rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 7:** Employment gap and output gap: Philippines

<!-- Start of picture text --> 4 0.03<br>3 0.02<br>2 0.01<br>1 0<br>0 -0.01<br>-1 -0.02<br>-2 -0.03<br>-3 -0.04<br>1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1<br><!-- End of picture text -->

**Figure 8:** Employment gap and output gap: Taiwan, China

<!-- Start of picture text -->
1.5 0.08<br>0.06<br>1<br>0.04<br>0.5 0.02<br>0<br>0<br>-0.02<br>-0.5 -0.04<br>-0.06<br>-1<br>-0.08<br>-1.5 -0.1<br>1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1<br><!-- End of picture text -->

_Source:_ See Table 1.

To investigate whether this visual relationship upholds empirical testing, the analyses above were re-run for the sub-sample of countries using data for agricultural and non-agricultural employment. In fact, for these countries, the data are available from 1996. This means that two crises can be included: the economic crisis of 2008-2011, but also the East Asian crisis of 1997 to 2000. It should be noted that the OLC’s presented in Table 5 include two quarters. They thus differ slightly from the short-run effects in the tables above. The corresponding one-period effects convey a similar same message though the estimates are murkier given the high variation during economic crises – for this reason the two-period estimates are presented in the table.

Table 5 conveys a number of interesting messages. First, by removing agricultural employment from total employment the OLC estimates are pronounced for both Korea and Taiwan, China. This contrasts with Table 4 and suggests that, when refining the analysis, there is a relationship between growth and jobs even in countries where the overall OLC is weak. Second, Table 5 conveys no convincing evidence that the OLC differs considerably in times of crisis and times of calm – at least with respect to nonagricultural employment: in Korea, the magnitude of the non-agricultural OLC fell consistently over the periods of study; in the Philippines it was pronounced in the Asian crisis but not detectable in other periods; and in Taiwan, China the OLC was most pronounced in the period of calm. This supports the message from Table 4 that there is no compelling evidence that the OLC differs substantially across the business cycle.

Finally, Table 5 provides some evidence for the argument that agricultural employment functions as a shock-absorber in East Asia. In times of crisis, the agricultural OCL is negatively signed, suggesting that agricultural employment responds to growth counter-cyclically. The effect is visible in all three countries in times of crisis but not during periods of clam. Thus, agriculture appears to be a fall-back option for some workers who lose their jobs in the ‘modern’ sector during an economic contraction. To the extent that the data on agricultural employment can be understood as a proxy for informal employment, the results suggest that a similar counter-cyclical relationship may hold in the informal sector, which presents an interesting avenue for future research.

10
periodic breaks. Table A-5 lists the resulting set of indicators and their sources.

We retain 14 distinct environmental indicators, covering different types of pollution, habitat preservation, and forest protection. Three come from the World Bank World Development Indicators: the ratio of CO2 emissions in kg to GDP (in PPP $), forest area (% of land area) and SO2 emissions. Eleven additional outcome measures are sourced from the EPI data. These include two measures related to Climate Change mitigation: black carbon intensity and greenhouse gas emissions (measured in terms of average annual rate of increase); three indicators related to Environmental Health (PM2.5 exposure, NOx Intensity, and Ozone Exposure, all measured using the number of age-standardized disability-adjusted life-years lost per 100,000 persons due to exposure to ground-level ozone pollution); and six measures of Ecosystem Vitality - a protected areas index (the proportion of biologically scaled environmental diversity included in a country’s terrestrial protected areas, with higher values reflecting better performance), the species habitat index (the proportion of suitable habitat within a country that remains intact for each species in that country relative to a baseline set in the year 2001), the sustainable nitrogen management index measuring the efficiency in application of nitrogen fertilizer with maximum crop yields (higher values indicating worse performance), a species protection index measuring the species-level ecological representativeness of each country’s protected area network, tree cover loss (five-year moving average of the percentage of forest lost relative to forest cover in 2000), and wetland loss (quantified using a five-year moving average of the percentage of gross losses in wetland areas relative to a 1992 baseline). For EPI indices, a higher value indicates (sometimes counter-intuitively) better performance with respect to the outcome of interest.<sup>8</sup> For labor protection we use two outcome variables: the _Mosley-Uno labor rights_ indicator, a measure of collective labor rights, and the _QOG worker rights_ indicator, a composite index measuring freedom of association in the workplace, the right to bargain collectively and a prohibition on the use of any form of forced or compulsory labor; a minimum age for the employment of children; and conditions of work with respect to min-

