rafmacalaba/data-use-mentions-extended huggingface dataset:
between 2006 and 2025, with the support of development partners (Carlitz 2017). The WSDP comprises of three smaller programs: 1) water resources management; 2) Rural Water Supply and Sanitation Program (RWSSP); and 3) urban water supply and sewerage. At the time of the initiation of RWSSP, water coverage in rural areas in Tanzania was estimated to be around 53 percent based on its 2002 Population and Housing Census (Ministry of Water, 2006)<sup>5</sup> . The RWSSP aimed at improving water coverage to its rural population to 1) at least 65 percent by 2010; 2) 74 percent by mid-2015; and 3) at least 90 percent by 2025<sup>6</sup> . The program is also largely funded by the World Bank and other international donors and is one of the biggest in the African region (Jimenez and Perez-Foguet, 2010; Gine and PerezFoguet, 2008; World Bank, 2008).

During the first phase of the WSDP (2007-15) implementation arrangements were decentralized to the district levels (Local Government Agencies- LGAs), with the intention of providing better technical support to the rural communities. The WSDP adopted a Community Driven Development (CDD) approach in the rural water sector, where the village community and its institution, the COWSO (Community Owned Water Supply Organizations), were given full ownership of their rural water system along with the associated management responsibility. Under this arrangement, the Ministry of Water and Irrigation (MOWI) set policies and guidelines and provided technical support to the Local Government Authorities (LGAs). The actual implementation of new water projects is facilitated by the LGAs under the President’s Office of Regional and Local Government (PORALG).

One of the more ambitious projects that went hand-in-hand with the country’s effort in improving water access was the creation of its Water Point Mapping (WPM) database. With the assistance of Water Aid which has previously done similar work in Malawi (Stoupy & Sudgen
# **III. What is the extent and distribution of tax compliance in Romania?**

**The imputation results show that the final imputed values using predictive mean matching (PMM) are reasonably close to those observed on the tax admin data** . The distribution of income in the EUSILC, tax data, and imputed EU-SILC is presented in Table 3. The mean income value in tax data and imputed EU-SILC are reasonably close.<sup>11</sup> The mean tax income is 1.2% lower than the mean survey income, and the mean imputed tax income is 6.6% higher than the mean survey income. As discussed below, our estimate of tax evasion in Romania is close to that of other countries in the region.

**Table 3. The distribution of survey income, tax income, and imputed tax income (2020 €).**

|**Statistic**|**Annual income based on**<br>**Survey data**<br>**(EU – SILC, gross labor**<br>**income)**|**Tax**<br>**annual**<br>**income-**<br>**observed value**<br>**(July 2020 * 12)**|**Imputed tax annual gross**<br>**income**<br>**(imputed EU-SILC)**|
|---|---|---|---|
|**Mean (without**<br>**censoring)**|11,300|11,164|12,051|
|**Mean(with censoring)**|10,863|.|10,191|
|**P1**|2,968|322|1,364|
|**P5
* 0.01_ Next, the _Property Rights*TAN_ interaction term is discussed. In developing countries, banks prefer immovable assets (i.e., a high degree of asset tangibility) such as land for collateral, which highlights an agency problem. The influence of asset tangibility on trade specialization can be magnified or diminished by the development of legal framework and other governance-related factors such as property rights and credit protection.

The interaction of property rights and asset tangibility with exporter size ( _Property Rights *TAN_ ) is insignificant. Strong property rights are not correlated with larger mean or median exporter size in industries characterized by higher asset intangible. However, despite the insignificance, the effects are significantly different in MENA and non-MENA regions ( _Property Rights*MENA_ ). One explanation is that the GCI Property Rights Index is higher in MENA than other regions, and offers more effective protection than in non-MENA. Property Rights in MENA also appear to be

> _23 We estimate regressions with average or weighted tariffs. Trade regulations are generally viewed to be much more restrictive in MENA countries. Weighted tariff rates in MENA average 17.4 percent, and 8.7 percent in non-MENA countries. Tariff data from the WDI is unavailable for Lebanon or Kuwait. Egypt, Iran, and Yemen have only two years of tariff data. The availability of tariff data does not always coincide with availability of EDD or business climate data._

20
6): 866-893. <u>htps://doi.org/10.1080/14719037.2015.1051572.</u>

- Dahlgren, P. (2009). Media and Political Engagement: Citizens, Communication, and Democracy. Cambridge University Press.

