rafmacalaba/datause-encoder-data huggingface dataset:
over the Akan ethnic group variable because the GLSS1 survey for 1987 Ghana has many (non-random) missing values for ethnicity.

Table 3: Discontinuities at the border of Cˆote d’Ivoire with Ghana

||Nat.|Linear|RD|Polyno|mial|Matc|hing|
|---|---|---|---|---|---|---|---|
|Bandwidth in km:|(1)|50<br>(2a)|75<br>(2b)|50<br>(3a)|75<br>(3b)|50<br>(4a)|75<br>(4b)|
|1986–8||||||||
|Log. Consumption p. c.|+0.75*** <br>(0.04)|+0.74*** <br>(0.23)|+0.64*** <br>(0.15)|+0.79*** <br>(0.23)|+0.65*** <br>(0.15)|+0.68*** <br>(0.07)|+0.68***<br>(0.07)|
|Head born in border<sup>_a_</sup>||+0.85*** <br>(0.18)|+0.68*** <br>(0.14)|+0.93*** <br>(0.18)|+0.70*** <br>(0.15)|+0.77*** <br>(0.05)|+0.72***<br>(0.04)|
|Log. Expenditures p. c.|+0.82***<br>(0.07)|+0.57*<br>(0.34)|+0.67*** <br>(0.25)|+0.70**<br>(0.30)|+0.57**<br>(0.24)|+0.54*** <br>(0.13)|+0.65***<br>(0.12)|
|Head born in border<sup>_a_</sup>||+0.45|+0.49**|+0.65**|+0.47*|+0.41**|+0.54***|
|||(0.28)|(0.22)|(0.26)|(0.24)|(0.16)|(0.10)|
|Stunted 6-59 m. (%)|-12***<br>(1)|-38***<br>(8)|-25***<br>(8)|-44***<br>(9)|-35***<br>(9)|-37***<br>(6)|-29***<br>(3)|
|Head born in border<sup>_a_</sup>||-33***<br>(8)|-11<br>(7)|-35***<br>(9)|-23***<br>(8)|-26***<br>(4)|-17***<br>(2)|
|Stunted 6-35 m. (%)|-6***<br>(2)|-26***<br>(7)|-16**<br>(6)|-32***<br>(9)|-27***<br>(8)|-28***<br>(5)|-21***<br>(3)|
|Electricity connection (%)|+17***|+34|+48**|+29|+33*|+31|+36**|
||(4)|(34)|(22)|(32)|(19)|(19)|(16)|
|Access to safe water (%)|+37***|+43|+26|+35*|+15|+25*|+31***|
||(3)|(29)|(16)|(22)|(20)|(14)|(8)|
|N C.d’Iv./ N Ghana|199/344|14/25|19/40|14/25|19/40|14/25|19/40|
|1993||||||||
|Stunted 6-35 m. (%)|+4***|-1|-6|-1|-4|+8|+6|
||(2)|(10)|(9)|(9)|(8)|(8)|(7)|
|Electricity connection (%)|+12***<br>(3)|+35***<br>(13)|+36***<br>(10)|+22*<br>(12)|+28**<br>(12)|+26**<br>(12)|+26**<br>(12)|
|Access to safe water (%)|+21***<br>(3)|+44***<br>(15)|+24*<br>(13)|+36**<br>(14)|+41***<br>(13)|+45***<br>(8)|+28***<br>(9)|
|N C.d’Iv./ N Ghana<sup>_b_</sup>|714/401|61/29|83/47|61/29|83/47|61/29|83/47|

_Source_ : Authors’ analysis based on data described in the text. _Coverage_ : PSUs in the bandwidth window (50 or 75 km from the corresponding border). _Notes_ : Consumption and expenditures at 1988 prices and exchange rates. Col. (2a) to (4b): controls for latitude, rainfalls, elevation, and distance to river are always included. a: Head born in border: Household head is born in a district lying along the border (see table 2 footnote d). b: Income survey pooled with DHS data on the Cˆote d’Ivoire side, DHS only on the Ghana side. Positive numbers indicate differences in favor of Cˆote d’Ivoire. Standard errors in parentheses. ***: _p < ._ 01; **: _p < ._ 05 ; *: _p < ._ 10 In 1986–8, significant BDs are found for log consumption per capita, ranging from +0.64 to +0.93 (i.e., indicating large income gaps at the border of the two countries) even if we restrict to households whose head is a native of border dis23
25

# V. References

Akin, John C., N. Birdsall, and D. de Ferrarci, 1987.<sup>Financing Health</sup> Services in Develoning Countries: An Agenda for Reform,<sup>Washington, D.C.:</sup> World Bank.

