rafmacalaba/datause-encoder-data huggingface dataset:
Figure 2a: Poverty Dynamics - Synthetic vs. Actual Panel Data by Sub-population Groups, Chile 1996 and 2006 <!-- Start of picture text --> Poor in 1996 and Poor in 2006 Poor in 1996 and Non-poor in 2006<br>40 70<br>60<br>30 50<br>40<br>20<br>30<br>20<br>10<br>10<br>0 0<br>0 10 20 30 40 0 10 20 30 40 50 60 70<br>Actual Actual<br>Non-poor in 1996 and Poor in 2006 Non-poor in 1996 and Non-poor in 2006<br>20 100<br>90<br>80<br>70<br>60<br>10 50<br>40<br>30<br>20<br>10<br>0 0<br>0 10 20 0 10 20 30 40 50 60 70 80 90 100<br>Actual Actual<br>Lower Bound Upper Bound<br>Estimation Estimation<br>Estimation Estimation<br><!-- End of picture text --> Data source: CASEN, 1996-2006.

Note: Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. The 45 degree line shows actual panel mobility. Upper bound estimations are based on 50 repetitions.

35
craft jobs that there have been more observational and experimental studies in developing countries — and generally the evidence from those settings is more positive than in OECD settings.

Among the observational studies, work analyzing the introduction of performance pay in the private sector finds nearly uniform support for the effectiveness of explicit incentive schemes. Observational studies of PRP for craft jobs in the public sector highlight that measurability of effort and output is more difficult in that environment and present slightly more mixed findings, but it seems the better output measurement and performance assessment are, the better performance pay works. The study of Brazilian

**Figure 4: Findings for craft and coping tasks by research quality and country context**

<!-- Start of picture text --> Relevant studies by quality (total 109) Findings for high quality (4 and 5) relevent<br>40 studies only<br>34 50<br>35 Failed Neutral Positive 44 Failed Neutral Positive<br>45<br>30 40<br>25 35<br>30<br>20<br>25<br>15 12 12 20<br>10 9 7 9 7 15 12 10<br>5 5 5 10<br>5 1 2 0 1 1 5 0 0 2<br>0 0<br>1 2 3 4 5 Craft Job Coping Job<br>1= lowest quality; 5 = highest quality<br><!-- End of picture text -->

**Table 3: Findings of high quality craft and coping studies by sector and country context**

|||**Craft**|**jobs**||**Copin**|**g jobs**|
|---|---|---|---|---|---|---|
||_Education_|_Health_|_Tax_|_Other_|_Public_|_Private_|
|**_OECD_**|**19**|**27**|**2**|**7**|**1**|**1**|
|_Positive_|12|16|1|6|1|1|
|_Negative or neutral_|7|11|1|1|0|0|
|**_Developing country_**|**6**|**4**|**1**|0|0|0|
|_Positive_|4|4|1||||
|_Negative or neutral_|2|0|0||||
|**Total**|**25**|**31**|**3**|**7**|**1**|**1**|

tax collectors is a good example of public sector work in which performance pay can be successfully implemented. Equally, Lavy's study of Israeli teacher competition (Lavy 2009) shows the ability of a well-designed performance scheme to function properly. At the same time, several observational studies identify problems with unintended consequences, generically subsumed under ―gaming‖ the incentive scheme, that can run counter to the original intentions of the reforms. With the current state of evidence though it remains unclear whether incidents of gaming have a net negative effect in the presence of increased productivity. Furthermore, while explicit incentive schemes certainly increase the opportunity for gaming, standard civil service arrangements have their own unintended incentive effects, i.e. employees will engage in behavior that increases the chances of easy work assignments or promotions and it is simply unknown whether existing forms of gaming are worse than similar behavior under performance pay.

Moving to studies that attempt to fulfill the gold standard of experimental design, the evidence overall again speaks in favor of the potential utility of performance pay for craft jobs. Comparing various laboratory experiments, the results suggest that indeed explicit performance incentives can work, but the studies employ easily measurable performance indicators and use fairly unrepresentative subject pools.

29
African countries.<sup>13</sup> Less than 10 percent of high income countries regulate or even have MFI institution as a category within regulatory framework.

