rafmacalaba/datause-encoder-data huggingface dataset:
5 reasons: (i) only about one-half of all migrants in Ghana remit, and (ii) about 50 percent of all remittance-receiving households in the survey do not have a migrant.<sup>4</sup> In Ghana, where family ties are very strong, households without migrants receive internal or international remittances from relatives (e.g. cousins, aunts, uncles) and close friends.<sup>5</sup> Table 1 presents summary data from the 2005/06 Ghana GLSS 5 Survey (sub-sample). Since we want to work with three exclusive groups of households, in this table and in all subsequent tables, we have dropped the 57 households that receive remittances from both internal and international sources. Of the remaining 3,884 households, 2,515 households (64.7 percent) receive no remittances, 1,159 households (29.8 percent) receive internal remittances (from Ghana) and 210 (5.4 percent) receive international remittances (from African or other countries).

# 2.  An Econometric Model of Household Incomes with Selection Controls

As noted above, if households with and without remittances differ systematically in their unobservable characteristics (e.g. skills, motivation, ability), selection bias may make the estimation results inconsistent. The purpose of this section is therefore to examine the extent of selection bias in the Ghana household data using the multinomial logit selection model developed by Lee (1983) and Bourguignon, Fournier, and Gurgand (2004).<sup>6</sup> The multinomial logit selection model is based on two equations: (i) a choice equation, which predicts the receipt of remittances; and (ii), an income equation, which determines household income, conditional on the receipt of remittances. Denoting the receipt of remittances in each group by r, r=1 (no remittances), r=2 (receive internal remittances), r=3 (receive international remittances), this can be summarized in the following equations:

* yr = zrγr+ ηr                                                                                  (1)
conservative estimate of the effect of social networks. Many of the variables that serve as controls in the regression equation (3) may also proxy for networks. For example, the language group and municipality fixed effects also capture some of the network effects at work. These are not counted as part of the network effect summarized by the parameter α , because the impact of these variables is likely to include many other factors in addition to networks.

Finally, the census data and our use of language attributes can only provide a proxy for social networks, and may not be the ideal indicator of networks. While recognizing that this empirical approach cannot completely measure the effects of complex social interactions, we do believe that it contributes to the study of differences in network effects due to unobserved differences between individuals, areas, and ethnic groups.

# **3. Results**

Equation (3) was estimated using a linear probability model (ordinary least squares) on the sample of all indigenous individuals contained in the 10% population sample of the 2000 Population and Housing Census.<sup>5</sup> To allow for potential differences in the way networks may operate based on the gender of an individual, equation (3) was estimated separately on the sample adult males and females 20-65 years of age as well as for the sample of teenage boys and girls between 12 and 17 years of age. In addition, to allow for possible differences in the role of social network effects across rural and urban areas, the males and females samples were divided further into a rural sample that included individuals residing in localities with less than 2,500 residents, a semi-urban sample that included individuals residing in localities with 2,500-

> 5  The combined number of fixed effects for localities and language groups is prohibitively large to allow estimation of probit or logit model with fixed effects.

9
- Blattman, Chris, Eric Green, Julian Jamison, Christian Lehmann and Jeannie Annan (2016) “The returns to microenterprise support among the ultrapoor: A field experiment in postwar Uganda”, _American Economic Journal: Applied Economics_ 8(2): 35-64.

- Blattman, Chris, Nathan Fiala and Sebastian Martinez (2014) “Generating skilled self-employment in developing countries: Experimental evidence from Uganda”, _Quarterly Journal of Economics_ 129(2): 697-752.

- Brudevold-Newman, Andrew, Maddalena Honorati, Pamela Jakiela and Owen Ozier (2017) “A firm of one’s own: Experimental evidence on credit constraints and occupational choice”, World Bank Policy Research Working Paper no. 7977.

- Bryan, Gharad, Shyamal Chowdhury, and A. Mushfiq Mobarak (2014) “Underinvestment in a Profitable Technology: The Case of Seasonal Migration in Bangladesh.” _Econometrica_ 82 (5): 1671–748

- Cesarini, David, Erik Lindqvist, Matthew Notowidigdo and Robert Östling (2017) “The Effect of Wealth on Individual and Household Labor Supply: Evidence from Swedish Lotteries”, _American Economic Review_ , forthcoming.

- Clemens, Michael and Erwin Tiongson (2017) “Split decisions: Household finance when a policy discontinuity allocates overseas work”, _Review of Economics and Statistics_ 99(3): 531-43.