Amenyah, Joseph. 1985. Prefeasibilitv Study National ,lealth Insurance Scheme. Ghana, mimeo manuscript. August.

Blair, Roger D. and Ronald J. Vogel. 1978. "A Survivor Analysis<sup>of Commercial</sup> Health Insurance". Journal of Business, July.

Borch, Karl. 1990. Economics of Insurance. Amsterdam, North<sup>Holland.</sup> Bruner-Jailly, J. 1988. Le Financement des Couts Recurrents de la Sante<sup>au</sup> Mali, Premiere Redaction, Geneva: World Health Organization. De Ferranti, David. 1985. PaXing for Health Services in Developing<sup>Countries:</sup> An Overview. World Bank Staff Working Papers No. 721.<sup>Washington, D.C.:</sup> World Bank.

Feldstein, Paul J. 1988. Health Care Economics, 3rd ed. New York,<sup>N.Y.:</sup> Wiley.

Fuchs, Victor R. 1982. Who Shall Live? New York, N.Y.,:<sup>Basic Books.</sup> Nigeria. Federal Ministry of Health. 1988. Report of the National<sup>Committee</sup> on the Establishment of Health Insurance Scheme in Nigeria.<sup>Vol I.</sup> Lagos, Niget.a: KOH. September. Vol II, Appendices to<sup>the ReRort of</sup> the Nati Comittee on the Establishment of Health Insurance Scheme Pauly, Hatk V. 1986. *Taxation, Health Insurance, and Market<sup>Failure in</sup> the Medical Economy". Journal of Economic Literature, 24 (2), June 1986: 629-675.

Vogel, Ronald J. 1987. Financing the Health Sector<sup>in Tanzania: A Public</sup> Exoenditure Review. Washington, D.C.: World Bank. December.
**Table 2: Matching results**

|**Treatment Variable:**<br>**Non-Cooperation**|**1-Nearest**<br>**Neighbor**<br>**Matching**|**2-Nearest**<br>**Neighbor**<br>**Matching**|**3-Nearest**<br>**Neighbor**<br>**Matching**|**Ra**|**dius Matchi**|**ng**|**Local**<br>**Linear**<br>**Regression**|**Kernel**<br>**Matching**|
|---|---|---|---|---|---|---|---|---|
|||||**r=0.005**|**r=0.01**|**r=0.05**|**Matching**||
||||**De**<br>|**pendent varia**<br>|**ble: Tax rev**<br>|**enues**<br>|||
|[1] Average Treatment on the Treated (ATT)<br>UsingGovernment Effectiveness|-2.181*<br>(1.164)|-2.066**<br>(0.979)|-2.061**<br>(0.968)|-2.260**<br>(0.893)|-2.382***<br>(0.836)|-2.508***<br>(0.742)|-2.778***<br>(0.795)|-2.495***<br>(0.760)|
|Number of observations|513|513|513|513|513|513|513|513|
|Number of Treated observations|104|104|104|104|104|104|104|104|
|Number of Control observations|409|409|409|409|409|409|409|409|
|||||**Robustn**|**ess checks**||||
|[2] Using Rule of Law|-2.364**|-2.779**|-2.840**|-3.062***|-3.068***|-2.784***|-2.968***|-2.779***|
||(1.201)|(1.139)|(1.129)|(0.838)|(0.870)|(0.802)|(0.759)|(0.812)|
|[3] Using Voice and Accountability|-2.032*|-2.162**|-2.333**|-2.484***|-2.450***|-2.484***|-2.751***|-2.492***|
||(1.064)|(0.981)|(0.961)|(0.843)|(0.773)|(0.711)|(0.739)|(0.702)|
|[4] Using Control of Corruption|-3.589***|-3.355***|-2.928***|-2.763***|-2.849***|-2.570***|-2.778***|-2.586***|
||(1.213)|(1.125)|(1.108)|(0.964)|(0.798)|(0.753)|(0.865)|(0.807)|
|[5] Using Regulatory Quality|-2.718**|-2.463**|-2.313**|-2.508***|-2.564***|-2.625***|-2.831***|-2.587***|
||(1.211)|(1.000)|(1.079)|(0.909)|(0.822)|(0.767)|(0.776)|(0.751)|
|[6] Using Political Stability|-2.612**|-2.556**|-2.812**|-2.956***|-2.901***|-2.871***|-2.892***|-2.847***|
||(1.237)|(1.112)|(1.151)|(0.904)|(0.865)|(0.858)|(0.911)|(0.834)|
|**Rosenbaum bounds sensitivity tests**|**1.2**|**1.3**|**1.3**|**1.5**|**1.6**|**1.7**|**1.9**|**1.7**|
|**Standardized biases (p-value)**|**0.21**|**0.33**|**0.56**|**0.76**|**0.78**|**0.94**|**0.21**|**0.94**|
|**Pseudo R2**|**0.04**|**0.03**|**0.02**|**0.01**|**0.01**|**0.01**|**0.04**|**0.01**|

