rafmacalaba/datause-encoder-data huggingface dataset:
-11-

An important reinterpretation of the Ravallion model is found in Faminow and Benson. They build upon Hotelling's model of locational interdependence which can be considered as spatial oligopoly. In particular, they note that short run integration as defined by Ravallion may be generated by collusive base point pricing. However, a rejection of short run integration and acceptance of long run integration is compatible with a model of market competition.

Inference from such a model depends, in part, on the nature of the market structure. Ravallian assumes a radial market with few, if any, local market linkages; Faminow and Benson discuss markets in which agents are not located at a few points but are spatial disbursed. As discussed below, the spatial nature of the markets studied lend themselves to the Ravallion model employed. Moreover, if two regions or markets specialize in different commodities, so that the radial structure pertains to more than one commodity, the model discussed above can be adapted to a multicomodity frame.vork. Tests for market segmentation in such a multicommodity framework would still be appropriate. Similarly, the IMC would be a rough measure of the local versus reference market influences, albeit the influence would work through the matrix of cross price responses. There is no particular reason, however, why the price transmittal would be exactly one for one from a particular commodity to another under either short or long run integration. Nevertheless, the magnitude of 8S, and [Ea,, + s,,] would still provide information that measures the net transmittal of shocks in one market to another.

Although cross price effects are generally not addressed in models of market integration they are explicitly recognized in general equilibrium and multimarket agricultural models. An understanding of the interactions of price
**Table 6**

**Regression Results of Public Transport Mobility depending on size of cities (country fe)**

|**(Megacities)**|**(Small cities)**|**(Intermediate)**|
|---|---|---|
|**PANDEMIC & LOCKDOWN EVOLU**<br>***|**TION**<br>***|***|
|**Covid death (per Million)**<br>-0.474<br>(0.079)|-1.241<br>(0.036)|-1.273<br>(0.044)|
|**Public transport closure**<br>-13.166***<br>(0.369)|-16.012***<br>(0.246)|-8.216***<br>(0.179)|
|**Public transport facial covering**<br>-0.498***<br>(0.209)|1.206***<br>(0.132)|1.501***<br>(0.104)|
|**CONTROLS**|||
|**Working days**<br>11.365***<br>(0.666)|12.932***<br>(0.527)|12.042***<br>(0.189)|
|**Min temperature**<br>-0.459***<br>(0.055)|0.361***<br>(0.031)|0.012<br>(0.026)|
|**Avg temperature**<br>0.488***<br>(0.056)|0.013<br>(0.029)|0.322***<br>(0.025)|
|**Precipitation**<br>-1.132***<br>(0.158)|-1.462***<br>(0.141)|-1.245***<br>(0.109)|
|**City density**<br>-0.008***<br>(0001)|-0.234<br>(0424)|-0.330<br>(0380)|
|.<br>**TRENDS IN MODAL COMPETITI**<br>***|.<br>**ON**<br>***|.<br>***|
|**Car registration**<br>1.521<br>(0.399)|0.720<br>(0.165)|0.410<br>(0.189)|
|**COUNTRY FE**<br>|||
|**Country FE**<br>Yes|Yes|Yes|
|**Monthly dummy**<br>Yes|Yes|Yes|
|**Constant**<br>-58.712***<br>(1.986)|-32.630**<br>(7.918)|-72.216***<br>(5.410)|
|**N**<br>6588|27,992|25,197|
|**N cities**<br>18|77|69|
|**Within R**<sup>**2**</sup><br>0.7495|0.6704|0.6828|
|**Between R**<sup>**2**</sup><br>0.9327|0.4666|0.8778|
|**Overall R**<sup>**2**</sup><br>0.7844|0.6434|0.7315|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent 19
Table 4. Bond Issuance Logit Regression Results