- Datta*, A., & Agarwal, S. (2004). Telecommunications and economic growth: a panel data approach. _Applied Economics_ , _36_ (15), 1649-1654.

- David, N. (2018). Democratizing Government: What We Know About E-Government and Civic Engagement. In: Alcaide Muñoz, L., Rodríguez Bolívar, M. (eds) International E-Government Development. Palgrave Macmillan, Cham. htps://doi.org/10.1007/978-3-319-63284-1_4.

- Davison R., Vogel D., Harris R. W., & Jones N. (2000). Technology Leapfrogging in Developing Countries: An Inevitable Luxury? Electronic Journal of Information Systems in Developing Countries, 1, 1-10. <u>htps://doi.org/10.1002/j.1681-4835.2000.tb00005.x.</u>

- Diergarten, Y., & Krieger, T. (2015). Large-scale land acquisitions, commitment problems and international law. _Law and Development Review_ , _8_ (1), 217-233.

19
capital and livestock. Thus the decreasing share of livestock in total agricultural capital was due in part to the rapid growth in treestock in the 1980s and 1990s, as well as the slowdown in the accumulation of animal stocks in the 1990s.

In addition to the time dimension, there is much cross-country variability in agricultural capital stocks. For purposes of comparison, we divide the sample into high-income countries versus middle- and lower-income countries (Table 2).<sup>18</sup> High-income countries achieved higher growth rates of fixed capital (6.2% versus 5.5%), while the growth of treestock was considerably faster in middle- and lower-income countries (6.6% versus 4.0%). The growth rate of livestock was only slightly higher in middle- and lower-income countries (3.8% versus 3.3%).

These growth patterns led to interesting changes in the composition of capital for each income group as shown in Table 3 and Figure 4. The shares of each component in total agricultural

> 18 The income classifications are based upon the World Development Indicators of the World Bank in 2000.

9
.1|64.0|14.9|6.1|0.9|18.0|9.9|
|2011|208.4|113.1|58.7|15.3|3.6|1.1|21.3|11.7|
|2012|205.1|111.5|55.6|15.1|3.2|1.4|22.9|11.9|
|2013|233.6|127.1|63.9|16.8|4.6|2.3|25.8|12.2|
|2014|251.9|140.3|64.8|18.9|4.5|2.1|28.0|14.4|
|2015|271.1|148.9|69.7|22.5|5.1|2.9|30.0|17.4|
|2016|299.9|168.2|71.6|27.1|5.9|3.5|33.0|21.2|
|2017|312.1|173.6|78.1|28.1|5.7|3.6|32.2|22.4|
|2018|331.1|189.4|80.1|29.1|6.9|4.1|32.5|22.2|
|2019|360.0|200.1|88.1|29.9|6.6|5.0|42.8|23.2|

_Note:_ This table presented total amounts of income components for each year in the final microdata. It includes survey and tax records after the merging of the two sources, but before scaling up components to macroeconomic aggregates.
the necessary upgrading of drainage systems and other infrastructure. Moreover, among households that rent, 74% provided a down payment to access the house, which could make it more difficult to move. Finally, since floods seem to affect different areas on different occasions, it is difficult to predict which areas will be exposed to floods, even with access flood models. Consequently, it will also be difficult for the market to price flood risk exposure correctly.

However, affected households consistently value their own dwelling at a lower price than non-affected households. Households owning their dwelling were asked to estimate how much the dwelling would be worth if someone bought it at the time of the survey. The self-evaluation showed a clear trend. Directly, indirectly and frequently affected households consistently estimated their dwelling to be worth less than the

> 23 The second phase of the Ramani Huria project launched in 2017 as part of TURP.

> 24 See Table 14 with results from simple t-tests analysis in Appendix 2.

18
2015); Mexico (Scott et al. 2018); Poland (Goraus and Inchauste 2016); Russia (Popova et al. 2018). Turkey: Own estimates based on Turkey 2016 HBS. Note: Argentina, Brazil, Chile, Mexico, and Russia are based on a $4/day poverty line in 2005 PPP. Croatia and Poland are based on $5/day poverty line in 2005 PPP. Turkey is based on 5.5/day poverty line in 2011 PPP. The figure shows the poverty rate for each income concept described in Figure 1.

