rafmacalaba/datause-encoder-data huggingface dataset:
**_Famly_** _P/ainnng Success_ **_in ho Cides In_** _Zaire_

_28_ waiting areas and in group meetings organized by churches, schools, offices, businesses, and neighborhoods. In addition, a weekly radio program answered questions and interviewed local residents about family planning and AIDS.

The purposes of the communication program were to publicize service delivery points and to give specific information on modem contraceptives, not to persuade people to limit child-bearing or vaguely to "plan their families." The information team included three full-time staff, plus several part-time persons with valuable community contacts and communication **s** kills.

The three Project activities were all underway by mid 1988, and service statistics reflected the intensified efforts. Whereas the two IMCK clinics had recorded fewer than 300 visits per clinic in 1987, this figure jumped to nearly 1500 in one facility and 3400 in the other in 1988 (Table 5). IMCK clinic visits remained at about the same level in 1989 and 1990. Meanwhile, nine other urban clinics were incorporated in the Project network and registered 1200 visits in 1988. They reached a peak of 2300 visits in 1989, representing one-third of the total clinic visits.

Table 6 shows clinical and social marketing output as Couple-Months-Protection (CMP) for the year preceding the Project and for the three Project years. Clinical services showed a fivefold increase to 37,000 CMP in the first Project year. Clinical CMP dropped slightly in 1989, then rose in 1990 to 42,000 CMP. Social marketing entered the Kananga scene in the second quarter of 1988; marketing sales provided only 5 percent of the total CMP in that year but rose to 22 percent in 1989 and 24 percent in 1990.

# C. Indicators **of** Success In Kananga

As of the 1982 contraceptive survey, 2.6 percent (± 1.2) of married women of reproductive age in Kananga used a modem contraceptive method. The 1987 baseline survey for the Kananga Project showed that prevalence had increased to only 4.1 percent (± 1.4). Project activities began in December 1987, 36 months later in December 1990, prevalence had reached 17.4 percent (±2.4).

Table 7 indicates that during the years of very low prevalence of modem contraception (1982-87), female sterilization was the method being used by the most women at the time of the survey (but even so, by fewer than 2 percent). In 1990, after three years of Project activity, women using pills and condoms were more numerous than women who had had a sterilization. NORPLANT, introduced in Kananga. in 1988, was already being used by more women than either vaginal tablets on IUDs.

Table **S.**

**Total Visits in Fandly Planning Clinics** of the Kananga Project Network In 1987-90

||1987|1988|1989|1990|
|---|---|---|---|---|
|IMCK Hospital<br>|171<br>|1,485|1,296|1,364|
|IMCK Polyclinic|275|3,379|3,233|3,292|
|Other urban clinics|60|1,200|2,261|1,167|
|Totals|506|6,064|6,790|5,823|
degree of informality. This is not surprising, given the financial constraints coupled with heightened uncertainty faced by informal firms during the crisis. Fully informal firms have a 25 percentage-point lower probability of investing in technologies and assets than formal firms.

_Fourth_ , the elasticity of job growth in informal firms with respect to sales recovery is significantly lower than that of formal firms. While informal firms are expected to have a lower probability of employment adjustment on both the extensive (lay-offs) and the intensive margins (cuts in salaries, benefits, and hours of operation), the result of lower responsiveness to employment growth illustrates that a higher share of informality could become a drag on the economy and contribute to jobless recovery post-crisis.

This paper makes two critical contributions to the literature on informality. _First_ , our unique and detailed data allows us to compute a multidimensional definition of informality. Using this measure, we quantify how financial constraints, uncertainty, and adjustment decisions vary in response to a shock by the margins of and motivations for informality. _Second_ , our detailed metrics on firm-level crisis response enables us to empirically test hypotheses and priors about informal firms in a crisis context, for instance, flexibility to make adjustments and, therefore, ability to rebound faster (Alfaro et al., 2020).

The rest of the paper is structured as follows. In Section 2, we explain the dataset’s characteristics, the construction and descriptive analysis of the main variables of interest, and the methodology implemented. Section 3 presents the main results of our analysis of the heterogeneity of firm-level constraints and decisions according to informality, and Section 4 concludes.