# **_3.4 Summary Statistics_**

Table 6 presents the summary statistics of the main indicators used in our analysis. As already mentioned, data on commercial banks are the most comprehensive. The table also shows substantial variance among countries in the different indicators. The mean (median) of deposit account penetration in banks is 996 (702) accounts per thousand adults with 25<sup>th</sup> and 75<sup>th</sup> percentile values of 270 and 1498, respectively, while the average balance lies around $7,725 (3,070) USD. On average, the average balance represents 2.6 times the GPD per capita (median = 0.94). The mean (median) of loan penetration in commercial banks is 299 (215) loans per thousand adults. The 25<sup>th</sup> and 75<sup>th</sup> percentile values for such variable are 42 and 431, respectively. The number of loans and the number of deposits per thousand adults in commercial banks are positively correlated, with a correlation of 0.72. In contrast, the average number of accounts or loans per thousand adults is much smaller in cooperatives, government banks, and microfinance institutions; for cooperatives, for example, the mean of such figure is 129. While the number of observations is smaller, these figures are not driven by sample selection.

The average (median) number of branches of commercial banks per thousand adults equals 16 (13). The penetration of branches among the population is positively associated with deposit and credit penetration, with correlations of 0.45 and 0.44, respectively. In contrast, the equivalent association with the proxy for geographic penetration is a bit weaker, with correlations of 0.34 and 0.35, respectively.

> 13 BCEAO countries did not respond to the survey. Recent years have seen a large number of African countries develop national microfinance strategies including the concept of special regulatory windows for deposit taking MFI banks designed to encourage the sector. See, e.g. Duflos and Glisovic-Mézières (2008).

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enterprises, and growth in construction and hotels are a symbol of economic growth. Services are more in the nature of private hospitals, clinics, schools, hotels and restaurants. How persistent employment in these sectors will be in the face of macroeconomic slowdown remains to be seen.

**Table** **<u>5: Sectoral Composition of Urban Formal Enterprises</u>**

|**Industry**|**Percent Formal Enterprises**|
|---|---|
|Wholesale/Retail Trade & Maintenance/Repair|40.7|
|Construction|19.8|
|Health,Education & Other Social Services|17.8|
|Other|12.8|
|Hotels & Restaurants|12.4|
|Transport,Storage Communication|5.4|
|Mining, Quarry,Electricity,Gas|3.1|
|Manufacturing|2.5|
|Agriculture & Allied|0.8|

Source: UES, 2004.

25. When disaggregated by size of enterprise, we find a surprising homogeneity in sector of operation, with trading dominating every category of enterprise – from the micro to the large (Figure 6). Thus, the picture of a predominantly petty trading economy in urban areas emerges. Among significant sectors, it is construction and hotel business where a relatively greater heterogeneity appears. Thus, while only about 11 percent of the smallest enterprises are in the construction sector, over 21 percent of the small and medium enterprises are in construction. Similarly, while only about 8-9 percent of the micro (smallest) and small enterprises are in the hotel business, this proportion increases to over 17 percent for enterprises with a turnover of over USD 1000.

<!-- Start of picture text --> Figure 6: Sectoral composition of Urban Formal<br>Enterprises by Turnover<br>40<br>35<br>30<br>Micro<br>25<br>20 Small-Sm/Med<br>15<br>10 Medium<br>5<br>0 Large<br>Source: UES, 2004.<br>% of firms<br>Agric/mining Manufact Utilities Construct Sale/repair Retail trade Hotel & rest Trans/comms Finance SocServs/Other<br><!-- End of picture text --> 26. _(ii) Informal sector:_ Informal enterprises are almost entirely (93 percent) in trading and petty maintenance (e.g. auto, bicycle, small machinery repair). About 3 percent of informal enterprises are in the hotel and restaurant business and 2 percent are in services (see Figure 7).

13
occur at the end of the year are thus not detectable immediately. Three consecutive years of interviews are thus needed: year _t_ and year _t+1_ to identify all births that occur in year _t_ , and year _t-1_ to observe the mothers’ (and their partners’) characteristics over a certain period before potential procreation.

Around 40% of women are observed not only for three, but for four consecutive years. Women observed for four years who have not had a child in the second year are included twice in our database (two person-years: two years of potential child arrival). Allowing for two potential ‘events’ increases the number of observations. In order to avoid estimation bias due to unbalanced panel data (the number of observed years may influence the probability of observed child arrival), we include “second event fixed effects” for individuals observed for the second time.

To reduce endogeneity, we not only observe activity status before child arrival, but over a certain period _before potential procreation_ of a child. This is possible as the SILC data contains information about labor market status on a monthly basis as well as about the quarter of births of children. For those women with child arrival (test group), we observe their activity status during the three months before procreation. For those women without child arrival in _t_ (control group), we arbitrarily chose a three months period during the year for _t-1_ .

