rafmacalaba/data-use-mentions-extended huggingface dataset:
14 _Fig 4: Horizontal inequity and reranking_ <!-- Start of picture text --> ostpayment<br>ncome X-P<br>990 _ X<br>900 /_<br>850<br>1000 1 100 EPrepayment income X<br><!-- End of picture text --> In short, differences in health care payments at each income level be sufficiently large for households starting off close to one another in the prepayment distribution to change their positions in the move to the postpayment distribution. This possibility was illustrated vividly in the World Bank's _Voices of the Poor_ exercise [11], which reported that in Lao Cai, Vietnam, a 26 year-old man had moved from being the richest man in his community to one of the poorest as a result of the large health care costs necessitated by his daughter's severe illness.

Such rerankings are captured in the move from the postpayment concentration curve to the postpayment Lorenz curve. They are captured numerically by R in eqn (3). The overlapping of fans (causing households to move up or down the income distribution as a result of health care payments) is conceptually distinct from horizontal inequity (the existence of fans). However, if horizontal differences are the usual source of non-zero values of _R,_ it seems unwise to try to make too much of the distinction between R and H. This is reinforced by the fact that although in the population at large there will be households on the same prepayment income, in a household survey such instances are rare. In empirical work, it therefore becomes necessary to define equals by reference to _bands_ of prepayment income, within which, for the purpose of the exercise, households are deemed to be equal. The choice of bandwidth inevitably affects the computed value of H, but also affects the computed value of R. Specifically, it seems to be the case that as the bandwidth is widened, _H_ falls and _R_ rises. However, the sum of _H_
represents a 7.5% increase over the control group. We find no effect in Rwanda, with a point estimate close to zero with small standard errors. One interpretation of these differential effects rests on the legal rights provided by the programs, which we explore in Section 5.

For those impact evaluations with a second round of follow-up data, Table 4 shows that treatment effects are insignificant across the board. This raises the question of whether we are identifying a true increase in cumulative fertility, or merely a shortening of birth spacing with no total effect. To compare the relative empirical support for these two competing hypotheses, we test whether the first follow-up and second follow-up treatment effect coefficients are statistically different from each other and show the resulting p-values in the last row of the table. Except in the case of Ghana, we cannot reject the hypothesis of equality between the two-periods coefficient, suggesting a positive overall effect on fertility.

Table 5: Probability of having had an additional child at follow-up

||_Ethiopia DOT_|_Ethiopia_<br>_WALN_|_Togo_|_Rwanda_|
|---|---|---|---|---|
||Bu|siness training||Land titling|
||(1)|(2)|(3)|(4)|
|Treatment follow-up 1|0.061**|0.067|0.141**|0.008|
||(0.030)|(0.080)|(0.056)|(0.016)|
|Treatment follow-up 2|0.030<br>(0.036)|-0.068<br>(0.116)|||
|Observations|1,380|145|386|1,971|
|Control group mean|0.273|0.283|0.136|0.155|
|p-val|0.270|0.325||
within WALN being located on the demand curve beyond the revenue threshold after which demand for children is income inelastic; women in the WALN sample’s control group have on average 1.3 more children than women in the DOT sample’s control group.

Turning to the land interventions, we detect a positive impact on women’s fertility in Benin and Ghana but not in Rwanda. Women randomized into the treatment group in Benin have 0.2 more children two years after the start of land demarcation, a 5% impact over the control mean (statistically significant at the 5% level). In Ghana, women in the treatment group have on average 0.26 more children than women in the control group 12 months after the beginning of demarcation, (also statistically significant at the 5% level). This effect

> 19This dataset does not contain information on the number of biological children of the beneficiary woman, so we compute the outcome as the number of children born post-intervention in the household. Given that women in our sample are married to or in a relationship with the head of the household, we assume that most of those newborns are the children of the women under study. To verify that our estimated impact is not driven by an increase in fostered children as women improved their financial status, we show that women older than reproductive age do not see any impact on their fertility (Appendix Table A8).

20While women in the Ethiopia DOT sample have 1.4 employees on average, this number is 3.6 in the WALN sample. Moreover, the monthly revenue in the DOT sample is 40,479 birr, but 72,000 birr in the WALN sample.

17
|31.8|1.34|1.37|
|Predicted matching|1,866||30.5|31.7|1.16|1.19|
|(B) Gini index (x100)|||||||
|Actual matching|1,866|34.2|34.3|34.0|-0.23|-0.19|
|Predicted matching|1,866||34.3|34.0|-0.24|-0.20|

_Note_ : _Predicted matching_ estimates poverty and inequality in 2020 using the methodology discussed in Section 3.3. _Actual matching_ maps income changes reported by each household in the phone surveys to the underlying 2019 distribution. Both methods rely on sectoral growth rates to estimate the size of household income shock. _Net COVID-19 impact_ is the difference between the 2020 distribution with COVID-19 and counterfactual distribution.

