rafmacalaba/datause-encoder-data huggingface dataset:
|||**Salary in 1**|**997**<br>**Entry Salary**|**Tenure in 1997**|**N**|
|---|---|---|---|---|---|
||**Female - Part II**|92.5|90.4|3.9|49|
|**Employees hired as**|**Male - Part I**|100|100|5|117|
|**YP after 1987**|**Male - Part II**|96.6|100.1|5.6|74|
||**Female - Part I**|90|97.1|4.4|83|
||**Female - Part II**|90.6|89.3|4.7|46|

We then examine the link between pay and performance. In 2001, performance ratings were introduced and staff were graded on a scale of 1 to 5. The system was strengthened in 2005 and again in 2010 to increase the payoff to higher ratings. Nevertheless, a common belief in the institution is that specific management practices may limit the ability of the WBG to reward high performers. One such practice is that staff should not receive ratings of 5 in two consecutive years and they should receive a 3 in the year that they are promoted.

If we assume that such ratings accurately reflect performance, we can use the performance data available from 2001 onwards in order to look at the link between compensation and performance. Between 2001 and 2015, with some changes, SRIs in our data are graded from 1 to 5, with 60 percent of staff in any given year falling in the average grade of 3, and 40 percent in grades 4 and 5. Grades 1 and 2 are for particularly poor performance, but are seldom used over the period of our data, accounting for less than 0.5 percent of all performance records.

**Table 4: Transition Fractions for SRIs in 2 Continuous Years**

|||**Fraction wh**<br>|**o received a SRI**<br>|**of _____ the next**<br>|**year**<br>|
|---|---|---|---|---|---|
|**Of staff who received a**|**1**|**2**|**3**|**4**|**5**|
|**SRI of__ thisyear**||||||
|**2**|0.10%|3.90%|67.80%|21.80%|6.50%|
|**3**|0.00%|0.40%|72.50%|22.80%|4.30%|
|**4**|0.00%|0.00%|43.00%|43.20%|13.70%|
|**5**|0.00%|0.00%|30.30%|45.40%|24.20%|
|**Total**|0.00%|0.38%|60.00%|30.80%|8.81%|

Patterns in the data are consistent both with rewards to performance and a preference for equality. For instance, of all staff who received a 5 in a given year, 24 percent went on to receive a 5 the next year (Table 4). Similar patterns obtain for GA-GD staff, GE to GH staff and GI+ staff and if we restrict our data only to the 2010-2015 period when SRI budgets tightened (tables available on request). On the other 36
# **1. Introduction**

Incomplete information about their rights and take-up procedures are possible reasons why poor people do not fully access the public services due to them, as has been often observed in developing countries (World Bank, 2004). There has been much recent interest in the scope for using information-based interventions to improve service delivery and governance. The premise is that a lack of information is a decisive demand-side factor inhibiting successful participatory action by poor people to get the services to which they are entitled.

There is some support for this premise from past studies. Strömberg (2004) reports evidence that US antipoverty programs have worked better in places with greater access to radios. For India, Besley and Burgess (2003) found that the governments of states where newspaper circulation is greater are more responsive in their relief efforts to negative agricultural shocks. In Uganda, Reinikka and Svenson (2005) found significant impacts on schooling of a newspaper campaign. Access to a televised soap opera in Brazil was found by La Ferrara et al. (2012) to lower fertility, especially among poor women. Jensen and Oster (2009) found that access to television led to less domestic violence and lower fertility rates in India. Not all studies have been supportive. Banerjee et al. (2010) are less encouraging on the scope for using information interventions to improve the monitoring of education service providers in India.

However, incomplete information is clearly not the only reason why poor people do not take up their entitlements.<sup>2</sup> Nor is knowledge exogenous to the forces that create their poverty. What one considers one’s “rights” in an Indian village (say) may depend more on what local officials and elites say than rather abstract central dictates in official legislation, far removed from the realities of daily life. Possibly people do not know their rights because there is no point knowing them when the reality of their lives does not admit those rights in practice. An information campaign will not then be sufficient for people to be willing and able to take action to get what they are due. The same factors that create poverty may make information about one’s legal rights largely irrelevant to one’s agency in accessing services.

