rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Summary and Conclusions**

In this paper, we have used new data from Mexico and Brazil to analyze relationships linking economic development, the size distribution of manufacturing plants, and exposure to industrial pollution. Prior work in this field has generally been limited to water pollution and to medium size plants, owing to lack of data. In contrast the present study examnines air pollution, and emissions of airborne suspended particulates in particular, and encompasses small plants (with 1-20 employees) as well as medium, and large plants (with more than 100 employees).

The data for Mexico indicate that emissions per employee decline with plant size in the aggregate and also within most 3-digit sectors. With the exception of a few sectors (such as basic foods, petroleum products and industrial chemicals) the small plants are the more pollution intensive per employee (and presumably per unit of output).

Some 69 percent of Brazil's municipios report having some manufacturing within their boundaries. Among these industrial municipios, the share of the standard dirty six industries falls continuously with increases in the mean wage of household heads (which is taken as a proxy for level of economic development). These same data also show that the share of the smaller plants (no matter whether measured by the fraction of employment or proportion of plants) is greater among the low wage municipios. Conversely, the large plants are relatively more important in the high wage municipios.

At first glance, these findings seem to imply that the pollution has a 'regressive' bias and that most of the damage is inflicted by small factories. However, the introduction of scaling variables reverses this conclusion: Almost all the pollution damage is in the highest-income areas, and most of it is generated by large plants. The reversal occurs because production volume, plant scale and population density are highly correlated with economic development in Brazil and these effects are not offset by Large power plants are a major source of particulate pollution, and including them would obviously increase estimates of deaths attributable to large plants. Motor vehicles are another major source of particulate pollution. For an estimate of their contribution to overall mortality in Brazilian municipalities, see Von Amsberg (1997).

17
10

# II) Subjective cardinal data

The first objection to studies--and results-of this Idnd is that the data generated by the systematic case review method are subjective. According to this skeptical view subjective data is unreliable and/or ordinal and therefore inappropriate for statistical analysis. In this section we show our data are subjective, yet reliable and cardinal. First, we argue that the automatic association of subjective phenomena with ordinal data is incorrect. Second, the degree of inter-coder agreement on the scoring of the major variables reveals that the subjective measurement error, while present, was a r.inor source of variation. Third, using techniques appropriate for ordinal data do not dramatically change the results, and the constraints imposed by linear regression analysis are also not rejected by the data.

# Subjective and objective. ordinal and cardinal

Economic theory presumes that objective phenomena (e.g. numbers of oranges, relatve prices) have a natral cardinal metric (e.g. real numbers) whereas intrinsically subjective phenomena (e.g. consumer utility) allow only ordinal comparisons (e.g. better or worse) especially intersubjectively'. This distinction is critical. While both cardinal and ordinal data same as the OLS: the t-statistics with White are 11.4 vs 10.6 (bivariate) and 5.33 vs. 5.25 (full multivariate). Second, in scoring variables, each co>de **r** ecorded their subjective assessment of the reliability of the score assigned. When these reliability measures were used to weight observations, the results were roughly the same. And when, because of a programming mistake, we weighted the observations by giving more weight to the less reliable observations, the coefficient point estimates were still the roughly the same. This is perversely reassuring: under the assumptions for consistency of OLS, weighted least squares estimation is also consistent kor any set of weights, even the exactly wrong weights.

eOrdinality stems directly from the basic theory of mapping a binary preference relation into a utility index. The restrictions imposed on the preference relation (complete, reflexive, transitive) imply that once a given utility index is derived any monotone transform of that index equally well represents the
twentieth century (Metcalf and Black 1915). Similarly, positive views were also expressed **in** a U.K. report on German agriculture (Cahill 1913). This favorable assessment was not affected by the disputes and divisions that afflicted the movement in Germany. It reflected the success of credit cooperatives in mobilizing resources and offering credit services to German farmers, artisans, and small traders and the avoidance of failures. As in the case of savings banks, these institutional inniovationis generated considerable interest in other countries and they quickly spread to several neighboring countries.

Credit cooperatives were created to meet the credit ne;'ds of small farmers in rural areas and those of artisans and small traders in 6rban areas, tlhus protecting them from the high rates charged by moneylenders. Like savings banks, credit cooperatives benefited from low transaction costs, as they often operated out of the kitchen of a farmer and relied on unpaid managers and staff. In addition, because of their reliance on local knowledge and peer monitoring, their loan losses were unusually low.

Credit cooperatives used the joint and several unlimited liability of members to raise funds from nonmembers and to makae loans on the security of the character, moral worth, industry, sobrietv, and thrift of their members, reinforced by peer pressure and mutual oversight. Thrift was motivated by the principle of unlimited liability and contributed to a high loan repayment record. Effective peer monitoring was facilitated by the small size and small area of operation of most credit cooperatives.

