rafmacalaba/datause-encoder-data huggingface dataset:
to the hiring process, some students suggested that the barriers generally erected by employers stemmed from their concerns about marriage, child-bearing, family responsibilities, and maternity leave. As a result, employers may choose male applicants who are willing to work overtime and at night, unlike women who cannot go out alone at night. One female undergraduate engineering student illustrated this point by saying, “In fact, one favors a man over a woman for the following reasons. People say that a woman has a family and has children, she should make sacrifices to the detriment of her career, and so she cannot take responsibility. The employer takes this point into account.” Although almost all participants agreed that discrimination exists, they also noted that the degree depends on the nature of work. Moreover, in some cases the employer is influenced by culture, traditions, and social norms. Participants noted that some occupations are best filled by women instead of men, especially those that require communication or contact with customers, such as an assistant, a secretary, or a receptionist. However, women are less preferred for occupations that require physical effort such as a police officer, taxi driver, soldier, and football team coach. Similarly, participants claimed that in these occupations workers would not accept a female boss. Thus for decision-making positions such as manager, the priority is always given to men, who are perceived to be authority figures, unlike women, who are perceived to be weak and emotional. This point was expressed by a male engineering student: _“_ Maybe some occupations require physical endurance. This automatically plays in favor of the man and creates discrimination.”<sup>9</sup>

# 5. Experimental design

To examine empirically gender-based discrimination by employers in the Tunisian labor market and to quantify its magnitude, we conducted a field experiment based on the correspondence method used by Bertrand and Mullainathan (2004) in their groundbreaking study. The experimental design consisted of sending fictitious and substantially identical male and female resumes in response to job advertisements. Any differences in callback rates could then be attributed to gender, and thus could be interpreted as gender discrimination. To the best of our knowledge, this is the first study of its kind conducted in the MENA region.

> 9 In industries that may require a higher degree of physical endurance, male employment is indeed higher than female employment. For example, in construction and mining, the share of male employees is over 95 percent (Tunisia Labor Market Panel Survey, 2014).

17
data from the entire experiment.

# **Reduced-form evidence on demand persistence**

In Table 2, we extend the specifications reported in Table 1 by including information on past discounts. Corresponding to the current discount dummies, we construct variables representing the number of months that the household faced the same type of discount in the previous 2, 3, or 4 months. In columns 1, 4, and 7 of Table 2, for instance, along with dummies for whether the household is currently receiving the 50 or 75 percent discount, we have the number of months out of the last 2, 3, and 4, respectively, that they received either of these discounts. In the water price specifications (columns 3, 6, and 9), we simply include the mean price that the household faced over the previous 2, 3, or 4 months, which incorporates any discounts. Note that, while specifications that include separate dummies for each type of discount are more flexible in terms of functional form, they are also likely to be the least precisely estimated given the rather high monthly serial correlation across discounts induced by the experiment. At any rate, the upshot of Table 2 is that greater discounts (or a lower average price) in the recent past leads to higher consumption in the current month conditional on the current discount. The evidence is strongest in the specifications with the effective water price ( _p_ -value=0.014 in column 3) and weakest, as expected, when we separate out the 50 and 75 percent discounts, in which case only the effect of past exposure to the 75 percent discount approaches significance.

# **Habit formation/learning through experience vs. alternative mechanisms**

Both provincial water authorities and the director of An Thinh utility told us they expected households to increase piped water consumption over time. Indeed, in its first 3 years of operation (preceding our experiment), piped water use among An Thinh’s customers was growing by an average of 0.9 percent per month.<sup>16</sup> While households had previously collected rainwater that needed to be stored in and pumped from dedicated tanks, piped water is more convenient and provides higher pressure; it also does not need to be filtered and boiled for cooking purposes like rainwater. As these benefits become manifest through continued use, An Thinh’s customers were expected to increasingly shift out of rainwater and into piped water, especially 16This is a within household estimate and thus accounts accounts for selectivity into An Thinh’s customer-base by connection month.

