rafmacalaba/datause-encoder-data huggingface dataset:
An F-test rejects the restricted form in which the model is the same when _D_ 93 _i_<sup></sup> 1 as _D_ 93 _i_<sup></sup> 0 ; F(2,128)=23.516; prob.<0.001. Differentiating the DPE between the 1993 benchmark and nonbenchmark countries doubles the share of the variance in ln( _P_ 05 _i_ /<sup>_P_</sup> 93 _i_ ) that is explained. The DPE is stronger for benchmark countries; Figure 6 plots the relationship. We now find that the estimated expected value of ln( _P_ 05 _i_ /<sup>_P_</sup> 93 _i_ ) is positive when ln( _Y_ 05 _i_ /<sup>_Y_</sup> 93 _i_ )  .0443, and this switch point is significantly different from zero (s.e.=0.058). Only when the growth rate (annualized log difference) exceeds 3.7% do we find upward pressure on the price level.

So, amongst the 1993 ICP participants one finds that the 2005 price levels are actually <u>lower</u> at given GDP per capita than those of 1993. To put the point another way, these results suggest that it is economic growth in developing countries that explains the upward shift in price levels implied by the 2005 ICP, rather than statistical factors such as the stricter quality standards in the 2005 ICP’s price surveys. The statistical-comparability problem appears to stem largely from the subset of 2005 ICP countries that had not participated in the 1993 ICP round.

Why is the Penn effect so much weaker for non-benchmark countries? Some explanations can be suggested. First, it might be conjectured that the ICP participation effect reflects the fact that the above model does not include all regressors used to predict the 1993 PPP for the non-benchmark countries.<sup>23</sup> The simplest way to check this is to see if the results change when one controls fully for the econometrically-estimated price level indices for the nonbenchmark countries in 1993, by re-estimating (6) in the following form:<sup>24</sup> The Penn effect is still weaker for the non-benchmark countries. So this is not the explanation. Second, possibly the ICP participation effect stems from non-linearities in the Penn effect, given that the non-benchmark countries tended to be poorer.<sup>25</sup> The following regression encompasses both the ICP participation effect and the negative interaction with initial GDP:

> 23 The missing variable is the secondary school enrollment rate.

> 24 Note that when _D_ 93 _i_<sup></sup> 0 , _P_ 93 _i_ is predicted based on the observed covariates used by the ICP.

> 25 The non-benchmark countries in the 1993 ICP had an (unweighted) mean GDP per capita of $2,268, as compared to $6,923 for benchmark countries.

13
17

# I1.6 Profit and Cost Dvnamics

The sharp contrast between the performance of AAA and A firms is brought out by Figure 9 on underlying profitability (see Glossary - underlying profitability attempts to track the profit rate on the basic business of firms, abstracting from sales of assets and net extraordinary gains). AAA firms are remarkably consistent over time. Interestingly, there was not much to choose between AAA and A firms at the start of the sample period; but the gap widens with time. Not surprisingly, the ratio of other income (mainly, sales and leasing of assets) to sales is the highest for A firms (Figure 10 plots this by quarter for 1991 and 1992).

<!-- Start of picture text --> Underlying Profitability Other Income/Sales<br>40.0X 12.0X<br>30.0o 10.0I.<br>20X.0. 8.0.O<br>I0.0X Cox.0X<br>0.0i 4.0X- E<br>-ZO.OX . -, -- .- . . . . ..... 2.0X- O . OX --<br>2 a9rv891 1|99090a OI91 91 91r11 91192*92 0.07. 191 191 o91 IV91 192 192<br>|-A^A - AA mw A i|A^AAA -AA l<br><!-- End of picture text -->

igure 9 Figure 10 The preceding discussion on adjustment is sumuarized in Figure 11, which shows the varying shares of underlying profits and costs in sales for 1990, 1991 and the first half of 1992. Costs are broken down into their main components consistent with the previous discussion on adjustment: materials and energy; labor; interest (working capital and investment loans; bank charges); and a residual category.Y AAA firms maintain underlying margins throughout and keep interest expenses in check. The increasing efficiency in materials and energy consumption is evident from the chart. Labor costs rise over time, but one must recall that these were compressed in 1990 to accommodate higher input and interest costs. For A firms, the story is one of rapidly growing interest payments and labor costs (in spite of the fastest rate of labor shedding among the sample firms). Profits all but disappear with a small underlying loss in 1992 (not visible from the chart). Given that materials and energy costs are amply covered, however, these 4Finns report two categories of costs: (a) costs of sales (without any breakdown); and (b) total costs per quarter, which are also broken down into individual cost items (nterat etc). Total costs refer to costs of production, which go partly into sales and partly into inventory accumulation. For Figure 11, the breakdown of the cost of sales is obtained by takng the individual cost items reported in total costs and scaling by the ratio of the costs of sales to total costs.
donor creates a public good for all other donors. Notice that this property of the equilibrium emerges because ML’s allocation rule combines both policy selectivity and poverty selectivity. A poverty-selective donor is inequality averse and aims to equalize resources provided policy levels are identical.

