rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7: Impact of PBF on health outcomes and health behaviors**

|**Variable**|**Baseline**<br>**Mean in**<br>**PBF**<br>**districts**|**Trend**|**PBF**<br>**impact**|**p-**<br>**value**|**N**|
|---|---|---|---|---|---|
|**Panel A: Children under 5**<sup>**a**</sup>||||||
|Weight-for-age||||||
|Mean Z-score|-0.421|-0.396|0.028|0.990|12290|
|Percentage below-2 SD|0.181|-0.001|-0.007|0.901|12290|
|Percentage below-3 SD|0.092|-0.015|-0.017|0.598|12290|
|Weight-for-height||||||
|Mean Z-score|0.183|-0.237|0.117|0.974|11236|
|Percentage below-2 SD|0.149|0.002|-0.033|0.652|11373|
|Percentage below-3 SD|0.082|-0.008|-0.018|0.659|11373|
|Height-for-age||||||
|Mean Z-score|-1.106|-0.014|-0.159|0.891|12460|
|Percentage below-2 SD|0.283|0.029|0.016|0.989|12803|
|Percentage below-3 SD|0.137|0.028|-0.012|0.377|12803|
|**Panel B: Adults aged 40 and above**<sup>**b**</sup>||||||
|Elevated blood pressure<sup>c</sup>|0.363|0.020|0.058|0.454|10535|
|Self-reported hypertension|0.264|0.028|0.063*|0.071|15450|
|Prescribed medication for hypertension|0.893|-0.019|0.044|0.128|3741|
|Took medication in past 24 hours<br>|0.676|-0.034|0.039|0.292|3714|
|Use local RHC or HH<sup>d</sup>|||0.076**|0.041|2002|
|**Panel C: Recently pregnant women**<sup>**c**</sup><br>||||||
|Use local RHC of HH<sup>d</sup>|||0.064|0.133|2879|
|ANC at the primary level<sup>e</sup>|0.769|0.029|-0.132|0.995|5,590|

Note: data from household surveys. Unless otherwise noted, regressions are of the difference-in-differences specification with facility fixed effects. Standard errors are clustered at the district and survey round level with wild bootstrapping. All regressions include controls for catchment areas’ exposure to the collaborative quality improvement and citizen scorecards interventions, region, household wealth quintile, and age.

a Sample of children under-5. Regressions include a control for the gender of the child. b Sample of adults 40 years and above. Regressions include controls for employment status, education level, gender, and marital status. c Sample of women with a live birth in the preceding two years. The indicators relate to care received during the most recent pregnancy. Regressions include controls for employment status, and education level.

d Respondents were asked whether they generally use the services offered by the staff of the local primary care facilities officially serving their catchment areas. The question was only included in the follow-up survey and the approach used for this outcome is the propensity score weighting specification. Standard errors are clustered at the district level. e Women reported to receive antenatal care at a rural health center, health house or through home visits. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 29
**Table 1: Household-Level Characteristics by Region**

||**Total**|**Central**|**Eastern**|**Northern**|**Western**|
|---|---|---|---|---|---|
|**Household Characteristics**||||||
|Number of Household Members|7.1|7.6|7.7|6.5|6.9|
|Household Headed by Female|2.6%|2.7%|1.9%|3.5%|2.0%|
|Percentage of Household Land Cultivated by Female|47.5%|42.7%|41.2%|45.5%|59.3%|
|Manager||||||
|Child Dependency Ratio (Number of Children Under 10|0.70|0.74|0.71|0.71|0.66|
|Years of Age/Number of Household Members 10 Years<br>of Age or Older)||||||
|Number of Children Under 2 Years of Age|0.56|0.59|0.58|0.57|0.50|
|Number of Female Adults (15 Years and Above)|1.58|1.70|1.65|1.40|1.63|
|Number of Male Adults (15 Years and Above)|1.75|1.82|1.80|1.64|1.78|
|Number of Female Young Adults (10-15 Years)|0.60|0.62|0.70|0.51|0.59|
|Number of Male Young Adults (10-15 Years)|0.58|0.63|0.66|0.53|0.52|
|**Sources of Income**||||||
|Household Earned Income from Non-Farm Enterprise|49.7%|55.1%|45.3%|57.8%|40.7%|
|Subsistence Farming Is the Most Important Source of|63.8%|51.7%|63.9%|75.6%|57.7%|
|<br>Earnings for Household||||||
|Commercial Farming Is the Most Important Source of|3.1%|2.2%|2.4%|5.5%|1.6%|
|Earnings for Household||||||
|Wage Employment Is the Most Important Source of|14.9%|18.8%|15.8%|6.7%|21.1%|
|Earnings for Household||||||
|Non-Agricultural Enterprises Is the Most Important|16.1%|23.2%|14.6%|11.2%|18.6%|
|Source of Earnings for Household||||||
|**Household Assets**||||||
|Value of Household Livestock (USD)|322|232|348|312|372|
|Value of Household Assets (USD - Includes House and|5178|5645|4692|3978|6765|
|Land)||||||
|Value of Household Land|3698|3211|3181|3245|5064|
|Value of Household Dwelling and Other Buildings|1187|1907|1159|562|1474|
|<br>Value of Household Physical Assets Excluding Land and<br>Buildings|293|526|352|170|227|
|Number of Observations (Households)|630|112|156|199|163|