We define an activity status as “stable” if it does not change during the observed period of three months. The following categories are created for women’s activity status during three months before (potential) conception of a child:

- Stable employment (self-employed, employed, full-time, part time)

- Stable unemployment

- Stable inactivity

- Stable student

- Other (retirement, military service, any change in the activity status over the three month period<sup>10</sup> )

The event “first child arrival” is observed for 7% of women in our database, while second child arrival is observed for 12% and third child arrival for 3% (see table 1).

**table 1. The endogenous variable “child arrival” in Turkey**

||time<br>period (year of<br>childbirth)|<br>number<br>of<br>observations|number<br>of events|prop. of<br>events<br>|
|---|---|---|---|---|
|1st child arrival|2007‐2010|5570|368|0,066|
|2nd child arrival|2007‐2010|2621|321|0,122|
|3rd child arrival<br>Data Base: EU-SILC LT 2|2007‐2010<br>006-2011|3880|115|0,030|

> 10 This change is not reported in further subcategories as only a very small minority of women is represented by this group – see descriptive statistics in table A to C in the appendix).

15
and the 37% wage premium found by Rao et al. (2003) in Calcutta, and more in line with the much larger premium found by Booranapim and Mainwaring (2002) in Thailand.

The next few rows in Table 3 report mean sexual behavior in the sample. For each variable, Column 1 presents the overall daily average, Column 2 presents the daily average for the round 2 sample only (which we present since this is the only sample for which measures of condom usage are available separately for vaginal and anal sex), and Columns 3-5 includes transaction level data. Column 1 indicates that, over all the days covered (including those in which they did not work), women have vaginal sex on 74% of days, anal sex on 22% of days, and oral sex on 19% of days. Women have a large amount of unprotected sex: they have unprotected sex on 18% of days, and have an average of 0.42 unprotected sex acts per day. All of these ...gures are slightly lower in round 2, perhaps due to seasonal di¤erences in demand or other factors (see Column 2). From Column 3, women have vaginal sex with 95% of clients, anal sex with 24% of clients, oral sex with 19% of clients, and unprotected anal or vaginal sex with 17% of clients. Though the round 2 ...gures are lower, it appears that unprotected anal sex - which greatly increases the risk of HIV infection - is not uncommon (occurring on 2% of days in Round 2).

One important distinction between clients is those that are considered regulars and those that are considered casuals. Though this distinction is not completely clear, regular clients have had repeated encounters with a given woman and may be considered a boyfriend, lover, or partner. In focus group discussions, women often cite emotional support and love as characteristics associated with a regular client. In contrast, casual clients are often not known to the sex worker before the transaction. Since casual clients can become regular clients over time, and many women have multiple regular clients, the distinction between regulars and casuals can be hard to de...ne. However, since SHCP had already been using the regular and casual client terminology, we allowed respondents to determine on their own if a particular client should be classi...ed as a regular or casual.

Columns 4 and 5 provide transaction level data on services provided to regular and casual clients. The major di¤erences between regular and casual clients are in anal sex and in unprotected sex: women are slightly more likely to have anal sex with casuals (26% of the time, compared to 19% of the time with regulars), but are more likely to have unprotected sex with 13
|High risk||(12.406)<br>0.226||(13.333)<br>-5.145|
|---|---|---|---|---|
|||(13.152)||(13.918)|
|Very high risk||34.475||28.242|
|||(22.465)||(21.922)|
|**Experienced flooding on business premises**: Yes|||6.293<br>(8.638)|6.691<br>(10.804)|
|**Region**|||||
|Dar es Salam|-6.670|-13.724|-0.664|-16.643|
||(10.830)|(12.373)|(8.665)|(12.718)|
|Dodoma|79.724<sup>***</sup>|65.343<sup>***</sup>|92.392<sup>***</sup>|62.301<sup>***</sup>|
||(18.285)|(22.399)|(19.276)|(22.574)|
|Tanga|67.414<sup>***</sup>|55.627<sup>***</sup>|71.338<sup>***</sup>|55.797<sup>***</sup>|
||(15.270)|(18.100)|(14.952)|(18.281)|
|**Sector**(Accommodation and food service|||||
|activities, Construction):|||||
|Communication and other services|5.194|7.133|4.263|7.198|
||(8.537)|(8.970)|(8.692)|(8.939)|
|Manufacturing, Mining, Water, Energy,|26.134<sup>*</sup>|26.336<sup>*</sup>|25.572<sup>*</sup>|26.492<sup>*</sup>|
|Agriculture|||||
||(15.686)|(15.788)|(15.432)|(15.930)|
|Transportation and storage|37.905|37.963|35.906|38.542|
||(27.076)|(27.032)|(27.277)|(27.060)|
|Wholesale and retail trade, Repair of motor|55.073<sup>***</sup>|55.895<sup>***</sup>|55.734<sup>***</sup>|54.987<sup>***</sup>|
|vehicles and motorcycles|||||
||(12.431)|(12.482)|(12.672)|(12.259)|
|Observations|765|765|763|763|
|Controls|YES|YES|YES|YES|
|Log likelihood|-4,734.909|-4,733.514|-4,724.925|-4,720.550|

