHANA 29

# Figure 2.5:: Selected Prices, Logarithmic Scale

**Index,19630100(log scale)**

<!-- Start of picture text --> 100000 _ Urban CPI<br>Bldg Cost<br>Min Wage<br>--- Rent Fuel Power<br>10000 Controlled Rent<br>1000<br>---- , - ----- -. .<br>10 0 I - -- --<br>70 72 74 76 78 80 82 84 86 88<br><!-- End of picture text --> Despite the inflexible nature of rent control when ruled by ministerial decree, and their consistently low level, rents in Kumasi appear to have remained within the levels set by the government and the Rent Control Officers during the early 80s (Tipple, 1988). At the same time, rooms with exclusive use of sanitation and water supply have commanded higher rents. During the 1960s and 1970s the official Development Plans noted the need to encourage private investment while directly building some houses for specific groups of people. But as the Consumer Price Index rose markedly, controlled rents lagged behind, with only a 19% rise between 1963 and 1975 (see Figure 2.5).

# Rent Control post-1979

The Armed Forces Revolutionary Council (AFRC) government of Flt-Lt Jerry J. Rawlings which ruled from June to September, 1979, imposed new rent levels as set out in Table 2.3. It should be noted that no distinction was made for the materials used in construction nor for the various levels of servicing which constitute "shared amenities." The 1980 survey of Kumasi, conducted within months of AFRC Decree 5 (see Tipple, 1984a), found mean and median monthly rents per room of C22 and C20.5
|||**ESPS**<br>|||**PSF**<br>||
|---|---|---|---|---|---|---|
||**Respondent**|**Number and list of items**|**Recall period**|**Respondent**|**Number and list of items**|**Recall period**|
|**Home produced**<br>**food**|Household<br>head|3 categories: Agricultural<br>products; Livestock<br>products; Fishing products.|Over the 12 last<br>months.|Household<br>head|3 categories: Agricultural<br>products; Livestock<br>products; Fishing products.|Over the last 12<br>months.<br>For each item they<br>produce, hunt, fish or<br>gather:<br>Respondent asked<br>how many months in<br>the year it is<br>consumed and the<br>monthly market<br>value|
|**Utilities**|Household<br>head|3 items: Water; Electricity;<br>Telephone (fixed and<br>mobile).|Over the two last<br>months.|Household<br>and cell<br>heads|4 items: Water; Electricity;<br>Fixed telephone; Mobile<br>phone.|Frequency is<br>reported by the<br>respondent who can|
|**Housing fuels**<br>**and combustible**<br>**materials**|Household<br>head|4 items: Gas; Charcoal;<br>Wood; Combustible<br>materials (candles and<br>petroleum).|Over the last 30<br>days (and if not<br>purchased, amount<br>usually spent over<br>a month).|Household<br>and cell<br>heads|1 item: Fuels and<br>combustible materials<br>(wood, charcoal, gas,<br>candles and petroleum);|choose among<br>twelve modalities<br>from every day to<br>every year.<br>Respondent is asked|
|**Durable goods**|Household<br>head|3 items: Furniture and<br>electrical appliances;<br>Transport means (car,|Over the last 12<br>months|Household<br>and cell<br>heads|2 items: Furniture and<br>electrical appliances;<br>Transport means (car,<br>motorcycle, bicycle...)|about both usual<br>expenditure and last<br>expenditure.|

5