rafmacalaba/datause-encoder-data huggingface dataset:
# **3.1.1 Examples of integrated flood management in practice**

**Integrated flood management schemes are naturally designed to fit in with waterrelated issues and can be part of a wider agenda such as urban regeneration or climate change adaptation.** The following examples illustrate real applications of the sort of integrated thinking and practices needed to tackle flood risk successfully.

# <u>Chengdu Urban Revitalization</u>

As part of a revitalization scheme of slum districts in Chengdu the inhabitants were moved away from the river bank into new accommodation. About 30,000 households were moved and this created space for a green buffer zone along the riverside (UN-HABITAT 2003a).

In 1985, the municipal government initiated the plan with the work carried out from 1993 to 1998. The plan had several best practice features including a clear target to reduce flood risk. In the past, households in the slums projected over the river and were often swept away by floods in rainy seasons. The rainy season brought constant vigilance so that evacuation could be effected. After the rain stopped, all the families had to deal with the mess brought about by the flood, resulting in great hardships, suffering and economic loss. As part of the plan the two rivers, Fu and Nan, were de-silted and widened thus reducing flood risk to a 200 year return period expectation (UN 2001).

Engagement of the local community ensured that the public participated in the scheme and resettlement was completed without litigation. Per capita living space rose by a factor of 1.4 and the relocation of the slum dwellers reduced congestion in the city (Jun 2003). There was also the benefit of 30 to 35% of owners gaining property rights which they had not held before. Green zones were created which improved the environment greatly and allowed for the construction of an award winning natural park area with water purification facilities which is now on the national tourist register.

- <u>Maputo, Matola, Xai Xai and Chokwe, Mozambique</u>

Mozambique stands on the confluence of many major South African rivers including the Zambezi. The Cahora Basso dam together with the Kariba dam in Zimbabwe serves several Southern African nations as a dual purpose system which generates electricity and also helps to control river flow. Previous regular flooding of the river basin was much reduced by the controlling of the output from the dams (ADPC 2006).

Dam building is not primarily a flood control mechanism but dams have impacts on river flows which clearly affect flood frequency and severity. Mozambique has been hit by 34 41
cantly larger than 1 would suggest that a recipient government matches on-budget health aid by increasing its own health expenditures.

To determine the degree of fungibility of off-budget health aid, however, we must compare _β_<sup>�</sup> _OFF_ to a different benchmark. Because off-budget health aid is not considered part of a government’s health expenditure _HSP_ even if there is no fungibility, a lack of fungibility for off-budget health aid occurs when _β_<sup>�</sup> _OFF_ is greater than or equal to 0, not 1. Off-budget health aid is fungible if _β_<sup>�</sup> _OFF_ is negative. For instance, if a donor finances the building of new hospitals with off-budget health aid, then fungibility would occur if the recipient government reacted by building fewer hospitals and reallocating some of its health spending to other sectors. In that case, the off-budget health aid of the donor is at least partly fungible because the total amount of resources devoted to the health sector (the sum of government health spending and off-budget health aid) increases by less than the amount of off-budget health aid.<sup>2</sup> Full fungibility occurs if _β_<sup>�</sup> _OFF_ is not greater than the marginal effect of unconditional resources _R_ minus 1, whereas a significantly positive coefficient for _HAIDOFF_ constitutes evidence of matching behavior by recipient governments.

We are now in a position to discuss how previous studies may have produced biased fungibility estimates. Some studies have relied on aid data reported by donors. These data are either collected directly from donors or obtained from databases managed by the OECD’s Development Assistance Committee (DAC) (e.g., McGuire, 1982; Khilji and Zampelli, 1994; Pettersson, 2007a,b). In this case, an equation of the following form is estimated: where _HAID_ = _HAIDON_ + _HAIDOFF_ is the total health aid, the sum of on- and off-budget health aid. The estimated marginal effect of health aid on recipient government health expenditures, _β_<sup>�</sup> , is used to evaluate whether aid is fungible; a _β_<sup>�</sup> value that is close to 1 is evidence of low fungibility, whereas an estimate that is close to 0 leads to the conclusion that health aid is mostly fungible. The OLS estimate of _β_ can be written as a weighted average of the OLS estimates of _βON_ and _βOFF_ in equation (1) (see, e.g., Lichtenberg, 1990): The weights depend on the sample variances of on- and off-budget health aid ( _σON_<sup>2and</sup><sup>_σ_</sup> _OFF_<sup>2;</sup><sup>_σ_2isthe</sup> variance of total health aid) and the sample covariance between on- and off-budget health aid ( _σON,OFF_ ).<sup>3</sup> Because off-budget health aid is not counted as part of government health spending even when it is used within the health sector, _β_<sup>�</sup> _OFF_ will be close to zero even if there is no fungibility. More generally, if on- and off-budget health aid are equally fungible, then we observe that _β_<sup>�</sup> _OFF_ = _β_<sup>�</sup> _ON −_ 1. As a result, the presence of off-budget aid in the donor-based aid measure lowers the estimated marginal effect of total health aid on health spending 3
# **Table A11:  Economist Intelligence Unit (EIU)**