- Dasgupta, Partha, and Debraj Ray (1986) “Inequality as a Determinant of Malnutrition and Unemployment: Theory _.” Economic Journal_ 96(384): 1011–34.

- de Hoop, Jacobus, Valeria Groppo, and Sudhanshu Handa (2017). “Household MicroEntrepreneurial Activity and Child Work: Evidence from Two African Unconditional Cash Transfer Programs,” <u>unpublished manuscript, accessed on 2/22/2018.</u>

- <mark>de Hoop, Jacobus and Furio C. Rosati (2014). “Cash Transfers and Child Labor”,</mark> _<mark>The World Bank Research Observer</mark>_ <mark>29 (2): 202–234,</mark>

- De Mel, Suresh, David McKenzie, and Chris Woodruff (2008) “Returns to capital: results from a randomized experiment”, _Quarterly Journal of Economics_ 123 (4), 1329–1372.

- De Mel, Suresh, David McKenzie, and Chris Woodruff (2014) “Business Training and Female Enterprise Start-up, Growth, and Dynamics: Experimental evidence from Sri Lanka”, _Journal of Development Economics_ , 106: 199-210.

- De Mel, Suresh, David McKenzie, and Chris Woodruff (2012) “One-time transfers of cash or capital have long-lasting effects on microenterprises in Sri Lanka”, _Science_ 335, 962–966.

- Fafchamps, Marcel, David McKenzie, Simon Quinn and Chris Woodruff (2014) “Microenterprise Growth and the Flypaper Effect: Evidence from a randomized experiment in Ghana”, _Journal of Development Economics_ 106: 211-226.

- Fiala, Nathan (2014) “Stimulating Microenterprise Growth: Results from a loan, grants, and training experiment in Uganda”, Mimeo. University of Connecticut.

22
Table 3. Leading causes of death by sex In adults aged 15-64 years in Egypt and Guatemala.

||Guate|mala|1984||Egyp|t 197|5||
|---|---|---|---|---|---|---|---|---|
||Males||Females||Males||Females||
|Total<br>deaths|10484||7240||68421||35702||
|1.|External|26.8|Infect/<br>parasit|18.8|Circul.|34.7|Circul.<br>|30.6|
|2.|Infect/<br>parasit|16.9|Neoplasm|10.3|External|11.3|External|8.3|
|3.|Circul.|7.0|Pneumonia|9.5|Other GI|8.1|Pneumonia|6.2|
|4.|Pneumonia|6.5|Circul.|8.0|Reasp.|6.9|Other GI|5.8|
|5.|Neoplasm|5|PCM|6.8|Neoplasm|6.0|Neoplasm|5.0|
|6.|PCM|4.0|External|5.8|Gen/urin|3.7|Gen/urin|3.6|
|7.|Liver|3.8|Resp.|4.2|Liver|3.3|Maternal|2.7|
|8.|Mental|3.2|Endocrine|3.8|Infect/<br>parasit|2.4|Infect/<br>parasit|2.1|
|9.|TS|3.1|Maternal|3.1|TB|1.9|Liver|2.0|
|10.|Resp.|3.0|TB|3.0|Pneumonia|1.7|Diabetes|2.0|
|11.|Endocrine|2.9|Cerebrov.|2.8|Cerebrov.|1.5|Pneumonia|1.9|
|12.|Other GI|2.(|Other GI|2.6|Diabetes|1.3|TB|1.6|

Source: WHO (1980), WHO (1987).

Notes: Disease categories as In Table 2. Abbreviations: Resp.= other respiratory diseases. Endocrine - other endocrine diseases, PCM = protein-calorie malnutrition, Circ.= circulatory diseases, Other GI = other diseases of the gastro-intestinal tract.

34
**Figure 5. Transparency Index and Rule of Law Index** (ICRG)

<!-- Start of picture text --> 6 00000 0<br>0<br>o<br>00 0 o<br>8<br>00 o ° 00° o O o °<br>_0 0 ° 00 00 0 0 oo 0<br>0~~~~~~~<br>8000 . 00 0<br>000 0 0§0 0 g<br>E 0 0 00 0 0<br>0 Og 0 0 00<br>0 0<br>0 0 oQ coo0 0<br>0~~~~~<br>1 ' 0 ' 6<br>trnsparency<br>6. Transparency Index and and Contract repudiation repudiation risk (ICRG)(ICRG)<br>6 - 8 o<br>0 0<br>0~~~~ 0 0<br>go 0 0 0<br>_80 - 0 0 00 0 00 00 0 00<br>*) 0 0 0 90<br>cm ~~~08 0<br>o 0 8 80 0 ° 0 0. 8 8 0 0 0 O 0 0 0 0 °<br>E _ 80 0<br>0o 0 0 oo o<br>0 0<br>0<br>0 0 0<br>1.23 0<br>1 ~~~~~~~~~~~~~~~~6<br>bansparency<br><!-- End of picture text --> Figure 6. Transparency Index and and Contract repudiation repudiation risk (ICRG)(ICRG) Each of the scatter plots indicate a negative association between the index and