Credit cooperatives were championed by men of high integrity and social standing, such as clergymen, wealthy philanthropists, or public officials. The protagonists emphasized self-help and developed a three-tier structure. Regional units facilitated the flow of funds across individual cooperatives and also provided valuable audit and control services, while national units represented the joint interests of credit cooperatives and provided a link to national money and capital markets. Urban credit cooperatives, whose area of operations were large, abandoned the concept of unlimited liability very early in their 21
# **1 Introduction**

Environmental and economic systems are deeply tied with one another, but consensus on the causal pathways is even in the most isolated settings seldom achieved. For instance: Does economic growth lead to environmental degradation or improvement? At the same time, to what extent does pollution take its toll on growth? The answers to both questions – and their interrelation – might tell us how places end up in pollution-poverty traps, or succeed in cleaning up the environment. The scope of these questions clearly calls for a holistic framework around the environmental-economic domain with both space and time dimensions. In this paper we introduce a framework that allows the researcher to model multiple interacting spatial time series.

Contemporaneous regression models alone often fail to provide conclusive evidence. This is mainly due to their inability to decide on the direction of causality. To pin down the direction, these models require the support of economic theory. Furthermore, standard approaches often confound direct effects with feedback effects. This leads many researches to adopt an instrumental approach, which often proves to be a non-trivial task. Time series offers invaluable insights to trace the arrow of causality. Univariate autoregressive moving average (ARMA) models are among the most fundamental statistical models to explore dynamics in observations that are collected sequentially over time. As we are interested in interactions between variables, we focus on their multivariate counterparts, known as vector autoregressive moving averages (VARMA). Moving averages are characterized by a cutoff in the auto-covariance functions. This implies that the effects represent parts in a model with short memory, while autoregressive parts represent long-memory effects. Short memory effects may relate to unobservable factors that slowly assimilate into the model, e.g. effects for which it takes time to be completely absorbed by a system. This is realistic for policy interventions in the context of economic systems, but it may also be realistic for natural phenomena. The ability to model effects that decay or remain free from feedback provides a framework to differentiate between long and short run causality as in Dufour and Renault (1998); Dufour et al. (2006) and Dufour and Taamouti (2010). This has added value when one is specifically interested in testing economic theories about the timing and duration of responses.

The VARMA constitutes the backbone of many studies on causality due to the strong 2
lx Most analytical attention has focused on Latin America's capital flight. In absolute amount, this accounts for a large share of the total capital flight--by our mneasure about 44 percent in 1985 and 37 percent in 1991 (see Figure 6).

**Figure 6: Top Ten Countries for World Bank Residual Measure Average Annual Flows 1981-1991**

<!-- Start of picture text --> M,Ilons of US$<br>4500<br>4000<br>3500<br>3000<br>2500<br>2000<br>1 500<br>1000U<br>0 4-'- -- - ~-- --<br>China Argentina Mexico Venezuela Poland Brazil Nigeria Egypt, Turkey Indonesia<br>Arab Rep.<br><!-- End of picture text --> Capital flight, however, is more widespread than previously thought, and is more important for many developing countries, not just Latin America. Relative to GDP, exports, and external debt, for instance, capital flight of Latin American countries is much less than most others. For Sub Saharan Africa, t': stock of capital flight at the end of 1991 (excluding misinvoicing) represented more than 85 percent of the region's GDP. By this measure, capital flight was, relatively, the largest for the Middle-East and North Africa region, with a ratio of a stock of flight capital to GDP of 118 percent. For **all** other regions, the ratio was 30 percent or less at the end of 1991. Least capital flight was in South-East Asia, only 15 percent of 1991 GDP.
middle-income countries from Eastern Europe / Central Asia. In each of these countries the survey covered between 900 and 1’583 households. We assess the impact of the crises on households using available indicators on changes in household income, consumption, education and health during 2008/2009. Table 2 provides an overview of the countries covered in this section. In order to benchmark the impact of the banking crises we compare changes in household well-being in countries that experienced a banking crisis to households in similar countries which did not experience a banking crisis (according to the classification of Laeven and Valencia 2012). The sample of non-crisis countries was selected to match the crisis countries based on per capita income and financial sector development prior to the crisis.

# **3.1 Data**

The LiTS 2010 questionnaire provides a wide range of information on how households were impacted by the global financial crisis in 2008/2009 and how they responded to economic difficulties in the crisis. While the survey does not provide quantitative data on changes in real household income it does provide information on whether the household experienced an income shock. Each household indicated whether one or more household members lost their job ( _Lost job_ ); working hours were reduced, wages were delayed, suspended or reduced ( _Lower wages_ ); or whether remittances were reduced or family members returned home from abroad ( _Less remittances_ ). If a household experienced any one of these three impacts we classify the household as having experienced an _Income shock_ .