15
|Basic plot/household/village controls|Yes|Yes|Yes|Yes|
|---|---|---|---|---|
|Observations|15,472|15,472|15,472|15,472|
|R-squared|0.082|0.083|0.103|0.103|
|**Tests:**Encroached + Encroached *Male = 0||3.48*||58.61***|
|Land taken + Land taken *Male = 0||5.94**||1.38|
|Unallocated land + Unallocated land*Estates = 0||4.21**||0.24|
|Estates + Unallocated land*Estates = 0||4.40**||0.01|
|Matrilineal+Matrilineal*Male=0||11.87***||0.01|
|Patrilineal+Patrilineal*Male=0||0.02||3.08*|
|Unknown+ Unknown *Male=0||0.04||1.47|

_Note:_ Basic controls at plot, household, and village level as well as district fixed effects are included throughout but not reported. Basic plot controls include land area and land topography. Basic household controls include the number of children, the number of adults, the number of old people, head’s characteristics (gender, age, education, birth place, and main job), ownership of durable goods, housing conditions, the value of livestock and poultry, and the value of agricultural assets. Basic village controls include access to the all seasonal road. Robust standard errors in parentheses clustered by village. *** p<0.01, ** p<0.05, * p<0.1.

21
18 3.7 billion people. Most rapid urbanization is taking place in low- and middle-income countries. In the IBNET database, the typical utility already has a density of about 66 connections per kilometer of network (compared to only 31 for the utilities in high-income countries). In addition, in many countries the actual connection density can be much higher than that what the typical utility faces. In Singapore, for instance, the connection density was no less than 240 connections per kilometer of network. The trend of higher connection density is partially neutralized by what is a decrease in population per connections once economic development takes off. In many utilities in low-income countries, the typical number of people per connection is high, but that tends to decrease over time driven by economic development and urbanization. A typical utility in a low income country served more than 9 people per connection, but that drops to less than 3 once a country moves to high-income status. The size and scope of the system which is mostly effected by government policies and urbanization policies is equally mostly beyond the control of utilities as larger systems tend to generate higher water losses. Moreover, wage rate levels affect water losses. This is mainly because as mentioned earlier by Garcia and Thomas (2001) the cost of reducing water losses shows up mainly in the form of labor and maintenance costs needed to repair the network to reduce losses. The higher the costs of labor are, the higher the cost of repairing water losses, and hence the less incentives for utilities to reduce water losses. Finally, the opportunity cost of water losses<sup>5</sup> also plays an important role. Careful planning of water and wastewater services upfront may help to alleviate some of the above-mentioned pressures.

Yet, even though some of the key drivers of NRW are beyond the control of utilities, utilities have room to manoeuver in reducing water losses through improving the efficiency with which they operate their water supply systems, and the opportunity cost of water losses<sup>6</sup> . Staff productivity as measured by the number of staff per 1,000 people served (the higher the number of staff per people served, the lower the staff productivity) is directly linked to water losses through the labor

> 5 In our sample we find that the best performing utilities in terms of NRW have significantly higher opportunity cost for water losses than poor performing utilities. The utility of Phnom Penh, Cambodia (Biswas and Tortajada, 2005) has a rather uncommon cost structure where the opportunity costs for water losses are relatively high.

> 6 In principle, utilities can manage their energy costs, but the costs of water resources and energy prices are beyond the control of utilities.  Moreover, energy costs are also driven by amongst others the outlay of the water supply network (pumping in the network drives energy costs), the location of the water source and topography.
be written as _r_<sup>_G_</sup> (2 _N_ + _T_ ) +<sup>�</sup><sup>_M_</sup> _m_ =1<sup>_r_</sup> _m_<sup>_R_(2</sup><sup>_Nm_+</sup><sup>_T_) + �</sup> _d∈{−_ 1 _,_ 1 _}_<sup>_r_</sup> _d_<sup>_D_(</sup><sup>_N_+</sup><sup>_T_) +</sup> � _Mm_ =1 � _d∈{−_ 1 _,_ 1 _}_<sup>_r_</sup> _m,d_<sup>_RD_(</sup><sup>_Nm_+</sup><sup>_T_), where</sup><sup>_N_is total number of countries (so 2</sup><sup>_N_</sup> is the overall cross-sectional dimension of the inflow-outflow data) and _Nm_ the number of countries in group _m_ . This yields the expressions: with ˆ and _ui,t_ is the estimated residual from the factor model with _r_<sup>_G_</sup> global factors and _rm_<sup>_R, r_</sup> _d_<sup>_D, r_</sup> _m,d_<sup>_RD_group, direction, and group-direction factors.The pa-</sup> rameter _c_ in the HQ criterion was set to 2, based on the criterion’s performance in residual bootstrap-based experiments.