|Dependent variable: Country-indu|stry monthly issuance a<br>|bove 2000-07 aver<br>|age (1=Yes, 0=No)<br>|<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|VIX|-0.603***<br>(0.185)||||
|RISK||-0.789***<br>(0.199)|||
|FED|||0.728***<br>(0.193)||
|LIBOR||||-0.494***<br>(0.0833)|
|GDPPC|1.543***|1.544***|1.435***|1.431***|
||(0.384)|(0.384)|(0.431)|(0.443)|
|GROWTH|0.0294**|0.0294**|0.0339**|0.0375***|
||(0.0133)|(0.0133)|(0.0156)|(0.0144)|
|EXT|0.00520|0.00520|0.00630|0.00709*|
||(0.00358)|(0.00358)|(0.00433)|(0.00411)|
|CA|-0.0329***|-0.0329***|-0.0315**|-0.0289**|
||(0.0121)|(0.0121)|(0.0124)|(0.0127)|
|PCRED|0.000204|0.000194|0.00255|0.00227|
||(0.00757)|(0.00758)|(0.00831)|(0.00791)|
|UST10Y|-0.0143|-0.0400|-0.0858|-0.196**|
||(0.100)|(0.0960)|(0.0990)|(0.0962)|
|Country fixed effects|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|
|Observations|79,464|79,464|61,824|69,048|
|No. of Countries|64|64|62|64|
|No. of Country-Industries|448|448|434|448|
|Pseudo R-squared|0.359|0.360|0.352|0.356|

Robust standard errors clustered on the country-industry level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 32
indeed sought to complement or supplement traditional enforcement actions (fines and penalties) with the adoption of structured information programs (or public disclosure programs) by which the environmental performance of industrial facilities is revealed. Programs such as the Toxics Release Inventory (TRI) in the United States (also implemented in Canada and Great Britain), or the Proper Prokasih program in Indonesia and the EcoWatch program in the Philippines are examples of structured information programs that rely on non-regulatory forces to create incentives for (mainly industrial) facilities to improve environmental performance.<sup>4</sup> While this may not be as well-known, the Republic of Korea (henceforth Korea) has developed its own extensive experience with the public disclosure of environmental performance of regulated facilities. Since the mid 1980s, the Ministry of Environment of Korea has published on a monthly basis a list of facilities in violation with existing Korean environmental laws and regulations. Over the sole period of 1993 to 2002, over 7,000 _violations_ have been reported on those lists, involving more than 3,400 facilities. As such, the Korean experience with a structured public disclosure program may very well be one of the most extensive experiences of this nature in the world.<sup>5</sup> In this paper, building upon the existing, albeit limited literature on this topic, we examine whether or not capital markets in Korea have reacted to the information contained in these monthly violation lists.

> 4 See Foulon et al. (2002), Lanoie et al. (1998), and World Bank (2000) for a description of such programs. 5 While it covers a larger number of years and facilities, the US TRI does not, by its very nature, focus necessarily on facilities in _violation_ of existing laws and regulations.

5
- 86-

# Annex 2: The Manually-Operated Bergen Toll Ring, 1986 to present

## A. Background Informadion"

93. The Bergen Toll Ring began on January 2, 1986 within a couple of years after the scheme was first proposed. Following Singapore, it is the second city to charge motorists for entering the Central Business Distict (CBD). The scheme involved the construction of tollgates at six entry points to the CBD of Bergen, Norway's second largest city.6<sup>7</sup> The tollgates are aU manualy operated, with about half of the lanes for motorists paying by cash or prepaid tickets and the other half reserved for motorists with seasonal passes (i.e., seson tickets) placed behind the windscreen. These passes could be purchased on a monthly, six-monthly, and yearly basis, their purpose being to allow traffic to go through the toUlgates at expreway speed. The enforcement of offer.ders is caried out by occasional videotape recording of license plates in these unattended lanes. Since its inception, the toll rates have remained at _NOKS [US80_ cents in 1990 figures] for light vehicles such as automobiles, with buses and light motorcycles being exempt. (Heavy vehicles are charged exactly twice the rate of light vehicles and the rate for monthly seasonal pass holders are set at 20 times the daily rate (SWderstr6m, 1988).) The hours of operation for entry have been set at Monday through Friday, 6 a.m. - 10 p.m. (except public - holidays) not quite daylight pricing. Note that the purpose of settng the toll at a low level is to increase revenues earmarked for road financing. (For purposes of charging for congestton, the toll rate should be higher to have a positive impact and should apply for a more selective time period. With its net toll revenue (totalling 25% of funds for a road program), Bergen was able to obtain a matching grant (25%), and when these grants are combined with regular

- "5 This nx is based piincipally on Lasn (1987, 1988). Extensive discussions with him and insights borrowed are gratfiuly acknowledged.