# **2. Data and Methodology**

# **2.1 Data**

To analyze the relationship between informality, financial constraints, uncertainty, and firm-level decisions, similar to previous studies like Apedo-Amah et al. (2020), Torres et al. (2023), Cirera et al. (2021), Avalos et al. (2022), Avalos et al. (2023), and Constantinescu et al. (2022), we rely on three rounds of data from the World Bank Business Pulse Survey (BPS).<sup>1</sup> In addition to the large set of variables analyzing firm performance, supply chain and international trade-related disruptions, financial fragility, and the different margins

> 1The Business Pulse Survey, which finished collecting data in 2022, includes data for around 150,000 firms in 87 countries.

4
13 in water scarcity across countries, Figure 1 presents the reduction in water per capita for Angola and for Austria (estimated β values are –49.12 and –2.49, respectively). Since our data on population includes both actual population for _t_ ≤ 2000 and forecasted levels of population for 2000 < _t_ ≤ 2050 the estimated scarcity level is both actual and perceived.

Values of α for countries sharing the same river are highly correlated and the same is true for the β values of river riparians. Therefore, we created principal component variables for various dataset specifications that include the information on the intercept ( α )— _Water scarcity intercept_ and on the slope ( β )— _Water scarcity slope_ . These variables incorporate the values of the intercept and the slope, respectively, of the two river riparians (Table 1). The hill shape behavior of the scarcity variables is captured via positive and negative signs for the linear and quadratic terms of _Water scarcity intercept_ and negatives signs for the linear and quadratic terms of _Water scarcity slope_ .

# **Governance**

We include variables in the analysis that measures the viability of institutions and governance in the countries that share the river so as to obtain a measure of domestic stability. We use a 7-year (19982004) average of the Corruption Perception Index (Transparency International, 2004). The notable features of this variable is that it is based on perceptional data in each of the river riparians, and uses a long-term average of each governance level measured as governance of country 1 and governance of country 2. A similar approach has been suggested in Kaufmann et al., (1999). We used two specifications for the governance variable. The first, _River riparians governance_ , is a simple summation of the value assigned to each riparian, country 1 governance and country 2 governance. Since both of these variables range from 1 to 10, the values of _River riparians governance_ range between 1 and 20, with higher values indicating better governance. The second governance variable introduces in the equation each country’s governance, _Country 1 governance_ , _Country 2 governance_ , and includes also an interaction term, _Country 1 governance_ × _Country 2 governance_ (appears as _Country 1_ × _Country 2_ ).

# **Trade**

We obtained two separate trade datasets. The first is the Direction of Trade Statistics (DOTS) Database IMFDOT that includes trade information for 184 countries for the period 1950-2004, in current US$. The second dataset is the United Nations Statistics Department (UNSD) dataset COMTRADE that includes information for 207 countries for the period 1962-2004 in current US$.
In order to disentangle the causality relationships between financial development, the investment to GDP ratio and GDP per capita growth, we apply a structural vector autoregression (SVAR) that imposes long-run structural restrictions on the relationship between the different variables (Blanchard and Quah, 1989). Specifically, we posit that the short-term real lending rate impacts financial development, the investment to GDP ratio and GDP per capita. Financial development, in turn, impacts the investment to GDP ratio and income per capita, while the investment to GDP ratio only impacts directly GDP per capita.<sup>7</sup> As the real lending rate is found to be non-stationary, first differences are applied for this variable. Annual data are used to estimate the regression system and lags of explanatory variables are included based on the Akaike information criterion making all residuals white noise.

The results in Table 3 suggest that financial development has a statistically significant long-run positive impact on both the investment-GDP ratio as well as on per capita GDP.<sup>8</sup> A one percent positive shock to financial development will generate a positive impact of about 0.15 percentage points on the investment to GDP ratio and a positive impact of about 0.22 percentage points on per capita income. The finding of a positive impact of financial development on per capita income even after controlling for the indirect impact through investment activity suggests that financial development enhances both investment and resource allocation. The estimated results also indicate that there is a positive and significant relationship between investment activities and per capita income. In the long-run a one percent positive shock to the investmentGDP ratio will generate a positive impact of about 0.22 percentage points on per capita income. The long-run response of financial development, investment and per capita income with respect to real lending rate changes, however, do not appear with the expected signs, as the results

> 7 Appendix 1 shows the exact derivation of this model.

> 8 Two other indicators for financial development, such as total deposits to GDP and M2 to GDP produce similar results regarding financial development—economic growth nexus.

