rafmacalaba/datause-encoder-data huggingface dataset:
<u>Table 7 : Housing Subsidies in Morocco – Detailed list and amount as of 2002</u>

|<br>_Category_|<br>_Provision_|_Subsidy_<br>_Amount_<br>_(million Mdh)_|
|---|---|---|
|**Fiscal Expenditures**|||
|Registration Fees|Reduced rate : 2.5% instead of 5% for dwellings with<br>value under Dh 350,000<br>Reduced rate : 1.25% instead of 5% for dwellings with<br>value under Dh 180,000|(total)<br>1428|
|VAT|Reduced VAT rate for housing sector (14% instead of<br>20%)<br>No VAT on self-construction<br>No VAT on social housing (area < 100 m2 and value ,<br>200,000 DH)|1020<br>340<br>177|
||ExemptionofVATon interestsfor mortgages|77|
|IncomeTax|Deductionof mortgageinterestsfrom taxableincome|602|
||Deductionof 40%from rental income|n.a.|
||Deduction of mortgage interests and capital from taxable<br>income, for wage earners buying social dwellings (Value<br><500,000Dhs)|406|
|Tax on Capital Gains|Total exemption after 10 years possession, partial<br>exemption after 5 years possession<br>Exemption of the first sale of social unit (area < 100 m2<br>and value ,200,000DH)|700<br>n.a.|
|Taxe Urbaine|Tax base cut by 75% for homeowners<br>Exemption for new unitsfor thefirst5 years|228<br>100|
||Exemption for social housing for all the duration of the<br>loan|13|
|Taxe d’édilité|Taxbase cutby 75%for homeowners|1,100|
|Article<br>19<br>(differential<br>cost)*|Construction of social housing units exempt form all taxes<br>and duties (nationalandlocal)|300|
|**Total**||**6,491**|
|**Direct Subsidies**|||
|Mortgage<br>Guarantee<br>Funds|Capitalization from the State for a 10-year period|95|
|Housing<br>Ministry|Investment<br>expenditures<br>for<br>slum<br>upgrading<br>and|450|
|Investment Budget|infrastructure servicing of theinformalsectorprograms||
|FSH|DedicatedHousingFund (funded by ataxoncement)|1,000|
|Interest rate subsidies on<br>mortgages|Still applies to loans originated until 2004|400|
|Exchange rate risks|Treasury covers the exchange rate risk on loans serving to<br>finance social housing programs|20|
|**Total**||**1,965**|
|**Land subsidies**|Provision of land parcels from the State domain to public<br>developers for construction of social housing, at prices<br>belowmarket|<br>**800**|
|**Grand Total**||**9,256**|

* The differential cost is calculated with reference to preexisting fiscal provisions for social housing Source : World Bank calculations (2004).

47
# **2.3 PMT algorithm**

PMT uses a ‘greedy’ approach to break the intractable variable selection problem down into a sequence of manageable tasks. At each step, it selects the best currently available option, until a stopping criterion is triggered. Such a myopic procedure can result in a selection that is very different to the optimal subset. However, the lack of theoretical guarantees is outweighed by its feasibility and results that tend to be credible, as evidenced by widespread policy adoption. A further benefit from the perspective of this paper is that a greedy algorithm produces a sequence of nested variable sets, which is a requirement for the adaptive approach laid out below. The original PMT algorithm, implicit in Grosh and Baker [1995], uses stepwise regression as the basic algorithm. It consists of the following key components:

- _Predictive model._ Ordinary least squares (OLS) regression is the workhorse of PMT. Starting with an intercept, one OLS model is trained for each un-queried variable.

- _Selection criterion._ The convention in stepwise regression tends to be the use of the Bayesian or Akaike information criterion for in-sample model selection [Friedman et al., 2001]. In forward selection, each variable is considered in turn, and that which provides the largest increase in the information criterion is added to the current variable set.

- _Stopping criterion._ Variable selection continues until the best available model in the current step offers no improvement in the chosen information criterion.