**Data Provider Description Website Data Source Type Respondents**

**Frequency Coverage Public Access**

**Description**

Economist Intelligence Unit Commercial business information provider headquartered in London, United Kingdom www.eiu.com Country Risk Service, Country Forecasts Expert Assessment Network of over 500 correspondents, reviewed for consistency by panels of regional experts Quarterly since 1997 Global sample of countries Full dataset is commercially available. Averages of sub-indicators are publicly available in this spreadsheet.

We use data on the components of these two data sources made available to us by the Economist Intelligence Unit. We use data from December of each year with exception of 1996 in which we draw data from first quarter of 1997.

||2007|2006|2005|2004|2003|2002|2000|1998|1996|
|---|---|---|---|---|---|---|---|---|---|
|**Voice and Accountability**||||||||||
|Vested interests|X|X|X|X|X|X|X|X|X|
|Accountablity of Public Officials|X|X|X|X|X|X|X|X|X|
|Human Rights|X|X|X|X|X|X|X|X|X|
|Freedom of association|X|X|X|X|X|X|X|X|X|
|**Political Stability and Absence of Violence**||||||||||
|Orderly transfers|X|X|X|X|X|X|X|X|X|
|Armed conflict|X|X|X|X|X|X|X|X|X|
|Violent demonstrations|X|X|X|X|X|X|X|X|X|
|Social Unrest|X|X|X|X|X|X|X|X|X|
|International tensions / terrorist threat|X|X|X|X|X|X|X|X|X|
|**Government Effectiveness**||||||||||
|Quality of bureaucracy / institutional effectiveness|X|X|X|X|X|X|X|X|X|
|Excessive bureacucracy / red tape|X|X|X|X|X|X|X|X|X|
|**Regulatory Quality**||||||||||
|Unfair competitive practices|X|X|X|X|X|X|X|X|X|
|Price controls|X|X|X|X|X|X|X|X|X|
|Discriminatory tariffs|X|X|X|X|X|X|X|X|X|
|Excessive protections|X|X|X|X|X|X|X|X|X|
|Discriminatory taxes|X|X|X|X|X|X|X|X|X|
|**Rule of Law**||||||||||
|Violent crime|X|X|X|X|X|X|X|X|X|
|Organized crime|X|X|X|X|X|X|X|X|X|
|Fairness of judicial process|X|X|X|X|X|X|X|X|X|
|Enforceability of contracts|X|X|X|X|X|X|X|X|X|
|Speediness of judicial process|X|X|X|X|X|X|X|X|X|
|Confiscation/expropriation|X|X|X|X|X|X|X|X|X|
|Intellectual property rights protection|X|X|X|X|X|X|X|X|X|
|Private property protection|X|X|X|X|X|X|X|X|X|
|**Control of Corruption**||||||||||
|Corruption among public officials|X|X|X|X|X|X|X|X|X|
|**Country coverage**|**150**|**152**|**127**|**125**|**120**|**120**|**120**|**120**|**120**|
|**Year of Publication**|**2007**|**2006**|**2005**|**2004**|**2003**|**2002**|**2000**|**1998**|**1996**|

49
therefore that many countries fail at making appropriate transitions or even fail to recognize that such a transition is needed....Ironically, old strategies become new weaknesses.” This suggests that the challenge which Latvia set for itself – becoming an innovative economy -- is especially daunting. In effect, Latvia must navigate not one, but two, simultaneous transitions. First, to succeed in the transition for the Factor-Driven to the Investment-Driven stage, Latvia it must upgrade the technological sophistication of its existing industries by absorbing knowledge produced outside Latvia. Unfortunately, current Latvian policies are not particularly geared to this challenge. And at the same time, to succeed in the Innovation-Driven stage Latvia will need to reform its NIS so that it makes better use its strong human capital endowment and historic tradition of scientific excellence. But as this report (and many others) suggest, Latvia is not particularly strong in this area either.