Note: Standard errors in parentheses computed using 500 bootstrap replications. * _p_ <0.10, ** _p_ <0.05, *** _p_ <0.01.

11
_Table 6: Impact of Electrical Conductivity on Net Primary Productivity, alternative thresholds, Mekong River Basin_

|DepVar: Δ log(netprimary productivity)|(1)<br>EC > 75ms/m|(2)<br>EC > 100ms/m|(3)<br>EC > 125ms/m|
|---|---|---|---|
|Electrical Conductivity exceeds threshold|‐0.0409<br>(0.0125)**|‐0.0578<br>(0.0138)***|‐0.0578<br>(0.0138)***|
|Precipitation (m/year)|‐0.201<br>(0.0236)***|‐0.202<br>(0.0236)***|‐0.202<br>(0.0236)***|
|Precipitation^2 (m/year)|0.0507<br>(0.00668)***|0.0508<br>(0.00667)***|0.0508<br>(0.00667)***|
|Temperature (C)|0.224<br>(0.0628)***|0.223<br>(0.0628)***|0.223<br>(0.0628)***|
|Temperature^2 (C)|‐0.00353<br>(0.00116)**|‐0.00352<br>(0.00116)**|‐0.00352<br>(0.00116)**|
|Year Fixed Effects|Y|Y|Y|
|Gridcell Fixed Effects|Y|Y|Y|
|Geographic Time Trends|Country|Country|Country|
|Distance to nearest station|50km|50km|50km|
|Cropland Threshold|>30%|>30%|>30%|
|N|3784|3784|3784|
|R‐sq|0.674|0.674|0.674|

Notes: Table shows results from estimating equation 1 for the Mekong River Basin sample via ordinary least squares. Each column is from a separate regression, and regressions vary by the threshold of EC used in the EC indicator variable. Standard errors clustered at the gridcell level are in parentheses. Stars denote level of statistical significance: + p<0.1, * p<0.05, ** p<0.01, *** p<0.001 22
Figure 6 : Emigration and Fertility Transition by Province in Morocco <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>0.0 5.0 10.0 15.0 20.0<br>Proportion of emigrants in 2000<br>Source: see Table 9<br>completed between 1982-94<br>Proportion of the transition of fertility<br><!-- End of picture text --> A key intermediate variable between emigration and demographic change populations left behind seems to be education. On one side there is overwhelming evidence that education is the single most important determinant of the transition of fertility in developing countries (Cleland & Hobcraft 1985, Jejeebhoy 1995), and MENA individual countries are not exceptions to this worldwide rule, even though this is not a one-to-one relation. In Egypt as well as in Morocco, declines in fertility have been associated with rising education, in particular among women.

On the other hand, MENA data support the hypothesis that migration has had an impact on the average education of non-migrant populations left behind, and that this impact varies according to the region which major streams of emigrants are destined to: emigration to the Gulf would have slowed down the progress of education in regions of origin, while emigration to the West would have sped it up.

22
and zero otherwise. ψc and ψt are country and year fixed effects, respectively. Xct includes timevarying country controls. All variables and their sources are described in section 3.3.

