rafmacalaba/datause-encoder-data huggingface dataset:
Fig. 4: Teledensity of fixed lines in a sample of North African countries <!-- Start of picture text --> Teledensity of fixed lines<br>12<br>Algeria<br>10 Tunisia<br>8 Morocco<br>Egypt<br>6<br>4<br>2<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br>% Population<br><!-- End of picture text --> Source: ITU data.

<mark>The slowing down of the public investment effort does not therefore totally explain the underperformance of the sector. In fact, the performance of the ministry of post and telecommunications during these years did not deviate from the overall performance observed in the rest of the economy (World Bank, 2002). Algeria was locked in a command economy involving large inefficient and overstaffed public administrations or enterprises. The poor performance of the telecommunications sector was probably due to inappropriate structural organization. The authorities wanted to change that situation and launched a comprehensive sector reform in 2000.</mark>

# **3 THE REFORM PROGRAM –**

In December 1999, the telecommunications network in Algeria counted approximately 1,600,000 fixed lines and 72,000 mobile subscribers. The number of Internet subscribers was estimated at 20,000 and cybercafes were only available in large towns. With respect to the country income per capita, connectivity was low and overall in poor condition. Conversely, the post and telecommunication ministry employed around 45,000 civil servants, and its labor unions were among the most powerful in the country. When the government announced its intent to reform the sector, most domestic and international observers expressed their skepticism. There was indeed a strong political will to proceed with a fast track but comprehensive telecommunications reform. The main objectives of the government were: (a) to use a successful telecommunications reform as a catalysis to structural reforms in other industries or sectors, and (b) to raise substantial fiscal proceeds through the sale of licenses. To achieve both objectives, the government pledged to implement market liberalization measures through competitive and transparent 6
heads. (Recall that we have used a weaker definition, closer to what could be implemented in practice given the information available to local administrators.) The theoretical target population identified by the decree turns out to be quite narrowly defined, representing a small fraction (5%) of the population at the baseline. About 45% of the heads have dependents aged less than 18 or incapacitated. Of those, only 12% of the sample of heads with dependents were unemployed as of October 2001 (80% were employed, and 8% were inactive).

Contrary to the theoretical target population, these practical eligibility criteria are quite broad and only slightly progressive (Table 7). These criteria, if perfectly enforced, would allow _Jefes_ to reach about 50% of the poor at a poverty line of about 100 pesos.

To help assess the distribution of gains from the program, Table 7 gives selected points on the concentration curve using the cross section samples, under the assumption of zero foregone income and for our preferred estimates of foregone income (discussed in detail later). Comparable results are also given for the preceding _Trabajar_ program. Figure 1 gives the complete concentration curves. _Jefes_ is clearly not as well targeted as _Trabajar_ . This is consistent with expectations that the work requirement was not as tightly enforced in _Jefes_ . However, spending on _Jefes_ appears to be better targeted than other categories of social spending in Argentina. World Bank (1999) (quoting Gasparini, 1999) presents estimates of the concentration curves for overall social spending indicating that the poorest 20% receive 22% of the outlays (30% for the sub-component of social services) while the next poorest 20% receive 20% (19% for social services).<sup>8</sup>

> 8 Analogously, the concentration curve shows that targeting performance for _Jefes_ is better than a median transfer program by international standards and Latin American standards, as indicated by the results of Coady, Grosh  and Hoddinott (2002),. A median social assistance program in Latin America is 60% more progressive than a neutral allocation (compared to 25% of a median transfer program in developing countries).