> governance indicators. Bar graphs looking at governance indicators and access to information tell a similar story as shown in Figures 7-10.

21
These results show that the problem of missing SDG gender measures is, in part, a problem with processing existing data rather than the lack of the primary data collection, since the underlying sources (typically household surveys) almost always (if not always) collect the sex of household members. If countries with a population estimate also reported data by sex, the SDG gender coverage rate would rise from 31% to 43%.

Evidence from other sources underscore the problem of available data not being reported. In a review of national statistics for 12 countries related to sex-disaggregated data on asset ownership, employment, and entrepreneurship, Bonfert et al (2023) find that such data exist but are not made available on average for 9 out of 24 indicators. Examining gender statistics from the UK, when comparing the Office of National Statistics (ONS) website for gender data to the reporting in the UN SDG site, we find that about 31% of gender-related SDG indicators (16 out of 50) are on the ONS website but not in the UN SDG site. On the other hand, only 4 of the 50 are in the UN SDG site but not on the ONS website. The remaining 30 SDG gender indicators are in both (21) or neither (9).

Turning to regional patterns in coverage, South Asia has the highest coverage rate of SDG gender indicators at 36% availability, about 5% higher than the global average (31%). There are no clear patterns in terms of which groups or types of indicators have higher or lower indicator coverage by region.

Notably, the high-income countries do not have higher coverage of gender-related SDG indicators (Figure 4). GDP per capita is not associated with better coverage of gender statistics in the UN SDG database (Figure 5). This is also the case for SDG indicators overall: high income countries do not have higher rates of reporting of all 181 SDG indicators (rates are high income 64%, upper middle income 72%, lower middle income 70%, and low income 65%). Yet, high 8
||Table9: Ai|d & bilateral O|IL -CASH - 2 <sup>_n_</sup>|<sup>_d_</sup>stage||
|---|---|---|---|---|---|
|)<br>|1<br>=<br>(<br>_V_<br>_cash_<br>_Pr_|(1)|(2)|(3)|(4)|(5)|
|_Ln(Fatalities)_|-0.060**|-0.042|-0.055*|-0.044|-0.043*|
||(0.023)|(0.027)|(0.028)|(0.027)|(0.026)|
|_Ln(Affected)_|0.014|0.008|0.001|-0.001|0.001|
||(0.009)|(0.010)|(0.011)|(0.010)|(0.010)|
|_Ln(GDP p.c.)_|-0.094*|-0.152**|-0.220***|-0.189***|-0.184**|
||(0.053)|(0.064)|(0.079)|(0.072)|(0.072)|
|_Ln(Population)_|0.045|0.028|0.027|0.030|0.031|
||(0.041)|(0.046)|(0.046)|(0.045)|(0.046)|
|_Trade (% of GDP)_|0.003**|0.002|0.002|0.003|0.003*|
||(0.001)|(0.002)|(0.002)|(0.002)|(0.002)|
|_Distance_|0.011|-0.017|-0.014|-0.017|-0.016|
||(0.013)|(0.015)|(0.015)|(0.015)|(0.015)|
|_Affinity index_|-0.129|-0.215|-0.243|-0.191|-0.183|
||(0.302)|(0.337)|(0.337)|(0.334)|(0.333)|
|_Common religion_|0.199**|0.175|0.170|0.149|0.147|
||(0.092)|(0.109)|(0.109)|(0.108)|(0.108)|
|_Common language_|-0.153|-0.289*|-0.277|-0.272|-0.276|
||(0.120)|(0.168)|(0.169)|(0.169)|(0.170)|
|_Former colony_|-0.369***|-0.553***|-0.557***|-0.551***|-0.567***|
||(0.129)|(0.166)|(0.166)|(0.168)|(0.164)|
|_Ln(GDP p.c.) donor_|-0.187***|-0.154**|-0.164**|-0.159**|-0.159**|
||(0.060)|(0.068)|(0.066)|(0.066)|(0.066)|
|_Fuel exports (% of_|-0.001|0.002|-0.004|0.001|-0.003|
|_merchandise exports)_|(0.002)|(0.002)|(0.004)|(0.002)|(0.005)|
|_Regulatory quality_||0.228*|0.390**|||
|||(0.119)|(0.157)|||
|_Fuel exports_×|||-0.008|||
|_Regulatory quality_|||(0.005)|||
|_Corruption control_||||0.302**|0.341**|
|||||(0.140)|(0.146)|
|_Fuel exports_×|||||-0.006|
|_Corruption control_|||||(0.007)|
|Disaster dummies|Yes|Yes|Yes|Yes|Yes|
|ρ|-0.102|-0.044|-0.057|-0.052|-0.052|
||(0.141)|(0.158)|(0.154)|(0.153)|(0.153)|
|Constant|-0.170|0.448|1.257|0.994|0.901|
||(0.955)|(1.028)|(1.171)|(1.106)|(1.098)|
|Log likelihood|-6631.837|-5529.674|-5527.802|-5526.460|-5511.599|
|N|3158|2632|2632|2632|2632|

