rafmacalaba/datause-encoder-data huggingface dataset:
considered: the presence of an inflation targeting regime, the degree of central bank transparency, the exchange rate regime, financial openness, trade openness, and the degree of fiscal sustainability. Inflation targeting regime and fixed exchange rate regime are dummy variables for which the presence of the indicated regime equals one. Exchange rate regime is determined using Shambaugh (2004). Central bank transparency and financial openness (capital account openness) are measured using indexes produced by Dincer and Eichengreen (2014) and Chinn and Ito (2017), respectively. Trade openness is measured as imports divided by domestic demand (domestic demand is defined as gross domestic product (GDP) + imports - exports). Fiscal sustainability is measured as the ratio of gross public debt to GDP.

The empirical exercise is undertaken in four steps. First, all variables are tested in a panel setting for unit roots.<sup>5</sup> Some tests do not reject the null hypothesis of nonstationarity of trade openness and gross public debt-to-GDP ratio (Table A.4.2). Second, since some variables (including the inflation targeting dummy, fixed exchange rate regime dummy, and financial openness index) are stationary, residual series are obtained from a panel regression of sensitivity of inflation expectations on these stationary variables. Specifically, the following model is estimated: where _β i,t_ is the time-varying estimate of the country-specific estimate of the elasticity of inflation expectations to inflation shocks, as explained in the discussion of regressions with time-varying parameters. _MPi,t_ is (i) a dummy variable that takes a value of one in countries with an inflation targeting framework or (ii) a measure of central bank transparency. _Xi,t_ includes a dummy variable that takes a value of one for countries with a fixed exchange rate regime and financial openness index. _Ѳ i_ captures country-fixed effects and _φ t_ refers to time fixed effects.

Third, the existence of cointegration between the residuals from the panel regression in model (5) and the gross public debt-to-GDP ratio and trade openness is tested by employing Pedroni’s (1999) cointegration test (Table A.4.3). The results indicate that the residuals are cointegrated with the two variables. Fourth, following Pedroni (2000, 2001), a grouped mean fully modified ordinary least squares (FMOLS) regression model and a grouped mean dynamic OLS (DOLS) regression model are estimated to correct for endogeneity bias and serial correlation. The dependent variable is the estimated residual from the panel regression in model (5). The independent variables are trade openness (measured by the import penetration ratio) and the gross public debt-to-GDP ratio.

> 5 This test follows Im, Pesaran, and Shin (2003); Maddala and Wu (1999); and Choi (2001). Although the time-varying parameters are constructed under the assumption of a random walk, most results of panel unit root tests reject the null hypothesis of nonstationarity.

36
# **Appendix Table 12: Children in monetary poor households in 2022 at the $6.85 line in 2017 PPP by income group**

|Income group|Children in<br>poverty<br>(millions)|Headcount<br>poverty rate (%)|Share of poor<br>(%)|Share of<br>population (%)|
|---|---|---|---|---|
|**Low income**|304.2|93.3|21.4|15.5|
|**Lower middle income**|903.6|83.4|63.6|51.5|
|**Lower middle income without India**|503.7|79.9|52.3|43.8|
|**Lower middle income without Nigeria**|802.8|82.1|60.9|48.9|
|**Upper middle income**|209.2|34.6|14.7|28.8|
|**Upper middle income without China**|152.2|38.6|15.8|27.3|
|**High-income**|2.8|3.2|.2|4.2|
|**Total**|1420.0|67.4|100.0|100.0|