15
Bank’s human capital index (HCI) and country statistical capacity index (SPI). (We provide more technical details on these methods in Appendix 1, Part A.) There are, unsurprisingly, pros and cons with each method. The simplest is the dashboard method employed by the United Nations (2018c), which analyzes the SDG indicators as is; that is, it tracks progress on each and every indicator. This method offers straightforward interpretation, but its disadvantage is that, with so many indicators, it is difficult to evaluate progress for them as a whole. For example, it is hard to compare a country with good poverty reduction but weak environment protection with another which has the opposite performance.

Sachs et al. (2017) combine the dashboard approach with an index that is an average (i.e., the arithmetic mean) of all the standardized indexes obtained from Equation (1). While this procedure is somewhat more complicated than the dashboard approach and requires making certain assumptions, it offers a way to evaluate overall progress for all the indicators. But note that this index is driven by data availability.<sup>25</sup> In fact, Sachs et al. (2017) also offer a dashboard-based approach to keep track of progress on each of the 17 goals.

An alternative to Sachs et al.’s (2018) method of constructing the SDG index by the arithmetic mean is to construct it using either the geometric mean or the product of all components. (The former is the latter raised to the power of _1/N_ , where _N_ is the number of components in the product.) Put differently, we can multiply them together in a similar way to the World Bank’s recent HCI (Kraay, 2018). Alternatively, after obtaining this product, we can then take the geometric mean, as does the UNDP’s human development index (HDI) (UNDP, 2010). The advantage of the latter is the emphasis on the lower values, that is, countries with weaker performance on one indicator

> 25 We offer further discussion in Appendix 1, Part A.

27
|**Product category**|**average dwell time**|**# of containers**|**proportion(containers)**|
|---|---|---|---|
|Chemicals& Allied Industries|16,4|5945|13%|
|Foodstuffs|24,2|5744|12%|
|Plastics/rubbers|21,5|4883|11%|
|Machinery/Electrical|24,3|4773|10%|
|Stone/Glass|22,9|4036|9%|
|Metals|19,9|3589|8%|
|Textiles|19,9|3571|8%|
|Vegetable products|21,4|3430|7%|
|Miscellaneous|23,1|2646|6%|
|Wood & Wooden products|18,0|2431|5%|
|Mineral Products|18,0|2418|5%|
|Transportation|26,2|1623|3%|
|Footwear/Headgear|16,2|593|1%|
|Raw Hides, Skins, Leather & Furs|18,4|558|1%|
|Other|30,9|261|1%|

**Table 6 - Distribution of average dwell time with respect to commodity group** (Source: CAMEROONIAN CUSTOMS, 2009)

# **_Cargo clearance patterns and efficiency of third parties_**

To finish with the description of cargo dwell time patterns the operations of third parties such as C&F agents, shipping agents or shipping lines<sup>18</sup> that play an important role in the determination of cargo dwell time also needs to be examined. The specific context of the port of Douala is of importance to interpret the high dwell times observed and among these specificities one might quote the following aspects:

#  **C&F market concentration**

|**Market share**|**2007**|**2008**|**2009**|**2010[1]**|
|---|---|---|---|---|
|**Top 3 C&F agents**|18%|17%|18%|20%|
|**Top 7 C&F agents**|31%|30%|31%|33%|
|**Top 20 C&F agents**|57%|51%|56%|56%|
|_Total number of agents_|_145_|_151_|_156_|_162_|

**Table 7 – C&F market concentration** (Source: CAMEROONIAN CUSTOMS, 2009 – proportion of number of declarations)

[1] until Nov 1<sup>st</sup> , 2010

> 18 We use the acronym C&F agents in this paper to refer to all clearing and forwarding agents namely Customs brokers, Freight brokers, Freight forwarders, etc. NVOCCs and shipping agents will be referred to collectively as shipping agents. In addition, the two largest C&F agents have merged in 2008 but have been considered independent for consistency purpose (the two brands are still in use).