# **3 Data**

We assemble a balanced panel data set on annual gross inflows and outflows, drawing from the International Monetary Fund’s Balance of Payments Statistics (BoP). The panel comprises 85 countries<sup>10</sup> and spans the years 1979-2015. We further group the sample countries into three categories: 19 advanced countries, 28 emerging countries, and 38 developing countries, as shown in Table A.1.

> 10We start by constructing a balanced panel comprising all the countries with complete data from 1979 to 2015. This yields a total of 98 countries. We exclude from this sample 13 very small countries with population fewer than 500,000 in 2005.

9
Finally, this equation indicates that realizations on the variables p, and s 3 , influence export decisions through their effect on _w(p,,sd and_ their effect on the expected future value of becoming an exporter now. Th1is latter effect implies, for example, that exchange rate movements that managers consider transitory will generally have less effect than equivalent movements that are viewed as long-term regime shifts.

m. The Pattern of Export Participation in Colombia Before discussing estimation issues and results, it is useful to introduce our data base, review the export enviromnent in Colombia, and provide some aggregate evidence on the pattern of export maiket participation during the 1980s.

The Data: The analysis in this paper is based on annual plant-level data collected as part of the Colombian manufacturing census for the years 1981-1989. This census, which covers all plants with 10 or more employees, provides information on each plant's geographic location, industry, age, ownership structure, capital stocks, investment flows, expenditure on labor and materials, value of output sold in the domestic market, and value of output exported. We have matched the individual plant observations across years to form a panel.<sup>7</sup> The data are particularly well-suited to analyzing export market participation because they allow us to observe transitions of individual plants into and out of the export market and to control for some important observable plant characteristics that are likely to affect the export decision.

The **Policy Regime** and Export **Particpation Rate:** Table **1** illustrates the basic paterns 7The census data and matching process are discussed in greak, detail in Roberts (1994).

**7**
<!-- Start of picture text --> Public Funding Private Funding<br>d<br>i8<br>s$<br>Bo x<br>g &<br>o°o<br>Donor Funding Other Funding<br>88<br>3<br>.<br>x 2 8<br>2<br>&<br>&<br>o o<br><!-- End of picture text --> <!-- Start of picture text --> Budget share from public sources<br>High income Low income<br>&<br>8<br>2<br>&<br>32 o Lower middle income Upper middle income<br>8<br>8<br>=<br>&<br>o<br>ME ow. ME <10% ME 10-25%<br>MON 25-50% ME 50-75% ME 75-100%<br>Graphs by inc_group<br><!-- End of picture text -->
39 Table 3

# Estimates of '1992' Effects on Dcveloping Country Exports of Goods in Constant 1987/8 Values (million Ecus)

||Addition<br>Primary|al<br>exports to EC<br>Manufacture|<br>Diversion<br>effects<br>s<br>(in all<br>manufactures)|% of<br>exports<br>to EC<br>|% of<br>total<br>exports|
|---|---|---|---|---|---|
|All developing countries|2804|4434|-5655|+1.5|+0.3|
|ACP|534|315|-477|+2.3|+1.0|
|Maghreb countries|244|370|-534|+0.9|+0.5|
|South Asia & China|86|920|-1125|-1.0|-0.1|
|Four Asian NiCs|12|2574|-4077|-6.1|-0.9|
|ASEAN countries|102|344|-464|-0.3|-0.0|
|Western Hemisphere|502|495|-751|+1.3|+0.3|
|OPEC|_1156_|515|-847|+3.8|+1.1|

Source: Page. 1991
**Table 3. WLS Regressions of** _ln_ ( _TFPR_ ) _/TFPR_ ) **on Different Dummies**

||(1)|(2)|(3)|
|---|---|---|---|
|_Old_|-0.210*|||
|(created _≤_1985)|(0.115)|||
|_Small_||0.189*||
|(5 to 19 workers)||(0.105)||
|_Large_||-0.007||
|(_≥_100 workers)||(0.130)||
|_Exported oriented_|||0.077|
|(_≥_60% exported)|||(0.140)|

_Note_ : The dependent variable is the deviation of log TFPR from the industry mean. In (1) the independent variable is a dummy for export-oriented firms. In (2) the independent variable is a dummy for old firms. In (3) dummies for small and large firms are the independent variables and the omitted group is medium size firms. Regressions are weighted least squares with industry value-added shares as weights. Entries are the dummy coefficients, with standard errors in parentheses. * indicates significance at the 10 percent confidence level.