> 8See further details on the dataset at `https://epi.yale.edu/.`

7
**Figure 4: Lorenz Curve and Inequality Coefficients by Region using NPS**

<!-- Start of picture text --> Lorenz Curve by Area Lorenz Curve by Area Lorenz Curve by Area<br>Tanzania 2008-2009 National Panel Survey Tanzania 2010-2011 National Panel Survey Tanzania 2012-2013 National Panel Survey<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumulative population proportion Cumulative population proportion Cumulative population proportion<br>Dar Essalam Other Urban Dar Essalam Other Urban Dar Essalam Other Urban<br>Rural Line of perfect equality Rural Line of perfect equality Rural Line of perfect equality<br>2008/09 2010/11 2012/13<br>Gini p90/p10 Income shares Gini p90/p10 Income shares Gini p90/p10 Income shares<br>Low. Top Low Top Low Top<br>quintile quintile quintile quintile quintile quintile<br>Mainland 38.2 5.2 6.9 45.6 38.6 5.4 6.8 46.1 40.8 6.2 6.1 47.4<br>Rural 31.9 4.1 8.2 40.5 32.7 4.2 8.0 41.0 35.2 4.7 7.3 42.8<br>Other<br>36.4 5.3 6.6 43.8 36.2 4.9 7.3 43.5 36.1 5.1 6.9 43.3<br>urban<br>Dar es<br>35.7 5.4 6.7 42.1 33.4 4.5 7.8 41.4 32.7 4.1 8.1 40.9<br>Salaam<br>1 1 1<br>.9 .9 .9<br>.8 .8 .8<br>.7 .7 .7<br>.6 .6 .6<br>.5 .5 .5<br>.4 .4 .4<br>.3 .3 .3<br>.2 .2 .2<br>.1 .1 .1<br>0 0 0<br><!-- End of picture text --> _Source:_ NPS 2008/09, 2010/11 and 2012/13.

# **C. Distributional pattern of growth**

Using changes in household consumption as the measure of growth, this section examines whether both NPS and HBS support the emerging signs of “pro‐poor” growth during the recent years. The Growth Incidence Curve (GIC) for HBS 2007–2011/12, which shows the percent change in average consumption for each percentile of the distribution, is downwardly sloped, indicating higher growth among the poorest (Figure 5). However, the pattern of real consumption growth using HBS differs from that using NPS as indicated by the upwardly sloped GIC for NPS 2008/09–2012/13 which suggests that the richer groups were the main beneficiaries of growth.

7
**t**

## Figure 14

# GHANA

### Revenue from Cocoa sales

<!-- Start of picture text --> 1977 Cedis/MT (Thousands)<br>6<br>4<br>c)<br>-2<br>-2 I I I I ' I " -1-- |<br>1974/75 1976/77 1978/79 1980/81 1982/83 1984/85<br>crop years<br><!-- End of picture text -->

**Producers Standard Cost LI Excess Costs Profits, taxes**

Standard Board costs **are** 1950-60 **average**
may be unwilling to respond to a follow-up. Therefore a listing stage was done which did not involve talking to the firm owner.

# **4.1. Listing Survey**

We started with a list of all 2,563 census blocks in Belo Horizonte. Using information from official government lists of registration, as well as a list of partially informal firms (firms who had acquired a license to display a sign outside the firm, but had not registered for a municipal or tax license according to the databases), we found that the number of formal and informal firms were reasonably highly correlated across blocks (0.67), reflecting that some census blocks are residential neighborhoods with few firms and others have more firms. Based on this, we dropped census blocks with below the median number of formal firms (11 formal firms) since these were likely to be mostly residential. This dropped 1,236 blocks. We then also dropped blocks in the top 5 percent of formal firm density, since high density blocks indicated high rise buildings with mainly formal firms and in which surveyors would not be able to enter without permission. We then stratified the remaining 1,260 blocks by sub-district, and randomly selected 600 census blocks to be listed, along with substitutes to be used in case some of the census blocks did not contain any informal firms.

The survey firm Gauss Estatística & Mercado was then hired by the Minas Gerais government through a public procurement process to undertake this listing survey. Listing consisted of enumerators visiting every firm operating out of a fixed building in the census block. It excluded individuals operating informally on the street since our interest was in larger informal firms, and excluded transportation firms since the rules for formalizing are different for them. Enumerators recorded basic information about the firm that could be observed without talking to the firm owner – the full street address, the business sector, the “fantasy name” of the firm (the name on a sign outside the firm if they had one), whether or not the business had a sign, the approximate area in square meters of the premises, and the approximate number of employees in the business. A photo was also taken of the firm to aid in subsequent identification.