14
34 usually negative interest rates, and their default rates are extremely high. In addition, budget loans are less transparent transfers than conventional subsidies. Under the current fiscal management system, it is much easier for regional governments to hide information about particular recipients of budget funds from both the federal government and the local legislature when these funds are disbursed as 'budget loans" or 'other expenditures" instead of "expenditure on a particular sector".

_Table 21. Determinants of Budget Loans, 1992-95_

||**1992**|**1993**|**1994***|**1995****|
|---|---|---|---|---|
||**(N=46)**|**(N=59)**|**(N=63)**|(N=79)|
|**Pre-transfer regional**|.010|.065||.074|
|**revenue**|(7.3)|(6.8)||(9.5)|
|**Federal transfers**|||.028<br>(3.1)||
|**Household incomes**|-.010|-.012||-.005|
||(-2.6)|(-3.2)||(-2.0)|
|**Meat production**||||1.47|
|**(1994/1991)**||||(2.8)|
|**Wage arrears (1993)**||.100<br>(2.1)|||
|**Index of Industrial**|||.315||
|**Output**|||(1.7)||
|**Share of loss-making**|n.a.|n.a.|-.912||
|**enterprises**|||(-3.8)||
|**Unemployment level**|||-2.86<br>(-2.0)||
|**Constant**|6.29|-6.39|51.1|-36.7|
||(0.6)|(-0.7)|(18.4)|(-2.3)|
|**Adjusted R**<sup>**2**</sup>|.61|.47|.29|.61|

*Excluding Chukotsky AO and Koryaksky AO. **Excluding Chukotsky AO.

Pre-transfer revenue was the most significant determining factor of loans to enterprises in 1992, 1993 and 1995. The exception to this pattern, 1994, is the year when per capita spending on loans dropped from 34 to 26, and also the year least successfully explained by the regression analysis. Transfers are significant only in 1994.

Note that in 1994, various indicators directly linked to industrial production proved to be significant in the determination of loans to enterprises, with better-off regions providing relatively more in loans than regions that demonstrated greater need. Thus, the index of industrial output has a positive effect on loans, while the percentage of loss-making enterprises negatively affects loans (the two variables are in some sense negative images of each other.) Similarly, regions with higher unemployment spend less on loans to enterprises. The same effect is seen in 1995: regions less affected by agricultural decline (measured by the meat production index) granted more loans that year.
Maksimovic (2008) found patterns consistent with these conjectures. Specifically, they found small firms were more financially constrained than others, and that they were unable to use trade credit and other informal sources of credit to alleviate those constraints. While trade credit could, in principle, be used as a substitute for lacking external finance, in practice credit from suppliers represented a smaller percentage of the total investment of small firms than of other firms. In regressions controlling for other relevant factors, the ratio of supplier credit to total investment was highest among medium-sized firms, though the coefficient was not significant across all specifications. Still, these patterns are suggestive of greater reliance on trade credit by mediumsized firms.

In terms of countries, poorer ones and those with low levels of financial and institutional development may be less likely to rely on trade credit than others. Financial underdevelopment could mean that there are fewer funds (notably bank credit) to redistribute via trade credit, while less developed market-supporting institutions imply heavy reliance on relational contracting to support trade credit arrangements, which limits their reach as illustrated again in the country case studies. For their part, advanced economies tend to have more formal financial development and better-developed courts and formal institutions that support arms-length contracting. These advantages can help to expand trade credit, but again those advantages also translate into greater access to many (formal) sources of external funding, and thus trade credit may not feature as heavily as a source of firm finance in those contexts.