Table 3 provides an overview of the share of households hit by different income shocks by country. Data is reported separately for high-income crisis countries, middle-income crisis countries and noncrisis countries. The table shows that the share of households hit by a negative income shock was substantially higher in middle-income crisis countries (62%) than in high-income crisis countries. In both regions a reduction in wages is the most common income shock followed by job losses and lower remittances. These findings confirm those reported in World Bank (2011) which shows that many more households in the region experienced a decline in real wages compared to those who actually lost a job.

The share of households hit by an income shock, as well as the type of income shocks that households were hit by are almost identical in middle income crisis countries and non-crisis countries. This finding suggests that in Eastern Europe the observed income shocks to households 13
|**Table 4: Impacts of Information o**|**n Business S**<br>Know a|**ervice Prov**<br>Provider|**ider Know**<br>Confiden|**ledge and**<br>t can Find|**Use**<br>Contacte|d a Provider|Use of Bu|siness Servic|e Provider|
|---|---|---|---|---|---|---|---|---|---|
||HR|Mktg/Acct|HR|Mktg/Acct|HR|Mktg/Acct|HR|Mktg/Acct|Either|
|**Panel A: Pooled Information Tre**|**atment**|||||||||
|Any Information Treatment|0.084**|0.032|-0.104|-0.039|0.054|-0.049|-0.006|0.006|-0.008|
||(0.039)|(0.042)|(0.108)|(0.109)|(0.035)|(0.039)|(0.023)|(0.016)|(0.025)|
|**Panel B: By Type of Information**|**Treatment**|||||||||
|Information Only|0.116**|0.066|-0.147|-0.024|0.094*|-0.011|0.037|0.009|0.030|
||(0.056)|(0.058)|(0.164)|(0.156)|(0.050)|(0.053)|(0.036)|(0.023)|(0.038)|
|Ratings Only|0.127**|0.052|-0.098|-0.173|0.080|-0.019|-0.024|-0.009|-0.033|
||(0.055)|(0.057)|(0.150)|(0.160)|(0.049)|(0.052)|(0.028)|(0.019)|(0.031)|
|Ratings + Negative Comments|0.009|0.007|-0.183|-0.150|-0.004|-0.068|-0.013|0.001|-0.012|
||(0.054)|(0.058)|(0.151)|(0.153)|(0.046)|(0.051)|(0.029)|(0.021)|(0.034)|
|Ratings + All Comments|0.078|0.003|-0.007|0.156|0.046|-0.094**|-0.021|0.019|-0.015|
||(0.053)|(0.055)|(0.146)|(0.141)|(0.047)|(0.047)|(0.027)|(0.023)|(0.032)|
|Sample Size|697|693|696|654|695|690|694|694|694|
|Control Mean|0.30|0.38|4.10|4.15|0.20|0.29|0.07|0.03|0.10|
|P-value: test alljointlyzero<br>Notes:<br>Robust standard errors in parenthe<br>_Know a Provider_is a binary outco<br>is on a scale of 1 to 5, where 5 de<br>is a binary outcome taking value o<br>is a binary outcome taking value o<br>and Mktg/Acct denotes marketing|0.066<br>ses. *, **, **<br>me taking valu<br>notes high co<br>ne if they con<br>ne if they hire<br>and accounti|0.722<br>* denote si<br>e one if the<br>nfidence in t<br>tacted this t<br>d this type<br>ng providers|0.713<br>gnificance<br>y say they<br>heir ability<br>ype of pro<br>of provider<br>. The final|0.268<br>at the 10, 5<br>know at le<br>to find this<br>vider in the<br>in the past<br>column is f|0.186<br>, and 1 per<br>ast one of t<br>type of pr<br>past six m<br>six month<br>or hiring ei|0.259<br>cent levels r<br>hese types o<br>ovider if the<br>onths; and_U_<br>s. HR denote<br>ther type of|0.421<br>espective<br>f provider<br>y need it; <br>_se of Busi_<br>s Human<br>provider.|0.815<br>ly.<br>s;_Confident_<br>_Contacted a_<br>_ness Service_<br>Resources pr|0.554<br>_can Find_<br>_Provider_<br>_Provider_<br>oviders,|

23
- 2.2 Empirical studies comparing the experience of structural change across developing regions and countries

Using data from both the national accounts and Demographic and Health Surveys (DHS),<sup>3</sup> and covering 19 SSA countries, McMillan and Harttgen (2014) show that between 2000 and 2010, the share of the labor force employed in agriculture declined by roughly 10 percentage points. This decline has been accompanied by a systematic increase in the productivity of the labor force, as it has moved from low productivity agriculture to higher productivity manufacturing and services. The decline has been accompanied by higher economic growth rates and poverty reduction. Both the declines of poverty and agriculture’s labor share have been more rapid in countries where the initial share of the labor force engaged in agriculture is the highest and where commodity price increases have been accompanied by improvements in the quality of governance.