The PMT algorithm is described algorithm 1 below, where _X_ is a design matrix with partitions, _Xsel_ , _Xtmp_ , and _Xcand_ are column-wise partitions thereof, _y_ is the consumption level per household member that PMT estimates to determine eligibility, _f_ () is an OLS predictive model, _AIC_ is the Akaike information criterion [Akaike, 1998].

# **2.4 Data and background**

## **The Indonesia context**

Indonesia uses a social registry to implement its PMT for targeting multiple programmes. Much of the country’s population lives clustered above the poverty line and despite marked improvements in welfare, vulnerability remains substantial. In 2018, the poverty rate was 9.8%, but 28% of Indonesians lived below 1.5 times the poverty line. As a result, a relatively small income shock can push around 20% of Indonesian households into poverty. Those living above this vulnerability line but below 3.5 times poverty line 9
# **Table 7. Likin Determinants**

# **Dependent variable: Likin per 1000 square km (in logs)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Taiping *Year 1880|3.184***|3.184***|||
||[0.227]|[0.227]|||
|Taiping *Year 1910|1.857***|1.857***|||
||[0.123]|[0.122]|||
|Taiping *Year 1953|1.442***|1.442***|||
||[0.095]|[0.093]|||
|Early Taiping *Year 1880|||2.763***|2.750***|
||||[0.217]|[0.217]|
|Early Taiping *Year 1910|||1.432***|1.417***|
||||[0.106]|[0.106]|
|Early Taiping *Year 1953|||1.124***|1.124***|
||||[0.073]|[0.074]|
|Late Taiping *Year 1880|||4.050***|4.078***|
||||[0.210]|[0.211]|
|Late Taiping *Year 1910|||2.732***|2.763***|
||||[0.084]|[0.087]|
|Late Taiping *Year 1953|||2.094***|2.095***|
||||[0.073]|[0.073]|
|Taiping *standardized Distance to Nanjing*Year 1880||-0.115***||0.068|
|||[0.041]||[0.074]|
|Taiping * standardized Distance to Nanjing*Year 1910||-0.110**||0.076|
|||[0.053]||[0.089]|
|Taiping * standardized Distance to Nanjing*Year 1953||-0.132***<br>[0.027]||0.002<br>[0.017]|
|Observations|1,064|1,064|1,064|1,064|
|R-squared|0.897|0.897|0.898|0.898|
|Number of prefectures|266|266|266|266|
|prefecture FE, year FE|Y|Y|Y|Y|

Note: For all regressions, the full sample contains the prefecture-year panel for 266 prefectures and four time periods, 1851, 1880, 1910, 1953. Only the estimation results of the key variables are presented.

Likin revenue are converted into 1887 silver teal.

Standard errors are robust, clustered at the prefecture level.

*** p<0.01, ** p<0.05, * p<0.1

52
the system. The overall budget went from USD 3.6 million in 2002, to USD 182 million in 2012. Of these, around 14 percent financed the family support service; 24 percent consisted of cash transfers; and 62 percent were transferred to other social programs in order to fill coverage gaps. Administrative costs remained relatively small: in 2009 (the latest year for which information is available), they represented around 3 percent of the total budget.

The cost per year of family support services, which includes salaries of social workers, varies significantly depending on the target group, from an average of US$ 132 for poor families, to US$ 474 for the homeless, to US$ 1,263 for children of prisoners. All of them include home visits, personalized work with beneficiaries, group activities, networking, and educational materials for the intervention; however variations on the frequency, level of complexity and specialization of the intervention strongly impact costs.

Remoteness also impacts costs significantly. The cost of the family support service ( _Programa Puente_ ), for instance, can vary between US$ 99 for municipalities of easy access and high geographic concentration, to US$ 531 for municipalities of difficult access and high geographic dispersion, where fewer visits can be attended per day (Table 2).