**Appendix Table 13: Children in monetary** **<u>poor households in 2022 at the $2.15 line in 2017 PPP in fragile countries</u>**

|**GMD sample**|**Extreme poor**<br>**(millions)**|**Headcount**<br>**poverty rate (%)**|**Share of extreme**<br>**poor (%)**|**Share of**<br>**population (%)**|
|---|---|---|---|---|
|Non-fragile states|168.9|10.1|50.6|79.8|
|Fragile states|164.7|38.6|49.4|20.2|
|**Total**|333.5|15.8|100.0|100.0|

|**Appendix Table 14: Ch**|**ildren in monetary po**|**or households in 202**|**2 at the $3.65 lin**|**e in 2017 PPP in fragile countries**|
|---|---|---|---|---|
|**GMD sample**|**Children in**<br>**poverty (millions)**|**Headcount**<br>**poverty rate(%)**|**Share of poor**<br>**(%)**|**Share of population**<br>**(%)**|
|Non-fragile states|538.2|32.0|65.7|79.8|
|Fragile states|281.4|66.0|34.3|20.2|
|**Total**|819.5|38.9|100.0|100.0|

**Appendix Table 15: Children in monetary** **<u>poor households in 2022 at the $6.85 line in 2017 PPP in fragile countries</u>**

|**GMD sample**|**Children in**<br>**poverty (millions)**|**Headcount**<br>**poverty rate(%)**|**Share of poor (%)**|**Share of**<br>**population(%)**|
|---|---|---|---|---|
|Non-fragile states|1045.2|62.2|73.6|79.8|
|Fragile states|374.8|87.9|26.4|20.2|
|**Total**|1420.0|67.4|100.0|100.0|

**Appendix Table 16: Children in monetary** **<u>poor households in 2022 at the $2.15 line in 2017 PPP by gender</u>**

|**Gender**|**Extreme poor**<br>**(millions)**|**Headcount**<br>**poverty rate (%)**|**Share of extreme**<br>**poor (%)**|**Share of**<br>**population (%)**|
|---|---|---|---|---|
|Female children|162.9|16.2|25.6|14.9|
|Male children|170.6|15.7|26.8|16.0|
|Female adults|158.5|6.6|24.9|35.2|
|Male adults|143.4|6.2|22.6|33.9|
|**Total**|635.4|9.4|100.0|100.0|

15
# Table 4: Legal Gender Disparities <u>and Labor Force Participation of Women Relative to Men</u>

|||||**O**|**LS**||||
|---|---|---|---|---|---|---|---|---|
|**Dependent Variable**|**Wo**|**men over me**|**n labor force**|**participatio**|**n rate (% of**|**population**|**ages 15-64),**|**2013**|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Legal disparities: Overall measure|-0.008***<br>(0.002)||||||||
|Legal disparities: Accessing Institutions||-0.017***|||||||
|||(0.005)|||||||
|Legal disparities: Using Property|||-0.039***||||||
||||(0.012)||||||
|Legal disparities: Getting a Job||||-0.008**<br>(0.004)|||||
|Legal disparities: Going to Court|||||-0.132***||||
||||||(0.030)||||
|Legal disparities: Providing Incentives<br>to Work||||||-0.013|||
|||||||(0.020)|||
|Legal disparities: Building Credit|||||||-0.005<br>(0.009)||
|Legal disparities: Violence Protection||||||||-0.009|
|||||||||(0.010)|
|Log of GDP per capita (constant 2005<br>US$)|-0.031**|-0.029*|-0.038**|-0.036**|-0.036**|-0.037**|-0.037**|-0.035**|
||(0.015)|(0.015)|(0.015)|(0.015)|(0.014)|(0.016)|(0.016)|(0.016)|
|Rule of law|0.058***|0.079***|0.082***|0.071***|0.081***|0.087***|0.087***|0.082***|
||(0.020)|(0.020)|(0.021)|(0.022)|(0.019)|(0.023)|(0.023)|(0.022)|
|region==East Asia & Pacific|-0.037|-0.032|-0.032|-0.065*|-0.069**|-0.055*|-0.054*|-0.071**|
||(0.034)|(0.030)|(0.033)|(0.034)|(0.031)|(0.030)|(0.031)|(0.033)|
|region==Europe & Central Asia|-0.018|-0.001|-0.014|-0.021|-0.013|-0.008|-0.000|-0.014|
||(0.024)|(0.017)|(0.020)|(0.026)|(0.021)|(0.021)|(0.020)|(0.023)|
|region==Latin America & Caribbean|-0.115***|-0.104***|-0.114***|-0.113***|-0.117***|-0.108***|-0.096***|-0.119***|
||(0.035)|(0.032)|(0.032)|(0.036)|(0.034)|(0.035)|(0.036)|(0.037)|
|region==Middle East & North Africa|-0.280***|-0.291***|-0.289***|-0.385***|-0.328***|-0.408***|-0.400***|-0.406***|
||(0.068)|(0.067)|(0.068)|(0.057)|(0.055)|(0.053)|(0.052)|(0.055)|
|region==South Asia|-0.197**|-0.200**|-0.172*|-0.247***|-0.195**|-0.252***|-0.238**|-0.256***|
||(0.087)|(0.085)|(0.098)|(0.093)|(0.082)|(0.095)|(0.096)|(0.093)|
|region==Sub-Saharan Africa|0.050|0.061|0.051|0.004|0.017|0.014|0.024|0.013|
||(0.050)|(0.046)|(0.049)|(0.050)|(0.045)|(0.047)|(0.048)|(0.049)|
|Constant|1.191***|1.079***|1.156***|1.206***|1.131***|1.133***|1.137***|1.131***|
||(0.138)|(0.135)|(0.135)|(0.141)|(0.130)|(0.140)|(0.142)|(0.142)|
|Number of observations|151|151|151|151|151|151|151|151|
|Adjusted R2|0.593|0.581|0.585|0.552|0.621|0.535|0.535|0.537|