# <u>B. World Bank Knowledge Economy Index (KEI)</u>

The World Bank’s KEI presents a mixed picture of Latvia’s progress toward becoming an innovative economy. For example, relative to its per capita GDP, Latvia’s KEI score is higher than expected. Nevertheless, in absolute terms, Latvia is behind all NMS’s except Bulgaria and Romania. In other words, Latvia is doing better than the statistical average. But Latvia is not competing against the average. It is competing against other NMS’s and, from that perspective, it is not doing well.

**Figure 17 KE Index in Latvia and Selected European Countries**

<!-- Start of picture text --> KE Index and GDP in Latvia and Selected European Countries, 2000<br>35000<br>30000 Ireland<br>Denmark<br>25000 Finland<br>Sweden<br>20000<br>Slovenia<br>15000<br>Czech Republic<br>Hungary<br>Slovak Republic Estonia<br>10000<br>Poland<br>Russian Federation Lithuania<br>Bulgaria LATVIA<br>5000 Romania<br>0<br>5 6 7 8 9<br>Knowledge Economy Index<br><!-- End of picture text --> _Source_ : WBIKAM 29
I of the data has an important bearing on the interpretation of the observed decline in household size. Table 3 explains how the data in the four years of the survey may be divided into 14 data sets, depending on whether or not the household forms one of the three panels. Set 1 ds'ta refer to households interviewed only once in 1985 and not revisited thereafter. Households in set 2 are those which were interviewed in 1985 with the intention of including them in the first panel, but which were not found again in 1986 (mainly because they had changed residence by the second year). The data in set 5 are then the replacements for these households in 1986. The panel households themselves are in sets 3 and 4, for the interviews in 1985 and 1986 respectively. The panel data sets for the other years are defined in a similar way.

Table 3

# CELSS: ane Data Sets. 1985-1988

- Set 1 Non-panel households, 1985 Set 2 Unmatched households from first panel, 1985 Set 3 First panel households, 1985 Set 4 First panel households, 1986 Set 5 Unmatched households from first panel, 1986 Set 6 Unmatched households from second panel, 1986 Set 7 Second panel households, 1986 Set 8 Second panel households, 1987 Set 9 Unmatched households from second panel, 1987 Set 10 Unmatched households from third panel, 1987 Set 11 Third panel households, 1987 Set 12 Third panel households, 1988 Set 13 Unmatched households from third panel, 1988 Set 14 Non-panel households, 1988

_5_ . .~~~~~~~~~~~~~~~~~~~~~~~I
When classifying countries on their crisis experience, we follow Caprio et al. (2014) and consider three states as opposed to the two states considered in the baseline model. Specifically, eight crisis countries labeled by Laeven and Valencia (2013) as borderline cases constitute a separate state in this alternative estimation. We thus specify and estimate an ordered-choice logit model in which the dependent variable is a dummy that takes the value 0 for countries with no crisis, 1 for countries with borderline crises, and 2 for countries with systemic crises. The results are reported in table A6. They support our baseline findings that housing bank supervision in the central bank can mitigate the positive effect of financial deepening: the interactions of CMiMa with the credit-to-GDP and deposits-to-GDP ratios are negative and significant. Overall, the resulting parsimonious regressions comprise the same explanatory variables that appear in our baseline parsimonious model. However, the baseline regression fits the data better than the regressions.

Next, we consider the estimation results using the CMiMa variable averaged over alternative time spans: 2000–03, 2000–07 and 2000–11 (table A7). In all three cases, the estimation results are very similar to and support our baseline results. This observed robustness to different averaging could be explained partly by the fact that there were only a few instances when bank supervision was relocated into or outside the central bank during the period 2000–07 (see the transition matrix in table A3 of Melecky and Podpiera 2013). In all cases, the interactions between CMiMa with private credit to GDP and deposits to GDP, respectively, are negative and significant at the 10% level and 5% level, respectively.

Finally, we test the robustness of our baseline results to an alternative functional form of the model by estimating a probit model that is characterized by a slightly different curvature of the assumed cumulative probability function. As we see from the estimation results reported in table A8, the probit results support our baseline estimation results. The coefficients on the interaction between CMiMa and credit to GDP as well as CMiMa and deposits to GDP are significant and negative. They reinforce the benefits of placing bank supervision in the central bank.