**Figure 6. Distribution of taxes and transfers across income deciles in Turkey, 2016**

22
0.0006|
|survey|||||(0.0433)|(0.0364)|
|Constant|0.3271***|0.6703***|0.3271***|0.6711***|0.0070|0.0277**|
||(0.0260)|(0.0246)|(0.0261)|(0.0248)|(0.0175)|(0.0132)|
|Panel Variable|District|District|District|District|District|District|
|Region x Year of Birth<br>dummies<br>|Yes|Yes|Yes|Yes|No|No|
|Age at Interview<br>dummies|No|No|No|No|Yes|Yes|
|DHS 2006 x Region x<br>Age dummies|No|No|No|No|Yes|Yes|
|Observations|3823|3055|3823|3055|9595|9267|
|No. of Clusters|75|75|75|75|69<sup>a</sup>|69<sup>a</sup>|
|R-Squared|0.1106|0.0368|0.1105|0.0367|0.2028|0.2975|

All specifications are estimated using the panel fixed-effects estimator. District casualties are expressed per 1000 inhabitants. Columns (1) to (4): Sample only includes individuals surveyed in the Nepal DHS 2006 and aged 5-9 (treatment group) or 16-19 years old (control group) at the start of conflict in 1996. Columns (5) and (6): Sample only includes individuals surveyed in Nepal DHS 2001 and 2006, aged 10 to 18 at the time of the survey.<sup>a</sup> DHS data collection was somewhat affected by the conflict in 2001, and so a number of districts were
particularly in developing countries, and uncertainty surrounding these political relationships can negatively impact firms‟ performances (Faccio, 2006, 2007; Fisman, 2001).

Column (3) presents the results of the 50% nominal devaluation and subsequent proprivate sector reforms on firm survival. Contrary to the idea that a revaluation of the exchange rate can help restore international competitiveness, we find that, on average, the reform had no significant impact on firm survival. However, we do find that the tradables sector responded favorably to the episode, reducing the likelihood of exit by about 30% relative to the service sector firms. Regarding firm size, we find that the reform lessened the disadvantage middle-size firms faced relative to large firms by 50%.

# **6. Conclusion**

We study firm dynamics using a database of all formally registered firms in Cote d'Ivoire from 1977 to 1997, which account for about 60% of GDP. Our analysis focuses on private sector dynamics over the business cycle. We also examine separate episodes of trade, fiscal, and monetary reforms and the relationship of these interventions with private sector dynamics.

First, we examine entry and exit patterns over this period and the role of new and exiting firms versus incumbents in job creation and destruction. We find that while the rate of job creation at new firms is quiet high at 8% on average, the absolute numbers of jobs added by new firms is small in absolute terms. Next, we examine survival rates and find that the probability of survival increases monotonically with firm size, but that manufacturing and foreign-owned firms face higher likelihoods of exit compared to service oriented and domestically-owned firms, respectively. We find that higher GDP growth increases the probability of firm survival, but this is a broad impact with no firm size disproportionately affected. In robustness checks we found that post-1987, size is no longer a significant determinant of firm survival for new entrants, suggesting that the operating environment for firms changed. Finally, we find that the trade and fiscal reform
# _How teachers are managed_

The mean reported gross monthly compensation for teachers is TZS 676,184 (USD 293). Education, relative speaking, is one of the better-paid sectors (RISE Baseline Report 2019). However, since 2000, most teachers have faced stagnating purchasing power at best. Also, the salary differences between teacher certification types have increased over this period ((RISE Baseline Report 2019).

Around 36 percent teachers believe that their school regularly recognizes and rewards teacher performance. However, only 25 percent said that student learning outcome is the key metric used by the head teacher to judge their performance. Nearly 84 percent of teachers believe actions are taken in case of poor performance. The action format most often taken, according to teachers, was a warning from the headteacher. According to teachers, the risk of dismissal or transfer because of poor performance is almost zero.

Around 49 percent agree with the following statement about the school leadership: “ _They will recommend me to be transferred or dismissed in case I receive too many bad performance evaluations_ .” In terms of support received (personal and professional) from the school administration and Government, only 17 percent and 23 percent, respectively, teachers feel satisfied.