# **3.2 Econometric issues**

Identifying the relationship between investment promotion efforts and FDI inflows poses several challenges. Perhaps the most important challenge is establishing the direction of causality. It could be argued that the choice of sectors to be targeted is endogenous; IPAs could be targeting sectors which already experienced high inflows. In our sector-level analysis, which compares FDI inflows to targeted and non-targeted sectors pre- and post-targeting, we use four different strategies to deal with the potential reverse causality. First, we include country-industry fixed effects, which take out unobserved time-invariant characteristics specific to country-industry combinations. If, for example, the mining sector in South Africa was chosen for targeting because of the endowment of gold and this endowment is also the reason for large FDI inflows into the sector, this is controlled for by the country-sector fixed effect. Second, we show that our results are robust to a specification with first, second and third lags. A change in FDI inflows is unlikely to explain a change in policy which precedes it, although the strategy is not robust to forward looking behavior of policy makers. Third, we investigate if the sectors targeted were different from other sectors in the years before the targeting started. We find no evidence of relatively successful or unsuccessful sectors being chosen for targeting. Fourth, as IPAs were asked in the Census about the reasons behind targeting a particular set of sectors, we show that the results hold even if we exclude targeted sectors in countries that made targeting decisions based on the past success or failure in attracting FDI to that sector.

The measures described above do not address the theoretical possibility that IPAs know which sectors will attract a lot of FDI in the future and choose to target them to show results. In the Census, IPAs were asked about who decided which sectors to target.<sup>9</sup> The incentive to target sectors that already have high expected FDI inflows may have been present at the agency board level, but it is harder to make the same case for the other entities. Of the 97 agencies that responded, only 6 said the decision was entirely left to the agency board, 24 reported the board having some input into the decision, and 67 said the agency board was not at all involved in the

> 9 The entities involved in the decision were: president’s office, prime minister’s office, ministry of foreign affairs, ministry of finance, ministry of industry, ministry of commerce, agency board or the decision was based on a national strategy plan. In some cases, several entities were involved.

12
courses of longer duration with more developed curricula and modules for different topics. In this new system, a student registered for a single course of about 24 hours that covered the entire stock trading program. It is important to note that since the inception of the education initiative in 2008, students in any given course met in the same classroom with the same instructor for the entire duration of that course. As a result, the social interactions in our setting are well-defined by classmates and by the time of the course.

For each course, the syllabus and supporting training materials were designed directly by the CSE. The rigidity of the program protects the CSE from conflict of interest issues. For instance, since brokerage companies are members of the exchange, when an instructor covers topics such as trading costs, buying on margin, or short sales, the training material is designed to avoid references or examples that could encourage investors to use of one brokerage company over another.

# **3.2. DATA**

Our analysis draws on three primary sets of data. First, we obtained data from the CSE on courses and participants. Each record includes detailed information on the beginning and end date of the course, matriculated students, cost of tuition, total number of hours, location, and name and curriculum vitae of the instructor. Table 1, panel A shows summary statistics on these courses for each year in the sample. In accordance with CSE directives, courses between 2008 and 2012 were shorter and less expensive (averaging 10.8 hours and 110 USD) than courses in the last four years of the sample (averaging 22 hours and 276 USD). Classes had 16 students on average, with few classes of over 30 students, and some with as few as 10 students. One limitation is that the CSE did not collect demographic or socioeconomic information on students. We classify students by gender using their first and middle names. We also determine the age range of each student at the time of each course using the national identification number. On average, female students represent 31% of the sample, and 55% of all students were over 30 years old at the time of the course.

Second, we use the CSE’s official record of equity trades and stock portfolio holdings for all investors in Colombia between 2006 and 2017. The CSE records every single transaction for listed equities, 17
Table 3 presents a set of regression results for Argentina, Brazil, and Mexico. The model using only negative information performed as expected – the explanatory variables for past defaults and missed payments were highly significant and positively impact default for all three countries. The specification of the model with both positive and negative information also provides results which are broadly consistent across countries. More importantly, in each country we found that <u>positive information adds to the explanatory power of the model. If lenders can better identify</u> risky borrowers, then they can reduce portfolio risk or extend more credit. We discuss this result in greater depth below.

The positive information variables also performed consistently between countries in the sample. Longer credit histories were highly significant and negatively associated with default. In Mexico and Argentina where data on collateral were available, the relevant coefficient was statsitically significant and greater collateral anmounts were associated with lower default probabilities. Ratings data were also shown to be valuable predictors of default. For example, in the case of Brazil a loan with a rating of “D” was twice as likely to enter default as one rated “C” and almost five times more likely than one with a “B” rating. Borrowers with more open credit lines (Argentina) or those with accounts in several banks (Brazil) were found to be more risky. In the only country where data on interest rates were used - Mexico - we found a positive and significant relationship between the probability of default and the interest rate.