5
subsidiary of a foreign company it is likely that the firm is part of a global supply chain; 3) productivity—foreign-owned firms are more productive and hence they are more likely to export.<sup>14</sup> Amongst the variables of interest, only ISO certification and the technological marketing tools are consistently positive and significant across all specifications. Firms that have ISO certification are 22% more likely to be exporters than firms that are not ISO certified. There is a close relationship of firms that have ISO certification and those that are able to export and therefore ISO certification can provide an important avenue for policy implementation. Having a website, on the other hand, increases the likelihood that the firm exports by 11%, whereas the use of email increases the likelihood of exporting by 9%.<sup>15</sup> The significance and high coefficient on email is probably picking up on the effect of the firm having access to the Internet.

The Enterprise Survey also includes the percentage of total sales from exports for each firm. This variable allows for the exploration of the importance of signaling and technological tools in the export intensity of each firm. For easier interpretation I use _ln(1 + % total sales from exports)_ as the dependent variable in the estimation of the tobit model. Table 7 presents the marginal effects conditional on the firm being an exporter. As with the probability of exporting, the productivity and size of the firm positively affect its export intensity. Foreign owned exporting firms sell 33% more in foreign

> 14 See Ferro (2010) for a more detailed discussion of the impacts of foreign ownership on productivity. 15 It is important to mention that out of the 17,535 firms that have their own website, 16,962 also use email, even though the correlation between the two variables is 0.57. Therefore, any positive marginal effect of using a website is in addition to that of using email. I tested for the inclusion of a variable that equals one if the firm uses both a website _and_ email instead of the reported variable, which equals one if the firm uses its own website. The results do not differ.

21
Table 2: Expectations and uncertainty

|||Expec|tations|||Uncer|tainty||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Conditional|(2)<br>Cont. initial<br>productivity|(3)<br>Conditional|(4)<br>Cont. initial<br>productivity|(5)<br>Conditional|(6)<br>Cont. initial<br>productivity|(7)<br>Conditional|(8)<br>Cont. initial<br>productivity|
||||**Panel A. B**|**aseline estimat**|**ions**||||
|Informality|-3.801**|-2.522|||2.877**|3.826**|||
||(1.630)|(1.984)|||(0.951)|(1.153)|||
||||**Panel B. By**|**type of inform**|**ality**||||
|Fully formal|||_Baseline_|_Baseline_|||_Baseline_|_Baseline_|
|Necessity|||-1.977*|-1.855|||1.828**|1.641**|
||||(1.111)|(1.229)|||(0.688)|(0.756)|
|De Soto|||-3.234**|-3.700**|||1.853**|1.709**|
||||(0.929)|(1.015)|||(0.600)|(0.650)|
|Parasite|||-4.224**|-4.514**|||0.751|2.020**|
||||(1.001)|(1.513)|||(0.615)|(0.858)|
||||**Panel C. By **|**margin of infor**|**mality**||||
|_PCA 1_|0.0475|0.983|||1.848**|2.628**|||
||(1.362)|(1.660)|||(0.793)|(0.950)|||
|_PCA 2_|-4.509**|-4.247**|||0.914|0.998|||
||(1.337)|(1.578)|||(0.786)|(0.928)|||
|Panel controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Initial productivity|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|13066|10391|13303|10501|13066|10391|13303|10501|

**N** otes: All continuous variables are winsorized at the 1% and 99%.

in sales across rounds of data collection.

Robust standard errors in parentheses

* p<0.10, ** p<0.05, ** p<0.01

-Expectations are higher for necessity firms with respect to the other two types of informal at the 10% level of significance in Panel B, cols. 3 and 4.

-Uncertainty is lower for parasite firms at the 5% level of significance in Panel B, col. 7, but no significant differences are observed in col. 8.

-The coefficients of PCA 1 and PCA 2 are statistically different at the 5% level in Panel C, cols. 1 and 2; and at the 10% level in panel C, cols. 5 and 6. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).
analysis (2014-2018). Each institution has its own mandate. This makes monitoring NTMs difficult, since there is no dedicated government institution to carry out NTM regulatory review and stocktaking (Munadi, 2019).