Likewise, Diao _et al_ . (2017) also use both data from the Groningen Growth and Development Center’s Africa Sector Database and the DHS to show that much of Africa’s recent growth and poverty reduction has been associated with a substantive decline in the share of the labor force engaged in agriculture over the period 1994-2008. This decline is most pronounced for rural females over the age of 25 who have a primary education; it has been accompanied by a systematic increase in the productivity of the labor force, as it has moved from low productivity agriculture to higher productivity services and manufacturing.

Haile and Moller (2018) examine the determinants of growth acceleration in West African Economic and Monetary Union (WAEMU) countries over the period 2011-17. Using the structural change lens as one of the methods of analysis, they find that growth was driven by static structural change and within-sector productivity growth. However, productivity growth was low in the subregion over the period of analysis.

de Vries _et al_ . (2015) analyze structural change in Africa and its implications for productivity growth during the past 50 years. According to this paper, during the early post-independence

> 3 DHS are nationally representative surveys designed to collect detailed information on child mortality, health, and fertility, as well as on households’ durables and quality of dwellings. In addition, DHS data include information on gender, age, location, education, employment status, and occupation of women and their partners between the ages of 15 and 59.

5
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE and agricultural resources and are investing heavily in the transport and energy facilities needed to support their operations. Because of Liberia’s policy of ensuring that such investments benefit the broader national economy, they could potentially contribute to meeting the country’s overall infrastructure spending needs.

Despite the potential for private investment, Liberia will likely need more than a decade to reach the illustrative infrastructure targets outlined in this report. Under business-as-usual assumptions for spending and efficiency, it would take at least 40 years for Liberia to reach these goals. Yet with a combination of increased finance, improved efficiency, and cost-reducing innovations, it should be possible to significantly reduce that time.

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in more than 40 Sub-Saharan countries, including Liberia. The results have been presented in reports covering different areas of infrastructure—ICT, irrigation, power, transport, water and sanitation—and different policy areas—including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Liberia, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Liberia is a low-income state richly endowed with natural resources; therefore, both low-income and resource rich benchmarks will be used to evaluate its performance. Detailed comparisons will also be made with immediate regional neighbors in the Economic Community of West African States (ECOWAS) and other states that have emerged from conflict such as the Democratic Republic of Congo and Rwanda.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2004 to 2009. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to ensure consistent comparisons across countries, the indicators and analysis were standardized. As a result, some of the indicators presented here may differ slightly from those that are routinely reported and discussed at the country level.

2
# **A.6 Cross-sectional results using data from U.S. Geological Survey (USGS) and CSCW diamond data.**

Since our empirical strategy requires data on production over time, we are not able to include all mines in the region. Hence, our results may not be generalizable to the effects of other, in particular smaller, mines. While we cannot completely overcome this problem, we show below that the cross-sectional results using all mines in the region obtained from USGS point in the same direction as our difference-in-difference results with the RMG data. The crosssectional results are shown in Panel A of Table A.15. We find that being close to a USGS mine is positively associated with being in services and sales and negatively associated with agriculture. These results are in line with anecdotal evidence pointing to female engagement in services and sales and that mining activities may compete with agriculture in terms of land use. There is significant diamond mining in Africa, and while the RMD includes some diamond mines, it does not capture all diamond mines. The RMG data set excludes all mines that produce only diamonds. To correct for this and explore the effects of diamond mining on local women’s employment opportunities, we use the CSCW data set. This diamond data set has GPS coordinates for the mines, but does not contain production data. The identification strategy here is thus the same as the one for the USGS data sets. The results are presented in Panel B of Table A.15. We find that being close to a mine is associated with a higher probability of engaging in sales and a lower probability of working in agriculture.

Table A.15: Cross-sectional correlation between being close to a USGS mine or a CSCW mine and our main outcomes.

|VARIABLES|(1)<br>Working|(2)<br>Service|(3)<br>Sales|(4)<br>Agriculture|
|---|---|---|---|---|
|_Panel A. USGS data._<br>usgs (20 km)|-0.003|0.009***|0.008***|-0.026***|
||(0.004)|(0.002)|(0.003)|(0.005)|
|Observations|525,180|525,180|525,180|525,180|
|R-squared|0.197|0.091|0.141|0.355|
|_Panel B. CSCV diamond data._|||||
|dia (20 km)|0.001|0.006*|0.028***|-0.053***|
||(0.009)|(0.004)|(0.010)|(0.013)|
|Observations|525,180|525,180|525,180|525,180|
|R-squared|0.197|0.091|0.141|0.355|

Robust standard errors clustered at the DHS cluster level in parentheses.All regressions control for year and region fixed effects, regional time trends, living in an urban area, age, years of education, and religious beliefs. *** p<0.01, ** p<0.05, * p<0.1 42