Recent evidence from Reyes, Roberts, and Xu (2021) is consistent with the idea that trade credit is associated with productivity growth, especially in middle income countries. Using data from the World Bank Economic Surveys (WBES) from 709 cities in 128 countries, those authors find a significant positive relationship between the city-industry average for accounts payable (scaled by total sales) and growth in labor productivity, after controlling for a comprehensive set of firm characteristics and descriptors of the business environment (including infrastructure quality, human capital, access to bank finance, barriers to firm entry/exit, labor regulation, and the tax environment). The positive link between the prevalence of trade credit and productivity growth is significant for lower middle-income countries, but not for the poorest countries (i.e., those with per capita incomes below $US1,000 measured in 2005), or relatively rich countries (those with per capita incomes above $US3,500). In addition, the significant link between trade 23
effect of the more exogenous corruption experiences question on confidence is always substantially smaller than the effect of the corruption perceptions question, consistent with the view that the former is less tainted by reverse causation.

Absent compelling instruments, we use an argument based on Leamer (1981) to provide a rough bound on the extent to which our estimates might reflect reverse causation. To make this concrete let y denote the portion of confidence that is orthogonal to all of the control variables, including the country fixed effects, in columns 2 and 4 of Table 3, and let x denote the same orthogonal component of corruption. The possibility of causal effects in both directions between corruption and confidence can be captured by the assumption that y and x are generated by the following system of two equations: (1) We are primarily interested in the slope coefficient  which captures the effect of corruption on confidence. However, we cannot identify this effect absent some instrument that shifts corruption without at the same time affecting confidence, i.e. we need to find a variable that is included in the second equation but excluded from the first.

Absent such an instrument, the problem is simply that there are four unknown parameters in this system ( , , and the two variances of the error terms), while there are just three moments in the data (V(x), V(y), and COV(x,y)).<sup>15</sup> However, we can still make progress by exploring how our estimate of would change given differing assumptions on the strength of the reverse causation captured by . To do this, express the three observable data moments in terms of the four unknown parameters, and then solve for conditional on a value of . Then by varying we can explore the robustness of our

> 14 An alternative approach sometimes used with survey data is to use the average of the corruption question across all observations within a pre-specified group, for example all respondents in the same city, as an instrument for corruption.  This is plausible as an identification strategy only to the extent that we think that the unexplained portion of confidence is uncorrelated across respondents within a group.  This assumption is difficult to justify in practice.

> 15 In fact things might be even more complicated, as we have assumed for simplicity that the covariance between the two structural errors is zero as well.  We justify this simplifying assumption by observing that in Table 3 we have already controlled for a large set of variables that might simultaneously be driving corruption and confidence.  Thus it is more plausible that the errors in the orthogonalized system here are independent.

21
and a flat benefit in Argentina. Each country restricted<sup>payout arrangements.We focus on</sup> urban workers, because social security coverage in<sup>ruralareas is very limited.</sup> 113riefr descrAijfons of tlhe new systems _Chile._ In 1981 Chile replaced a mature traditional government-run<sup>pay-as-you-go</sup> defined benefit system with a new multi-pillar<sup>system thatincludedadefined</sup> contribution pillar buttressed by a public pillar in<sup>theformofaminimumpension</sup> guarantee (M1PG). The old system was insolvent, having<sup>promised benefits that exceeded</sup> contributions, with the disparity differing across<sup>individualsinunevenwaysthat</sup> encouraged evasion. The object of the reform was to<sup>makethesystem largelyfunded</sup> (except for the MPG), and therefore fiscally sustainable;<sup>tolink benefitsmore closelyto</sup> contributions, thereby reducing the tax element and<sup>theincentivetoevade;and to make</sup> the redistributive element very explicit and<sup>targeted.</sup> A new system of private pension funds (AFP's)<sup>was started,that competed for the</sup> mandatory payroll contributions of workers. These contributions<sup>are10%of payrollfor</sup> investment plus about 3% for administrative fees<sup>andrequisitepremiumsfor disability</sup> and survivors insurance (all data on administrative<sup>and insurancecostsare from Jameset</sup> al 2000 and 2001). Affiliation with the new<sup>system ismandatoryfor newemployeesin</sup> the labor force, voluntary but encouraged for workers<sup>already in the laborforceatthe</sup> date of the reform and for the self-employed.<sup>Almostallemployees are nowin thenew</sup> system. Upon retirement (age 65 for men, 60 for women),<sup>workers candrawupon their</sup> accumulated savings in the form of gradual withdrawals<sup>thatarespreadoverboth</sup> spouses' lifetimes or an annuity that had to be joint<sup>for marriedmen.(Earlier retirement</sup> and lump sum distributions are permitted if<sup>theaccountbalanceislargeenoughto</sup> finance an annuity that exceeds a specified floor).<sup>Allmediumandlong-term financial</sup> transactions, including annuities, are price-indexed<sup>inChile, andmany indexed</sup> instruments are traded. Those who have worked<sup>atleast20years areguaranteeda</sup> minimum pension (MPG) that is financed from general revenues.<sup>TheMPGisnot</sup> formally indexed but so far has risen over time faster<sup>than prices,on par with wages,on</sup> an ad hoc basis. It is based purely on the individual's<sup>own pension, without taldng other</sup> income or family circumstances into account.<sup>5</sup> 9
available. Instead, these data were provided to us at the two digit industry level, by state and by employment size. There are as many as 12 size bins in each state/industry. These are 0-2 workers, 3-5 workers, 6-10 workers, 11-15 workers, 16-20 workers, 21-30 workers, 31-50 workers, 51-100 workers, 101-250 workers, 251-500 workers, 501-1000 workers and 1001 or more workers. So, for example, an observation in the data we obtained from INEGI is then the number of firms employing 6-10 workers in the textile industry located in the state of Jalisco.