- 67 Four of the toilgates hve two lanes - one for motos payg cash and pepaid (coupon book) tices and another for use of seasonal passes - and two of dte paalld tollge have four lanes - two for cash and prepaud tckets and the other two for seasonal passes. Thus, the tota mumber of les in the 6 toll **_p_**<sup>**_1_**</sup> **_as_** is 16.
from otherwise lack of demand for farm labor during lean seasons. In addition, there could be a direct increase in income if the size of the transfer is larger than the consumption smoothing requirements of affected households, thereby have a long term impact on household overall consumption. To assess the impact among EGPP participants we use both estimates of _the monthly food and nonfood consumption reported at the time of the survey_ as well as _total annual household consumption estimated across the year_ as proxies of poverty. However, identifying any program impact on poverty will depend on having information on the counterfactual consumption levels of EGPP beneficiary households in a withoutprogram situation during a specific lean season. The survey questionnaire instrument allows us to identify beneficiary households in the various phases of the EGPP under implementation in each calendar year. Thus we take advantage of the _design_ and _timing_ of the household survey questionnaire to identify a reasonable comparison group of households to estimate the impact of participation in EGPP on: (i) monthly consumption at the time of the survey; and (ii) annual consumption within the past year of the survey. For (i) we compare monthly consumption among EGPP beneficiary households who participated in different EGPP phases at endline (i,e. identification is based on with or without treatment); while for (ii) we compare annual consumption among EGPP beneficiaries households with varying levels of participation in EGPP phases (i.e. identification is based on the intensity of the treatment). We use these differences in the timing of participation in EGPP among beneficiaries during the baseline and endline to run a set of difference-indifference regressions, controlling for differences in observable household characteristics: where _t_ stands for the period ( _t = 0_ for the baseline, and _1_ for the endline). _Yit_ is the outcome of household _i_ in period _t_ . _Ti_ represent the treatment status of the household: it takes the value 0 if the household is in the control group and 1 if it is in the treatment group. _Xit_ is a set of additional observable characteristics,  _i_ is unobserved time invariant household specific factors that may affect consumption and participation in EGPP, and  _it_ is an error term.  measures the difference between treatment and 12
21

# **Table 4.10: RuralUrban Poverty Incidence in Nigeria by Agro-climatic Zones, 1985-93 (Poverty Line=N395.41)**

**_RURAL_**

|||**1985/86**|||**1992/93**||
|---|---|---|---|---|---|---|
|**Region**|**P0**|**P,**|P2|**P0**|P,|**P2**|
|Northern|0.549|0.220|0.115|0.442|0.198|0.117|
|Southeast|0.434|0.158|0.075|0.260|0.117|0.070|
|Southwest|0.419|0.141|0.064|0.271|0.113|0.062|
|_All Rural_|_0.495_|_0.194_|_0.098_|_0.364_|_0.167_|_0.098_|
|||**1985/86**|_URBAN_||**1992/93**||
|**Region**|**P0**|**P1**|P2|**P0**|**P1**|**P2**|
|Northern|0.342|0.106|0.046|0.403|0.160|0.086|
|Southeast|0.273|0.072|0.027|0.226|0.097|0.057|
|Southwest|0.324|0.087|0.034|0.230|0.084|0.044|
|_All Urban_|_0.317_|_0.093_|_0.038_|_0.304_|_0.140_|_0.076_|

Source: NCS, 1985/86 and 1992/93.