_Note:_ Controls include number of employees, firm age and time since last disaster. All coefficients refer to a standard linear regression model. Standard errors are clustered on the ward level and take sampling weights into account:<sup>*</sup> p<0.1<sup>**</sup> p<0.05<sup>***</sup> p<0.01

**Table D.4: Logistic regression results for probability of water tank ownership**

|||P(water tank<br>|ownership= 1)<br>||
|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|
|**Region**(Dar es Salaam)|||||
|Dodoma|0.063|0.059|0.161|0.444|
||(0.639)|(0.650)|(0.637)|(0.696)|
|Tanga|0.641|0.310|0.598|0.888|
||(0.483)|(0.555)|(0.558)|(0.571)|
|**Direct disaster risk perception**(no risk)|||||
|Low risk|0.701|||0.712|
||(0.651)|||(0.696)|
|Moderate risk|0.905|||1.174|
||(0.640)|||(0.786)|
|High risk|1.767*|||1.686|
||(0.969)|||(1.278)|
|Very high risk|0.979|||1.005|
||(0.798)|||(0.965)|
|**Indirect disaster risk perception**(no risk)|||||

42
**Table 24. Governance-related drivers of legal and regulatory obstacles: Public agency capacity**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0752|0.0659|||0.143|0.134|||
||(0.0591)|(0.0579)|||(0.108)|(0.115)|||
|Export % of sales|-0.00169**|5.49e-06|-0.00130*|0.000306|-0.00321**|-0.000151|-0.00254*|0.000329|
||(0.000767)|(0.000769)|(0.000782)|(0.000776)|(0.00139)|(0.00150)|(0.00146)|(0.00157)|
|Share of sourcing via imports|0.00182**|0.000989|0.00179**|0.000997|0.00322**|0.00194|0.00323**|0.00198|
||(0.000810)|(0.000767)|(0.000837)|(0.000791)|(0.00147)|(0.00149)|(0.00156)|(0.00158)|
|High-income source country|-0.207**|-0.0923|-0.220**|-0.101|-0.374**|-0.138|-0.402**|-0.163|
||(0.0853)|(0.0863)|(0.0862)|(0.0869)|(0.160)|(0.172)|(0.166)|(0.179)|
|>250 employees|-0.0549|-0.0492|-0.0704|-0.0678|-0.0814|-0.0757|-0.126|-0.114|
||(0.0766)|(0.0775)|(0.0762)|(0.0778)|(0.140)|(0.150)|(0.144)|(0.154)|
|Cumulative inv. >US$10M|0.111|-0.00978|0.0899|-0.0313|0.213|-0.0252|0.177|-0.0732|
||(0.0735)|(0.0724)|(0.0730)|(0.0724)|(0.136)|(0.144)|(0.138)|(0.148)|
|Years in country|-0.000680|-0.00141|-0.000227|-0.000852|-0.00127|-0.00420|-0.000194|-0.00289|
||(0.00236)|(0.00217)|(0.00227)|(0.00213)|(0.00423)|(0.00435)|(0.00429)|(0.00442)|
|Level of foreign ownership (%)|-0.00250*|-0.000383|-0.00261**|-0.000325|-0.00459*|-0.000866|-0.00492**|-0.00102|
||(0.00133)|(0.00139)|(0.00127)|(0.00131)|(0.00256)|(0.00288)|(0.00249)|(0.00280)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.024|0.139|0.053|0.163|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "In general, how much of an obstacle were the following factors in this country? [Capacity of public agencies]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

41
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