rafmacalaba/datause-encoder-data huggingface dataset:
# **Advertising, marketing and solicitation- foreign Score 0.5**

Advertising and marketing by engineering professionals is not prohibited. Same provisions apply to domestic and foreign suppliers. However, foreign suppliers licensed to practice in Tanzania are not allowed to use their foreign supplier’s name in advertising and marketing activities.

# **Advertising, marketing and solicitation - domestic Score 0**

Advertising and marketing by engineering professionals is not prohibited.

# **Fee setting - domestic Score 1**

Fees and prices are regulated – there are binding minimum prices for all engineering services. According to ERB these measures regarding fees are applicable to domestic services providers only (they do not apply to foreign engineering service providers licensed to practice in Tanzania).

# **Fee setting- foreign Score 0**

Fee regulations do not apply to foreign engineering service providers licensed to practice in Tanzania).

# **Licensing requirements on management Score 0**

No restrictions.

# **Movement of people – Temporary Score 0.5**

Case by case decision

# **Other restrictions (Addition categories) Score 1**

Restrictions on use of firm's international name, restrictions on hiring professionals and government procurement restrictions towards foreigners.

# **Sources** :

Dee, P. (2005), “A compendium of barriers to services trade”, prepared for the World Bank, <u>http://www.crawford.anu.edu.au/pdf/staff/phillippa_dee/Combined_report.pdf</u> 117
the export flows of virtually all exporting firms, and provides detailed information on the product exported, the destination market, and the value and quantity exported. Export values in these data are free-on-board, thus excluding any duties or shipping charges.

**Management practices survey** : We further use data from _Inqu ́erito `as Pr ́aticas de Gest ̃ao (IPG)_ [Management Practices Survey] for 2016. IPG is a non-periodical survey conducted by INE, which collects information on the perceptions of top executives about the management practices of their firms. The 2016 survey was the first and only of its kind collected in Portugal. It seeks to evaluate the importance of management practices for firm productivity, as well as other key indicators that make it possible to evaluate differences in productivity between Portuguese firms. IPG employed a stratified sample of firms operating in Portugal covering the whole non-financial private sector in 2016, excluding micro firms (with fewer than five employees). The sample is representative by sector (20 sectors corresponding of aggregations of the 2-digit level of the CAE), firm size and age, as well as belonging (or not) to a conglomerate. The IPG survey includes questions seeking to evaluate management practices in three main areas: (1) Strategy, monitoring and information; (2) Human Resources; and (3) Management and social responsibility systems. We selected 18 variables that are closely related to those adopted in Bloom and Reenen (2007). Following their approach, our measure of management quality was constructed by z-scoring (normalizing to mean 0, standard deviation 1) the 18 individual questions in IPG, taking averages, and then z-scoring the average. This process yields a management practice score with mean 0 and standard deviation 1.

**Actual and forecasted GDP growth** : We further use yearly information on actual and recently forecasted GDP growth from the World Economic Outlook (WEO) of the International Monetary Fund (IMF). WEO is usually published twice a year (in April and September/October). It presents IMF staff economists’ analyses of global economic developments during the near and medium term. Every April and October, the WEO provides year-ahead and current-year GDP growth forecasts. We refer to the year for which the forecast is being made as the target year. Forecasts made in the Fall WEO before the target year are called year-ahead forecasts and those made during the Spring target year are called current-year forecasts. During our sample period, forecast data are available for 195 countries. As shown by An, Jalles, and Loungani (2018), IMF growth forecasts are virtually identical to other growth forecasts from the private sector, and can therefore be regarded as consensus forecasts from a wide variety of market analysts from both the official and private sectors. After merging these IMF data with ECI we were left with 174 destinations, which account for 99.7% of all exports in 2006. Table A2 reports the export shares to the main destinations in 2006, both in the full ECI data and in