20
**Figure 7.The share of minimum wage earners across birth cohorts**

<!-- Start of picture text --> 50.00%<br>40.00%<br>30.00%<br>20.00%<br>10.00%<br>0.00%<br><!-- End of picture text --> Source: _Own estimates based on administrative tax data_

**The distribution of minimum-wage individuals showcases significant disparity throughout the country. In the region exhibiting the highest prevalence of minimum-wage earners (Suceava county— 38%), this percentage is nearly twice as high as in the region with the lowest frequency of minimumwage earners (Sibiu—21%). Figure 8 shows the proportion of minimum-wage** earners at the county level (NUTS 3 regions).

**Figure 8. Share of minimum wage earners by NUTS 3 regions**

<!-- Start of picture text --> 45.00%<br>40.00%<br>35.00%<br>30.00%<br>25.00%<br>20.00%<br>15.00%<br>10.00%<br>5.00%<br>0.00%<br>Suceava Harghita Vaslui Maramures Giurgiu Bistrita-Nasaud Neamt Botosani Brailia Constanca Olt Calarasi Vrancea Salaj Tulcea Bihor Buzau Teleorman Galati Valcea Ialomita Dolj Mehdinti Satu Mare Mures Caras-Severin Gorj Dambovita Bacau Hunedoara Arges Iasi Prahova Ilfov Covasna Alba Arad Cluj Bucharest Timis Brasov Sibiu<br><!-- End of picture text --> _Source: Own estimates based on administrative tax data (January 2020)._

# **IV. Ex-ante Evaluation of Policy Reform**

**In this section, we evaluate the potential ex-ante distributional impacts of the alternative minimum wage policy on the labor market outcomes, relying on micro-simulation methodologies and administrative tax data.** More precisely, we employ microsimulation techniques to gauge the proportion of individuals who may lose their jobs and the wage increments experienced by those who remain employed. In particular, we simulate the impact of the increase in the minimum wage on wages, employment losses, and the

31
following analysis for two reasons: (1) the 264 importers in the first category represent 98 percent of the imported value; and (2) informal traders in the second category cannot be distinguished in the IT system from traders who import secondhand clothing for personal use (the same problem that applies to importing cars).

In the second step, we explore how secondhand clothing is imported by these 264 importers, taking into account that importing secondhand clothing may be a main or a secondary activity. Table 4 describes the characteristics of the secondhand clothing import mix.

**Table 4. Secondhand Clothing Import Mix Characteristics, Douala port, Cameroon**

||Number of<br>importers<br>(share of<br>total<br>imports of<br>secondhand<br>clothes)|Average share<br>of declared<br>value of<br>secondhand<br>clothes|Number of<br>imports by PM<br>(legal entities)<br>and PP (real<br>persons)|Median, standard<br>deviation, and<br>maximum values<br>(CFAF)|
|---|---|---|---|---|
|Importers who import<br>only 6309|96 (37%)|23.0%|11 PM; 75 PP|Median: 30 million<br>SD: 92 million<br>Max: 432 million|
|Importers who import<br>mainly other products<br>(more than 60% of<br>total value)|9 (3%)|0.7%|1 PM; 8 PP|Median: 24 million<br>SD: 24 million<br>Max: 83.7 million|
|Importers who import<br>50/50 (between 45 and<br>55% of total value of<br>otherproducts)|10 (4%)|6.0%|3 PM; 7 PP|Median: 19 million<br>SD: 555 million<br>Max: 1.787 billion|
|Importers who import<br>mainly 6309 but not<br>exclusively (between<br>45 and 1% of total<br>value of other<br>products)|146 (56%)|70.0%|20 PM; 126 PP|Median: 67 million<br>SD: 274 million<br>Max:1.6 billion<br>Min: 8 million|

_Source:_ ASYCUDA system.

_Note:_ 6309 refers to worn clothing and other worn articles. PM = personne morale or legal entity; PP = personne physique or real person; SD = standard deviation.

One main conclusion that can be drawn from these data is that secondhand clothing importers rarely import clothing as a minor commodity in terms of value; nearly all importers who import secondhand clothing do so as a main activity. This sector is dominated by informality. However, albeit informal, this sector provides significant revenues, and the dominant business model 13
FIGURE 5

# ZAMBIA: FINANCIAL TAXES

<!-- Start of picture text --> Mur I<br>17<br>Is<br>14<br>13<br>CL 12<br>10<br>T<br>7<br>4<br>3<br>2<br>1981 1G9 1083 1084 108S Gm 1987~ 1GMs<br><!-- End of picture text --> 45
Lack of innovativeness is also reflected in the small proportion of innovating firms introducing new innovation outputs such as patents (Table 11), although regional innovation leaders present values above the average for the ECA and Africa regions. However, laggards are below international standards. Other innovation outputs such as trademarks or licensing are more important than patents for all the countries considered.

