rafmacalaba/datause-encoder-data huggingface dataset:
the parallel-trend assumption in difference-in-difference analyses).

## **Signs of a “bad” natural experiment (“bad IVs”)**

With these guidelines in mind, we highlight some danger signs of a “bad” natural experiment, one that does not make a credible case for the exogeneity of its key right-hand-side variation.

It is often difficult to make an _a priori_ case for rhetorical plausibilty when seeking to achieve causal identification using only cross-sectional data (data at just a single point in time for each observation). With cross-sectional data, the researcher cannot control for fixed effects for each unit (e.g., individuals or households). Inclusion of unit fixed effects in a regression helps control for time-invariant differences across units, but requires panel data (more than one observation per unit). Only in unusual cases can one make a case for rhetorical plausibility. An example is Shrestha (2017), who uses cross-sectional Nepalese data to analyze the impact of a change in an educational requirement for a migration opportunity varies plausibly exogenously across cohorts (birth-year groups) and localities.

Another danger sign is simply using one existing variable in a dataset as an instrumental variable (IV) for the key endogenous (right-hand-side) variable of interest, without a clear argument for the rhetorical plausibilty of the instrument. Examples of variables that have been used to instrument for the migration decision include network instruments, such as leave-one-out instruments (instrumenting own migration with that of others)<sup>5</sup> , or village lagged migration rates; being the oldest child in a household; the gender of oldest child; parental education; proximity to transport; and proximity to remittance services. These variables are often found to be correlated with migration (or related outcomes like remittances), and thus are argued to have a strong “first stage” in IV estimation. But each one is quite likely to violate the IV exclusion restriction: the assumption that the instrument affects the second-stage outcome of interest only via the instrumented endogenous variable. The exclusion restriction cannot be tested directly, so it is crucial to make a strong rhetorical case for its plausibility.

# **4.1 Selection on Observables by Regression and Matching approaches**

Before discussing how natural experiments can be used, we note that the traditional way of estimating impacts has typically been based on an attempt to control for observable factors in regression or matching. Consider estimating the impact of migration on income in an equation like equation 1. In the absence of a randomized experiment, we might be concerned that migrants and non-migrants differ in ways that also determine income. For example, migrants may tend to be younger, more educated, and male. The hope with linear regression and matching is that controlling for these observable differences will be sufficient to allow the regression error term to be uncorrelated with migration, so that equation 2 holds. For

> 5McKenzie (2021a) discusses the issues involved in more detail

14
is, as Mackenzie et al. (2004) note, organic matter stores approximately 112 kcals of energy per 12 gC; hence, 1 gC is equal to approximately 9.33 kcals. However, some of the energy in NPP is lost postharvest owing to transport and processing, crop residue left in the field, and roots. We thus use the adjustments for each of these factors proposed by Imhoff et al. (2004) to convert our NPP kilocaloric measure into kilocalories of final agricultural production available for human consumption.<sup>18</sup> This approach suggests that 1 gC/m<sup>2</sup> /year of NPP can be converted into approximately 1 kilocalorie available for human consumption. Given that global estimates of the cost of the minimum amount of kilocalorie per person per day (2100) is approximately $1.25,<sup>19</sup> then one unit of NPP would be valued at roughly 0.06 cents. The annual monetary benefits of a dam can then be derived by using our estimated coefficient, which gives the marginal additional annual benefit to each square meter of cropland, and the fact that basins have, on average, 15 km<sup>2</sup> of cropland.