note: *** p<0.01, ** p<0.05, * p<0.1, Huber/White standard errors reported Omitted region: North America 28
|**Table 1: Heter**|**ogeneity with res**|**pect to hotel si**|**ze**||
|---|---|---|---|---|
|Dependent variable: SBM Efficiency|**(1)**|**(2)**|**(3)**|**(4)**|
|Volatility in Occupancy*Number of|-0.157***|-0.180***|-0.155***|-0.182***|
|workers (logs)|||||
||(0.049)|(0.051)|(0.049)|(0.053)|
|Volatility in Occupancy|0.464***|0.493***|0.444***|0.548***|
||(0.127)|(0.116)|(0.122)|(0.157)|
|Number of workers (logs)|0.107*|0.115*|0.132*|0.118|
||(0.058)|(0.065)|(0.068)|(0.069)|
|Central region Y:1 N:0||0.005|-0.043|0.040|
|||(0.024)|(0.042)|(0.080)|
|Eastern region Y:1 N:0||0.210***|0.221***|0.241***|
|||(0.046)|(0.057)|(0.072)|
|Northern region Y:1 N:0||0.012|0.074|0.046|
|||(0.039)|(0.064)|(0.113)|
|Sabah & Sarawak Y:1 N:0||0.146*|0.115|0.155**|
|||(0.071)|(0.069)|(0.069)|
|Age of hotel (logs)|||0.023|0.066|
||||(0.049)|(0.040)|
|Part of a chain Y:1 N:0|||-0.154|-0.101|
||||(0.109)|(0.064)|
|Women's ownership (proportion)|||-0.071|-0.077|
||||(0.130)|(0.189)|
|Finance major obstacle Y:1 N:0|||0.015|-0.006|
||||(0.121)|(0.101)|
|Occupancy rate|||0.117|0.138|
||||(0.186)|(0.192)|
|Number of power outages|||-0.002|-0.013|
||||(0.020)|(0.023)|
|Largest owner is manager Y:1 N:0|||-0.048|0.002|
||||(0.055)|(0.055)|
|Website Y:1 N:0|||-0.141*|-0.065|
||||(0.079)|(0.096)|
|Low competition Y:1 N:0|||0.017|0.025|
||||(0.041)|(0.064)|
|Intermediate competition Y:1 N:0|||-0.000|0.042|
||||(0.093)|(0.103)|
|Two-star hotel Y:1 N:0||||-0.118*|
|||||(0.065)|
|Three-star hotel Y:1 N:0||||0.019|
|||||(0.060)|
|Four- or five-star hotel Y:1 N:0||||0.110|
|||||(0.114)|