**Table 4.11: Rural/Urban Poverty Decomposition by Agro-climatic Zones, 1985-93 (Poverty Line=N395.41)**

**_RURAL_**

|||**1985/86**|||**1992/93**||
|---|---|---|---|---|---|---|
|**Region**|**P0**|**P1**|**P2**|**Po**|**P1**|**P2**|
|Northern|61.8|70.3|72.5|67.9|56.1|78.8|
|Southeast|21.2|17.3|16.2|16.8|8.2|17.1|
|Southwest|17.1|12.4|11.3|15.3|7.2|4.1|
|_All Rural_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|
|||**1985/86**|**_URBAN_**||**1992/93**||
|**Region**|**P0**|**PI**|**P2**|**Po**|**PI**|**P2**|
|Northern|48.4|_55.5_|58.4|57.6|71.3|68.5|
|Southeast|24.9|19.4|17.7|19.1|15.2|15.1|
|Southwest|26.7|25.1|23.9|23.2|13.5|16.4|
|_Al/ Urban_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|_100.0_|

Source: NCS, 1985/86 and 1992/93.

Since the North did not show substantial improvements, one could use Table 4.12 to see which states did well and which did not. Focusing on the poverty measures of Bauchi, Kaduno, Kano and Sokoto in alphabetical order, Kaduna shows a 25.1 percentage points decline in headcount, 8.3 percentage points decline in depth and 3.4 percentage points in severity. The state of Bauchi shows 3.1 points decline in headcount, 1.1 points increase in depth and 0.1 percentage
equilibrium simulation with post-simulation analysis of household impacts.” This approach is best characterized by its two-step nature, whereby the general equilibrium model is first simulated to get commodity and factor price changes. These are then fed into a micro-simulation framework that calculates the effects on individual, or highly disaggregated representative households. Various poverty measures can be applied to assess the distributional effects of shocks.<sup>9</sup> An obvious limitation of post-simulation analysis, at least in the view of general equilibrium practitioners, is that the reactions of households to commodity and factor price changes in the post-simulation analysis are not transmitted back to the general equilibrium model. Although this absence of feedback is not satisfactory from a theoretical point of view, the resulting measurement error is likely to be small. This is due to the fact that: (a) spending patterns tend to be a function of household income per capita, so the transfer of income between two households with similar income levels (e.g., rural non-farm labor and agricultural labor) has little effect on aggregate demand, and (b) the change in spending patterns as households become wealthier is a gradual one, and the change in the overall income _distribution_ stemming from trade policy changes is also modest (recall the results from Cogneau and Robilliard above). Therefore, the change in aggregate demand due to changes in the distribution of income across strata and across income levels is likely to be very small. So ignoring the impact of such changes on equilibrium prices is most likely not very damaging.<sup>10</sup> The simplest micro-macro syntheses are really just an extension of the partial equilibrium, cost-of-living studies to include a full vector of commodity prices as well as factor prices. Chen and Ravallion’s (2003) study of the poverty implications of China’s WTO accession offers a good example of this approach. In this work, the authors combine disaggregated household survey data with trade 9 A few studies in this general category are: Robilliard, Bourguignon, and Robinson (2003); Ianchovichina, Nicita, and Soloaga (2001); Hertel, Ivanic, Preckel, and Cranfield (2004); and Friedman (2001). Summaries are provided in the tables of Reimer (2002).

10 Informal discussions with authors working in this area confirm this fact. Both Thomas Rutherford and Maros Ivanic report that equilibrium results obtained with a single aggregate household differ little from those obtained with CGE models which disaggregate a large number of households.

17
2009, the effect was positive but small, whereas between 2010 and 2018 the effect was larger. For example, in 2018, a 1% increase in real GDP per capita growth led to a 0.62pp increase in the _f_lfp_r_ (Figure 13).<sup>15</sup>

# **viii) Male labor force participation**

Men's and women's labor force participation rates have a positive relationship that reflects complementarity in their work (Figure 14). Men have maintained consistently high participation rates of around 73% for decades with minor fluctuations. In comparison, women began with a low participation rate of 18% in the 1960s but increased to 61% in the 2010s (Figure 15), which implies that male participation rates remained high and were not impacted as women began to participate more (Tenjo and Ribero, 1998).

**Figure 14: Effect of the** **_m_lfp_r_ on the** **_f_lfp_r_**

<!-- Start of picture text --> 3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>1960 1970 1980 1990 2000 2010 2020<br>Range of variation Male labor force participation rate<br>Percentage points<br><!-- End of picture text --> Source: Own calculations.