Consequently, providing extra resources to small enterprises would produce higher returns than providing resources to larger ones. Credit constraints, which typically have more incidence on small firms, can partly explain why small companies cannot acquire more resources to grow. Export-oriented firms exhibit 7.7 percent higher TFPR (regression 3). Keeping in mind the large standard errors associated to the coefficient, this result suggests that more resources should be allowed to export-oriented business. At first sight this seems a counter-intuitive result since it is normally expected that these firms face large positive distortions resulting from better access to credit or preferential treatment in export processing zones. Yet, this “positive” distortions can be more than compensated by other “negative” distortions linked to the overall “anti-export” biais of economic policy (see Section 2 and 6).

# **5.2 TFP gain from Reallocation**

Before analyzing results in this sub-section, it is important to highlight that this exercise makes no allowance for measurement error or model misspecification. Such errors could lead us to inflate efficiency gains from resource reallocation. Table 4 provides TFP gains (in %) for Morocco and other developed and developing countries from equalizing TFPR across firms in each industry group. Results are obtained using WBES2013 instead of the richer data in WBES2007 because the computation of TFP gain needs the most recent measures of _physical productivity_ .

Full liberalization would boost aggregate manufacturing TFP in Morocco by 83.8 percent. Morocco’s TFP gains from liberalization is well above the one in the US. This is not surprising since, as shown in Table 1, the level of market distortions in Morocco are much higher than in the US.

Morocco’s benefits from liberalization are also very much in line with those 15
||(316.9)|(380.9)|(325.7)|(386.2)|(323.6)|
|---|---|---|---|---|---|
|KVIP|342.5|319.2|350.6|488.0|345.2|
||(261.5)|(259.5)|(263.0)|(327.6)|(256.6)|
|Bucket/pan|239.9|30.39|270.6|244.7|217.8|
||(174.9)|(191.9)|(192.4)|(207.2)|(202.6)|
|Public toilet|-68.77|-74.11|-98.57|30.20|-54.14|
||(93.18)|(113.5)|(96.52)|(105.0)|(105.3)|
|Waste disposal:||||||
|Collected|ref.|ref.|ref.|ref.|ref.|
|Burnt by household|-346.9|-570.0|-397.6|-591.5|-361.6|
||(565.8)|(561.0)|(611.7)|(628.9)|(565.7)|
|Public dump|126.7|149.7|134.5|207.0|109.5|
||(206.5)|(196.0)|(209.6)|(279.2)|(201.2)|
|Dumped indiscriminately|-648.7*|-797.0*|-627.8|-839.7*|-621.5|
||(382.8)|(407.3)|(380.7)|(431.0)|(386.7)|
|Expenditure quartiles:||||||
|Q1|||||ref.|
|Q2|||||-16.01<br>(240.3)|
|Q3|||||51.85<br>(246.8)|
|Q4|||||120.5<br>(255.7)|
|NEIGHBORHOOD FIXED EFFECTS|NO|YES|NO|NO|NO|
|Constant|2,008***|2,077***|1,971***|1,024|1,849***|
||(363.0)|(411.4)|(384.8)|(610.7)|(538.7)|
|Observations|484|484|484|376|484|

_Table 20: Total annual housing costs_<sup>_17_</sup> _by household and neighborhood characteristics._

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Unaffected|ref.|ref.|ref.|ref.|ref.|
|Affected|-151.7**|-141.0*|-129.3|-158.5**|-151.9**|
||(65.03)|(77.09)|(77.98)|(71.53)|(63.76)|
|High-elevation|||ref.|||
|<br>Low-elevation|||-147.8|||
||||(111.5)|||

17 Total annual housing costs include annual rent for households that pay some form of rent, annual fees payed to traditional chiefs for those who pay fees to these entities, annual pay of construction loan (this concerns very few households). Households that occupy their dwelling without paying any rent or costs (occupation of a dwelling owned by family, etc.) have a total annual housing cost equal to 0. Households that own their dwelling have been ruled out of the analysis.