# **5.2 Random allocation of growth rates**

In our simulation, we assigned the same growth rates within five groups of households. These five groups were identified from the income loss, gain, and no change probabilities in rural and urban areas. Our method requires that the growth rates of these five groups aggregate to the growth in per capita GDP. However, within a group, there are infinitely many combinations of growth rates that would allow us to match growth in per capita GDP. In what follows, we will relax our assumption that within each group all households have the same growth and test how alternative growth realizations impact the poverty and inequality estimates.

27
the last WIOD release available (release 2016, see below for more details on the available tables’ versions);

- <u>`year(`</u> _#_ `)` , sets the year to be used for the analysis; the _default_ is the last available year: 2014 for the WIOD tables ( `wiodn` ), 2015 for TiVA tables ( `tivan` ), 2015 for the Eora Global Supply Chain Database tables ( `eora` ). Not needed for userprovided tables;

- `info` , shows the data sources and the versions of the loadable ICIO tables.

## **2.1.2 Examples**

`icio_load` can be used for the following purposes: 4
literature based on the United States and domestic issuance data, we find that firms from around the world switched away from the markets most affected by crises. The inclusion of international issuances in the analysis is a key element to find this pattern. By increasing issuances in alternative debt markets, firms compensated (partially and sometimes fully) for the decline in lending in the crisis-hit markets. These compensations at the firm level are also observed at the economy-industry level within the four markets we analyze. Because the different debt markets in which firms borrow are not identical, debt maturity and currency denomination changed as firms moved across them. Consequently, we obtain different dynamics on the aggregate volume and terms of financing when considering the four debt markets than when focusing on each market individually, as the literature usually does. Overall, the analysis of more complete markets shows evidence consistent with market-specific credit contractions during crises due to supply-side shocks, which prompted firms to change the borrowing location. These changes had significant effects on the volume and terms of financing during the crises analyzed.

There is, however, substantial heterogeneity across firms. On the one hand, relatively larger firms (the ones with access to international markets) are more capable of cushioning the consequences of crises by moving abroad when there is a domestic shock or moving back home when the crisis happens abroad. These firms also drive our aggregate results given that they captured most of the issuance activity we observe within countries. On the other hand, firms issuing only in domestic markets (typically smaller firms) could face declining financing, shorter maturities, and could be crowded out by the firms returning to domestic markets during shocks in foreign markets. During domestic banking crises, firms issuing only in domestic markets would be constrained by the evolution of these markets, perhaps lacking similar alternative markets to compensate for the domestic shock. Hence, when implementing policies aimed at mitigating the effects of shocks, policy makers might want to focus on constrained (smaller) firms that are not be able to tap different markets
census data. The presence of a recent census in Burkina Faso provides a valuable opportunity for evaluating this method. As with every SAE application, the performance of different methods will depend on the country context and the characteristics of the available survey and auxiliary data they are applied to. Evaluations of the estimates therefore remains of paramount importance.

The paper is organized as follows. Section 2 describes the data sources and the process of integrating geospatial and survey data. Section 3 presents the core of the small area methodology, model selection and assessment, small area estimation, mean squared error estimation and measures to assess the small area estimates for all countries of focus in this paper. Section 4 presents an evaluation exercise using recent census and survey data in Burkina Faso. This allows us to compare small area estimates produced with geospatial covariates to small area estimates produced using covariate information from census microdata. The results of the evaluation exercise add new insights to the body of literature on the use of geospatial data in small area estimation and motivate the use of the unit context model with geospatial data in the four remaining countries that lack up-to-date census data. Section 5 presents experimental point and uncertainty estimates for all countries using the unit context model. The paper concludes with a summary of the main findings and areas for further research.

# **2. Data sources and geospatial data integration**

In this paper, we use geospatial covariates because, as shown in Table 1, the most recent censuses in the four focus countries were conducted in 2014 in Guinea, 2012 in Niger, and in 2009 in Chad and Mali. If more recent census data existed, using these data would be the preferred option. For example, several variables routinely collected in censuses such as household size, education, and sector of employment have been shown to be highly predictive of household welfare. Estimates based on recent census data are expected to be more accurate and precise than estimates based on geospatial data, which is often only available at an aggregated level (see for
9|63.8|440|100.0|92.7|82.7|378|100.0|89.2|75.1|58.7|
|4.A. IMSS no, Census no|348|31.8|31.0|28.4|68|_15.5_|77.9|75.0|62|16.4|61.3|48.4|62.9|
|4.B. IMSS yes, Census no|79|7.20|86.1|74.7|26|5.91|100|80.8|34|8.99|97.1|73.5|50.0|
|4.C. IMSS no, Census yes|337|30.7|88.4|77.2|168|38.2|92.9|81.0|110|29.1|88.2|76.4|54.5|
|4.D. IMSS yes, Census yes|332|30.3|97.9|84.6|178|40.5|97.2|87.6|172|45.5|98.3|84.3|61.6|

Only those firms in business for more than three years could have been enumerated in the 1989 Census of businesses. IMSS refers to registration of the firm's paid workers with the Mexican Social Security administration. Only paid workers have to be registered with IMSS.