To proxy the life span of a dam, we take advantage of the fact that the South African dam register classifies many dams in terms of their safety risk. More specifically, dams are assessed structurally to determine whether they pose any risk in terms of economic loss and are then categorized accordingly. We take the mean age of those classified as significant risk as a proxy for when they have reached the end of their life span. The mean age of dams within this category in our sample was found to be 45 and 34 years for large and small dams, respectively.<sup>20</sup> Using our estimated coefficients from the third column of table 5, the estimated life spans, and the assumption that there is cropland downstream from each large dam, we 41
|**HS**||||Year|||
|---|---|---|---|---|---|---|
|<br>**code**<br>|**_Type of products_**|2001|2002|2003|2004|2005|
|**2 digits**|||||||
|24||10.8|10.8|10.5|10.5|10.5|
|||_(9.5)_|_(9.5)_|_(9.2)_|_(9.2)_|_(9.2)_|
|||93<br>|93<br>|93<br>|93<br>|93<br>|
|25|_SALT_|5.2|5.2|5.2|5.2|5.2|
|||_(1.5)_|_(1.5)_|_(1.5)_|_(1.5)_|_(1.5)_|
|||53|53|53|53|53|
|26|_ORES SLAG AND ASH_|5.0|5.0|5.0|5.0|5.0|
||_,_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|||111|111|111|111|111|
|27|_MINERAL FUELS OILS_|5.0|5.0|5.0|5.0|5.0|
||_,_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|||267|267|267|267|267|
|28|_INORGANIC CHEMICALS_|5.3|5.3|5.3|5.3|5.3|
|||_(1.5)_|_(1.5)_|_(1.7)_|_(1.7)_|_(1.7)_|
|||570<br>|570<br>|570<br>|570<br>|570<br>|
|29|_ORGANIC CHEMICALS_|5.1|5.2|5.3|5.2|5.1|
|||_(0.7)_|_(1.9)_|_(3.2)_|_(2.0)_|_(1.1)_|
|||79<br>|79<br>|79<br>|79<br>|79<br>|
|30||9.2|9.2|9.2|9.2|9.2|
||_PHARMACEUTICAL PROD_|_(3.3)_|_(3.3)_|_(3.3)_|_(3.3)_|_(3.3)_|
|||37<br>|37<br>|37<br>|37<br>|37<br>|
|31|_FERTILIZERS_|10.0|10.0|10.0|10.0|10.0|
|||_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|||67|67|67|67|69|
|32|_TAN DYE EXTRACTS_|5.3|5.3|5.3|5.3|5.4|
||_,_|_(1.7)_|_(1.7)_|_(1.7)_|_(1.7)_|_(2.2)_|
|||59|59|59|59|59|
|33|_ESSENTIAL OILS_|8.6|8.6|8.6|8.6|8.6|
|||_(4.8)_|_(4.8)_|_(4.8)_|_(4.8)_|_(4.8)_|
|||36|36|36|38|38|
|34|_SOAPS LUBRICANTS_|12.8|12.8|13.1|12.5|12.6|
||_,_|_(4.2)_|_(4.2)_|_(4.9)_|_(5.1)_|_(4.3)_|
|||35|35|35|35|35|
|35||5.0|5.3|5.5|5.6|5.6|
||_MODIFIED STARCHES_|_(0.0)_|_(1.2)_|_(1.8)_|_(1.9)_|_(2.1)_|
|||10|10|10|10|10|
|36|_Glues_|20.0|20.0|20.0|20.0|20.0|
|||_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
||_Pyrotechnic materials and products_|61|61|61|61|61|

27
interview length at the question-, module-, individual-, and household-level, and also to link it with the information on household, individual and interviewer attributes.

# **3. Duration and Costs of Household and Individual Interviews**

Using the setup discussed above, we turn to examining country-specific insights on respondent burden - specifically descriptive statistics for the duration of each household and individual questionnaire module, and for the duration of household and individual interviews. Given oftenlimited resources in conducting multi-topic surveys, the descriptive analysis in this section can provide insight on which modules might be more costly and complex to implement than others, aiding in survey design decisions. We also show how interview length estimates can be used to provide estimates of monetary costs of household and individual interviews.

# **3.1 Duration Estimates**

Table 3 provides key descriptive statistics regarding the time burden of household questionnaire modules while Table 4 provides the same for the individual questionnaire modules. Countries are presented side-by-side for ease of comparison. The mean and median duration estimates for each module are computed over the entire household sample concerning the household questionnaire modules and over the entire age-eligible sample of household members (which varies by module, say education versus health versus labor) concerning the individual questionnaire modules. Most modules tend to have a median interview length of less than 10 minutes per interview, with a few exceptions that are noted below. We also find consistent patterns across countries in terms of which modules take the longest and ranking of modules by interview length.