The coefficient of 0.50pp in the 1960s suggests a weak positive relationship between men's and women's labor force participation rates. As men's participation rates increased by one percent, women's participation rates increased by only half a percentage point. The scarce participation of women during this period, around 18%, may have contributed to this result. In contrast, the coefficients of 1.29pp and 1.23pp during the 1970s and 1980s suggest a much stronger positive 15 Although our estimations do not cover the period of the Covid-19 pandemic, it is important to mention that the participation of women in the labor market was the most affected by the economic downturn, which is in sharp contrast with previous economic crises where men's employment was more affected. According to GarcíaRojas et al. (2020), the pandemic expelled women from formal to informal occupations, occupied to unemployed, and unemployed to inactivity; consequently, more women were pushed into domestic work.

24
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2

# **I.   INTRODUCTION**

Emerging markets (EMs) represent a relatively large and rising share of world GDP. Taken together, EMs now constitute the largest block in the world economy and contribute the most to global growth (Figures 1 and 2). From a trade standpoint, advanced economies (AEs)’ imports are sourced in large part from EMs (about 50 percent) and EMs have also become important destinations for AE’s exports (Figure 3 and 4).<sup>2</sup> EMs are as a block the world’s largest consumer of commodity including metals and energy (Figures 5 and 6). The EM block is thus expected to be a large source of (cyclical) spillovers to the rest the world. To date, however, little is known about these spillovers. Indeed, the spillover literature has focused mostly on spillovers from AEs.<sup>3</sup> While AEs were arguably the most important sources of spillover for most of the second half of the 20<sup>th</sup> century, it is high time to consider the EM block as not just the destination of these spillover effects but also the origin—considering the growing importance of EMs. Some 2 It should be noted however that trade data used in the analysis are on a gross basis and thus double or multiplecount vertical trade or flows in intermediates. Trade flows in gross basis may overestimate the extent of trade links between AEs and EMs. That said, to the extent that supply chains are at least in part regional in nature the issue of double counting when considering trade links between AEs and EMs is less prevalent. Trade in value added allows incorporating the specificity of the new business model on which global manufacturing is based. Due to limited country coverage of value-added trade data we are however unable to use such data in our analysis.

3 See for example Österholm et al. (2007) and Utlaut et al. (2010) for studies of spillover from AEs to Latin America and to Emerging Asia respectively. See also Erten (2012) for a study of spillover emanating from the Eurozone to emerging markets.

(continued)
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particularly because of the presence of biases in the control group in the recall of information contained in intervened units (Table 13 and 14).

The balancing between treatment and control enhanced statistical inference through the application of other matching methods. Table 12 also shows the interaction effects using nearest neighbor and kernel matching, with the same imputation techniques on outcome variables into the DHS surveys. The results show that for the impact evaluation surveys the nearest neighbor and kernel estimations are statistically significant and positive for communication and motor skills with the presence of TV, radio and other communication assets. Yet, such effects are still low. However, with the imputed DHS surveys the results are predominantly statistically insignificant, but with similar levels of magnitude of the coefficients compared to those estimated with the impact evaluation surveys. Such results indicate that the presence of contamination in information transmission between comparison groups introduced biases in observed characteristics that could lead to overestimation of treatment effects.

# **<u>Conclusions</u>**

Understanding hygiene promotion programs aimed at changing hygiene and sanitation behaviors and ultimately preventing diseases in developing countries is important for policy makers, practitioners, researchers, and societies as a whole. Furthermore, the relationship and pathways between hygiene promotion programs and children’s mental development is little understood. To obtain a picture about the effectiveness of a hygiene information campaign program in Senegal, we used two rounds of an impact evaluation survey with possible sources of biases and contamination between comparison groups. Yet the data provided useful information to explore some pathways of hygiene information transmission across rural households and developmental outcomes of children. At early ages, children’s cognitive development is observed through a wide range of behaviors and skills. However, poor hygiene and inadequate sanitation will potentially prevent children from developing these developmental foundations, particularly when they face recurrent disease and children’s caregivers provide poor hygiene practices. In this respect, infants from low-income 28