14
Table 3: LPM results for regressions as correlations

||**(1)**<br>**h1**|**(2)**<br>**h5**|**(3)**<br>**e6**|**(4)**<br>**h8**|
|---|---|---|---|---|
|Index * ln(D/L)|0.016|0.025|‐0.041**|0.003|
||(0.310)|(0.215)|(0.025)|(0.881)|
|Constant|0.265***|0.432***|0.135***|0.188***|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Observations|8775|7694|7654|7773|
|R2A|0.142|0.140|0.033|0.143|
|R2W|0.000|0.000|0.001|0.000|
|RMSE|0.397|0.454|0.380|0.359|

Note: * p<0.10; ** p<0.05; *** p<0.01, representing p‐values not standard errors. The dependent variable h1 stands for whether a new product / services has been introduced over the last 3 years? Yes = 1 | No = 0. The variable h5 whether the firm has new process introduced over the last 3 years? Yes = 1 | No = 0. The variable e6 whether the firm has used technology licensed from a foreign company? Yes = 1 | No = 0. The variable h8 whether the firm spent on new R&D (excl. market research) in last 3 years? Yes = 1 | No = 0. The term (D/L) is comprised of non‐capitalized computer software expenditures over labor. Fixed effects by country, sector and year applied separately. Robust standard errors clustered by country‐sector.

Table 4: Probit estimates for regressions as correlations

||**(1)**<br>**h1**|**(2)**<br>**h5**|**(3)**<br>**e6**|**(4)**<br>**h8**|
|---|---|---|---|---|
|Index * ln(D/L)|‐0.014|0.121|‐0.336***|‐0.122|
||(0.887)|(0.283)|(0.001)|(0.297)|
|Observations|9988|8855|9276|8933|
|LR chi2(10)|1193.96|1072.52|215.74|947.12|
|No. groups|32|32|24|12|
|Loglikelihood|‐4690.1|‐5207.8|‐4045.7|‐3475.1|

Note: * p<0.10; ** p<0.05; *** p<0.01, representing p‐values not standard errors. The dependent variable h1 stands for whether a new product / services has been introduced over the last 3 years? Yes = 1 | No = 0. The variable h5 whether the firm has new process introduced over the last 3 years? Yes = 1 | No = 0. The variable e6 whether the firm has used technology licenced from a foreign company? Yes = 1 | No = 0. The variable h8 whether the firm spent on new R&D (excl. market research) in last 3 years? Yes = 1 | No = 0. The term (D/L) is comprised of non‐capitalized computer software expenditures over labor. Fixed effects by country, sector and year applied separately. Data is grouped by sector.

24
online appendix

# **A.1.3 EBM descriptive statistics**

Table A1: Masks in EBM

||Manufacturer<br>(1)|Retailer/Trader<br>(2)|Importer<br>(3)|
|---|---|---|---|
|# sub-Districts|44|137|91|
|# mask purchasing sub-Districts|44|135|91|
|# mask selling sub-Districts|14|52|15|
|# frms|410|1,408|1,109|
|# of mask purchasing frms|393|1,240|1,083|
|# of mask selling frms|23|285|51|
|# receipts|965|25,864|5,041|
|# of mask sales|1,025|26,261|5,112|
|# of masks|1,070,200|1,080,615|482,186|
|Mask sales, RwF|415,697,270|507,994,654|795,044,037|

_Notes:_ Summary statistics, either counts or total values, on mask sales in EBM during our study period are reported in this table. Column 1 reports statistics on sales by manufacturers, Column 2 reports statistics on sales by non-manufacturers excluding mask importers, and Column 3 reports statistics on sales by non-manufacturer mask importers.

Table A2: Mask supply chains in EBM

||Sales to (destination)<br>sub-District|Purchases from (origin)<br>sub-District|
|---|---|---|
||(1)|(2)|
|sub-Districts|2.43 (2.36)|11.29 (11.32)|
|Firms|3.00 (3.75)|29.57 (40.58)|
|Receipts|14.85 (32.86)|68.93 (105.35)|

_Notes:_ Average counts on mask sales in EBM during our study period are reported in this table, with standard deviations in parentheses. Column 1 reports counts for sales to each destination subDistrict (excluding destination sub-Districts with no purchases), while Column 2 reports counts for sales from each origin sub-District (excluding origin sub-Districts with no sales).