Table 3 shows that the average total household interview ranges from 82 minutes in Cambodia to 120 minutes in Tanzania. The **food consumption** module emerges as the longest module to administer across countries, with an average module duration of 22 to 26 minutes, reflecting a very small amount of time per question — about 0.16 minutes or less — conditional on answering.<sup>9</sup> Other top time-consuming modules across countries are (i) **non-food consumption** (5 to 13 minutes per module, or 0.19 minutes or less per question), (ii) **housing** (6 to 7 minutes for the module, or 0.20 minutes or less per answered question), and (ii) **household roster** (13 to 23 minutes for the module, or 0.29 minutes or less per answered question). The average duration for the administration of the entire set of household-level asset rosters for **land** , **livestock** , and **apartments** ranges from 6 to 7 minutes, resulting in an average duration of 0.20 minutes or less per answered question.

> 9 Although most questions within modules are comparable across-countries, the survey designer can modify or add questions that are of interest to the country and fit more with the country’s context. Given this, not all questions across countries are created the same and there might be some variation.

9
Table C.9: National Estimates of Effect of Years of Schooling on Number of Children Born - DHS

||**Number of**|**Years of**|**Number of**|**Number of**|
|---|---|---|---|---|
||**Children Born**<br>(OLS)|**Schooling**<br>(First Stage)|**Children Born**<br>(Reduced Form)|**Children Born**<br>(2SLS)|
||(1)|(2)|(3)|(4)|
|Years of Schoolingizy|-0.162<br>(0.016)<br>[0.000]||||
|Add’l Years of Free||0.115|-0.057||
|Schooling<br>�<br>_I_<sup>_F P E_</sup><br>_zy_<br>�||(0.053)|(0.021)||
|||[0.035]|[0.009]||
|Add’l Year of MTI||0.175|-0.012||
|No Script Change||(0.087)|(0.025)||
|�<br>_I_<sup>_MT I−T_</sup><br>_zy_<br>�||[0.049]|[0.636]||
|Add’l Year of MTI||-0.128|0.092||
|with Script Change||(0.073)|(0.041)||
|�<br>_I_<sup>_MT I_</sup><br>_zy_<br>�||[0.085]|[0.031]||
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I−T_</sup><br>_zy_||-0.010|-0.003||
|||(0.008)|(0.002)||
|||[0.239]|[0.204]||
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I_</sup><br>_zy_||-0.001|-0.002||
|||(0.007)|(0.003)||
|||[0.930]|[0.342]||
|�<br>Years of Schoolingizy||||-0.365<br>(0.147)<br>[0.001]|
|First Stage F-Statistic||6.91||6.91|
|Number of Clusters|58|58|58|58|
|N|24,898|24,898|24,898|24,898|

_Source_ : Author’s analysis based on data from the Demographic and Health Survey (DHS) in years 2005, 2011, and 2016.

_Note_ : The dependent variable is either years of schooling in column 2 or number of births in the other three columns. � Years of Schooling _izy_ is the reported years of schooling from the data; Years of Schooling _izy_ is the predicted level of schooling, instrumented with the free primary education (FPE) intensity measure _Izy_<sup>FPE</sup> , two mother tongue instruction (MTI) intensity measures _Izy_<sup>MTI-T</sup> and _Izy_<sup>MTI</sup> , which denote the measures for MTI regions without and with script change, respectively, and the interactions for regions in which two interventions occurred. All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects, zonespecific linear trends, and a cubic for age. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.25
The engineering profession has an exclusive right to perform the following services: design and planning, representation for obtaining permits (signature of designs), tender and contract administration, project management including monitoring of execution, planning and managing maintenance, survey sites, testing and certification and expert witness activities. There is no law prohibiting a foreign provider with a commercial presence in Kenya from providing these services. The engineering profession has a shared right to provide the following services: feasibility studies, environmental assessment, and construction cost management. There is no law prohibiting a foreign provider with a commercial presence in Kenya from providing these services. Apart from design and planning, which can be done elsewhere and sent to Kenya, a foreign provider supplying services (i.e., without commercial presence in Kenya) will need a work permit in order to provide these services.