_Notes:_ Probit estimates. Coefficients reported; robust standard errors in parentheses. Dependent variable is cash, a dummy that switches to 1 if the bilateral aid contribution was cash. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

23
13 specific cases where the market fails to do so.<sup>7</sup> Hence, long-term elasticity estimates were used as a basis for linearizing the supply functions. Thus, the simulations performed in the following section should be understood as generating comparisons between alternative longrun equilibria after all relevant planting changes have taken place.

Moreover, since we are concerned with the long-run we abstract from the demand for stocks and focus on consumption demand. We assume for simplicity that the residual output exported by the small producers - - those not included in our group of nine major producers is given and does not respond to changes in the world price. We then set the demand at the world price equal to the exports of the nine major producers. Finally, we abstract at this stage from the possibility of smuggling, say between Ghana and Cote d'Ivoire. This added constraint on the power of some countries to set export taxes will be taken up at a later stage. Table 1 presents some of the basic data used in the analysis. The output levels (in thousands of metric tons)and the shares correspond **Lo** 1986, with the Cote d'Ivoire at 585 (36Z), Brazil at 329 (20Z), Ghana at 219 (13Z), Malaysia at 125, (8Z), Cameroon at 118 (7%), Nigeria at 110 (7Z), Ecuador at 85 (5Z), Indonesia at 32 (2?), and Oceania at 30 (2Z). Table 1 also shows the 1986 world price, tax rate,8 and domestic prices, the long-run supply elasticities and the corresponding slopes and intercepts of the long-run supply curves.

7 Power to affect the price in the market for a country's exports of a competitively produced commodity is a case where optimal export taxes will raise welfare for the exporting country.

8 The tax rates correspond to 1982 and 1983. Reliable estimates of export tax rates for 1986 were not available.
the resulting weights are used in estimating a probit model with the sentinel data that includes individual information combined with the geographical/administrative variables (some of the latter coming from the census and other data linked in using GIS) to obtain a set of parameter estimates on correlates of HIV status (with no claim to causality). In the third stage, these parameter estimates are applied to the identically defined variables in the DHS (or similar) survey to predict HIV prevalence for each target woman in the DHS sample. Aggregation is then performed to obtain an estimate of prevalence at the desired geographic level.

The estimates produced using this SAE approach are then compared to those derived directly from the sentinel data in Malawi. The SAE estimates suggest lower HIV prevalence than is found in sentinel data. We further compare SAE estimates with “goldstandard” estimates of HIV prevalence obtained directly from the 2004 Malawi DHS. At the national level, the SAE methodology yields an estimated prevalence of 13.2% in the 2000 DHS. This is very close to the 13.3% estimate obtained directly from the 2004 DHS. We note that there are good reasons to expect little change in HIV prevalence between 2000 and 2004 and so we regard this close agreement between SAE estimate for 2000 and observed prevalence in 2004 as indicating that the SAE method is yielding sensible estimates. Close agreement between the 2000 SAE estimates and 2004 DHS estimates is also found at the regional level and, to a considerable extent, also to the district level in Malawi. An important area for future research is to understand better the circumstances underlying those cases where district level predicted prevalence rates in the DHS 2000 disagree with 2004 district estimates. There are many factors that could underlie this disagreement, and it is important to acknowledge that they could stem also from non-response problems in the 2004 data.