A8
that a similar price variance for non-statutory and statutory plots reveals the existence of information asymmetry.

Second, the spatial pattern of land tenure transformation is dierent according to whether brokers are informed or not. In general, informed brokers progressively attempt to transform land tenure across the urban space in the interval [ _<u>x, x</u>_ <u>]</u> , while uninformed brokers attempt to transform the whole urban space beneath some distance from the city center _x_<sup>_∗_</sup> . This highlights a spatial dierence in terms of transaction frequency between the two versions of the model: in the uninformed case, property right conversions abruptly cease beyond the critical location _x_<sup>_∗_</sup> , whereas in the informed case, there can be a smooth phasing out of property right conversions over [ _<u>x, x</u>_ <u>]</u> .

# 6 Empirical analysis

## 6.1 Data

We use the data of a unique survey of 1 _,_ 655 land plots that were transferred as unbuilt land plots in Bamako, Mali, between 2009 and 2012 (Durand-Lasserve et al. 2015). Current information was obtained at the year of the survey (2012) and retroactive questions were asked regarding the situation at the time of the transaction (between 2009 and 2012). The database reports characteristics such as price, tenure status, location (GPS coordinates), intended land use (i.e., residential vs. agricultural), surface area, infrastructure and services, municipality, distance to paved main road and river, as well as details on buyers and sellers.<sup>9</sup> The sampling ensures extensive coverage of the Bamako greater area, at regular intervals along paved main roads extending outward from Bamako. The coverage of the survey area makes it possible to draw conclusions regarding the characteristics of the transfers and provides the best possible sample that could be constructed in the Malian context for an empirical analysis. Figure 3 displays the land plots on the Bamako map.

We drop the observations located farther than 40 km from the city center, those without residential or agricultural use and those without some missing data, which leaves 1 _,_ 259 observations. We consider two types of land rights: non-statutory rights, which includes both customary land and other plots held without any property rights, and statutory rights, which includes land plots with property rights such as permits to occupy and title deeds. We also consider two land uses: agricultural and residential. Therefore, combining land right and use types, we focus on three categories: statutory residential, non-statutory residential and non-statutory agricultural. We exclude statutory agricultural plots because they make for a tiny minority of plots and lie outside the scope of our model. This reduces our sample to 1 _,_ 150 land plots.

> 9Information on each plot was collected by a team of investigators through a variety of local informants (neighbors, informal brokers, customary chiefs, buyers, users, sellers, and elected local ocials).

20
# _Clinical performance in outpatient consultations_

The competence of health personnel is a major problem in several countries and especially in rural and poorer areas. Clinical performance in outpatient consultations will be measured through Patient Case Simulations (also called “health vignettes”). With this methodology one of the surveyors act as a case study patient with some specific symptoms. The clinician who is informed of the simulation is asked to proceed as if the enumeratror is a real patient, while another enumerator acts as an observer. High quality performance in outpatient consultations entails at least the following: (i) To systematically arrive at a correct diagnosis (or preliminary diagnosis); (ii) To provide an appropriate treatment (or referral); and (iii) To reveal important information to the patient about which actions to take (e.g., how to take medicine, what to do if the patient does not get better, etc.). The methodology presents several advantages: (a) All clinicians are presented with the same case study patients, thus making it easier to compare performance across clinicians; (b) The method is quick to implement, and does not require waiting for patients with particular diagnoses; (c) We avoid intrusion and ethical issues that would arise if we were studying real patients’ cases. The method also has its drawbacks. The most important one is that the situation is a not a real one and that this may bias the results.<sup>9</sup>

# _Time spent counseling patients per day_

This measure will be based on aggregating data from the observational study of medical personnel. In the observational study the clinician is observed during a short period. Then combining data on number of patients treated per day (or week) with the observational data on the time spent on each patient, it is possible to calculate the total time spent counseling patients per day.