## **Multidisciplinary practices Score 0**

There are no restrictions on cooperation between engineering professionals and other professionals. The same applies to foreign suppliers.

## **Advertising, marketing and solicitation Score 1**

Advertising and marketing by Kenyan professional engineers as well as foreign suppliers is prohibited.

## **Fee setting Score 0.5**

Prices /fees in the engineering services applicable to the private sector and other institutions outside the government are not regulated. In the case of professional <u>engineering services rendered to the government,</u> prices/fees are determined the Ministry in charge of engineering services but as of 2010, this function will be performed by the Engineering Registration Board (ERB). The ERB will set the prices/fees to be paid for professional engineering services rendered to the government; the service providers will be expected to compete on the technical aspect only.

## **Licensing requirements on management Score 0**

No restrictions.

**Movement of people - Temporary Score 0**

No restrictions.

**Other restrictions (Addition categories) Score 0.33**

Restrictions on hiring professionals: Investment Promotion Act 2004 (cap 172) section 13.1. The employment of foreign natural persons for the implementation of foreign investment shall be agreed upon by the contracting parties and approved by Government.

# **Sources:**

Dee, P. (2005), ―A compendium of barriers to services trade‖, prepared for the World Bank, <u>http://www.crawford.anu.edu.au/pdf/staff/phillippa_dee/Combined_report.pdf</u> Nguyen-Hong, D. (2000), ―Restrictions on Trade in Professional Services‖, Productivity Commission Staff Research Paper, Ausinfo, Canberra. Available at: http://www.pc.gov.au/research/staffresearch/rotips World Bank Regulatory Survey in East Africa conducted in the context of the Project ―Trade in Professional Services in East Africa‖ in 2009.

World Bank Survey on Applied Policies in Services conducted by Development Research Group, in 2008-2009.

111
-16-

GHANA <!-- Start of picture text --> 0.50 140%<br>- 120%<br>ft40A A<br>- 100%<br>0.30 - 80%<br>0.20 60%<br>p 40%<br>0.10 20%<br>3.00 . . . . . . . .0%<br>71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87<br>M1/GDP __M2/GDP<br>(M2-M1)/GDP INFLATION<br><!-- End of picture text --> FIGURE 2a
for the groups of high-volatility and medium-to-low-volatility countries (using the 75<sup>th</sup> percentile as threshold). In both cases we consider only the restricted sample of countries (that is, without outliers and dynamically unstable observations). The distributions appear to be basically symmetrical. We can see that the groups of crisis and highvolatility countries not only have more negative average short-run coefficients but also have more dispersed distributions, with fatter tails particularly towards the negative portion of the spectrum.

# **C. Classical Growth Regressions: The role of financial volatility and crisis**

So far our analysis has used a novel empirical estimator to distinguish between short- and long-run effects of financial intermediation on economic growth. This methodology uses the time-series dimension of the data at least as intensively as the cross-country dimension. It represents a departure from the typical empirical growth literature in which high-frequency movements in the data are averaged out prior to estimation. Typical panel-data growth studies work with country data averaged for periods of 5 or 10 years and, therefore, are likely to combine short- and long-run effects. In previous sections of the paper, we have argued that the contrasting effects of financial intermediation come from different aspects associated to the process of financial development --financial depth and fragility. The growth literature has emphasized the role of financial depth and found a positive and significant effect on growth (see King and Levine 1993 a, b; and Levine, Loayza, and Beck 2000). In order to provide further support to the arguments developed in the paper, we would like to use the typical growthregression framework to analyze whether financial fragility is also a relevant determinant of growth.<sup>6</sup>

# **_Data and Methodology_**

We work with a pooled (cross-country, time-series) data set consisting of 82 countries and, for each of them, at most 8 non-overlapping five-year periods over 19602000. See Appendix 1 for the list of countries in the sample.