To overcome these issues, we use a novel comprehensive NTM data-base built by the World Bank on the basis of data collected by the Economic Research Institute for ASEAN and East Asia (ERIA) in collaboration with UNCTAD (Cal`ı et al., 2021). The UNCTAD/ERIA data identifies the stock of NTMs applied by Indonesia as of 2015. The data is cleaned extensively to address coding and interpretation errors and made time-varying by tracking the individual NTMs applied to each product before and after 2015.<sup>10</sup> The data uses the most disaggregated NTM classification available, i.e. 3-digit MAST level. This classification is the most appropriate for policy analysis as it identifies individual measures introduced or modified by each agency. A total of over 60 3-digit NTMs are available in the data set, each with values coded between 0 and 1 to signify when they were in effect, that is, the data varies at the month-product pair.

In addition, we complete the trade policy data with import tariff data collected by the UNCTAD Trade Analysis Information System (TRAINS) data set. For Indonesia this data includes yearly Most Favored Nations (MFN) import tariff at the HS 10-digit product level before 2017 and HS 8-digit level from 2017 onwards. We then convert all of the tariff data into the 10-digit used for the NTMs as well.

# **3.3 Japan: The main regional destination for Indonesian and Chinese exports**

We base the analysis on a single individual export market in order to make the empirical analysis manageable and to focus on a similar set of exporters facing the same shock. Several

> 10ERIA has updated the data and the latest one is 2018, where some of these coding errors may have been addressed. Cal`ı and Montfaucon (2021) details the process and the dissimilarities and coding errors corrected by the data. Additionally, Cal`ı et al. (2021) also provide more details on the construction of the data and the institutional context.

11
the reduction in returns to schooling and end up increasing the contribution made by education to within-tradeable sectors’ inequality. The same pattern is somehow shown in the years following NAFTA and the crisis, with the proportion of inequality attributable to educational endowments decreasing in the non-tradable sectors and increasing the tradable ones. This evidence is consistent with the results found by the trade-relative wages literature and also by other studies undertaking decomposition analysis.<sup>23</sup> Our results show that the sectoral redistribution caused by the combination of the peso devaluation and the enactment of NAFTA had as a consequence an increase in the proportion of inequality accounted for by skill endowments in the tradable sector (manufacturing and agricultural).

# **5 Summary and Conclusions**

The present study analyzes the underlying causes behind the high _levels_ of Mexican household income inequality during the 1990s. In order to capture the sectoral disparities observed during this period, total household income was divided into income derived from activities taking place in different sectors of the economy. Our discussion focused in the importance played by sectoral disparities, skill endowments, and their market returns to account for total income inequality.

The results from conventional income factor decomposition `a la Shorrocks (1982) shows how little we actually know about the causes behind the high levels of income inequality in Mexico. More than 50 percent of total income dispersion is accounted for by income derived from entrepreneurial activities and returns to financial assets. These are income sources with few structural interpretations and that nevertheless account for a large income proportion of household situated at the very top of the income distribution.

Trying to add more economic structure to the conventional non-parametric decompositions, we decompose income inequality using the semi-parametric approach developed by Morduch and Sicular (2002) and Fields (2003). We find that skill endowments accounted for, at most, 25 percent of total household income distribution in Mexico during the 1990s. Our results show that the proportion of inequality attributable to skill endowments increased during periods of macroeconomic stability and economic growth and reduced during the crisis. This pattern is, to a great extent, explained by

> 23A literature review on the trade versus relative wages studies can be found in Hanson (2003). Lopez-Acevedo (2000), De Hoyos (2005b) and Legovini (2005) undertake decompose household income distribution to analyse the importance of returns to schooling in the trade-inequality debate.

24
42

# Table (4.2) MAXMUM LIKELIHOOD PROBIT ESTIMATES OF THE STATUS PARTICIPATION EQUATION Sub-Saharan Africa

|Variable|<br>Coefficient|Std. Error|t-Stat|2-Tail Sig.|
|---|---|---|---|---|
|GDP2|-18.695|8.856|-2.111|0.043|
|GD121|-8.381|4.272|-1.962|0.059|
|DEBT21|<br>2.338|1.186|1.971|0.058|
|REER2|0.012|0.008|1.473|0.151|
|POL2|-0.452|0.205|-2.202|0.035|
|Note:|Log likelihood=-12.496; p|seudo R squared=0.47|||