# <u>Budget</u>

_Effective health expenditure reaching primary clinics (in % of the health budget)_ Similar as for education.

# _Delays in salaries_

Similar as for education.

# **2.4.3. Discussion**

Referring back to the service delivery production function (1), we are trying to measure effort

> 9 However, several studies that have compared Patient Case Simulations with Direct Observation of clinical performance have concluded that there is a high correlation between the performance scores under these different alternatives (Leonard and Masatu, 2007, Das, Hammer and Leonard, 2008). On balance, we therefore think that this approach is suitable for measuring clinical performance.
migration. In addition, they use geographic variables and passport costs as instruments to control for potential endogeneity problems.

# **Forced Displacement**

The study of forced displacement is often associated with people fleeing from conflict. This may include, but is not limited to, generalized violence, armed conflict or persecution. As defined by the 1951 Convention on Refugees, a refugee is ‘a person who is outside his or her country of nationality or habitual residence; has a well-founded fear of being persecuted because of his or her race, religion, nationality, membership of a particular social group or political opinion; and is unable or unwilling to avail him- or herself of the protection of that country, or to return there, for fear of persecution’ (UNHCR, 2007, p. 3). While many individuals fleeing persecution may seek assistance across international borders either in the form of asylum or through temporary assistance, others may fear being returned and choose to remain anonymous.

Accurately recording refugees in data is extraordinarily difficult. The level of accuracy often depends on the destination of the refugees, the size of the displacement, and whether the refugee chooses to integrate into the destination community. The United Nations High Commissioner for Refugees (UNHCR) maintains a statistical database of refugees across the world. The data are usually reported by local UNHCR offices and reflect host-country data. In the developing world, this information largely incorporates refugees in camps, but may not include those who have integrated into society. In 25 industrialized countries, the data are estimated based on individual-country recognition of 40
in the economy. Amaral & Quintin (2010) use the database compiled by Beck _et al._ (2000) and estimate an intermediated capital to output ratio of 2.2 for the US economy. This database does not contain financial market information for Afghanistan and hence we are unable to estimate the intermediated capital to output ratio for the country. In the absence of this estimate, we calibrate � by matching the ratio to 1.1 which approximately corresponds to the estimated ratio for India. As the credit markets in India are likely to be much more efficient than those in Afghanistan, the chosen value of the intermediated capital to output ratio is a conservative estimate of the actual level of imperfections present in the country. Together with tax and ability parameters, a credit market efficiency �<sup>���</sup> of 0.27 pins this targeted value. Table 4 reports the values of the calibrated parameters and the targets used in the exercise.

**Figure 5: Size Distribution of Firms in Afghanistan**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 ‐ 19 20 ‐ 99 100 and above<br>Number of Employees<br>2014 2008<br>Share of Total (%)<br><!-- End of picture text -->

**<u>Table 4: Calibration to Afghanistan: Estimates and Targets</u>**

|**Parameter**|**Estimate**|**Target**|
|---|---|---|
|Real rate of interest:�|3.66|Annual real rate = 4%|
|Depreciation rate:�|0.81|Annual depreciation rate = 8%|
|Rental rate:�|4.47|���|
|Production function parameters:��, ��|(0.28, 0.57)|Capital share = 1/3<br>Concavity = 0.85|
|Discount rate:�|0.46|Excess savings to investment = 40%|
||**Calibrated Join**|**tly**|
|Ability distribution parameters:��, ��|(-2.80, 1.1)|Labor share of firms < 20 workers = 69%<br>Labor share of firms�100 workers = 6%|
|Credit market efficiency:�|0.27|Intermediated capital to output = 1.1|

24
incumbent party and its performance at the ballot box (Olivera and Lora 2005), however this situation, as shown in Table 2 and Figure 6, may be quite different by 2030.

