rafmacalaba/datause-encoder-data huggingface dataset:
Fig. 3 depicts the first difference of the log of monthly prices. Lao prices are on average the most volatile, with the episodes in 2006 and 2010 standing out. The price spike in 2010, which led to an export ban criticized by the IMF, implied a price rise of over 50% between September 2009 and September 2010. Thai prices are stable until 2006, but then become highly volatile for a couple of years. This is arguably because of a close link to world market prices for non-glutinous rice, as Thailand is a large exporter of both glutinous and non-glutinous rice. Vietnamese prices show some within-seasonal fluctuations, as expected for an agricultural product, but there are no clear episodes of high volatility. Table 1 provides some additional information on volatility by reporting standard deviations for first and twelfth log differences and measures of deviation from the normal distribution (skewness and kurtosis). This confirms that Vietnamese prices are the most stable.

Table 1. Data description, first and twelfth differences

||Lao PDR|Thailand|Vietnam|
|---|---|---|---|
|Mean (1<sup>st</sup>difference)|0.008|0.008|0.009|
|Std. Dev. 1st difference|0.051|0.043|0.024|
|Std. Dev. 12th difference|0.161|0.263|0.099|
|Skewness (1<sup>st</sup>difference)|1.098|1.231|0.717|
|Kurtosis (1<sup>st</sup>difference)|8.217|8.910|4.078|
|Observations(1<sup>st</sup>difference)|143|143|143|

Figure 3. Prices in growth rates (log first differences) 10
Table 1 presents a decomposition of interest rate spreads for Uganda and shows that high operating costs are one important factor, while loan loss provisions reflecting loan losses and reserve requirements are rather minor components of the interest rate spread. The highest component of interest rate spreads, however, is the high profit margin, consistent with limited competition, but also a high risk premium. Comparing domestically- and foreign-owned banks, we see that domestic banks show much higher spreads, due to higher lending rates, which most likely reflect a riskier loan portfolio. The spread decompositions indicate that domestic banks generate high profit margins from a group of borrowers that repay their loans at a higher rate than clients of foreign banks. The relatively high overhead costs and low profit margins for the foreign banks could be consistent with the idea that they deal with a set of “blue-chip” clients whose projects are more costly to evaluate and maintain. In addition, higher wages might add to their costs, though, higher costs could also result from foreign banks’ propensity to invest more, including in IT and technology to develop new products.<sup>vi</sup>

**<u>Table 2: Explaining Overhead Costs in Africa</u>**

||Overhead costs|
|---|---|
|African banks|605|
|Rest of World banks|451|
|**Difference**|**154**|
|Of which: Contractual framework|12|
|Non-interest income|93|
|Bank size|18|
|Equity-asset ratio|5|
|Other bank characteristics<br>|-3|
|Inflation|11|
|Africa residual|18|

Source: Authors’ calculations using data from Bankscope.

Table 2 uses regression analysis to relate bank-level variation in overhead costs to bank- and country-level characteristics and compares banks in Africa with banks in nonAfrican developing countries. As overhead costs is one of the major components of interest rate spreads, we regress overhead costs in 2011 for a cross-country sample of banks on (i) the 15
The DID results show that displaced workers due to plant closings experience a reduction in wages of -4.7 percent, a moderate increase of 0.53 hours of work per week, and no impact on the probability of working in the formal sector. Compared to OLS estimates, DID reduces the magnitude of the impact of plant closings on wages, from -7.5 to -4.7 percent, and the effect on hours of work from 1.4 to 0.53 hours per week, while the DID coefficient on probability of working in the formal sector is tiny and thus not statistically different from zero. We interpret this change in the magnitude of the estimates as evidence that OLS might be inflating the impact of plant closings on labor outcomes.

The rest of the paper is structured as follows: Section II describes the data. Section III describes the empirical specifications, outlines the robustness tests and discusses the results. Section IV presents the conclusions.

# **II. Data**

The _ENOE_ is a household survey that collects detailed information on labor force status, wages, employment, and demographic characteristics of the labor force. The survey has been collected quarterly since 2005 and is representative at the national and state levels. The survey in each quarter includes approximately 121,000 households. The survey is structured as a rotating panel; each household is interviewed for five consecutive quarters, and in each quarter, one-fifth of the sample is replaced. We restricted the sample to individuals between 15 and 65 years old. We use selected phases of the survey which implemented the extended questionnaire for the period 20052019, which excludes the COVID-19 economic crisis.