# _What teachers do_

For _what teachers do_ , we report data from _Teach_ . Broadly, teachers in our sample score high (average rating of 3-3.5 out of 5) in providing a supportive learning environment and in setting positive behavioral expectations in the classroom. However, they score low (average rating of 1.5-2 out of 5) on providing students with feedback, in perseverance, and in social and collaborative skills.

We also capture what teachers do through selected questions in the student survey: While 89% of students reported that their teacher explains in another way if they do not understand something, only 61% said that teachers write on their notebooks while correcting their work. About 70% of students said they were afraid of their teacher.

# _What teachers believe_

Finally, for _what
We then assess these income effects empirically by estimating beneficiaries’ WTP for electricity in three low‐income countries with pockets of households living in extreme poverty ‐ Burkina Faso, Senegal, and Rwanda. Using survey data from unelectrified rural areas in each of these three countries collected between 2010 and 2013, we calculate stated WTP for different electricity access technologies, such as a solar lamp, a solar home system, and a grid connection. We then analyze its determinants by regressing stated WTP on the household income level, household characteristics, and other control variables.

We find that households put a high priority on having electricity and are willing to dedicate more than 10 percent of their monthly expenditures to paying for electricity. Consistent with the theoretical model, we also find that the household WTP increases with household income. A 1 percent increase in households’ expenditures increases their WTP for electricity access by around 0.2 percent. Moreover, we see that the WTP increase with households’ income is nearly twice as large for grid electricity than for a low‐cost off‐grid technology, such as a solar lamp.

Nonetheless, the overall WTP for electricity access by the rural households covered in the study is low enough that it is not sufficient in itself for covering operational and capital costs of extending on‐grid electricity supply to poor rural households over the short‐ and medium‐term. These results suggest concentrating efforts to bring electricity service to very low‐income households in rural areas on ultra‐low‐ cost off‐grid technologies, such as solar lanterns and solar home systems. This recommendation is consistent with providing service that meets the basic needs of low‐income rural households, as shown previously by Samad et al. (2013) and Grimm et al. (2017, 2018) in the context of solar home systems and even Pico‐PV.

As noted above, if aggregate gains to households from improving provision of public services are high, subsidized grid electrification to improve community electricity access may be desirable from a
We estimate the relationship between the concepts of financial inclusion and stability by using the best available cross-country data. For the inclusion measures, ��<sup>�</sup> , we assume that they comprise of four orthogonal components: ��<sup>�</sup> , the aggregate component ; ��<sup>�</sup> , the component specific to economic agent (firms or individuals) ; ��<sup>�</sup> , the component specific to the type of financial service (payments, savings, credit, insurance),,; and ��<sup>��</sup> , the measurement error that comes with any empirical data.

On aggregate, the ��<sup>�</sup> component will prevail and capture the systematic variation across all financial inclusion indicators. When dissecting the empirical measures by type of economic agent, ��<sup>�</sup> ���<sup>�</sup> will prevail with the possibility that ��<sup>�</sup> dominates. Recall that ��<sup>�</sup> and ��<sup>�</sup> are orthogonal so that ��<sup>�</sup> disappears in aggregation across economic agents. Similarly, when dissecting the empirical measures by type of financial service, the ��<sup>�</sup> ���<sup>�</sup> components will prevail with the possibility that ��<sup>�</sup> could dominate ��<sup>�</sup> . In the latter case, the service-specific results (the covariance between inclusion in savings and stability) will be different from the aggregate results. In all cases, there is the possibility that the measurement errors (a white-noise process) associated with the empirical measures could be so large that they will prevent identification of
independence and flexibility of work arrangements are used as the main ranking criteria. What is clear is that lower-tier informal wage employees and self-employed receive the lowest median remuneration, validating the tier classification.

Over all countries, one in three people of working age is engaged in lower tier informal jobs, compared to 20 percent of people in formal wage jobs, 6 percent in upper tier informal jobs, 5 percent in unemployment and 38 percent who are inactive. Lower-tier informal wage jobs and self-employment yield the lowest earnings and account for most jobs in lower income countries (accounting for 48 percent of the working age population in LICs as opposed to 31 percent in LMICs, 21 percent in UMICs and 13 percent in HICs). Table A4, also in the appendix, presents a breakdown of the proportion of adults (15+) in each category, by income status and by region. There is a clear increase in the proportion of workers in higher forms of employment as country income increases; 37.3 percent of adults in high-income countries are classified as formal wage employees, while just 10.2 percent of adults in low-income countries are classified as such.