**Table 2 Composition of the global middle class in 2000 and 2030 (%)**

||2<br>|000<br>|2<br>|030<br>|
|---|---|---|---|---|
||Pop.|Income|Pop.|Income|
|Poor (per capita income below the average of Brazil)|81.7|29.1|61.9|15.5|
|Middle class (per capita income between Brazil and Italy), of which:|7.9|14.6|16.6|14.4|
|High income country nationals|3.7|7.4|0.8|0.8|
|Low and middle income country nationals, of which:|4.1|7.2|15.8|13.6|
|East Asia and Pacific|1.5|2.6|8.9|7.8|
|Eastern Europe and Central Asia|0.9|1.6|2.3|2.0|
|Latin America and Caribbean|1.1|2.0|1.9|1.6|
|Middle East and North Africa|0.4|0.7|0.8|0.6|
|South Asia|0.0|0.0|1.7|1.4|
|Sub-Saharan Africa|0.2|0.3|0.3|0.2|
|Rich(percapitaincome at orabove the average of Italy)|10.4|56.3|21.5|70.1|
|Total|100|100|100|100|

Source: Authors’ calculations based on simulations from the GIDD.

Our estimates show that the GMC is likely to grow from 430 million in 2000 to 1,147 million in 2030, more than doubling its share in the global population (Table 2). Furthermore, the composition of this group of consumers is likely to change radically: while in 2000 developing country nationals accounted for 52 percent of the GMC, by 2030 they are likely to represent 95 percent of its population (Table 2). The biggest contributors to the increase in the number of the GMC members are the Asian countries led by China and India. These two countries alone are responsible for nearly two-thirds of the entire increase in the GMC, with China accounting for 52 percent of the rise in the GMC population and India adding another 12 percent (Bussolo et al., 2007). At the same time, the re-orientation of the global middle class towards citizens of low and middle income countries is very broad-based, with all six World Bank developing regions contributing to the growth in the GMC. As shown in Figure 6, nearly all developing countries in our sample see an increase in the proportion of the GMC members in their populations. There are only six exceptions to this observation, and five of them—the 17
Table 12: Political Credit Risk Ratings

||(1)<br>short term risk (0-7)|(2)<br>short term risk (0-7)|(3)<br>short term risk (0-7)|(4)<br>mid term risk (0-7)|(5)<br>mid term risk (0-7)|(6)<br>mid credit risk (0-7)|
|---|---|---|---|---|---|---|
|peace|-0.455***|-1.017***|-0.990***|-0.197**|-0.366**|-0.418***|
||(0.156)|(0.264)|(0.242)|(0.0806)|(0.142)|(0.119)|
|frst fve years of recovery||0.699***|0.356||-0.136|0.106|
|||(0.181)|(0.294)||(0.183)|(0.175)|
|fragile peace||0.352|||0.334***||
|||(0.294)|||(0.0982)||
|fragile peace (ftted values)|||1.648*|||0.824*|
||||(0.913)|||(0.480)|
|Country FE|YES|YES|YES|YES|YES|YES|
|Observations|3,619|3,619|3,613|3,600|3,600|3,594|
|R-squared|0.006|0.029|0.028|0.004|0.015|0.016|
|Number of countries|203|203|203|203|203|203|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The dependent variable in columns (1)-(3) is a measure of the short term risk of credit default on a scale from 1 (low risk) to 7 (high risk) provided by ONDD. The dependent variable in columns (4)-(6) is the ONDD proxy of the mid term risk of credit measured on the 1-7 scale. “Peace” is a dummy that indicates when a country is not in conflict, i.e. violence intensity is above a threshold of 0.008 battle-related deaths per 1000 inhabitants. “First five years of recovery” is a dummy that indicates if the end of the most recent conflict has been less than five years ago. “Fragile peace” is a dummy that takes a value of 1 if a year is peaceful, within five years of the last conflict and reverts back to conflict within the next five years. Fitted values of “fragile peace” is coming from a regression of “fragile peace” on “share of the population excluded from political power”, “share of the population that is discriminated” and “refugees per 1000 population”.
45