37
<!-- Start of picture text --> Customs clearance time (days)<br>18<br>16 14.8<br>14<br>12<br>10<br>7.68<br>8<br>5.35 5.54<br>6 4.83<br>4.16 3.83 3.72<br>4 3.03 3.31<br>2<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text -->

**Figure 5 – Time necessary to perform Customs clearance formalities in the port of Douala** Source: Synthèse sur les délais de passage portuaire, GUCE, Direction générale des études et pilotage de la performance, Septembre 2010

A threshold of three days seems to be a down limit for time efficiency of manual procedures and the GUCE is aiming today for a full dematerialization of procedures to achieve better performance. In parallel the customs administration has recently introduced performance contracts to ensure better efficiency of customs operations in the Port and one indicator (time release) tracks the time period between broker’s registration and customs officer’s assessment. From 70% declarations assessed the day it is lodged, the percentage increased to 90%.

All these observations make us conclude that customs clearance performance does not seems to be a priority issue anymore in the reduction of dwell time in Douala. Of course there is still way for improvement, in particular in the pre-shipment inspection process or the establishment and lodging of customs declaration.<sup>25</sup> A major step would be to encourage wide use of pre-arrival declaration that is still far from being widely practiced by brokers and shippers.

Other areas of improvement include the efficient connection of Port authority to the electronic GUCE, the introduction of a single payment and the training of customs brokers to avoid errors in declaration. There are also many operational issues with the current pre-inspection system with long delays before validation of pre-inspection and collection of statements at the preinspection service office in Douala. This paper does not examine these issues currently discussed by all port stakeholders and customs administration. The point is that all IT investments (and infrastructure or equipment investment in port operations) will only result in improvement if behavioral inefficiencies are effectively tackled.

> 25 An action plan to reduce processing time at SGS, the pre-shipment inspection service, is in place. Reference time of 8h30 is much exceeded today (1 day and 5h on average in 2009 according GUCE).

19
Donaldson and Storeygard (2016). Early applications to the measurement of economic activity and welfare include Henderson et al. (2012), which also established the value of these data as predictors of economic outcomes (see also Pinkovskiy and Sala-I-Martin, 2016). These early studies confirm that national income can be accurately predicted using NTL data.<sup>2</sup> Economists have since expanded the set of predictors extracted from remote sensing data and increased the spatial resolution at which economic welfare including poverty is estimated. Jean et al. (2016) is among the early studies to use a comprehensive set of predictors derived from remote sensing data to estimate poverty at the small area level. The studies vary in the resolution of imagery used, with higher resolution images allowing for a richer set of predictors that can be extracted from them (see e.g. Marx et al., 2019). The increased accessibility of remote sensing data combined with the continued demand for highly disaggregated poverty data has prompted a surge in the production of poverty maps in recent years. In the years following Jean et al. (2016), dozens of remote sensing-based poverty and/or household welfare maps have been produced, see e.g. Blumenstock et al. (2015), Blumenstock (2016), Bosco et al. (2017), Burke et al. (2021), Engstrom et al. (2021), Imran et al. (2014), Pokhriyal and Jacques (2017), Steele et al. (2017), Watmough et al. (2016), Watmough et al. (2019), Zhao et al. (2019), and Lee and Braithwaite (2022).

This highlights the importance of assessing the accuracy of remote-sensing-based poverty maps. One logical entry-point in this regard is an assessment of how closely a poverty map obtained using remote sensing data tracks a poverty map derived from population census data. Such assessments are currently missing from the literature. Existing studies typically validate their small area poverty estimates by comparing them against direct-estimates obtained from the household survey. However, these direct estimates are subject to large sampling error and cover only a small subset of the total number of areas. They thus fail to confront what motivates the use of small area estimation methods to begin with. Engstrom et al. (2021) is a notable exception. That study uses the census-based small area estimates of poverty obtained using the ELL approach as the dependent variable in their model employing predictors derived from remote sensing data as independent variables. This does not allow, however, for a validation of a poverty mapping approach that does not use any population census data (i.e. an approach that combines household survey with remote sensing data).