In sum, the results generally indicate that the SAE methodology introduced here can potentially hold a lot of promise. First, it produces estimates of prevalence that are lower then those derived directly from sentinel data. This is good news because in all countries where recent DHS surveys have included direct HIV testing the findings confirm that sentinel data overestimates prevalence (UNAIDS, 2007). Second, the SAE methodology produces estimates of prevalence which are quite close to the “goldstandard” prevalence rates obtained from a nationally representative survey that directly collects information on HIV prevalence. Third, given the right set of data (and 18
the private sector and real GDP growth during the three years prior to the start of the crisis. We call this variable CREDIT GROWTH.

For the aggregate economic recovery, we construct proxies for economic output losses suffered due to a crisis. We use two approaches here. One way is comparing, in real terms, the pre-crisis GDP level of a certain country with the GDP level during the following years until the pre-crisis level is reached. This approach considers pre-crisis GDP growth rates to a country’s trend or potential growth rate. The pre-crisis, trend GDP growth is calculated as the average of GDP growth rates from year t-3 to t-1, where year t is the start of the crisis. Then, each GDP growth rate from year t onwards is compared to the trend until the trend growth is reached. The output loss is defined as the sum of the difference between the actual and the trend growth rate over all the years until trend growth is reached again. This approach follows the methodology used in IMF’s World Economic Outlook (1998), although we recalculate and update the data to include some recent crises. We call this variable OUTPUT LOSS (IMF).<sup>10</sup> The second version of the output loss variable results from using the Barro (1991) growth model to estimate a country’s GDP potential growth rates. We refer to this variable as OUTPUT LOSS (BARRO). Instead of using pre-crisis data to estimate the trend growth rate, we obtain predicted values of per capita GDP growth from equation (2) in Barro (1991, p. 410). The exact equation is: Average GDP per capita growth from 1960 to 1980 = 0.0302 - 0.0111 * GDP per capita in 1960 + 0.00051 * (GDP per capita in 1960)<sup>2</sup> + 0.0323 * Secondary school enrollment rate + 0.0270 * Primary school enrollment rate - 0.122 * Government consumption/GDP - 0.0200 * Number of

> 10 We also counted the number of years a country needs to get to the same GDP level as prior to the crisis. We ran the same regression for this variable and found similar results as for the two output loss measures.

17
with the sole exception of Venezuela. Of the 33 PCRs which collect ratings, 26 distribute these ratings back to the financial institutions. As public credit registries become tools to monitor and analyze forward-looking provisions, they may also be used to analyze capital.<sup>20</sup> Many public credit registries, including the world’s oldest PCR in Germany, were established in response to a banking crisis prompted by defaults of large borrowers. Monitoring credit risk concentrations and informing individual banks of these risks remains a key objective for most public credit registries. At the same time, in many developing countries improving access to credit also has high priority. Information from the public credit registry can serve both objectives by allowing banks to better assess the creditworthiness of borrowers at the same time that supervisors use the data to monitor for risks. However, there are tensions which are created by these different objectives and which can affect the design of the public registry and the data collected.

One of these tensions concerns the size of loans to be included in the PCR. From a lender’s perspective it would be preferable to have data available on all loans regardless of size, since credit reports are particularly important for consumer and small business lending. Supervisors, however, may prefer to focus the database on large loans which pose a systemic threat. Also, by establishing a minimum loan size for inclusion in the PCR, supervisors can drastically reduce the size of the database, making it easier to manage and to enforce quality standards.<sup>21</sup> Figure 1 provides information on the minimum loan sizes included in PCRs by region measured by the ratio of the cutoff amount to GNI per capita. In the World Bank survey 23 countries did not have any minimum loan size or had a very low minimum loan size indicating the important role these registries play as a provider of information to financial institutions. Virtually all

> 19 In some PCRs where the rating is on the credit line, collateral may also affect the rating.  Another view however is that the rating should be that of the borrower and that collateral only enters in determining the level of provisioning to ensure greater comparability of ratings across borrowers.

> 20 We come back to this in the discussion on banking regulation below.

> 21 Some years ago, the cost in terms of computer hardware may also have been an issue however given the effective doubling of computing power every two years (so-called Moore’s Law – see <u>http://www.intel.com/research/silicon/mooreslaw.htm) and the even faster development of computer storage media,</u> this does not seem to be the relevant constraint.  As indicated in the text the constraint is really related to the management and particularly on the issue of ensuring reasonable quality control.

18