When the survey uses the extended questionnaire, it collects detailed information on individuals’ labor history. The survey instrument asks whether individuals have ever experienced a job 6
African groundnut exports did not materialize. Instead, they posit that the main constraints on these exports came from the supply side and that in the case of edible groundnuts the regulations may have actually stimulated trade by enhancing the safety of the products in the eyes of EU consumers.

Thus, while the impact of SPS and TBT regulations on developing country exports is quite complex and they cannot all be considered as NTMs used for protection of domestic industry, these regulations certainly have the potential of doing so. The problem is that in defining these as NTMs it is necessary to identify intent—which is inherently difficult to demonstrate, as well as resolve a variety of complex scientific issues.

The WTO undertook an exhaustive analysis of NTMs and especially SPS and TBT measures in its 2012 annual trade report. Its conclusions are important to report here. It states (p.143) “The existing empirical literature finds that at the aggregate level TBT/SPS measures may not be associated with lower trade”, (also p.153) “TBT/SPS measures do not unambiguously increase or decrease trade. In general TBT/SPS measures have positive effects for more technologically advanced sectors, but negative effects on trade in fresh and processed goods.” Given these difficulties, it is important not to lump TBT and SPS measures with other non-trade barriers which quite unambiguously restrict trade such as quotas or trade remedies. To do so will result in ‘crying wolf’ too often and missing true increases in protection. The only recommendation is for continued vigilance, monitoring and transparency through timely reporting and discussion in the WTO and other relevant international bodies entrusted with the setting of these standards.

58
# **FAM-low (standard quality)**

||**MK per MT**|**USD per MT**|**Product Form**|**Location**|
|---|---|---|---|---|
|**Farm gate price**|148,049|1,057.49|Baled tobacco|TAMA depot|
|**Assembled raw material**|164,450|1,174.64|Baled tobacco|Price at auction|
|**Ex-factory price**|||||
|Product 1|385,545|2,753.90|Un-boxed T&T (55%)|Factory|
|Product 2|-|-|Trash|Factory|
|**Final traded price**|||||
|Product 1|511,000|3,650.00|Boxed T&T Tobacco|cif Europe|
|**FAM-high (better quality**|**)**<br>**MK per MT**|**USD per MT**|**Product Form**|**Location**|
|**Farm gate price**|163,919|1,170.85|Baled tobacco|TAMA depot|
|**Assembled raw material**|180,895|1,292.11|Baled tobacco|Price at auction|
|**Ex-factory price**|||||
|Product 1|415,445|2,967.47|Un-boxed T&T (55%)|Factory|
|Product 2|-|-|Trash|Factory|
|**Final traded price**|||||
|Product 1|532,000|3,800.00|Boxed T&T Tobacco|cif Europe|

**Table 17: Summary of key data from the farm-level analysis of burley tobacco production**

||**Yield**<br>**(MT/ha)**|**Kg**<br>**Fertilizer***|**USD per**<br>**MT**|**%**<br>**Foreign**|**Tax as %**<br>**DVA**|**Total SV**<br>**(USD/MT)**|**Net Profit**<br>**(USD/ha)**|
|---|---|---|---|---|---|---|---|
|**Malawi (2007)**||||||||
|FAM-low|0.90|400|1,057|50%|14%|759.05|332.89|
|FAM-high|1.25|600|1,171|55%|17%|736.65|617.75|
|**Zambia (2006/07)**||||||||
|FAM|1.25|600|1,200|41%|9%|975.16|443.28|

79. As shown in Figure 20, key burley tobacco production costs at the farm-level include fertilizer, seed (mainly due to nursery establishment costs), chemicals and labor. Beyond the farm-gate, the major cost component is the foreign costs attributed to the high transport costs.

**Figure 20: Burley, composition of farm-level SV**

<!-- Start of picture text --> Burley, Composition of Farm-level SV (USD per MT)<br>Depreciation<br>1,000<br>900 Labor<br>800<br>700 Transport to<br>depot<br>600<br>Baling &<br>500 club<br>400 Sprayer hire<br>300<br>Chem<br>200<br>100 Fert<br>-<br>Low High Low High Seed<br>USD per Ha USD per MT<br><!-- End of picture text --> 80. At the assembly level, the major costs that reduce producers’ net profit include the high cost of transport and intermediation especially at the assembly level, including the Auction Floors. Transport costs to the floors constitute about 9 percent of the total proceeds (see Figure 37
We also construct a variable XCONST that is the average for the years 1990-99 of the Polity IV sub-index on executive constraints. XCONST measures the extent of institutionalized constraints on the decision-making powers of chief executives. XCONST ranges from 0 to 10 with a higher score indicating less _de facto_ operational independence (and more accountability) of the country’s chief executive. XCONST captures the extent to which the political system enables minorities or non-elites to influence political elites in regular ways.