**Table 2: Cost of the** **_Programa Puente_ , by geographic area**

|**Type of Municipality**|**% Municipalities**|**% Families**|**Total yea**<br>**(Av.**|**rly cost per f**<br>**2008/09; US$**|**amily**<br>**)**|
|---|---|---|---|---|---|
||||**Min.**|**Max.**|**Mean**|
|**Difficult access – high**<br>**geographic dispersion**|15.6|6.1|128|531|181|
|**Combination of localities**<br>**with easy and difficult**<br>**access**|71.8|63.2|119|133|126|
|**Easy access – high**<br>**geographic concentration**|12.6|30.7|99|128|105|
|**ALL**|**100**|**100**|99|531|132|

Source: Authors’ compilation based on program information.

17
Table 9. Endogeneity: A Control Function Approach

||(1)|(2)|(3)|
|---|---|---|---|
|Rate Diferential|1.271<br>(1.164)|||
|Host-KF METR|-0.003<br>(0.004)|||
|Non-Host Component||2.930**<br>(1.483)|1.866<br>(1.375)|
|Host Component||-4.924***<br>(1.223)|-4.271***<br>(1.363)|
|Residual (Rate Dif.)|1.490<br>(1.226)|||
|Residual|-0.001|||
|(Host METR)|(0.017)|||
|Residual (Host Comp.)||-0.486|0.513|
|(Non-Host Comp.)||(1.376)|(1.268)|
|Residual||3.140***|2.512**|
|(Host Comp.)||(1.100)|(1.231)|
|Observations|13,483|12,765|12,077|
|Pseudo R2|0.153|0.169|0.186|
|Fixed efects<br>IVs (no. of lags):|Parent, Year<br>CIT Diferential (2)<br>Host METR (2)|Parent, Host-Year<br>Host Comp. (2)<br>Non-Host Comp. (2)|Parent, Host-Year<br>Host Comp. (2)<br>Non-Host Comp. (2)<br>Host METR (2)<br>VAT and PIT Rev. (1)<br>Host Pop. (0)|

_Notes:_ The dependent variable is the I/K ratio for FAI. The specification is our baseline PPML, augmented with residuals from first stage regressions of tax variables. The residuals are predictions from first-stage regressions, with the instruments included as indicated in the table. We: 1) regress endogenous (tax) variables on instruments and controls, to retrieve predicted residuals, then 2) estimate PPML regressions with the first stage predicted residuals as controls. With this approach standard errors tend to be underestimated. Fixed effects are included as indicated in the table. Macroeconomic variables as well as bilateral gravity variables are included as control variables. Standard errors in parentheses are clustered by host. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.

57
pronounced focus on informality in Bolivia when analyzing the quality of employment has made less visible other dimensions of a good job such as working conditions, stability and the linkage between wages and the job.<sup>3</sup> This paper proposes a Job Quality Index (JQI) that encapsulates key elements that define a good job. It builds on the literature on measuring the quality of jobs, and also incorporates country-specific considerations related to Bolivia’s regulatory and institutional labor market context. We employ a Principal Component Analysis (PCA) to facilitate the aggregation of various labor dimensions into a composite value, with each component representing a distinct job quality aspect. Additionally, the usage of the PCA allows to endogenously select the job dimensions’ weights and avoid any arbitrary assignment. These dimensions are derived from the Bolivian Household Survey, which offers extensive information on workers’ labor conditions, including contract types, pension enrollment, monthly wages and other relevant factors. The analysis utilizes data from 2007 to 2021 to assess the evolution of these labor conditions over time, examining the economic boom in Bolivia during the past decade as well as the pandemic crisis.

We find that job quality mostly remained constant during the analysis period, hovering around 0.46 (in an index between 0 and 1, where values closer to 1 indicate higher quality). At the same time, the index highlights high variation in the quality of jobs across groups. For example, men have jobs with a quality 28% better than those of women, representing a gap that has been persistent over time. We also find that workers in micro and small firms have jobs with a quality 0.17 points below that of self-employed workers (representing a quality 42% lower).

Overall, these results suggest that Bolivia faces challenges in increasing job quality as evidenced by a relatively steady measure even during an economic boom. In addition, it shows the disparities that exist and persist among different groups. Namely, women, workers in micro and small firms and workers in rural areas face jobs of lower quality compared to their respective counterparts. Policy implications developed from these results could address dimensions with lower satisfactory levels, such as access to social security, and at the same time, target and extend coverage on disadvantaged groups.