**Table** ZZ2.1

**Contraceptive Mthod.** by Source AMag Current Usors for Selectd Counttrieu

|Country(Year)|Cantraceptive<br>trevance<br>vovrNrnt<br>Nationde|-<br>(Percent)|Commerciala<br>(Percent)|no<br> <br>(Percent)|0th.rb<br>(Percent)|
|---|---|---|---|---|---|
|Africa<br>||||||
|Kenya**(1984)**|17|51.3|*.4|32|1.1|
|<br>Liberia (19866|6|31.1|1.3|49.2|2.3|
|<br>Senegal (1986)|12|4S.0|50.0|-|5.0|
|<br>aire(1984)||64.1|28.7|3.6|3.5|
|<br>Zimbabwe(1984)|**36**|**42.6**|9.2|46.2|2.0|
|Asia||||||
|Bangladesh<br>(1935)|25|||||
|<br>**Korea**(1965)|70|58.0c|42.0c|||
|Wepalt(19l)d's|15|73.9|2.7|20.4|2.9|
|<br>Pakistan (1965)|9|66.6|26.5|-|6.7|
|Sri<br>Lanka (1967)|55|64.4|7.9|2.9|4.6|
|Thailand**( 1 984 )e**|65|76.0|19.7|0.7|1.6|
|Latin Amrica||||||
|SaM-dros 7 19S5)|37|34.4|33.6|21.6|10 4|
|Belse<br>(1965)|37|36.0|30.0|-|30 0|
|Dolivia(1983)|26|7.0|93.0|-|-|
|Drtail (1986)|65|15.0|65.02|||
|<br>Colombia**( 1986 )e**|**66**|34.0|43.6|2 1 <sup>.</sup>6 <sup>h</sup>|1.1|
|<br>Costa Rica(1955)|66|66.0|21.5|<br>22.1|1.4|
|<br>DominicanRepublic (1|966)<br>46|44.0|44.0|4.0|4.0|
|<br>Ecuador(1967)|<br>40|37.4|39.2|15.4|6.5|
|ElSalvador (1967)|46|49.7|36.1|-|12.2|
|Guatemla(1983)|25|31.8|16.1|30.3|11.7|
|<br>Haiti(1983)|7i|32.9|67.1|-|-|
|<br>Honduras<br>(1984)|35|27.9|22.0|.32.9|2.4|
|Jamica (1963)|51|66.9|30.2|-|2.9|
|Mexico(1978)|48J|15.6|77.4|0.0|6.4|
|<br>Panama (1979)|63|6S.9|23.4|-|10.7|
|<br>Paraguy<br>In.d.)|36|-|100.0|-||
|<br>Peru(1966)|41|56.0|33.0|-|11.0|
|NearCast||||||
|Egypt**(1984)e**|30|30.0|69.4|1.3|1.1|
|<br>tLbanon (1984)|53|1.2<br>|40.0|56.6|-|
|Morocco (1 984)e|26|**5$.4**|40.0|-|1.6|
|<br>Tunisia(1983)|41|77.7|21.4|-|0.6|

Source: CPS CHS: **Bogue et** *I. (1967): U.S. (1967).

- a. icludes private physicians, hoapitals. pharmacies, nd any other private, non-MO. b. Unspeciied source, may encoepas tuOt wben private, nonprofit$ **are** not **a** category, and my include coercial **where** it **Is** not **a sparate** catetgry.

- C. Source allocation date **are** for 1979. d. Based on nonusers **as vell as users. .** includes currently mrried winen oly.

- f. Thirty percent uncertain **as** to ource of contraceptives. g. Source allocation data **are** tor 1963. h. Preotailia only. t. Oly 40 percent of usrs **use** modern contraceptive **mthods.** j. Prevalence data ist tre 1962.

VP: Table 12t.1