Through this process more than 10,000 firms were listed during January and February of 2011 (appendix 3 provides a timeline). They were then matched by Gauss against two databases of formal firms – a database from PBH of 140,628 firms with municipal registration, and a database from JUCEMG (the Chamber of Commerce) of 117,350 firms with state registration. This was used to eliminate the “definitely formal” firms, i.e. firms who appeared on both of these lists, giving a sample of 7,852 listed 11
each case, the use of different PPP conversion factors produced a significant re-alignment of the regional profile of poverty.

The release of the 2011 PPP data places us once again in the position that new PPP data are changing our perception of global poverty. The 2011 data suggest that developing countries’ incomes in PPP-adjusted dollars are significantly higher than indicated by the 2005 data. This has created the anticipation that new PPP data would lead to a large decrease in the global count of poor in the world. To highlight the change, commentators in the press and bloggers seem to now be making the erroneous assertion that adopting the new PPP data will extract millions of people from poverty overnight.<sup>5</sup> Their true standard of living is unchanged whether the 2011 PPP data are used or not. The issue is whether the new PPP data improve the correct assessment of who is poor, as judged on a common threshold for cross country comparisons. Of course, for the World Bank and other international organizations, this is not purely an analytical exercise but issues of communication are also concerns.

The presumptive trade-off is to use the new PPP data and run the risk of being perceived as conveniently manipulating the data to achieve the poverty goal, or break the methodological approach in how the poor are counted in order to keep the target as challenging to reach as when the 3 percent goal was announced. However, our analysis suggests that this concern may be a false trade-off. This paper presents evidence that if the global poverty line based on the 2011 PPP data is re-estimated based on the same set of national poverty lines that define the $1.25 line in 2005 PPPs, the revised 2011 global count of the extreme poor increases slightly, and the path to ending poverty by 2030 continues to be ambitious.

> 5 These assertions are presumably not intended to be taken at face-value, but are made to purposely highlight how sensitive our understanding of poverty rates is to underlying data and assumptions.

5
||**Sub-domain: Political**||
|---|---|---|
|**Presence of rules (institutions)**|**Enactment of rules (institutions)**|**Existing data**<br>**sources/indices**|
|_Formal_<br>•Number of international instruments<br>and conventions on civil and political<br>rights (including the right to<br>participate) ratified (3)<br>•Index of Political Rights (1)<br>(constitutional support for free, fair<br>and regular elections; accountability<br>of monarchy)<br>•Index of Civil Liberties (1) (freedom<br>of association and political<br>organization)<br>•Civil Society Index (2) (freedom of<br>association and political organization)<br>•Number of laws/Acts protecting<br>freedom of association and political<br>organization (4)<br>•Number of formal rules of<br>inclusion/exclusion in political life<br>(e.g. India: formal rules for<br>percentage inclusion)|_Formal_<br>•Index of Political Rights (1) (fair electoral process;<br>elected representatives endowed with real power;<br>effective opposition parties; freedom of association<br>enforced)<br>•Civil Society Index (2) (freedom of expression<br>enforced)<br>•Index of Civil Liberties (1) (freedom of expression<br>enforced)<br>•Number of cases alleging discrimination filed per<br>year<br>•Number of cases alleging discrimination won per<br>year<br>•Informal (cultural)<br>•Number of people influenced by tribal/religious<br>leaders in their voting choice per election<br>•Number of reported cases of local elites using<br>informal hierarchical power relationships as form of<br>social control per year<br>•Number of women participating in political<br>processes per year<br>•Number of people from ethnic/religious minorities<br>participating in political processes per year<br>•Number of women in positions of political influence<br>per year<br>•Number of people from ethnic/religious minorities in<br>positions of political influence<br>•Number of reported cases of local feudal or patron-<br>client power relations per year<br>•Number of private armed groups operating per year|(1) Freedom House<br>(2) CIVICUS<br>(3) Office of the UN High<br>Commissioner for Human<br>Rights<br>(4) US State Department –<br>Country Reports on Human<br>Rights Practices<br>Where no data source is<br>identified, data must be<br>collected using the national<br>survey or in-depth research<br>techniques.|
||**Sub-domain: Service Delivery**||
|**Presence of rules (institutions)**|**Enactment of rules (institutions)**|**Existing data**<br>**sources/indices**|
|_Formal_<br>•Number of laws/acts ensuring<br>freedom of information (1)<br>•Number of international instruments|_Formal_<br>•Number of national data systems accessible to the<br>public as percentage of total number of data systems<br>•Percentage of real annual budget allocation in line|(1) Privacy International –<br>Country Reports<br>(2) Office of the UN High<br>Commissioner for Human|

67
## _Background publications_

- Miller, K., R. Miller, A. Askew, M. C. Horn, and L. Ndhlovu. 1998. "Clinic-based family planning and reproductive health services in Africa: findings from situation analysis studies." Population Council.