We obtained from INEGI both the data for all firms and the data for domestic firms, those for which no part of the capital is foreign owned. As we are interested in domestic institutions, we focus on the data that exclude foreign owned firms. Results including the foreign firms are similar to those we report below. We eliminate several industries that are dominated by government-owned firms: oil and gas extraction, coal mining, water and electricity. We also eliminate the fishing industry, both because the industry remains dominated by cooperatives established with significant government assistance and because the regional location of fishing is determined by geography. Finally, the census data do not include firms involved in agricultural production, though agricultural processing firms are included. There are 32 states and 25 two-digit sectors, resulting in 800 potential state/sector data points. However, some sectors have no employment in some states, so for most state/industry regressions we have about 740 observations.<sup>9</sup>

> 9 One drawback of the data is that they are derived from plant level data. Our framework and most of the theories explaining firm size distributions refer to enterprise level data rather than plant data. Unfortunately, there is no way to aggregate the data at the enterprise level, and even if we could, it is not clear what institutional measures we would use for firms operating in multiple states. We discuss this issue further below.

18
Product market regulation (PMR) is measurable through a methodology developed at the OECD relying on the OECD regulatory indicators questionnaire. The methodology and key findings of the PMR for OECD countries are presented in Nicoletti et al. (1999) and Conway et al. (2005). The PMR indicators summarize information on economy-wide and industry-specific regulatory provisions.

The PMR indicators are designed to reflect regulations that have the **potential to restrict competition** in areas where competition is viable. By construction, they have a number of features which make them useful not only for analysis, but, more importantly, for policy advice, since they allow to pinpoint specific policies that hamper competition in product markets. First, PMR indicators are focused on enacted **policies** and not on outcomes, implying that they are ‘ **objective** ’, in that they are not based on opinion surveys. Second, since the summary PMR indicator is constructed as the average of well defined components, PMR scores can be related to **specific underlying policies** , thus providing precise inputs in the phase of policy recommendation. Finally, PMR indicators focus on regulatory measures that affect the economy at large and can therefore be considered as **comprehensive** measures of regulatory restrictiveness. Their advantages notwithstanding, PMR indicators are not designed to capture **informal** regulatory practices nor the effective **enforcement** of regulations, since they are only concerned with formal compliance with a number of criteria.

Data were collected for Bulgaria for the purpose of the present exercise, covering four of the six “sections” of the OECD product market regulation database.<sup>2</sup> The first section deals with general regulatory policy issues, concerning public ownership; market access and competition issues; market structure and vertical relationships in utilities and other network industries. The second section covers regulatory and administrative policies, such as processes and capacities in the public administration. The third section covers regulation in transportation industries, focusing on market access, business conduct, and market structure in road freight, railways and passenger air travel. The final section covers regulation in retail distribution, focusing on the regulatory environment, industry behavior and prices. Information from Doing Business 2006 was used for a fifth section of the PMR - administrative burdens on startups.

The structure of the PMR system is shown in Figure 1. The system is composed of 16 basic or ‘low-level’ indicators, each capturing a specific aspect of the regulatory regime as described in Box 1. The basic indicators are progressively aggregated in more comprehensive policy areas. The highest level of aggregation corresponds to the summary measure of product market regulation in the country.

> 2 The OECD questionnaire adapted for Bulgaria is provided in the Annex. For a comprehensive reference see www.oecd.org/eco/pmr.

5