As is standard in the literature, our growth regression equation is dynamic in the sense that it includes the initial level of per capita output as an explanatory variable. In 16
_Table 8 - The asymmetric effect of good and bad shocks on social cohesion. Shock is measured as the year-on-year difference in cyclical component of log(USD to IRR rate) in each month. The cyclical component is computed using Hodrick-Prescott filter. Bad shock is defined as shock * (shock > 0) and good shock is defined as – shock * (shock < 0). All regressions are estimated for the matched sample of HEIS households._

||**A**|**id**|**Tran**|**sfer**|**Free**|**goods**|**Dona**|**tions**|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Afghan**|1.456***|1.610***|0.188|0.306|1.049***|1.149***|-0.139**|-0.112|
||(0.213)|(0.425)|(0.180)|(0.364)|(0.132)|(0.255)|(0.060)|(0.114)|
|**Shock (Cyclical Xrate)**|0.283||0.781**||0.477***||0.390***||
||(0.312)||(0.307)||(0.180)||(0.119)||
|**Bad shock**||0.654||0.733||0.878***||0.086|
|||(0.579)||(0.511)||(0.302)||(0.198)|
|**Good shock**||0.397||-0.918||0.344||-1.032***|
|||(0.942)||(1.194)||(0.529)||(0.353)|
|**Afghan * Shock**|0.629||-0.888**||-0.826**||-0.116||
||(0.588)||(0.446)||(0.346)||(0.175)||
|**Afghan * Bad shock**||0.253||-1.171||-1.103*||-0.175|
|||(1.003)||(0.753)||(0.570)||(0.290)|
|**Afghan * Good shock**||-1.319||0.351||0.309||-0.046|
|||(1.783)||(1.536)||(1.125)||(0.531)|
|**Constant**|15.199***|15.047***|16.166***|16.195***|14.390***|14.227***|12.176***|12.301***|
||(0.105)|(0.224)|(0.131)|(0.278)|(0.060)|(0.116)|(0.041)|(0.077)|
|**Observations**|187|187|170|170|927|927|1994|1994|

30
From each household in this census, a member was selected at random using a Kish grid. Households with existing Microcred clients were excluded from the census but those that were clients of other financial institutions were included. In total, 8,002 individuals were interviewed in the census. The timeline of the experiment is presented in Figure 2.

We used data from a pilot in which a small sample from the census were offered the possibility to open a savings account to identify respondents with a high propensity for takeup.<sup>9</sup> The 2,500 individuals with the highest probability were selected and randomly allocated into a control and four different treatment groups of 500 each. Individuals in the treatment groups were offered monetary incentives to open a savings account and were encouraged to open the account at a Microcred agent or branch using the 2x2 design described above.

Household visits took place between January and March 2015. During the visit, tablets were used to collect baseline data about respondents’ demographics, household characteristics, and financial assets, and their credit and saving behavior, use of money transfer services and bill payment methods, as well as their awareness and use of mobile money services. A total of 2,201 respondents were successfully interviewed, 374 respondents in the control group and 1,827 respondents from the four different treatment groups.

Table 1 reports summary statistics from the census data for the whole sample (Panel A) and the one selected for the study (Panel B). The full census sample is comparable to the Global Findex Database because both use a Kish grid to select the sample among household members. Study participants, however, were selected because they had a high propensity to open a savings account, and thus, the baseline sample and the census differ. Panel B reports that 53 percent of respondents are female, less than in the full census sample in Panel A. Forty-one percent of baseline respondents are heads of households, who are typically the managers of household finances. While that could have contributed to a high propensity to open an account, the proportion of household heads in Panel B is actually similar to that of the full census in Panel A. The average and median proportion of self-employed respondents, respondents with credit, as well as respondents with a savings account, are higher in the baseline sample than in the census sample. The same holds for respondents’ average and median age and individual monthly income.

Baseline respondents in Panel B are on average about 38 years old, obtained 7 years of schooling and have an average monthly income of about 184 USD. The average monthly

> 9 The pilot had a sample of 53 individuals and the statistical model to predict take-up of the account turned out to be underpowered and inaccurate. In the full sample of individuals offered to open the savings account, there was no difference in the propensity to actually open the account between those with high and low propensity to open one.

8