This paper contributes to three strands of the literature. First, it expands the research

> _Doble Aguinaldo_ , restrict SME growth in Bolivia.

> 3For instance, Hovhannisyan et al. (2022 _a_ ) summarize the principal dimensions that overlap across different frameworks proposed by international panels of experts at the macro and micro levels.

2
<!-- Start of picture text --> Figure 2. Poverty headcount ratio<br>Elderly from EHH1 & EHH2<br>Elderly from EHH2<br>Elderly from EHH1<br>Poverty line = 50% of median per capita income, OECD equivalency scale adjusted household size<br>Poverty headcount ratios for the population & its subgroups estimated using the same poverty line<br>Elderly are household members aged 60 years or more<br>Argentina Bolivia Brazil ChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHonduras MexicoNicaraguaPanamaParaguay PeruUruguayVenezuela, RB<br>0.50<br>.5<br>0.41<br>0.39<br>.4<br>0.36 0.36<br>0.35 0.35<br>0.32 0.32<br>0.29 0.31 0.29 0.31 0.30 0.28 0.31 0.30 0.31<br>.3 0.27<br>0.26 0.25<br>0.23 0.24 0.23 0.23 0.23 0.23<br>0.22 0.20 0.21 0.22 0.20 0.19 0.21 0.20 0.21 0.19<br>.2 0.16 0.16 0.17 0.18 0.16 0.17 0.17<br>0.15 0.14<br>0.13<br>0.11<br>0.09<br>.1 0.08 0.08<br>0.06 0.06<br>0.03<br>0<br><!-- End of picture text --> 29
# **Table 4. The Effect of Land Title on Investment: Instrumental Variable Regressions**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents parameter estimates from the first and second stage of instrumental variable regression of investment on Titling Firm dummy variable. In Column (1) and (2), the results of the first stage estimate are presented. In Column (3) and (4), the results of the second stage estimate are presented. The instrument we use in (3) is whether the Shenzhen government was the controlling shareholder of the firm when it was first established. The instrument we used in (4) were the shares of Shenzhen government as the percentage of total shares of the firm when the firm was first established. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that is White-corrected. The *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

||Titling<br>First sta<br>(1)|Firm<br> <br>ge OLS<br>(2)|<br>∆(Investme<br>IV: Shenzhen<br>government as<br>controlling<br>shareholder when<br>established<br>(3)|nt/PP&E)<br>IV: Shareholding<br>percentage of<br>Shenzhen<br>government when<br>established<br>(4)|
|---|---|---|---|---|
|Shenzhen gov’t as controlling|0.694***||||
|shareholder when established|(9.032)||||
|Shareholding percentage of Shenzhen gov’t||0.808***|||
|when established||(9.603)|||
|Titling Firm|||0.322***|0.315***|
||||(2.771)|(2.964)|
|ROA|||0.001|0.001|
||||(1.442)|(1.442)|
|Tobin’s Q|||0.003|0.003|
||||(1.581)|(1.579)|
|Industry FE|Yes|Yes|Yes|Yes|
|Exchange FE|Yes|Yes|Yes|Yes|
|Adj. R<sup>2</sup>|0.424|0.471|0.028|0.028|
|# of Obs.|1250|1250|1250|1250|
|First stage F-test|91.327|110.383|||