There are large returns to policy selective aid: For example, if _β_ = 0 _._ 5, _r_ 1 = 40, _r_ 2 = 30, and _m_ 1 + _m_ 2 = 60, then the public good with non-policy selective aid equals 0.5. With policy selective aid, the public good is nearly ten times as large, namely 4.7, and policy increases from the no-aid policy level of 0.00625 to 0.37. If the donor were able to implement all of its aid to produce the public good without aid being diverted to produce the private good (no aid fungibility), then the public good would be equal to 4.5. Thus, policy selectivity not only mitigates aid fungibility but it also gives recipients an incentive to invest more of their own resources into the production of the public good _G_ .<sup>7</sup> Notice that both recipients are better off taking part in the contest for aid. Recipient 1’s payoff in the noaid situation equals 40.25 whereas its payoff in the equilibrium described above equals 42.36. Since recipient 1 has more resources than recipient 2, it receives less aid in equilibrium. This implies that if it is rational for this recipient to participate in the contest for aid then it is also rational to do so for recipient 2. This numeric example shows that a contest for aid can give recipients strong incentives to improve policy. If _β_ increases to 0.9, then recipients’ incentives to invest in the public good without aid are reduced and the public good equals 0.387 instead of 0.5 as before. However, the contest for aid now is substantially more competitive. In fact, the FOCs produce _p_<sup>_M_</sup> = 1 as a solution. However, this is not an equilibrium as recipients have an incentive to deviate to the no-aid policy level given the opponent plays _p_<sup>_M_</sup> = 1. However, if _r_ 1 = 35, _r_ 2 = 25, and _m_ 1 + _m_ 2 = 60, then _p_<sup>_M_</sup> = 1 is the unique equilibrium outcome in this game. Compared to before, total resources remain at 120. Thus, given total resources in an economy, there is always a distribution of resources in terms of _r_ 1, _r_ 2, and aid budgets such that policy choices are part of a pure strategy equilibrium in the game.<sup>8</sup>

# **3.2.2 Endogenous Delegation**

Proposition 2 assumes delegation. We need to show that donors are indeed better off delegating aid to ML. The case for delegation is immediately apparent if _α_ 11 = 1. In this case, donor 1 cares only about the public good for recipient 1, and donor

> 7Cordella and Dell’Ariccia (2007) find a similar result when analyzing the benefits of ‘conditional aid’ as opposed to ‘project aid.’

> 8This statement can be proven easily. For _rj_ = 0 for _j_ = 1 _,_ 2 each recipient is strictly better off to participate in the contest for aid. The equilibrium is unique and in pure strategies in this case.

19
**<u>Table 2: Households’ land rights, tenure, and demand for documentation</u>**