27
|14%|25%|62%|-|
||Importer|37%|22%|44%|72%|-|
||Exporter|37%|33%|47%|89%|-|
||Two-way|30%|38%|44%|74%|-|
|Turkey|No Trader|2%|3%|4%|46%|-|
||Importer|36%|24%|36%|7%|-|
||Exporter|10%|10%|12%|67%|-|
||Two-way|9%|23%|27%|92%|-|

Source: Author’s elaboration from Enterprise Survey (2014) <u>Difference across ownership groups: In all the countries of the SAR region except in Pakistan, foreign-</u> owned firms are more innovative both in technological and non-technological terms. However, differences in the innovation rate of domestic and foreign firms are relatively minor in the case of India when we look at product and process innovation. While this pattern is similar to the observed in ECA; it is different to the one displayed in Africa, where domestic firms introduce more products than foreign-owned ones. These findings are statistically different for product and process innovation in Bangladesh, product and process innovation simultaneously in India, marketing in Nepal, and process innovation and marketing in Pakistan.

15
economy _i_ , %Internet users _i_ , log GDP per capita ln(GDPpc _i_ ), and youth (age 20-35) population share, Youth share _i_ . This specification allows us to control for the scale effect and interpret the results as the correlation of other independent variables with the intensity of generative AI usage. We include the potential channels that may affect generative AI usage at the country level, indicated as vector _Xi_ . We also include the month fixed effects _δt_ to control for seasonality as well as global macro conditions in this panel regression specification. Notably, we use the GDP per capita and other indicators from 2022 or the most recent available data prior to 2022. This selection is driven by data availability and aims to mitigate reverse causality where the widespread adoption of generative AI adoption might influence key economic indicators, such as GDP per capita and exports of digitally delivered services. The summary statistics for the dependent variables are presented in Table A1, and the summary statistics and data source of independent variables are listed in Appendix Table A2.

Table 3 juxtaposes factors affecting the usage of ChatGPT and Google as a comparison. In Column (1), we regress ChatGPT’s monthly traffic (log) on four basic variables of an economy: GDP per capita, the percentage of population using the internet, population, and the percentage of the population aged 20-35. The coefficient of log population is slightly over 1, indicating an approximate unity scale effect. Internet user share is strongly related to monthly visits. A one percentage point increase in internet user share is associated with a 3.8% rise in ChatGPT visits. This coefficient is above 1%, implying a potential spillover effect among internet users: ChatGPT spreads faster and is more intensively used as the internet user base grows. We include the percentage of the population aged 20-35 to control for the demographic structure of an economy. The positive coefficient is consistent with the previous descriptive evidence that youth population is the main user of ChatGPT. Before adding other factors, the
3 diversification. We also draw implications for policy actions by nonrestricted countries and suggest that the recent increase in anti-dumping cases may be linked to this two-tier quota allocation practice.

# II. Two-Tier Quota Allocation Systems

Countries wishing to take into account export volumes to nonquota markets uasually rely on a combination of two criteria for the allocation of export licenses to the restricted country: (1) unit value of firm's exports in letter of credit; (2) export volumes to nonquota countries. In a previous paper, Bark and de Melo (1987), we show that taking into account unit value of firms' export is consistent with a government's objective of maximizing foreign exchange revenues and will usually lead to quality upgrading. The other criterion, export volumes to nonquota countries, is often used to encourage export diversification.

The usual practice then is to adopt a two-tier quota allocation mechanism: a 'basic' allocation based on export shares to the restricted market; and an open' allocation based on export shares to the nonrestricted market. For example, a two-tier quota allocation mechanism is among the criteria used for allocating rights to export clothing in Malaysia. 2/ Given that the multi-fiber agreement is so widespread, there is not much scope for diversification towards nonrestricted markets. Further evidence is available at the country level. Table 1 shows how Korea implemented her allocation criteria in 1985 (Rhee (1984)). What is striking is the uniformity of criteria across commodities subject to VERs. Furthermore, Rhee documents unofficial statistics by exporters' associations suggesting that the open quota is usually 152 to 202 of the total quota.
2001-05 periods. This effect was particularly large in South Asia, where it reached 2.7 percent per year. The authors shows that if Sub-Saharan African economies would cut the gap between their level of infrastructure and the average level of infrastructure in Pakistan or India by 50 percent, Central African low-income countries would

> 21 Using data from Uganda, Reinikka and Svensson (1999), for example, find that unreliable provision of electricity is a significant deterrent to investment.