The social psychology of an information campaign is clearly relevant here. We learn from psychology that information absorption is a selective, choice-based, process. If new public information about one’s rights under the law is in an uncomfortable dissonance with long-

> 2  Useful overviews of the arguments and evidence on factors relevant to the success of information campaigns can be found in Keefer and Khemani (2005) and (in the context of immunization campaigns) Cappelen et al., (2010).

2
bilaterally and disaggregated by gender.<sup>11</sup> Of the 3,500 sources detailed in the overarching Global Migration Database, 1,107 were suitable for analysis once repeated censuses were removed or combined. Of these, 951 record data disaggregated by gender, as reported in table 1.

# **{Table 1 about here}**

Despite the large number of primary sources, there are still inevitable gaps (table 2). This might be because a particular destination country did not conduct a census in a given decade or disseminate the relevant bilateral or gender-specific information. The majority of the migrants omitted from these censuses are in the Middle East and Africa. The countries of the Middle East are often reticent about releasing data, while many countries in Africa have a long history of conflict. Nonetheless, the 68 countries for which there are complete data account for 68 percent of the world migrant stock in 2000. The 17 countries for which there is only one census account for less than 2 percent of the total stock. The data for earlier decades reflect an identical pattern.

# **{Table 2 about here}**

# _II. HARMONIZING THE MATRICES_

Given the complexities of the underlying data, several major challenges arise in constructing global bilateral migration matrices. The most critical were explained above. In some cases, there is no choice but to recognize that the underlying processes that generated the data are less than ideal and to accept the data at face value. In others, every effort has been made to standardize the data.

# _Defining the Master Country List_

Over the period covered by the 1960–2000 censuses used to construct the global bilateral matrices of migrant stocks (1955–2004), the global political landscape underwent fundamental changes. Many countries, especially in Africa, Oceania, and the Caribbean, gained their independence. Following the end of the cold war, many countries redrew their political boundaries. Some fragmented into smaller nation states, such as the Soviet Union, Czechoslovakia, and Yugoslavia, and others reunified following an extended period of separation, such as Germany and Yemen.<sup>12</sup> cases, each origin countries‘ migrant stock as a share of the total is calculated in 1950 and these shares are applied to the 1960 total.

11 There is little standardization in the age brackets that countries use to record migrants‘ age. This is the main reason why an analysis of migrants‘ age is omitted from the current study.

- 12 Small border changes and territorial disputes are ignored.

8
Policy ReseaRch WoRking PaPeR 4500

# **Abstract**

This paper combines household survey data with event data on the timing and location of armed conflicts to examine the impact of Burundi’s civil war on children’s health status. The identification strategy exploits exogenous variation in the war’s timing across provinces and the exposure of children’s birth cohorts to the fighting. After controlling for province of residence, birth cohort, individual and household characteristics, and province-specific time trends, the authors find that children exposed to the war have on average 0.515 standard deviations lower height-for-age z-scores than non-exposed children. This negative effect is robust to specifications exploiting alternative sources of exogenous variation.

This paper—a product of the Growth and the Macroeconomics Team, Development Research Group—is part of a research project entitled Post-Conflict Transitions focusing on development issues particularly relevant to countries recovering from conflict. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at akresh@ad.uiuc.edu.

_The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post-Conflict Transitions Project can be found at http://econ.worldbank.org/programs/ conflict ). An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org ._ Produced by the Research Support Team
|Table 2 Land transactions||
|---|---|
|Sample:|farm land >0.6 ha in 2007 farm land <=0.6 ha in 2007|
|Dependent: change in|land self-cultivated land self-cultivated land self-cultivated land self-cultivated|
||Total Rent-in Total Rent-in|
|Real wage growth: Non-agriculture|-0.0265 0.0546 0.1745 0.0521|
||(0.35) (2.46) (1.79) (0.69)|
|Real wage growth: Agriculture|0.0130 0.0988 0.0254 -0.0034|
||(0.15) (6.39) (0.34) (1.75)|
|Years of schooling (age 20-55; 2007)|-0.0405 -0.0448 0.0104 0.0074|
||(1.33) (2.81) (0.92) (0.43)|
|Age (age 20-55; 2007)|-0.0269 -0.0191 -0.0099 0.0007|
||(1.48) (1.97) (1.61) (0.13)|
|Female (age 20-55; 2007)|0.0827 -0.0494 -0.2305 -0.0220|
||(0.12) (0.23) (0.79) (0.30)|
|Number of hh members (age 20-55; 2007)|0.1341 0.0403 0.0098 -0.0139|
||(1.62) (0.76) (0.19) (0.28)|
|Province fixed effects|yes yes yes yes|
|R squared (within)|0.0175 0.0332 0.0169 0.0038|
|Number of observations|451 451 457 457|

Numbers in parentheses are absolute t values using robust standard errors.