- Miller, R., K. Fisher, K. Miller, L. Ndhlovu, I. Maggwa, A. Askew, D. Sanogo, and P. Tapsoba. 1997. The Situation Analysis Approach to Assessing Family Planning and Reproductive Health Services: A Handbook. New York: Population Council.

# _RAND_ **_Surveys_**

RAND has supported the design and implementation of Family Life Surveys (FLS) in developing countries since the 1970s. Currently available country surveys include Malaysia (1976-77, 1988-89), Indonesia (1993, 1997, 1998, 2000), Guatemala (1995), and Bangladesh (1996). Here, the Indonesia and Bangladesh surveys are discussed in further detail.

## **The Indonesia Family Life** Survey

The Indonesia Family Life Survey (IFLS) is an ongoing, multi-topic longitudinal survey. It aims to provide data for the measurement and analysis of a range of individual- and household-level behaviors and outcomes. The survey has collected data at individual and household level, including indicators of economic well-being, education, migration, labor market outcomes, fertility and contraceptive use, health status, use of health care and health insurance, intra-household relationships, and participation in community activities. In addition, community level data are collected. These include detailed surveys of service providers (schools and health care providers) in the selected communities. The first wave of the survey (IFSL1) was conducted in 1993/94, covering approximately 7000 households. The IFLS2 and IFLS2+ were conducted in 1997 and 1998, and a further wave (IFLS3) was planned for 2000.69 Re-interview rates of over 90 percent were achieved in the second and later waves of the survey.

In respect of the health facility survey, visits to local health care providers were carried out, and staff representatives were interviewed about the staffing, operation, and usage of their facilities. The surveys covered (i) government health centers and sub-centers; (ii) private clinics and doctors, midwives, nurses, and paramedics; and, (iv) community health posts. For each community, up to 3 government health centers, 6 private clinics, doctors, etc., and 2 community health posts were surveyed. Health care providers were selected on the basis of information provided by household respondents about where they normally seek care.

Different questionnaires were used for the respective providers to reflect differences in organization and scope of services. In general, the questionnaires collected data on availability and prices of services, lab tests, and drugs; availability of equipment and supplies; direct observations about the facility's cleanliness and other features that might influence its attractiveness to patients. In addition, five hypothetical patient scenarios or vignettes were

> 69 The IFLS2+ covered a sub-sample (25 percent) of households and was designed to assess the effects of Indonesia's economic crisis.