The validation exercise presented here provides a clean comparison between poverty maps obtained with and without population census data. Employing data for Malawi, we first obtain small area estimates of poverty that combine the Malawi household

> 2More recent examples of empirical studies that use remote-sensing data to track local economic activity include Nhu and Noy (2020), Marx et al. (2019), and Van der Weide et al. (2018), among others.

4
perspective on what we could expect as market participants receive better information.

The magnitudes of the impacts we find are also quite large. The gains in learning alone compare favorably to other interventions, both in absolute terms and relative to normal yearly gains (the treatment effect is 42 percent of the average yearly gain experienced by children in our sample).<sup>32</sup> Similarly, the gain in enrollment represents a cost per marginal child enrolled of $20, which is significantly lower than several programs that are currently regarded as quite successful in low-income countries (Akresh et al., 2013). In terms of overall welfare implications, it is noteworthy that the cost of providing information was similar to the decline in school fees. Specifically, the cost of the report card exercise was $1 per child, and the fee savings were approximately $3 per child in private schools. With nearly one-third of all children enrolled in private schools in these villages, the total cost of providing information for all children is comparable to the decline in fees. This partial analysis would suggest that the entire improvement in test scores is free of cost if only the welfare of households is considered.<sup>33</sup> Finally, our results also help inform the ongoing debate on public versus private education in low-income countries where public sector failures are common. Much of the debate about how to improve learning focuses on public schools. In fact, it is especially telling that many interventions and studies focus

> 32 A recent meta-study (McEwan, 2013) of over 70 educational intervention studies from developing countries finds that the largest mean effects were around 0.15 standard deviations (for interventions with computers or instructional technology).

> 33 A comparison with other cost-benefit calculations in the low-income country educational literature shows the usual practice is to focus only on the welfare of households and their children. For instance, in cases where improvements have come through greater effort by teachers, their welfare cost is not accounted for in the cost-benefit analysis. However, a complete welfare analysis would exclude the decline in fees, since it is a direct transfer from the schools/teachers to parents and focus on the enrollment and test score gains alone. Even by that criteria though, the returns to this intervention are significant.

29
**Table 3.   Oversight Recommendations by Sector**

(As percent of total)

||**1999-**<br>**2001**|**2002-**<br>**2004**|**2005-**<br>**2007**|**HIC**|**MIC**|**LIC**|
|---|---|---|---|---|---|---|
|**Banking**|**46.5**|**42.8**|**42.6**|**42.9**|**45.7**|**37.4**|
|**_a. Prudential_**|44.5|40.2|39.4|41.4|42.9|34.0|
|**_b. Market performance_**|2.0|2.6|3.2|1.5|2.8|3.4|
|**Insurance**|**16.7**|**19.2**|**16.5**|**20.1**|**17.3**|**17.3**|
|**_a. Prudential_**|15.3|17.9|15.7|18.9|16.3|15.5|
|**_b. Market performance_**|1.4|1.3|0.8|1.2|1.0|1.8|
|**Pensions**|**6.6**|**7.2**|**9.5**|**4.8**|**9.4**|**5.2**|
|**_a. Prudential_**|5.6|6.3|8.1|4.8|7.7|5.2|
|**_b. Market performance_**|1.0|0.9|1.5|0.0|1.7|0.0|
|**Microfinance**|**2.2**|**4.8**|**5.5**|**0.0**|**2.3**|**16.2**|
|**_a. Prudential_**|1.8|4.3|5.2|0.0|2.0|14.7|
|**_b. Market performance_**|0.4|0.5|0.3|0.0|0.2|1.5|
|**Other NBFIs**|**5.0**|**4.9**|**4.7**|**4.1**|**4.8**|**5.9**|
|**_a. Prudential_**|4.8|4.7|4.4|3.9|4.6|5.4|
|**_b. Market performance_**|0.2|0.2|0.3|0.2|0.2|0.5|
|**Capital Markets**|**12.9**|**10.7**|**9.0**|**12.6**|**10.2**|**10.6**|
|**_a. Prudential_**|11.5|10.5|9.0|11.9|9.8|10.3|
|**_b. Market performance_**|1.4|0.2|0.0|0.7|0.4|0.3|
|**Global Oversight**|**10.1**|**10.5**|**12.1**|**15.5**|**10.4**|**7.5**|
|**_a. Prudential_**|10.1|10.5|11.8|15.5|10.3|7.5|
|**_b. Market performance_**|0.0|0.0|0.3|0.0|0.2|0.0|