49
Table 1: Employment Shares and Quality of Infrastructure

|||OLS|||GAM||
|---|---|---|---|---|---|---|
||Agriculture|Manufacturing|Services|Agriculture|Manufacturing|Services|
|log(_Li_)|-0.063**|0.022**|0.041**|-0.063***|0.021***|0.042***|
||(0.003)|(0.002)|(0.001)|(0.004)|(0.002)|(0.003)|
|log(_Gi_)|-0.027|0.016|0.011**|-0.016***|0.006**|0.011***|
||(0.006)|(0.007)|(0.001)|(0.004)|(0.002)|(0.003)|
|log( <sup> ̄</sup>_Ii_)|0.023*|-0.006|-0.017***|0.016***|-0.005***|-0.010***|
||(0.003)|(0.003)|(0.000)|(0.003)|(0.002)|(0.002)|
|log(_Gi_)_×_log( <sup> ̄</sup>_Ii_)|0.002|0.003|-0.005*|0.007**|-0.001|-0.005**|
||(0.002)|(0.001)|(0.001)|(0.003)|(0.002)|(0.002)|
|Num.Obs.|1114|1114|1114|1114|1114|1114|
|R<sup>2</sup>|0.352|0.343|0.243|0.606|0.543|0.550|
|_S_(lat, lon)||||0.000|0.000|0.000|

Data: IPUMS-International for Brazil (2000-2010), DNIT and ANEEL. Geographic regions are consistent across years. Robust standard errors in parenthesis. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Panel OLS controls for latitude and longitude. Panel GAM shows semiparametric regressions with thin-plate spline in latitude and longitude, _S_ (lat, lon) shows the approximate p-value of the thin-plate spline.

tween the quality of roads and electricity, year fixed effects ( _μt_ ), and the function _S_ (lat _i_ , lon _i_ ) that controls for the possible spatial correlation of the errors. In particular, The first three columns in Table 1 show the OLS estimates of (1) in which the variables latitude and longitude enter the regression linearly. A second set of results in that table, labeled as GAM (Generalized Additive Model), follows Kelly et al. (2023) and considers a thin-plate spline specification of _S_ (lat _i_ , lon _i_ ); see Appendix A for details.

Table 1 shows that locations with larger shares of employment in manufacturing and services tend to have larger populations. Better quality of electricity is associated with smaller employment shares in agriculture and larger employment shares in manufacturing and services. Better road quality is associated with larger employment shares in agriculture and lower employment shares in manufacturing and services. The interaction term shows that the quality of electricity (roads) raises the marginal effect of improving the quality of roads (electricity) for agriculture. For services, the quality of roads (electricity) raises the marginal effect of improving the quality of electricity (roads). These interaction results indicate possible complementarities between the transportation and electricity networks.

The salient feature of our model is that the local electricity and road quality affects firms’ 8
Table 7: Health care spending as reported in household data†

||(1)<br>Official provider<br>fee|(2)<br>Unofficial provider<br>fee|(3)<br>Lab and x-rayfees|(4)<br>Transportation fees|
|---|---|---|---|---|
|Post indicator|1811.58|2052.12*|1048.64|123.03|
||[1475.25]|[1057.18]|[711.54]|[201.09]|
|PBF/Post interact|-1495.83|-2254.12*|-1473.44*|-455.41|
||[1538.26]|[1305.64]|[779.60]|[288.36]|
|Control 1/Post interact|-334.73|-2736.04|-521.02|-495.14**|
||[1506.73]|[1778.02]|[868.01]|[241.38]|
|Control 2/Post interact|-1378.05|-1422.67|-639.27|-368.79|
||[3969.75]|[1244.33]|[885.00]|[236.41]|
|p-value PBF vs. C1|0.191|0.750|0.051|0.880|
|p-value PBF vs. C2|0.974|0.392|0.128|0.732|
|p-value PBF vs. C3|0.332|0.086|0.060|0.116|
|Baseline mean C3|1689.22|2183.33|1603.09|910.30|
|N|2374|2261|2292|2365|

* = p < 0.10, ** p < 0.05, *** p< 0.01

<mark>† Results from difference-in-differences regression models examining the effect of PBF on health care spending in the last 4 weeks among respondents in the household survey. Regression model adjusted for individual (age, sex) and household control variables (number of individuals in the household, housing type, house ownership, water source, and type of sanitation). Standard errors were clustered at the health facility level.</mark> 40
33