34
Table 11 Theil's Inequality Coefficients and their Compositions 1981 - 1986 (Dynamic)

|VariableB|T|B|V|C|
|---|---|---|---|---|
|Xa<br>|0.0522<br>|0.3846|0.0000|0.6154|
|Xi<br>|0.0885<br>|0.5569<br>|0.4210|0.0221|
|Xs<br>|0.0465<br>|0.0014<br>|0.0165<br>|0.9821|
|Ya<br>|0.0536<br>|0.7323<br>|0.1793<br>|0.0884<br>|
|Mi|0.0627<br>|0.8964|0.0009|0.1026|
|Mo|0.1085|0.9293|0.0333|0.0373|
|Ms<br>|0.1395<br>|0.7813|0.1010|0.1177|
|Qa<br>|0.0724<br>|0.9717<br>|0.0183<br>|0.0100<br>|
|Qi<br>|0.0409<br>|0.7141<br>|0.1473<br>|0.1386<br>|
|Cp<br>|0.0119|0.9297|0.0426|0.0276|
|ip|0.1532|0.9841|0.0060|0.0099|
|T|0.1094|0.758¶1<br> <br>|n.'216<br>|0.0195|
|Pd<br>|0.016<br>|0.009?<br>1<br>|<br>13:15<br>|0.5142|
|Pda|0.0275|0.1399|(.(008|0.8593|
|Pdi|0.0234|0.0000|0.4431|0.5563|
|I'M|to.0076|0.0003|0.1453<br>|0.3544|
|Rd<br>|0.0122<br>|0.1319<br>|n0.s;Si<br>|0. '930|
|F<br>|0.0746<br>|0.0125<br>|ii.027:3|0.9602|
|Hf|0.0546|0.2266||**0.**IX18<br>64R6|
|z|0.0239|0.8173|0.0038|0.1789|
|DD|0.0582<br>|0.4573|1<br>z.|I. :,1Ol|
|TD<br>|0.0272<br>|0.7744|0.0800|0.1456|
|SD|0.0504|0.1345|0.425(i|o.1398|
|L|0.0894|0.6023|0.0014|0.3963|
|GBb|0.0311|0.3140|0.0069|0.6790|
|Fa<br>|0.0370<br>|0.1182|0.0006|0.8813|
|Fl<br>|0.0524<br>|0.0242<br>|0.6888<br>|0.2870|
|CAb|0.0498|0.0022|0.2016|0.7962|
|B|0.0441|0.3179|0.0024|0.6797|
|D<br>|0.0263<br>|0.8120<br>|0.0216|0.1665|
|CAp<br>|0.0600<br>|0.2909<br>|0.0463<br>|0.6628|
|GBp<br>|0.0449|0.0064|0.6328|0.3608|
|Hb|0.1141|0.0523|0.4196|0.5281|
|X|0.0563|0.6315|0.1080|0.2605|
|XN|070588|0.6400|0.1363|0.2237|
|M<br>|0.0719<br>|0.9536|0.0104|0.0360|
|MN|0.0709|0.9599|0.0079|0.0322|
|TBD|0.1430|0.7277|0.0215|0.2508|
|CAD<br>|0.2215<br>|0.8831|0.0037|0.1132|
|Y<br>|0.0247<br>|0.9057<br>|0.0008|0.0935|
|Dg|0.2578|0.7589|0.0031|0.2381|
|Pdh|0.0140|0.2666|0.0132|_!i.i?02_|

Not I '1 wil ie-equality coefficients. B= Fraction of error due to I3ias. It , ;, I ^I. cticde to Different Variation. C Fraction of error due to Different Covariation.
large firms. The share of total employment in large firms grows by around 3 percent, while both medium and small firms face a drop in their employment share.

**Experiment 2.** In the second counterfactual experiment, we examine what happens in Guinea when the detection probability can be improved by 50 percent while the distribution of losses stemming from the business environment is left untouched. When the detection probability is increased from 0.0634 to 0.0951, sales not reported for tax purposes decline from 53.73 percent to 43.88 percent, corresponding to a 18.34 percent drop in firms’ tax evasion in Guinea. The improvement of the institutional quality raises the expected costs related to tax evasion activities as monitoring and auditing increase. Consequently, the incentive for firms to underreport their sales for tax purposes declines.

In this tax neutral experiment, the output per worker is nearly 6.33-fold as large as the initial output per worker of the country.<sup>11</sup> Compared to the baseline findings on Guinea, a 50 percent improvement in the institutional quality causes the aggregate TFP to rise by 208 percent and boosts the entry of firms by 173 percent. Similarly, the aggregate capital increases by 173 percent. These important changes in aggregate TFP and the entry of firms might explain the substantive growth in output per worker. This experiment facilitates the emergence of medium firms as their share in the distribution of firms increases by 8.71 percent while the share of small firms decreases by 0.98 percent for a constant share of large firms. The evidence is consistent with the literature on the missing middle in the size distribution of firms in developing countries, where the quality of the business environment is identified as

> 11Although the change in the output per worker might appear high, they are in line with similar evidence in the literature. IMF (2003) shows for instance that improving the quality of institutions in Cameroon (-0.72) to the average level of institutions in the sample (0.13) generates almost a 5-fold increase in income per capita in Cameroon. Rodrik et al. (2004) find that one standard deviation increase in institutional quality generates a 6.4-fold difference in income per capita. Similarly, Acemoglu et al. (2001) show that improving the quality of institutions in Nigeria to the level of Chile could lead to a 7-fold increase in Nigeria’s income. Also, Hall and Jones (1999) show in a cross-country regression that difference in institutions between Niger and the United States which is only 3.78-fold is more than enough to explain the 35-fold difference in output per worker. The contribution of Hall and Jones (1999), one of the most important in the literature, highlights how a small change in institutions can generate an exponential variation in output per worker.

27