18
Table A3: Balance test saturated regressions

||Property<br>Value|Front<br>Metres|House<br>type|Tenant<br>Male|Tenant<br>Age|Bill<br>amount|N Bills<br>paid 2019|Digital<br>payment|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**_A. Blocks with 80%_**|**_treated:_**||||||||
|Treated|0.01|_−_8.27|_−_0.00|_−_0.00|_−_0.14|2.81|0.05|_−_0.00|
||(0.02)|(17.77)|(0.00)|(0.01)|(0.40)|(7.81)|(0.09)|(0.01)|
|Untreated|0.00|_−_1.76|0.00|0.00|_−_0.53|6.27|_−_0.06|_−_0.00|
||(0.02)|(20.70)|(0.01)|(0.01)|(0.53)|(12.95)|(0.12)|(0.01)|
|**_B. Blocks with 50%_**|**_treated:_**||||||||
|Treated|0.01|12.65|_−_0.00|_−_0.00|_−_0.47|1.16|0.03|0.00|
||(0.02)|(20.38)|(0.01)|(0.01)|(0.50)|(9.21)|(0.11)|(0.01)|
|Untreated|0.01|25.30|_−_0.00|_−_0.00|_−_0.42|1.88|0.02|0.01|
||(0.02)|(20.66)|(0.01)|(0.01)|(0.48)|(9.66)|(0.11)|(0.01)|
|**_C. Blocks with 20%_**|**_treated:_**||||||||
|Treated|0.02|32.57*|_−_0.01|0.01|0.10|5.94|0.07|_−_0.01|
||(0.02)|(16.79)|(0.01)|(0.01)|(0.54)|(9.55)|(0.12)|(0.01)|
|Untreated|0.02|19.14|_−_0.01|_−_0.01|0.12|1.32|0.00|0.00|
||(0.02)|(14.05)|(0.00)|(0.01)|(0.40)|(7.77)|(0.09)|(0.01)|
|Mean Pure Control|13.64|841.50|0.91|0.62|19.15|368.66|6.71|0.35|
|Observations|64,932|68,808|68,808|46,419|52,714|68,808|68,808|38,112|
|Number of clusters|3,979|3,981|3,981|3,973|3,976|3,981|3,981|3,968|

Notes: This table shows balance test regressions to formally test for differences in observable characteristics between the treatment and control groups. Each column corresponds to a separate regression (equation (12) in the text). The dependent variables in each column are: (1) the log of assessed property value; (2) the front metres of the property; (3) an indicator for the property being a house versus a house with a store; (4) whether the tenant is male; (5) a proxy for the tenant’s age (first two digits of the ID); (6) the amount paid in the bill corresponding to December 2019 (including zeroes); (7) the number of bills paid in 2019 (the maximum is 12); (8) for those who paid, whether they did so digitally. The row _Mean Pure Control_ displays the constant of each regression, corresponding to the average of the dependent variable for accounts in blocks with no treated units ( _Tg_ = 0). Missing/non-missing indicators for the dependent variables with missing observations (columns 1, 4, 5 and 8) are also balanced between groups (results not reported). Standard errors clustered by blocks are reported in parentheses. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01

# **A.4 Further Details on Within-Group Assignment Mechanisms**

**Fixed Margins.** The within-group treatment is often assigned by choosing a fixed (i.e. nonrandom) number of treated units within each group. Given _Tg_ = _t_ , suppose the researcher wants to assign a

A-8
The key to any safe-keeping function is regulation that assures the integrity of the system and requires procedures to be subject to audit (Makin, 2009). Back-up systems are needed to ensure that account information can be recovered in case of physical destruction or theft. For cases such as M-PESA, the accounts that contain deposits from customers need to be kept separate from the accounts of Safaricom, even though M-PESA is not a separate company. As it happens Safaricom created a special trust to safeguard the accounts and it is important that strict separation is maintained between the accounts of M-PESA and Safaricom so that the custodianship function is kept distinct from the operations of Safaricom. The records associated with the holding of accounts facilitate the imposition of anti-money laundering (AML) regulation (Chatain, Zerzan, Noor, Dannaoui and de Koker, 2011).