<u>Table 11. Innovation outputs (% of innovating firms)</u>

||**Bangladesh**|**Pakistan**|**India**|**Nepal**|**SARaverage**|**ECA**|**Africa**|
|---|---|---|---|---|---|---|---|
|Patent|8%|2.2%|8%|1.3%|5%|2.5%|6%|
|Other than patents|18%|5%|32%|1.7%|14%|-|17%|

Source: Author’s elaboration from Enterprise Survey (2014) <u>Table 12. Links between innovativeness and firm characteristics</u>

|||**Bangladesh**|**India**|**Nepal**|**Pakistan**|
|---|---|---|---|---|---|
|No innovators|age|18.70|22.54|13.65|22.15|
|Imitators|age|18.42|20.39|18.21|34.24|
|radical|age|16.46|20.51|16.57|28.25|
|No innovators|size|162.09|95.99|12.89|168.94|
|Imitators|size|228.16|134.64|28.89|176.99|
|radical|size|183.04|144.23|25.30|434.45|
|No innovators|exporter|20.63%|18.93%|5.83%|19.71%|
|Imitators|exporter|24.73%|18.35%|7.46%|26.90%|
|radical|exporter|35.46%|32.11%|2.74%|0.86%|
|No innovators|foreign|0.78%|0.45%|0.06%|0.25%|
|Imitators|foreign|3.07%|0.40%|0.00%|0.00%|
|radical|foreign|1.00%|0.77%|5.49%|0.78%|
|No innovators|R&D intensity|4.44|17.43|0.31|11.45|
|Imitators|R&D intensity|4.94|24.22|0.43|70.08|
|radical|R&D intensity|11.68|31.71|2.62|214.17|
|No innovators|sales per worker|8.42|10.04|8.58|7.71|
|Imitators|sales per worker|8.58|10.13|9.03|8.87|
|radical|sales per worker|8.84|10.11|8.48|9.44|
|No innovators|value added per worker|7.90|9.09|7.96|9.09|
|Imitators|value added per worker|7.95|9.10|8.32|10.14|
|radical|value added per worker|8.11|9.31|6.97|9.44|

Source: Author’s elaboration from Enterprise Survey (2014) R&D intensity is correlated with the novelty of innovation. Table 12 shows the average firm characteristic for each “novelty group”: (i) non-innovators, (ii) imitators (new to the firm only), and (iii) radical innovators (new to the national or to the international market). There are two types of patterns across country groups. For leaders, radical innovators are younger, more middle or larger sized, exporters and national. For laggards, innovators tend to be relatively older, larger, non-exporters, and more likely foreign. Further, there seems to be a clear mapping from R&D intensity to more radical forms of innovation, in the sense that firms that have a higher level of R&D intensity tend to introduce more radical innovations. Third and more surprising is the fact that while innovators tend to have larger labor productivity than non-innovators, except for Nepal, there are no clear differences between more radical innovators and imitators.

19
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# 2.1 _Main variables – description and motivation_

Summary statistics of all the variables used are provided in table A2. The outcome or dependent variable is (log of) labor productivity of the firm. Labor productivity equals the total sales (in USD) of the firm in a typical month over the last year divided by the total number of workers (paid plus unpaid and including the owner if applicable) in a regular month over the last year. Total sales are converted to USD using official exchange rate taken from World Development Indicators (WDI), World Bank.

For our main explanatory variable, we define a female-owned firm as a firm where the largest owner is female. All other firms are male-owned. For over 96 percent of the firms in our sample, the largest owner is also the main decision maker. Thus, there is almost no distinction in our sample between the largest owner and the manager (main decision maker).<sup>7</sup> Table A1 provides the percentage of female-owned firms by country in the sample. Ghana has the highest percentage of female-owned firms (64 percent) followed by Guatemala (57 percent). At the other end of the spectrum are Mali (18 percent), and DRC (23 percent). The average for the whole sample is 44.9 percent.

We account for several factors that are found important for the productivity of businesses. We account for firm size using (log of) the total number of employees in a regular month. Informal firms of different sizes may show different levels of productivity. Large informal firms may have more resources, but they may face transformation and evasion costs that smaller informal firms are not burdened with (Amin and Islam, 2015). It is also well-established that formal firms are more productive than informal business (La Porta and Shleifer, 2014). Thus, the degree of

> 7 Restricting the sample to only those firms where the largest owner is also the main decision maker makes no noticeable difference to any of the results discussed.

10