# **Table 6.  Effects of Remittances on Poverty for Non-Remittance and Remittance-Receiving Households, Ghana, 2005/06**

||**Rec**<br>**no rem**|**eive**<br>**ittances**|**Receive i**|**nternal rem**<br>**Ghana**|**ittances (from**<br>|**Receive int**<br>**Afri**|**ernational re**<br>**can or other**|**mittances (from**<br>**countries)**|**Internal**<br>**remittances**<br>**vs. no**<br>**remittances**|**International**<br>**remittances vs.**<br>**no remittances**|
|---|---|---|---|---|---|---|---|---|---|---|
||Observed<br>(1)|Predicted<br>(2)|Observed<br>(3)|Predicted<br>(4)|Counterfactual<br>(5)|Observed<br>(6)|Predicted<br>(7)|Counterfactual<br>(8)|(4) vs (5)|(7) vs (8)|
|Poverty|30.24|21.26|35.43|12.81|41.86|4.87|.98|8.27|||
|headcount ( %)|||||||||-69.40|-88.15|
|Poverty gap (%)|7.13|5.67|10.40|9.48|16.57|.21|.09|.90|-42.79|-90.00|
|Squared poverty|.0007|.0006|.0030|.0031|.0068|.00006|.00003|.00006|||
|gap (%)|||||||||-54.41|-50.00|
|Gini coefficient|.43|.29|.41|.26|.25|.39|.27|.23|||
||||||||||4.00|17.39|
|Mean per capita<br>household<br>expenditure<br>(thousand cedis)|6,402|5,278|5,545|4,740|3,577|12,600|11,000|6,201|32.51|77.39|
|N|2515|2515|1159|1159|1159|210|210|210|1159|210|

Notes: Columns (1), (3) and (6) show observed household per capita expenditure. Columns (2), (4) and (7) show predicted household expenditures, using equation for households of type s, with households of type s (equation 10). Columns (5) and (8) use equation (11), which is for households with no remittances on households with internal remittances (column 5) and households with international remittances (column 8). These estimations adjust the selection term as explained in section 5 of paper. Poverty calculations made using poverty line of 3,066,582 Ghanaian cedis/person/year, which is the 1998/99 Ghana poverty line, updated for inflation. In 2006, US$ 1.00 = 9,000 Ghanaian cedis.

Source: 2005/06 Ghana GLSS 5 Survey (sub-sample).
there are 5 primary factors of production indexed by _f ∈ F_ .

Table 1 sumarizes the equilibrum conditions and associated variables. The non-linear system (of 1,364 equations and variables) is formulated in GAMS/MPSGE and solved using the PATH algorithum. We proceed with a description and algebraic representation of each of the conditions itemized in Table 1.

# **1 Dual representation of technologies and preferences**

Technologies and preferences are represented in the Kenya model through value functions that embed the optimizing behavior of agents. Generally, any linearly-homogeneous transformation of inputs into outputs is fully characterized by a unit-cost (or expenditure) function. Setting the output price equal to optimized unit cost yields the equilibrium condition for the activity level of the transformation. That is, a competitive constant-returns activity will increase up to the point that marginal benefit (unit revenue) equals marginal cost. In the case of the Kenya model not all transformations are constant returns, so there are exceptions. In general, however, we will use the convention of setting unit revenues (left-hand side) equal to unit cost (right-hand side) and associating this equilibrium condition with a transformation activity level.

Agents in Kenya wishing to purchase a particular good or service _g_ face an aggregate price _PA_<sup>_g_</sup> . Let _ts_<sup>_g_</sup> equal the sales tax rate such that (1 _− ts_<sup>_g_</sup> ) _PA_<sup>_g_</sup> is the net-of-tax price of the aggregate of domestic, foreign, and FDI (if applicable) varieties of _g_ . In constructing the aggregate, we will rely on the following notation for the component prices: _PD_<sup>_g_</sup> Gross price of domestic output ( _∀g ∈ G_ ),

- _PMr_<sup>_g_Grosspriceofcross-borderimportsfromregion</sup><sup>_r_ofBusinessServicesandCRTS</sup> goods ( _∀g ∈_ ( _I ∪ K_ )),

- _Pr_<sup>_g_Dixit-Stiglitzpriceindexonregion-</sup><sup>_r_varieties(</sup><sup>_∀g∈_(</sup><sup>_I∪J_)).</sup>

2