> 22 Before road improvements, women had to spend an average of two hours per day collecting and carrying wood for fuel. Butane gas, used extensively in urban areas, did not reach the rural areas due to the high transport and distribution costs. Initially, a bottle of butane cost 20 Dh. Following improvement of the road, the price dropped considerably, to as low as 11 Dh, making it affordable for many families (World Bank 1996).

14
-13-

# IV. EFFECTIVE COST OF BORROWING

The derivation of the real lending rate (the effective cost of borrowing) draws on Ersel & Sak (1987). The main contribution of their work is the incorporation of the costs incurred due to the obligation to maintain low-interest compensating balances. In many developing countries such procedures are used routinely as a device to evade ceilings on lending rates.

Table 1 below gives the details of the calculation of the effective cost of borrowing for Turkey during the period 1980-1986. Line A gives the nominal interest for lending as shown in the Quarterly Bulletin of the Central Bank of Turkey. Line B is the sum of all commissions and taxes on loans. Line C simply adds A and B. Line D calculates the compounded rate. This is necessary because the Quarterly Bulletin presents quarterly rates annualized without compounding; this clearly underestimates the actual year-to-year cost of borrowing.

There is no direct information on compensating balances. We therefore follow the procedure suggested in Ersel and Sak (1987). Regressions of commercial deposits (DP) on Loans (L) were run across banks for each each year: DP - bo + b1 L The value of the coefficient bl is interpreted as the average compensating balance in that particular year. The logic behind this specification is that commercial deposits are held either for transactions purposes or as compensating balances. But the amounts held for transactions purposes should be uncorrelated with the amount of loans made by the bank and are hence reflected in the coefficient bO. Compensating balances on the other hand are clearly a function of the value of loans made by the bank. Line E presents
16 To study the effect of geopolitics, we consider a classic spatial autoregressive (SAR) model (see LeSage and Pace, 2009) as follows: where is a N-by-1 vector that contains growth shocks in N countries. captures the importance of spatial dependence. yy is a N-by-N spatial weight matrix that captures the distance between ρρ countries. We measure the spatial interdependence using geographical distance. The diagonal ρρ elements are zero. The off-diagonal elements are the inverse of the distance between country capitals. Next, the weight matrix is standardized so that the sum of row is unity.

To ensure a balanced sample, we use G7 countries in the AE block, and Brazil, China and Russia in the EM block. We estimate the growth shocks _y_ by filtering the growth rates using an AR(1) model. The SAR model is then estimated using maximum-likelihood estimation. The estimates of is 0.125 and is highly significant with a p value of 0.004. The evidence thus suggests the existence of spatial dependence. To measure the spatial effect, we rewrite the model as ρρ (1 −ρ )<sup>−1</sup> . The effect from the innovation _u_ is magnified through the spatial multiplier yy= (1 −ρ )<sup>−1</sup> . uuTable 7 reports the spatial multiplier matrix. A shock affects the home country by more than unity because of the “spillback”. Generally, the spillover is around 0.02. For neighboring countries, this effect could be much larger. For example, China has a spillover of 0.045 on Japan. Overall, the spillover from geopolitics are relatively small in comparison to the aggregate spillover documented earlier.
reading (5 items), writing (3 items), math (4 items), social (6 items), physical (3 items), problem-solving (6 items), technical (6 items), management (4 items), and digital (13 items). The questionnaire also pilots questions related to environmental skills (4 items) and the ability to work from home (1 item). The module requests that respondents rate each of the skills according to the frequency with which they are performed ranging from 1 for “Never” to 7 for “Hourly or more often.”

The SDS skills module was redesigned after carefully reviewing O*NET and other skills measurement surveys, particularly PIAAC and STEP, and taking into consideration experience from the Vietnam and Indonesia pilots. The module also reflects issues raised in the literature about the vagueness and complexity of O*NET wording and responses (Handel 2016). **Appendix 1** provides a comparison of the O*NET and SDS skills instrument. Several important changes were made.

- <u>Additional skills. The SDS instrument includes 56 skills while O*NET includes 35. Changes</u> were made after reviewing the skills concept and the PIAAC and STEP’s skills at work modules, which collect data on 46 and 56 skills, respectively). The SDS instrument adds complexity levels (for example, 4 levels of math), removes or merges highly related skills to prioritize and reduce the number of questions (for example, active listening), and adds skills that are growing in importance due to trends reshaping the skill and task content of work (for example, digital, environmental, and care skills).

- <u>Simplified language. O*NET skills concepts may be difficult to understand and interpret for</u> workers not accustomed to thinking in terms of skills taxonomies and concepts. To avoid misinterpretations that could result in measurement error, the Vietnam and Indonesia pilots introduced plain language definitions