In some cases, the behavior of explanatory variables was uneven across countries. For example, evidence on the effect of loan size on default probability was mixed. Loan size exhibits a strong negative impact on default in Brazil, as well as in Argentina where the coefficient is also negative but much weaker. In Mexico, however, loan size is shown to be positively linked to default, but only weakly significant.

The simple credit scoring model similar to the one reported above was then used to define default probabilities for borrowers in country samples, using both the negative-only estimation coefficients and those from the complete information model with both positive and negative data.

> 8 The PCR in Brazil also contains information on days past due, however loans are grouped into buckets of 15 to 60, 61 to 180 days past due and so on.  Since 90 days past due is used as a definition of default in our exercise, this PCR

9
# **1. Introduction and Background**

Understanding peoples’ productive activities over the course of the year is important both to document how people earn their livelihoods and also to ensure that poverty programs are well designed and well-targeted. Measuring employment is especially challenging in settings where productive activities are informal, with irregular intensity of participation, and seasonal, where much of the effort and earnings are concentrated in specific periods of the year (Feuerbacher et al. 2020). These characteristics are particularly relevant in rural labor markets in low-income countries that rely heavily on employment in the agriculture sector (Fink, Jack, and Masiye 2020; World Bank, 2017).

The prevailing survey methodology literature considers shorter recall periods to be more accurate, often relying on a standard seven-day recall window (Durazo et al. 2021). However, relying on reported labor activities over the most recent seven days is likely to miss individuals’ full range of labor contributions and work activities over a full year, especially in highly seasonal and informal labor markets. To address this gap, standard data collection practices typically also include questions about productive activities of each household member over the past year.<sup>1</sup> While existing evidence is mixed, use of such a long window may lead to losses in data and data quality.<sup>2</sup> In this paper, we use a survey experiment to test the effect of recall windows on reported annual work behaviors, contrasting reported labor participation using a long (annual) recall method to those resulting from a set of quarterly interviews that follow a similar structure and set of questions, but over shorter, 90-day, recall windows.

A commonly used format for annual recall labor questions in multitopic household surveys asks the household’s primary respondent to report on their labor activities over the preceding year, including type of activity, periods worked, and intensity of participation. These questions are asked about their main activity over the year as well as any secondary activity. While there is often a stated preference for having each household member respond for themselves, frequently, either when that respondent is unavailable or when survey budget resources are insufficient to allow for

> 1 The most common format for large multi-topic surveys such as the World Bank’s Living Standards Measurement Study is to ask about activities in the last seven days in detail, with follow-up questions regarding the last 12 months (see Durazo et al. 2021).

> 2 See de Weerdt, Gibson, and Beegle (2020) for an excellent review of the survey methods literature that includes an overview of both recall windows and proxy reporting.
When the supply of casual manual labor is positive, it is decreasing in both αi and C0i, and increasing in Wi. Equation (3.1) can also be written as: αi = Wi/Ci. The intuition here is that Wi/Ci reveals the preference of the household for this type of work: a household with a high disutility attached to doing this type of work will ask for a high wage rate.

We do not assume that everyone has the same preferences. There are clearly idiosyncratic differences in the disutility attached to doing casual labor, which may reflect personal labor histories, caste, gender and demographic characteristics. A natural choice for the common preference parameter in making inter-personal comparisons of welfare is α�= Q50 Wi , the � Ci � median of the observed ratio of the wage rate to consumption per capita. However, we will test sensitivity to this choice.

The above formulation assumes that the individual is free to supply any amount of labor to casual work—that there is no involuntary unemployment. This assumption can be questioned; some workers may be unable to get as much work as they would like at the going wage rate. We will use a specially designed survey that allows us to identify a sub-sample of those who are not rationed, to test if this alters our main results.

# **3. Data**

We use two household data sets. The first is the 66th round (July 2009-June 2012) of the Employment Schedule (“Schedule 10”) of the Government of India’s National Sample Survey (NSS). This dataset includes household characteristics (including demographics, NREGS participation, social groups) and information on household members’ education, principal and subsidiary activity and time disposition during the week ended (block 5.3). The questionnaire also includes a block on monthly household expenditures during the last 30 days (block 9). We observe the weekly supply of casual manual work and also the daily wage rate for this type of work during the week ended (in block 5.3). The daily wage rate of a household is defined as its total earnings from casual manual work divided by total number of days spent on such work across all household members during the week ended (status code 41, 42 and 51). Table 1 gives summary statistics on Li, Ci and Wi.

7