Next, we construct a variable COALITION that takes a value of one if the government of the country is a coalition government, and zero otherwise. The variable COALITION is averaged for the years 1975-97 and is based on data collected by the World Bank. For more details on this database, see Beck et al. (2001). Unfortunately, we do not have cross-country data on political campaign contributions from interest groups, unlike Kroszner and Strahan (2001) in their study of state-level deposit insurance limits in the United States. Therefore, we cannot control for the effect that money contributions may have on the voting patterns of politicians.

Second, we control for structural differences within the banking industry. Private interest theory emphasizes how different interest groups can provide funds and votes to politicians who, in turn, control the regulatory decision to raise the coverage level of deposit insurance. Private interest theory thus predicts that support for high coverage levels is more likely in countries with greater political influence of risky banks relative to that of safe banks. Public interest theory, on the other hand, would imply that support for high deposit insurance coverage is less likely in countries where the share of risky banks is large because the social costs of deposit insurance are directly related to the size of the 10
<!-- Start of picture text --> Figure B3 Returns to Migration<br>.16<br>.12<br>2007<br>.08<br>.04<br>2001<br>.00<br>-.04<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br>Quantile<br>Coefficient<br><!-- End of picture text --> Figure B4. Returns to Employment in Agriculture <!-- Start of picture text --> .00<br>-.05<br>-.10<br>2001<br>-.15<br>2007<br>-.20<br>-.25<br>-.30<br>-.35<br>-.40<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br>Quantile<br>Coefficient<br><!-- End of picture text --> 50
23 Table 9: Standardized Slope Coefricients (based on Tables 7 and 8)

||Equity<br>Nominal<br>interest<br>rates|Real<br>interest<br>rates|Bon<br>Nominal<br>interest<br>rates|d<br>Re ii<br>interest<br>rates|
|---|---|---|---|---|
|||Lati|n America||
|PCS|1.678's|6.816n"|7.320|12.614|
|Return|_NS_|NS|NS|NS|
|Price-<br>earning<br>ratios|-8.598|-5.764|-7.506|-4.375|
|P11|-18.141|-11.066|-20.316|-13.406|
|||Asia|||
|Credit<br>rating|1.974|1.937|30.030|29.230|
|Return|1.180|1.180|1.570|1.638|
|Price-<br>earning<br>ratios|-1.916|-1.948|-12.334|-11.596|
|P11|-2.007|-1.564|-10.001|-10.494|

Notes: Variables other than the principal components have been standardized; PCS: first principal component of credit rating and secondary market prices (see notes tor Table 5); Pll: first principal component of interest rates and US industrial production index; NS: not significant.

The results vary by region and type of flows (and whether real or nominal interest rates are used). For Latin America, the sums of the absolute value of the standardized coefficients for PCS, return, and price-earnings ratio are about the same as the coefficients for the global factor (PII). The eplication for this region is that global variables are as important as domestic variables in explaining both bond and equity flows. For Asia, the country-specific variables are three to four times more important than the global variables in explaining both equity and bond flows. The credit rating variable is particularly important in explaining bond flows (a standardized coefficient of about 30) to this region.
poverty for all age groups. The coefficients are larger negative values for the younger age groups. Similarly, Chile’s graph of poverty against positive behaviors in Figure 4b shows a statistically significant downward trend of behavior against poverty (proxied by parental education).<sup>30</sup> This negative trend is significant for the younger cohort as well, with an even larger magnitude.

While poverty is a reasonable candidate as an indicator in identifying at-risk groups,<sup>31</sup> we cannot conclude that economic class is the sole source of clustering since a close look at the clusters shows that not everyone in the at-risk groups is poor. The figures in Annex C use the density of the various possible general levels of education of heads of households for each cluster and demonstrate that poverty is correlated with risk level, however each risk cluster has youth with parents of varying education levels. Furthermore, we do not suggest that poverty causes risky behavior, but, poverty, for whatever reason, can be used to identify ex ante which youth have a higher propensity for engaging in risky behaviors.