# **B.  Statistical analysis**

To avoid introducing a selection bias by cherry picking across the whole FSAP reports and to limit the number of recommendations, the database is limited to the key recommendations drawn from the summary tables (generally) placed at the end of FSAPs’ executive summaries. In cases where such a summary table was unavailable (this was often the case in the earlier FSAPs), the recommendations were inferred from the executive summaries. When the recommendations in the summary table or executive summary were deemed to be excessively general, an effort was made to infer more specificity from the main body of the FSAP report (there were only a few such cases).

When recommendations could genuinely be placed into multiple rows of the matrix, they were copied into each of the rows. While such overlapping is difficult to avoid completely (some recommendations will inevitably fall in the interface between different items of the grid, particularly when they are not too specific), it was quite infrequent, suggesting that the grid does a good job on the whole in mapping up one for one the universe of recommendations.<sup>5</sup> 5 Take for example the case of a recommendation aimed at improving the registration and use of collateral. Because it touches on aspects of information infrastructure (e.g., the collection and storage of information 7
# **Figure 1: Evaluation Design**

<!-- Start of picture text --> Treatment roll-out<br>2014 2015 2016<br>QII QIII QIV QI QII QIII QIV QI<br>TT<br>Group A<br>(257 households)<br>Treatment Group<br>Early receivers of<br>workfare offer<br>Group B (504 households) TT<br>(247 households)<br>CC T<br>Group C<br>(253 households)<br>Control Group (507<br>households)<br>CC T<br>Group D<br>(254 households)<br>1011 eligible households<br><!-- End of picture text --> Note: The shaded areas refer to the time period when the group served as treatment or control. For treatment groups, different types of shades indicate the implementation period (midline) versus the period following the cessation of the public works program, with the endline therein (approximately nine month after completion).

33
> contrast, over the periods 1987-96 and 1988-97 there were marked inequalities between the poor and better-off. The VLSS data are more directly comparable given in each year the quintiles are quintiles of live births, and the ranking variable is the same (equivalent

> consumption). What the VLSS results suggest is that the national reductions in child mortality uncovered in the previous sections have not been spread evenly across the

> Vietnamese population-the upper socioeconomic groups have seen appreciable reductions in child mortality, but the poorer Vietnamese children have seen little improvement in their survival prospects in recent years. By using the concentration curve

> device, we can get round the problem of the DHS data being quintiles of households.

> Figure 5 shows the concentration curves for under-five deaths, the nonalignment of the markers for the DHS data with the quintile cut-offs reflecting the higher fertility of poorer households. It is striking how close the 1997 DHS and the 1998 VLSS concentration curves are to one another, and how far they are from the curve for the 1993 VLSS.

**Figure 4. Trends in under-five mortality in Vietnam, by consumption quintile**

**Changing inequalities in child survival, 1993-98**

<!-- Start of picture text --> 70.0 __<br>60.0<br>50.0<br>01<br>'~40.0- 193VS<br>8 _w"' E l!1~~~~~~~~~~~~ 1 997 VDHS5<br>30.0 -_ 1998 VLSS<br>20.0<br>10.0 _ __'ti I<br>10.0 I!I<br>1 2 3 4 5<br>quintiles<br><!-- End of picture text --> 9