||<br>**Total**|<br>**Regi**|<br>**on**|||**Province**|||**Head m**|**arried**|
|---|---|---|---|---|---|---|---|---|---|---|
|||**Rural**|**Urban**|**Lusaka**|**North**|**East**|**West**|**South**|**Yes**|**No**|
|No. of parcels|1.527|1.646|1.234|1.225|1.511|1.760|1.747|1.570|1.559|1.456|
|Area (median)|1.200|1.600|0.103|0.800|1.000|1.215|0.540|3.000|1.280|0.800|
|Of which used by males only*|0.622|0.631|0.600|0.637|0.746|0.657|0.507|0.727|0.806|0.173|
|... share used by women only*|0.283|0.268|0.316|0.327|0.227|0.250|0.323|0.274|0.079|0.778|
|... share used jointly with women*|0.110|0.130|0.064|0.045|0.058|0.122|0.216|0.004|0.143|0.031|
|Has right to sell land|0.394|0.261|0.721|0.748|0.369|0.256|0.189|0.208|0.386|0.412|
|... if yes, women have a say|0.397|0.433|0.370|0.368|0.332|0.308|0.417|0.256|0.211|0.820|
|Has right to bequeath land|0.428|0.318|0.699|0.752|0.472|0.216|0.297|0.241|0.426|0.435|
|... if yes, women have a say|0.410|0.450|0.371|0.379|0.283|0.345|0.502|0.189|0.240|0.799|
|Has some document|0.314|0.150|0.718|0.674|0.235|0.111|0.153|0.176|0.300|0.346|
|Has formal title|0.109|0.011|0.349|0.306|0.013|0.022|0.003|0.060|0.098|0.134|
|... if yes, woman included|0.347|0.376|0.344|0.322|0.147|0.489|1.000|0.345|0.121|0.724|
|Has incomplete title|0.071|0.023|0.189|0.167|0.032|0.014|0.026|0.064|0.068|0.076|
|... if yes, woman included|0.326|0.252|0.348|0.402|0.501|0.118|0.322|0.226|0.112|0.765|
|Has informal certificate|0.072|0.073|0.069|0.068|0.072|0.038|0.093|0.019|0.077|0.060|
|... if yes, woman included|0.241|0.240|0.243|0.205|0.116|0.046|0.419|0.000|0.091|0.683|
|Has no title|0.884|0.970|0.675|0.731|0.973|0.969|0.971|0.898|0.899|0.851|
|...... if yes, would like title|0.543|0.495|0.713|0.825|0.602|0.590|0.282|0.272|0.568|0.484|
|... if yes, mean WTP (ZMK)|754.01|666.53|968.14|949.21|756.39|521.01|556.61|464.37|803.84|613.83|
|... if yes, median WTP (ZMK)|350|250|500|500|150|200|350|100|350|200|
|Tenure perceived insecure|0.428|0.450|0.372|0.426|0.428|0.713|0.209|0.110|0.450|0.377|
|Tenure perceived very insecure|0.262|0.280|0.219|0.241|0.302|0.486|0.080|0.056|0.278|0.227|
|No. of sample households|4,888|3,703|1185|491|525|761|491|566|3,455|1,433|
|Population size|1,709,593|1,214,953|494,640|184,147|167,765|327,931|150,558|251,872|1,186,526|523,067|

_Source_ : Own computation from 2017/18 Quarterly Labor Force Survey conducted by Zambia’s Central Statistical Office. Sampling weights adjusted with non-response probabilities are used to estimate the mean values.

_Note_ : Questions on area and gender-differentiated transfer rights were administered only in quarters 3 and 4 (to a total of 2,637 households).

*The sum does not add up to one as households own more than one parcel with different ownership structure. The figures should thus be interpreted as the share of households with at least one parcel owned by male only, female only or jointly.

22
65

## Table III.10

# Distribution of Average Price Paid for Contraceptives by Area of Residence for Jamaica and Thailand (1984 U.S. Dollars)

|Price Ran¶jesa|Urban|Rural|
|---|---|---|
|Jamaica|||
|Free|50.4|45.5|
|S.01-.20|17.0|10.1|
|$.21-.50|17.8|27.0|
|$.51-2.00|8.8|9.5|
|>$2.00|6.0|7.9|
|Thailand|||
|Free|29.3|46.8|
|$0-$.47|11.3|13.2|
|$.48-$1.10|15.4|8.8|
|$1.11-$4.26|14.1|12.2|
|>$4.26|29.9|19.0|

Source: Akin, 1984.

a. See Table 111.9 for exchange rates used for conversions.

**n.Tabe 111.10**
and September-December. The PoI data has information on one’s employment status, time usage, and demographic characteristics like gender, education level, and marital status. The PoI data shows how much time a person spends on household activities, at work, and traveling. Reported time on travel is spent by a person traveling from one place to another for various purposes, including work-related activities. The CP survey does not ask specific questions about time spent commuting to work, searching for a job, or on leisure.

We use six waves of PoI from May-August 2020 to January-April 2022 (or from the 20th to the 25th wave). We also match the households in the PoI data to those in the CP data. Appendix Table D2 lists the study periods for the two sectional data sets. We restrict our sample to women (or households having women) aged between 15 and 65 at their in the data. Table D3 lists the variables used in the first appearance Appendix analysis and their definitions. Appendix Table D4 presents the summary statistics of our study sample. Panel A displays the household characteristics in December 2020. Differences between households in treated vs. control HRs regarding rural residence, number of people, and per-capita income and expenditures are relatively small. In panel B, we compare women in treated HRs to those in control HRs in May-August 2020. The distributions of age, marital status, and education are comparable for the two groups of women. Women in treated areas are less likely to participate in the labor market, but conditional on participation; they are more likely to be employed. They also tend to more time on household activities and work but less time on travel than women spend in control areas.