_Living in a rural area_ is a candidate for a proxy for risk; in both countries, rural youth are over 10 percentage points more likely to be in risk types I, II, and III than are urban youth, and ten percentage points less likely to be risk-free (Tables 3a and 3b). Figures 5a and 5b confirm this correlation, demonstrating that living in a rural area is negatively correlated with positive behaviors and outcomes at the 1% level. That said it is important to note that not all rural youth are at risk, and that most at-risk youth live in urban areas, since more of the general population lives in urban areas.<sup>32</sup> Again, in Chile living in a rural area may be more risky than in Mexico, since the more at-risk clusters in Chile are more rural compared to the national average than Mexico.

Ordered logistic regressions relating the risk level to poverty level and living in a rural area (when controlling for age and gender as well as when not doing so) demonstrate a large, significant and negative relationship for both variables as shown in Table 5. This

> 30 Similarly Chile had two variables available to represent poverty: parent/caregiver education level and the economic class variable from the survey data (nse – nivel socio-economico).  The “nse” variable was constructed by the Chilean National Institute of Youth using parental/caregiver education and occupation or an average of six durable goods in the event that the other information was not available.

> 31 Indeed, Blakely et al. (2005) used income level to estimate relative risk levels.  Their risk factors are: tobacco, drinking alcohol, not having access to safe water/sanitation, indoor air pollution exposure and obesity. 32 In Mexico, the rural indicator was constructed to equal 1 if rural (< 2500 inhabitants), 0.5 if living in a small to medium sized town (2500 to 99,000) and 0 if living in a large urban setting.  The rural indicator in Chile equals 1 if rural with more than 2000 and less than 5000 inhabitants and 0 if urban with more than 5000 inhabitants.

20
Hi INTRODUCTION Table 9 Population and Percentage Distribution by Geographic Region and by Income Group, 1985.2100

|||Pop|utati|on<br>(mill|ifons|)||Percen|toje<br>of|worldp|opulatio|n|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Region or|||||||||||||
|income<br>group|1985|1990|2000|2025|2050|2100|1985|1990|2000|2025|2050|2100|
|World|4,844|5,285|6,204|8,415|10,03|5<br>11,330|<br>100.0|100.0|100.0|100.0|100.0|100.0|
|Less developed<br>(a)|3,666|4,074|4,939|7,078|8,716|10,020|<br>75.7|77.1|79.6|84.1|86.9|88.4|
|More developed<br>(a)|1,179|1,211|1,265|1,336|1,319|1,310|<br>24.3|22.9|20.4|15.9|13.1|11.6|
|Ufrica|559|<br>651|<br>870|1,587|2,275|2,962|<br>11.5|12.3|14.0|18.9|22.7|26.1|
|East Africa|231|<br>271|<br>367|<br>700|1,034|1,383|<br>4.8|<br>5.1|<br>5.9|<br>8.3|10.3|12.2|
|West Africa|204|<br>238|<br>320|<br>602|<br>873|1,152|<br>4.2|<br>4.5|<br>5.2|<br>7.2|<br>8.7|<br>10.2|
|North Africa|124|<br>142|<br>182|<br>286|<br>368|<br>427|2.6|<br>2.7|<br>2.9|<br>3.4|<br>3.7|<br>3.8|
|America|667|<br>721|<br>826|1,040|1,146|1,192|<br>13.8|13.6|13.3|12.4|11.4|10.5|
|Latin America and Caribbean|<br>399|<br>441|<br>525|<br>703|<br>814|<br>864|8.2|<br>8.3|<br>8.5|<br>8.4|<br>8.1|<br>7.6|
|Northern America|268|280|302|336|332|<br>328|5.5|<br>5.3|<br>4.9|<br>4.0|3.3|<br>2.9|
|Asia|2,823|3.100|3,664|4,901|5,728|6,288|<br>58.3|58.6|59.1|58.2|57.1|55.5|
|East and Southeast<br>Asia|1,650|1,781|2,038|2,5n3|2,719|2,822|<br>34.1|33.7|32.8|29.7|27.1|24.9|
|South Asia|1,058|1,186|1,454|2,108|2,614|2,977|<br>21.8|22.4|23.4|25.1|26.0|26.3|
|Southwest<br>Asia|115|<br>132|<br>172|<br>290|<br>396|<br>490|2.4|<br>2.5|<br>2.8|<br>3.4|<br>3.9|<br>4.3|
|Europe<br>and U.S.S.R.|771|<br>787|<br>813|<br>848|<br>843|<br>842|15.9|14.9|13.1|10.1|<br>8.4|<br>7.4|
|Oceania|25|27|31|39|42|<br>44|0.5|0.5|0.5|0.5|0.4|0.4|
|Income<br>group (b)|||||||||||||
|Low|2.7S1|3,004|3,627|5,165|6,343|7,315|56.0|56.8|58.5|61.4|63.2|64.6|
|Lower-middle|580|<br>648|<br>792|1,145|1,416|1,609|12.0|12.3|12.8|13.6|14.1|14.2|
|Upper-middle|422|462|545|748|891|<br>989|8.7|8.7|8.8|<br>8.9|<br>8.9|<br>8.7|
|High|767|789|<br>830|884|869|<br>869|15.8|14.9|13.4|10.5|8.7|7.7|
|Nonreporting nornemrber|365|382|410|474|515|<br>548|7.5|7.2|6.6|5.6|5.1|4.8|