# 2.3 Methods

We collapse all labor data to the country-survey year level, using the provided survey weights. We reweight the data in two ways. Our preferred specification gives each country equal weight and reweights the data to correct for heteroskedasticity driven by the number of observations in the sample surveys. We calculate the average number of survey observations for each country, Ih we denote as . Putting these together, our final weight for each country-year cc NN<sup>�</sup> is ~~,~~ where is the number of survey waves for country . Giving each country equal weight NN<sup>�</sup> cc NNcc NNcc cc avoids skewing the results towards countries with more frequent surveys. Meanwhile, correcting 12
12.81|1.38|**7.65***|0.00|
|2008|Math|11|29.90|14.79|0.79|29.67|14.18|1.24|**0.23**|0.88|
|2008|Science|5|51.47|17.03|0.97|48.70|19.76|1.97|**2.77**|0.17|
|2008|Science|9|46.88|18.43|1.03|36.87|17.21|1.89|**10.01***|0.00|
|2008|Science|11|44.49|22.62|1.20|42.52|23.60|2.07|**1.97***|0.40|
|2008|Arabic|5|54.38|22.76|1.28|47.51|22.89|2.18|**6.88***|0.01|
|2008|Arabic|9|56.66|19.84|1.07|48.47|17.58|1.86|**8.20***|0.00|
|2008<br>Source:|Arabic<br>Authors’ ca|11<br>lculations|63.95<br>based on NA|14.58<br>fKE 2006 an|0.76<br>d 2008 data|62.52|17.55|1.53|**1.44**|0.36|

<mark>Note: SD=standard deviation; SE=standard error</mark> The overall trend is that the academic
what time paths of growth drivers, in particular investment, is essential to realize a given growth path.

# **3. Quantitative Analysis**

Each period of the model corresponds to a year which implies that all growth rates are annual in nature. A desirable feature of the model is that it is parsimonious with regards to parameters. There are only three parameters that need calibration – labor share of income � , �� depreciation rate � and initial capital‐to‐output ratio ~~.~~ ��

- **Labor share of income:** ���. ��. The Penn World Tables 8.1 does not contain the labor share data for the case of Bangladesh. An alternative source that provides guidance for this parameter is the Global Trade Analysis Project (GTAP). The GTAP database contains information that can be used to calculate the aggregate labor share.<sup>5</sup>

<!-- Start of picture text --> 0.60<br>0.50<br>0.40<br>0.30<br>5 See Hertel, Tsigas and Narayanan (2002) on details regarding the calculation of labor shares in the GTAP 0.20<br>database.<br>0.10<br>7<br>0.00<br>Bangladesh LMI ‐ Mean LMI ‐ Median<br>Labor Share of Income<br><!-- End of picture text -->

**Source: PWT 8.1, GTAP**
ii∈jj YYii _i_ FF. ii **Herfindahl-Hirschman Index** ii _(HHI)_ in industry j at time _t_ is defined as the sum of the squared market YY 2 shares of firms in an industry: H = ∑ O <u>O i</u> . �∑ O i � ii ii **Import** in industry j is the share of total imported raw materials out of total material inputs. HHjjii ii,ii∈jj ii,ii∈jj iiO ii

**Import Labor (log)** is total number of paid workers in an industry. **Market share of the four largest firms** _(MS4)_ in industry j at time _t_ is defined as = ∑ <u>,2,3,4,∈j</u> O i ii=1∑ O iiOi ii ~~,~~ where _i_ indicates the rank of firms in sector _j_ with 1 representing the largest firms, 2 the MMSS4jjii second largest firm, 3 the third largest firm and 4 the fourth largest firm.   ii,ii∈jj iiO ii

**Natural rate of entry** is the percentage of new corporations (firms that are not more than one year old) in US industries, averaged over the period 1998-99. Source: Klapper et al. (2006).

**Number of firms** is the number of firms in industry _j_ in year _t._

**Output (log)** is total real output in an industry; nominal output was deflated using three-digit industrylevel deflators obtained from the Indonesian statistical office.

39