'p~~~~~~~
# **Table 2. Forecasting Equations for First Differences in US Interest Rates 1992M5-2004M6**

||**Coefficient value**<br>|**s (absolute t-ratios)**<br>||
|---|---|---|---|
|**Explanatory**<br>**variables: changes**<br>**in logs of:**|**US Tbills**|**10-year Treasuries**|**Corporate Hi-Lo**<br>**Spread**|
|**Producer Price**||||
|**Lag 1**|**2.400 (1.29)**|**4.438 (1.68)**|**-4.359 (.76)**|
|**Lag 2**|**2.308 (1.23)**|**-2.677 (1.01)**|**4.176 (.73)**|
|**Lag 3**|**-2.716 (1.48)**|**-4.973 (1.90)**|**.356 (.06)**|
|**Retail Sales**||||
|**Lag 1**|**2.751 (1.95)**|**6.266 (3.13)**|**-9.099 (1.62)**|
|**Lag 2**|**5.643 (3.84)**|**6.826 (3.27)**|**1.745 (.39)**|
|**Lag 3**|**2.791 (1.99)**|**3.612 (1.81)**|**-2.408 (.56)**|
|**Capacity**<br>**Utilization**||||
|**Lag 1**|**10.124 (3.54)**|**5.524 (1.36)**|**4.291 (.49)**|
|**Lag 2**|**3.604 (1.24)**|**.425 (.10)**|**-3.769 (.42)**|
|**Lag 3**|**7.128 (2.38)**|**.707 (.16)**|**-11.098 (1.21)**|
|**M2**||||
|**Lag 1**|**-3.081 (1.37)**|**8.563 (2.67)**|**-12.034 (1.74)**|
|**Lag 2**|**-2.082 (.96)**|**-.547 (.18)**|**-1.761 (.26)**|
|**Lag 3**|**-3.601 (1.57)**|**1.201 (.37)**|**-3.812 (.54)**|
|**Constant**|**-.0324**|**-.1303**|**.1163**|
|**No. of obs.**|**146**|**146**|**146**|
|**R2**|**.3418**|**.1796**|**.068**|
|**p-value**|**.0000**|**.0000**|**.0000**|

20
ratios. Odds ratios represent the odds of a child death (for example) with improved sanitation relative to the odds of a child dying without improved sanitation: where _P(d)_ denotes the probabilities of a child dying with sanitation ( _S)_ and without sanitation (U), respectively. Odds are different from probabilities as they refer to the probability that an event occurs relative to the probability that an event will not occur, whereas probabilities only represent the probability that an event occurs. For small probabilities the odds (and the respective odds ratio) are, however, very similar to the corresponding probabilities.

We pool all relevant child-year observations in our regressions. The implicit assumption underlying this pooling is that the _marginal reduction_ in the mortality risk is the same for all children in the included age range (0-60 months). To adjust for differences in the _absolute mortality risk_ driven by different ages of exposure, we include child-age (cohort) fixed effects in our empirical model. In addition, we control for various child level characteristics that are commonly used as predictors for child mortality in the health literature: age, sex, whether the child is the first born, and time intervals (in month) between the preceding and succeeding births.

To deal with omitted variable bias concerns, we include an extensive list of control variables including mother’s age and education, mother’s marital status, household size, and urban or rural residence. DHS surveys do not contain any direct information on the income or consumption of households. To overcome the lack of a direct income measure, we include household assets as proxies for permanent income. The assets we use in our final specification are electricity, radio, TV, and bicycle. Most DHS surveys include an asset index based on principal component analysis as suggested by Filmer and Pritchett (2001). While these indices do well in describing the relative wealth ranking within a country, asset quintiles are hard to compare across countries with highly heterogeneous income and asset levels. We thus chose to directly control for a set of durable assets available in all surveys.<sup>12</sup> To avoid potential

> 12 Other potentially important control variables to limit omitted variable bias are indicators related to health preferences, such as the vaccination status of the child and whether the child was breastfed. The problem with vaccination and breastfeeding in cross-sectional studies is reverse causality: children who die at a very young age will never be vaccinated or breastfed. We could use the vaccination and breastfeeding status of living siblings to approximate or instrument the vaccination and breastfeeding status of dead children. The validity of such an approach is however, debatable, and the improvements achievable in the face of the resulting sample restrictions (identification can only be reached in households with at least two children) are uncertain.

12