# **3.2 Delhi Primary Survey**

As explained above in footnote 28, we cannot include Delhi in our baseline analysis. To complement our inquiry, however, we use primary data collected in February 2020 by the Gesellschaft f ̈ur Internationale Zusammenarbeit (GIZ) India (Mahendru, 2022). This survey is specifically designed to evaluate changes in bus usage among women in Delhi following the implementation of the _Pink Slip_ scheme. The survey collected responses from 2,025 women, of which 1,294 had been using the bus before the scheme’s introduction; we refer to this group as “continuous users.” Additionally, 231 respondents who began using buses after the scheme’s introduction are termed “new users.” The remaining 500 respondents who did not use buses before or after the scheme are labeled as “non-users.” The was selected at attractions and across sample randomly major generation points 6
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Figure 4. Poverty Estimates 2009-2013 Rest of the Country <!-- Start of picture text --> Host communities<br>80<br>70<br>60<br>50<br>40<br>30<br>20 21.1 20.8 19.3 18.4 17.9<br>10<br>0<br>2009 2010 2011 2012 2013<br>Settled Migrant Households in the Rest of the Country (>4 years in Turkey)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10 9.1 10.7 9.3<br>7.0 6.6<br>0<br>2009 2010 2011 2012 2013<br>Recent Migrant Households (Rest of the Country, <=4 years in Turkey)<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>16.1<br>13.3 13.2<br>10<br>7.3<br>5.2<br>0<br>-10<br>2009 2010 2011 2012 2013<br>Poverty Rate (%)<br>Poverty Rate (%)<br>Poverty Rate (%)<br><!-- End of picture text --> _Notes_ : Regionally deflated, OLS imputation model, with time-invariant characteristics. Vertical line represents standard error. Poverty line is $5/day PPP per capita. The Near Syrian Border region (NSB) includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, TR62. Time in Turkey is based on the date of arrival of the head of household. Results displayed at the 95% Confidence Interval.

_Source_ : LFS 2009-2014. World Bank author’s calculations.

23
Table (D4) Impacts of the Program on Physical Health

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Subjective health (Scale 1-4)|-0.12|(0.11)|[0.32]|3.34|(0.70)|1,610|
|Disability level 1 (=1 if disabled)|0.10|(0.06)|[0.17]|0.61|(0.49)|1,610|
|Disability level 2 (=1 if disabled)|0.06|(0.08)|[0.37]|0.41|(0.49)|1,610|
|Disability level 3 (=1 if disabled)|-0.00|(0.06)|[0.71]|0.26|(0.44)|1,610|
|Disability level 4 (=1 if disabled)|0.04***|(0.01)|[0.01]|0.01|(0.08)|1,610|
|Respondent hunger last week (=1)|0.23***|(0.07)|[0.01]|0.35|(0.48)|1,604|
|Adults hunger last week (=1)|0.17***|(0.06)|[0.01]|0.38|(0.49)|1,610|
|Respondent hunger last week (Days)|0.65***|(0.22)|[0.01]|0.91|(1.46)|1,604|
|Adult hunger last week (Days)|0.55***|(0.21)|[0.01]|0.79|(1.36)|1,444|

**_Notes_** :

- The table shows the regression results on pre-specified physical health outcomes using the phone survey midline data. Each row is its own dependent variable.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- The outcomes that require definitions are: Disability is defined using the Washington Group Short Set on Functioning and equals 1 if the respondent is classified as disabled and 0 otherwise. Disability 3 is the recommended cutoff and as we moce from Disabiloty 1 to Disability 4, disability level becomes more extreme. Respondent (and adult) hunger equals 1 if the respondent went to bed hungry on at least one day in the last week and 0 otherwise.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age).

- In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 6 in the pre-analysis plan.

7
# **2  MEASURING SERVICES TRADE RESTRICTIVENESS**

The method used to generate the STRI is an updated version of the STRI developed by the World Bank a few years ago In updating the World Bank STRI, the main principles were maintained but several improvements were introduced; namely the use of an algorithm and an automatized aggregation method to construct the STRI. Introducing these technical improvements removes the subjective bias that may have existed in the previous World Bank STRI. The improved methodology will be briefly described below, and more information may be found in Borchert et al. (2019).