In the case of M-PESA the cash merchants perform a function related to safe-keeping, namely registration of the account. They establish the identity of the owner, process the request for account opening and perform checks required by AML regulation. They are thus subject to more regulation than is required for the pure cash merchant function. This means that M-PESA, the “safe-keeper”, takes on responsibility to train account openers in requisite procedures like knowyour-customer protocols required by AML regulation and to supervise implementation. In principle, however, there is no reason why the account opening function needs to be bundled with the cash merchant function<sup>7</sup> . There could also be different tiers of cash merchants. M- PESA today distinguishes between wholesalers and retail agents with different limits on their accounts. There might also be pure cash merchants with no responsibility for registration, such as the smaller street cash merchants that may be entering the market now. Abroad, cash merchants that facilitate remittances via the M-PESA system also need not perform registration functions or be branded as an M-PESA agent.

# **Transferring money**

Poor people often transport their money themselves or give it to friends or to a bus driver to take to their relatives. Safer and cheaper means of transport are hugely in demand. The issue is 13
# **Table 3: region and country of birth of foreign born blacks**

**Caribbean and Latin America: Africa: Other: 70.2% 25.15 % 4.65% Including: Including: Including:** Jamaica: 26% Nigeria: 6.8% UK: 1.5% Haiti: 19.3% Ghana: 3.4% Trinidad: 7.2% Ethiopia: 3.0% Guyana: 4.7% Liberia: 1.6% Barbados: 2.6% Kenya: 1.1% Grenada: 1.3% Africa, non specified: 4.12% Belize: 1.1% Bahamas: 1%

West Indies, non specified: 1.16% Data from the 2000 Census IPUMS data (5% sample), population aged 25-65.The estimations use the sampling weights provided with the IPUMS data.

31
# Frame-of-Reference Bias in Subjective Welfare Regressions

## Kathleen Beegle, Kristen Himelein and Martin Ravallion<sup>1</sup>

_Development Research Group, World Bank 1818 H Street NW, Washington DC, 20433, USA_

> 1 This paper would not have been possible without the work of the _Tajikistan_ Living Standards Survey Team, including Diane Steele, Sasun Tsirunyan, Vladimir Kolchin, Farhod Khamidov, Oleksiy Ivaschenko and staff of the National Statistics Committee of Tajikistan ( _Goskomstat_ ). We thank Gero Carletto and seminar participants at IFPRI and the AEA annual meetings for very useful comments. All views are those of the authors and do not reflect the views of the World Bank or its member countries.
with sector dummies. This exercise yields an insignificant result and hence we are not reporting it. This means that the effect is similar across sectors. Recall that we are working with four-digit sectors within manufacturing so this result is expected. If we worked with sectors like construction, services or agriculture, then the results might differ. Even when the bulk of foreign investment in Turkey is in services, we focus exclusively on the manufacturing sector, where the calculation of firm level productivity is more accurate, as it benefits from correct measurement of capital and materials, and the results are comparable across countries.

**Table 5: Foreign ownership and firm employment**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||∆ln(L)|∆<sup>2</sup>ln(L)|∆<sup>3</sup>ln(L)|∆<sup>4</sup>ln(L)|∆<sup>4</sup>ln(L)|
|∆ln(FO)<br>∆<sup>2</sup>ln(FO)|0.000<br>(0.003)|0.353***<br>(0.003)||||
|∆<sup>3</sup>ln(FO)|||0.585*<br>(0.25)|||
|∆<sup>4</sup>ln(FO)||||-0.293***|-0.123***|
|||||(0.001)|(0.001)|
|Observations|34,128|32,231|28,344|18,138|18,138|
|Year Fixed Effects|Yes|yes|yes|yes|yes|
|Sector Fixed effect|Yes|yes|yes|yes|yes|
|Sector-Year Fixed effect|No|no|no|no|yes|

_Source:_ authors’ calculations based in the Orbis dataset for Turkey. _Notes:_ The regressions are estimated by GLS. LHS is log employment. FO is transformed as (FO _/_ 100)+1. FO is the share of foreign-owned equity. ∆<sup>_k_</sup> indicates the change between year _t_ and year _t_ − _k_ where _k_ = 1 _,...,_ 4. Standard errors clustered at the firm level are in parenthesis. *** , **, *, denote significance at 1%, 5%, and 10% levels.