44
Table 8: **Total of direct and indirect imports as a share of gross costs**

||1992|1995|1997|2001|2004|2007|
|---|---|---|---|---|---|---|
|Agr, Forestry, Fisheries|0.077|0.103|0.097|0.100|0.114|0.121|
|Processed Foods|0.137|0.157|0.156|0.153|0.150|0.153|
|Energy Extraction|0.341|0.333|0.530|0.562|0.689|0.771|
|Minerals nec|0.083|0.128|0.092|0.094|0.113|0.121|
|Beverages and Tobacco Products|0.071|0.085|0.088|0.092|0.113|0.116|
|Textiles|0.217|0.243|0.283|0.298|0.287|0.274|
|Wearing Apparel|0.147|0.147|0.168|0.168|0.189|0.169|
|Leather Products|0.152|0.201|0.189|0.201|0.188|0.179|
|Wood Products|0.107|0.127|0.148|0.152|0.169|0.169|
|Paper Products, Publishing|0.142|0.197|0.173|0.174|0.158|0.163|
|Chemical, Rubber, Plastic Prods|0.356|0.401|0.403|0.395|0.398|0.427|
|Mineral Products nec|0.083|0.101|0.123|0.125|0.139|0.155|
|Ferrous Metals|0.229|0.264|0.248|0.219|0.234|0.278|
|Metals nec|0.225|0.294|0.269|0.251|0.277|0.339|
|Metal Products|0.106|0.148|0.169|0.172|0.198|0.227|
|Transport Equipment|0.130|0.187|0.223|0.231|0.268|0.279|
|Machinery and Equipment nec|0.183|0.236|0.311|0.346|0.359|0.367|
|Manufactures nec|0.118|0.145|0.175|0.189|0.175|0.180|
|Electricity, Gas, Water|0.071|0.131|0.157|0.148|0.168|0.215|
|Construction|0.068|0.078|0.096|0.100|0.126|0.136|
|Trade and Transport Services|0.163|0.179|0.235|0.226|0.273|0.315|
|Other Commercial Services|0.103|0.160|0.159|0.176|0.225|0.248|
|Public Services|0.028|0.052|0.048|0.044|0.044|0.047|
|Dwellings|0.005|0.011|0.005|0.004|0.005|0.010|

15
learning poverty (LP) at 53 percent and the simulation baseline at 54 percent. For two regions, Latin America and the Caribbean and Middle East and North Africa, the results were the same, since no countries were missing from the simulation. In Africa, despite the countries missing due to unavailable NLAs, the difference was only 2.5 percentage points, with the original LP at 87 percent and simulationbased LP at 84 percent. East Asia and the Pacific is the exception: with China missing from the simulations, the average difference was close to 12 percentage points.

**Table 6 Population and country coverage by country groups**

|||All countries<br>|||Low- and middle<br>Number of<br>|-income countries<br>||
|---|---|---|---|---|---|---|---|
|Country|group|Number of<br>countries with data|Number of<br>countries total|Population<br>coverage (%)|countries with<br>data|Number of<br>countries total|Population<br>coverage (%)|
||Overall|100|217|81.1|62|144|79.7|
||East Asia and Pacific|13|37|86.6|6|23|86.9|
||Europe and Central Asia|33|58|84.0|12|23|74.0|
||Latin American and the<br>Caribbean|16|42|86.8|16|30|88.4|
||Middle East and North<br>Africa|14|21|71.4|6|12|68.8|
||North America|2|3|100.0|N/A|N/A|N/A|
||South Asia|5|8|98.1|5|8|98.1|
|Region|Sub-Saharan Africa|17|48|46.1|17|48|46.1|
||High|44|79|97.7|6|10|99.7|
||Upper middle|27|60|91.8|27|58|92.2|
|level|Lower middle|16|47|75.8|16|46|76.0|
|Income|Low|13|31|63.3|13|30|64.8|
||Part 1|38|73|93.0|N/A|N/A|N/A|
|typeb|IBRD|39|68|91.4|39|68|91.4|
|Lending|IDA / Blend|23|76|56.0|23|76|56.0|

a Low- and middle-Income countries are Part 2 countries eligible to borrow from the World Bank Group and include high-income IBRD clients. b Lending types: Part 1 countries do not borrow from the World Bank Group; International Bank for Reconstruction and Development (IBRD); International Development Association (IDA); and IDA-eligible based on per capita income levels and are also creditworthy for some IBRD borrowing (Blend). Notes: Data include only assessments since 2011. Source: Author’s calculation using the Global Learning Assessment Database, UIS enrollment data, and UN population numbers.

The previous section established the main properties and axioms for the learning poverty headcount measure and discussed how some advantages and weaknesses of different choices of aggregations in terms of the monotonicity axiom and the monotonicity sensitivity axiom. In what follows, this paper 23