The construction of the STRI follows the same principal steps as in Borchert et al. (2012):

- 1) the **selection** of key restrictions entering the STRI; 2) the **determination** of the level of restrictiveness of individual measures (or bundles of measures if they are conceptually intertwined, which we call ‘synthetic measures’); and

- 3) the **aggregation** of measures into indices at the sector-mode level, sector level, and economy level, respectively.

# **2.1  Selection**

While the Services Trade Policy Database (STPD) contains a large number of measures, the index is based upon a subset of key restrictions to trade in services, approximately 115 regulations and policies. The choice of key restrictions entering the quantification exercise was driven by a number of considerations: 1) to cover the most significant restrictions – this was based on the previous analysis conducted by the World Bank as well as expert advice; 2) to respond to recent regulatory developments perceived as having a discernible impact on trade in services, e.g. screening policies, restrictions relating to cross-border data flows; 3) to ensure comparability with the 2008-2011 World Bank exercise; and 4) to adapt to – and be consistent with – the coverage of measures in the OECD database, which served as a fundamental source of information for economies covered by both the STPD and the OECD STRI. The list of measures selected for the construction of the STRI is provided in Annex 5 of Borchert et al. (2019).

# **2.2  Determination of level of restrictiveness**

Trade policies are assessed by looking at specific types of measures for each subsector and mode. For example, for commercial banking, we obtain separate measures for the cross-border supply, commercial presence and the presence of natural persons. We assign a score depicting the level of restrictiveness to each measure. For each individual measure, we generally consider 6 distinct levels of restrictiveness, each of which attracts a different (monotonically increasing) score:

1. not restrictive;

2. minimal procedural/transparency issues; 3. minor restriction; 4. “intermediate” category to reflect measures that are neither minor nor major restrictions; 5. major restriction; 6. service provision not possible.

Table 1 provides examples of concrete policy measures and associates a score to measures for each stage.

7
The final figure W is divided by population in order to obtain the wealth per capita.

With respect to the crude oil sector, the robustness of the estimation has been challenged by the intrinsic volatility and fragmentation of some essential pieces of information, such as the price and the costs. As it is known, the international price of crude oil has been characterized by highly unpredictable trends, highly sensitive to a large number of market factors, including psychological ones, and as such very hard to predict. For these reasons, this paper assumes a constant price (adjusted for inflation over time) equivalent to USD100 a barrel. As for the total costs of production, the difficulties in obtaining punctual information on the Mauritanian sites led to opt for regional averages for Africa, provided by the online database of the USA Energy Info Administration (45 $/Baril).

In the case of gas, this paper assumes a unit price of 8.5 $/mmBTU and fixed costs of production equivalent to USD 15 million a year<sup>68</sup> . A USD 600 million upfront investment is estimated initially, while the production is assumed to start from 2016 and to be equivalent to 15.3 million mmBTU, with exhaustion time of the resource estimated in 22 years<sup>69</sup> . Also in this case a 3 percent inflation rate is computed.

The recent discovery of gas in the Mauritanian subsoil is a decisive addition to further stimulate and intensify the economic development of the country, with a strong potential to effectively incentivize poverty eradication objectives. As a matter of fact, Mauritania suffers from serious shortages of power resources, current electricity production costs are high<sup>70</sup> , and the country capacity will not be in a position to address this issue by using sustainable methods before a number of years. By 2017, aggregate demand of energy is expected to grow higher than 500MW, spurred by an increase in domestic demand but mainly driven by a skyrocketing demand from mining companies for the development of new projects (approx. 400MW).

Gas-exploration campaigns offshore allowed the identification of massive gas resources in the Banda field, currently operated by Tullow Oil, which are supposed to be larger than 1 Tcf<sup>71</sup> , and thus sufficient to supply a gas power plant with a capacity of at least 350 years for roughly 20 years. The World Bank is currently supporting the Government with a Guarantee project (USD100 million) meant to facilitate the conclusion of an ambitious development program of the gas field.

> 68 Based on World Bank staff estimates, also used for the Banda field gas-to-power project.

> 69 This is in line with the feasibility studies conducted on the amount of the gas deposit. In addition, a 22-year cap allows dealing with the exact same period of time, with 2038 as the final year.

> 70 Due to the high price of energy commodities (hydrocarbons) and to considerable loss ratios.

> 71 Tcf is a volume measurement of natural gas, corresponding to one trillion cubic feet (1,000,000,000,000 cubic feet).

34