Table 5 runs similar regressions as above but this time using log employment as the dependent variable. As we can see, there is an initial increase in employment but after four years this effect turns negative. This may mean that domestic content requirements imposed on multinationals upon arrival, in terms of hiring local workers, would vanish after the fourth year. This is not surprising since the four-year mark is also when productivity starts improving. Clearly, efficient use of labor explains part 10
# **Table A2: Sector Distribution Ben Ali Firms 2010 (by Broad Sector)**

|||**_Con_**|**_tributio_**|**_ns of Be_**|**_n Ali f_**|**_irms_**<br>||**_Sec_**|**_tor Aggrega_**|**_te_**|
|---|---|---|---|---|---|---|---|---|---|---|
||**_BA_**<br>**_Firms_**|**_Firms_**|**_Y_**|**_L_**|**π**|**Gross**<br>**π**|**_Gross_**<br>**_Losses_**|**_Y_**|**_L_**|**_Profits_**|
||**#**|%|%|%|%|%|%|Sum|Sum|Sum|
|Telecommunications (Information and<br>communication)|13|1.05|59.28|25.27|77|78|80|1265|9396|267|
|Cars and motorcycles - sales and|||||||||||
|repair|11|0.41|28.43|6.36|45|39|0|2182|7758|134|
|<br>Finance and insurance|11|1.86|20.51|18.61|48|21|4|2820|10444|125|
|Transport and storage|14|0.94|17.96|6.90|-17|7|23|1227|13780|47|
|<br>Other manufacturing activities|2|||||||284|3515|21|
|Fabrication of rubber|2|||||||675|7451|10|
|Fabrication of other mineral products|2|||||||1215|14969|60|
|Chemicals and pharmaceuticals|1|||||||1139|7163|75|
|Professional, scientific, technical|||||||||||
|<br>activities|9|0.16|3.14|2.29|-2|1|13|1149|18267|193|
|Construction|18|0.41|2.00|0.97|1|4|6|2911|51163|103|
|Real Estate|0|||||||118|1483|-59|
|Cars and transport|1|||||||1439|25482|72|
|Education and human health services|2|||||||520|13093|123|
|Hotels and restaurants|9|0.17|1.17|1.15|0|2|0|1349|39828|-417|
|Administration and support services|9|0.42|0.62|0.09|0|1|1|1112|65898|55|
|Commerce (gross) - excluding cars|||||||||||
|and motorcycles|24|0.36|0.84|0.57|1|1|0|10735|30842|357|
|Agriculture, forestry and fishing|6|0.93|0.65|0.76|807|1|9|547|12348|0|
|Manufacture of textiles|2|0.07|0.44|0.44|0|0|0|2760|102969|174|
|Manufacturing of paper, printing|1|||||||646|5923|20|
|Manufacture of food products,|||||||||||
|<br>beverages and tobacco products|2|||||||4557|30517|123|
|Metallurgie and fabrication of metal<br>products|2|||||||1741|15854|83|
|Manufacturing of ICT, electronics,|||||||||||
|optics|3|0.45|0.21|0.15|1|0|0|2953|29086|177|
|Retail - excl cars and motorcycles|5|0.04|0.12|0.04|0|0|2|4733|28838|154|
|Extractive Industries|1|||||||836|5108|110|
|Repair and installation of machinery|0|0.00|0.00|0.00|0|0|0|404|4509|34|
|Manufacturing of leather and leather|||||||||||
|<br>products|0|0.00|0.00|0.00|0|0|0|586|18415|35|
|<br>Manufacturing of wood|0|0.00|0.00|0.00|0|0|0|56|1608|6|
|<br>Furniture|0|0.00|0.00|0.00|0|0|0|320|6707|6|
|Repair of computers|0|0.00|0.00|0.00|0|0|0|48|532|-4|
|<br>Other Personnel services|0|0.00|0.00|0.00|0|0|0|79|3206|1|
|Other|6|0.77|0.17|1.49|-1|0|5|938|3801|116|
|Total|155|0.23|5.03|1.72|16|11|6|51341|589952|2203|

Note: Table only includes firms with Y>0 and L>0. Y=output in millions of USD, L=employment (wage workers), π=net profits, Gross π =Gross profits (e.g. only including firms which report positive profits). Empty cells indicate that the number of observations was too small to safeguard statistical anonymity.

37