a. "More developed" comprises Europe, U.S.S.R., Northern America (United States and Canada), Australia, New Zealand, and Japan. "Less developed" comprises the rest of the world.

b. Criteria for income grours are given in World Bank 1989. See listing in Exhibit 1.
carries out new combinations and looses that character as soon as he has built up his business and settled into running it.” (Schumpeter, 1942). Thus, new and young firms are more likely to retain the “entrepreneurial spirit” alluded to by Schumpeter. Other useful proxies for entrepreneur might be firms where an individual or family is the largest shareholder; whether the firm is managed by the owner; and if the firm is registered as a sole-proprietor (relative to a limited-liability partnership or corporation.) In the later case, the person who starts the business, the entrepreneur, is actually running the business. In the paper we focus on young firms as a proxy for ‘entrepreneurial’ firms, while also exploring other samples of firm ownership and legal types.

The rest of the paper is organized as follows: Section 2 presents our data and descriptive statistics, Section 3 presents our regression results on the relationship between firm age and usage of financing, Section 4 presents our results on the differential impact of business environment on usage of financing by young versus older firms, Section 5 presents robustness checks, and Section 6 concludes.

# **2. Data and Summary Statistics**

The WBES dataset includes firms across multiple sectors (manufacturing, services, agriculture, and construction). The database includes both quantitative and qualitative information on firm characteristics, including sources of finance, barriers to growth, access to infrastructure services, legal difficulties, and corruption. The dataset also includes some measures of firm performance, such as multiple years of historical data on employment and sales.

6
accountability", "government stability", "law and order", and "corruption". Other examples include large cross-country surveys of individuals such as the Afro- and Latino-Barometer surveys or the Gallup World Poll, which ask quite general questions such as: "is corruption widespread throughout the government in this country?".

The main advantage of such outcome-based indicators is that they capture very directly the views of relevant stakeholders, who take actions based on these views. Governments, analysts, researchers, opinion- and decision-makers should, and very often do, care about public views on the prevalence of corruption, the fairness of elections, the quality of service delivery, and many other governance outcomes. In other words, outcome-based governance indicators, as distinct from indicators of specific rules that we have discussed above, provide direct information on the _de facto_ outcome of how the _de jure_ rules are actually implemented: the distinction between rules "on the books" and practice "on the ground".

But against this major strength there are also some significant limitations. The first we have already discussed at length above. Outcome-based indicators of governance, and particularly where they are general ones, can be difficult to link back to specific policy interventions that might influence these governance outcomes. This is the mirror image of the problem we discussed above: rules-based indicators of governance can also be difficult to relate to outcomes of interest. A related difficulty is that outcome-based governance indicators may be too close to ultimate development outcomes of interest, and so become less useful as a tool for research and analysis. To take an extreme example, the recently-released Ibrahim Index of African Governance includes a number of ultimate development outcomes such as per capita GDP, growth of GDP, inflation, infant mortality, and inequality. While such development outcomes are surely worth monitoring, including them in an index of governance risks making the links from governance to development tautological.

Another difficulty has to do with interpreting the units in which outcomes are measured. We have noted that rules-based indicators have the virtue of clarity -- either a particular rule exists or it does not. Outcome-based indicators by contrast are often measured on somewhat arbitrary scales. For example, a survey question might ask respondents to rate the quality of public services on a 5-point scale, with the distinction 13