6
Table 3: Heterogeneous effects of gender quota on outcomes at the concentration level

||Acc|ess to IHEs|||Score related|outcomes|||
|---|---|---|---|---|---|---|---|---|
||Share<br>females|Share females<br>low-SES|Total avg.<br>score|Female avg.<br>score|High-SES fe-<br>male avg. score|Low-SES<br>female<br>avg. score|Male avg.<br>score|Female to male<br>avg. score ratio|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Estimators|||||||||
|Panel A: CEELM|||||||||
|OLS|0.17***|0.04***|-4.36|-17.94***|-21.12***|-3.65|0.88|-0.07***|
||(0.03)|(0.01)|(3.46)|(5.19)|(6.43)|(7.77)|(3.23)|(0.02)|
|Borusyak et al. (2021)<sup>_ζ_</sup>|0.17***|0.04***|-0.35|-18.12***|-18.06***|-9.09*|5.57|-0.09***|
||(0.02)|(0.01)|(4.17)|(4.44)|(4.61)|(4.78)|(4.18)|(0.01)|
|Sun-Abraham (2020)|0.186***|0.045***|-1.69|-13.11**|-17.72***|-6.37|4.48|-0.06***|
||(0.036)|(0.008)|(3.44)|(5.22)|(6.27)|(9.93)|(3.17)|(0.01)|
|_Control group mean_|0.18|0.05|260|259|260|256|260|1|
|N|748|748|748|748|688|528|748|748|
|Panel B: Non-CEELM|||||||||
|OLS|0.06***|0.02***|-2.27|-7.84***|-10.99***|-4.88*|-0.52|-0.03***|
||(0.02)|(0.01)|(1.84)|(2.14)|(2.57)|(2.53)|(2.08)|(0.01)|
|Borusyak et al. (2021)<sup>_ζ_</sup>|0.05***|0.04***|-2.10|-7.80***|-6.75***|-6.64***|-0.48|-0.03***|
||(0.02)|(0.01)|(1.75)|(1.87)|(2.01)|(1.87)|(1.84)|(0.01)|
|Sun-Abraham (2020)|0.046**|0.019**|-1.47|-6.98***|-10.61***|-3.128|-0.03|-0.03***|
||(0.02)|(0.008)|(2.02)|(2.32)|(2.60)|(2.97)|(2.33)|(0.008)|
|_Control group mean_|0.31|0.12|216|215|219|214|217|0.99|
|N|2339|2339|2339|2339|2007|2140|2339|2339|
|Concentration FE|✓|✓|✓|✓|✓|✓|✓|✓|
|University*year FE|✓|✓|✓|✓|✓|✓|✓|✓|
|Academic pro*year FE|✓|✓|✓|✓|✓|✓|✓|✓|
|Year FE|✓|✓|✓|✓|✓|✓|✓|✓|

Note: Highly competitive academic programs encompass concentrations within computer science, economics, engineering, law and political science, and medical studies, collectively referred to as CEELM programs. Each coefficient is from a separate regression. The dependent variables in columns 1 and 2 are the share of females and share of females from low socioeconomic backgrounds, which can be interpreted as average percentage point increase in the share of females in treated concentrations owing to the quota introduction. In columns 3–7, the dependent variables are overall average score, average score for females, average score for high- and low-SES females, and male students average score at concentration level, respectively; it can be interpreted as points decrease/increase. Column 8 denotes female to male average score ratio at concentration level. All the models in Panels A and B include the above stated fixed effects. Standard errors are clustered at concentration level and reported in parentheses. Significance level: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01 _ζ_ Owing to the imputing nature of the estimator by Borusyak et al. (2021), the actual number of observations used to estimate the result is slightly lower than that with the OLS and Sun-Abraham (2020). The _N_ represents the number of observations used to compute the OLS and Sun-Abraham (2020) estimates.
reasonably accurate at the regional level, but the loss of mangroves may be much larger in some countries. Furthermore, the mangrove database shows the extent and shape of the mangrove area, but does not indicate the status of the mangroves; for example, patchiness, health, size of trees, etc. Studies have shown that specific characteristics of mangroves are important for protection from storm surge. For example, if stands are not dense enough, they provide insufficient resistance to wave energy, but if the stand is too dense, waves may simply pass over.

Conversely, factors that may underestimate coastal protection and resources at risk include: geographic limitations of the data, elevation measurement error, a lack of GDP and population estimates to 2100 and the conservative estimates from direct exposure. With regards to geographic limitation, some small‐island nations in Africa, Asia and the Pacific, and Latin America are not included in our analysis due to lack of data. The elevation data (SRTM) has measurement error due to signal interference from surface features such as dense canopy (or high forest cover percent see Shortridge and Messina 2011) and built‐up up environments, which would under estimate risk by overestimating height. For exposure estimates, we used 2005 data for population and GDP in absence of reliable country‐specific projections of coastal population and GDP out to 2100; and we did not consider potential growth in the coastal economies over time. Direct exposure estimates for calculating vulnerable population and GDP are also conservative estimates and do not consider the losses from proximity or network effects due to mangroves at risk (e.g. the economic loss generated from a to the transportation network).

Finally, we do not know if there is any positive or negative bias introduced by the following: rounding of elevation data, spatial allocation methods and the functional form of the distance decay function. The unit of measurement for the SRTM data is meters and the rounding may introduce a positive or negative bias. Spatial allocation methods used for estimating population and economic activity in coastal areas have infrastructure and land cover information in the model (e.g. Bright et al. 2006) and may have bias at the local level. The literature has limited 25
14

# 4.1 The **CFA** zone

Relevant data is not really available for the franc zone. The extensive control of interest rates exercised by the authorities in the two CFA zone has resulted in fairly wide differentials of these controlled rates vis-a-vis Paris. This is especially true in respect of bank lending rates for lending which has been refinanced by the Central Bank at a subsidised rate. Naturally, such refinancing is rationed and so the relevant rates of interest are not market-clearing. Revisions in the administered rates have been rather infrequent. For example the normal rediscount rate of the BEAC stood at 9 per cent from late 1982 until early 1986, a period during which Paris interbank rates fell from over 1.3 per cent to less than 9 per cent.

The most frequently revised rates have been the BCEAO's so called Taux du Marche Monetaire (TMM) which apply to bankers' deposits with the Central Bank and to some non-preferential redicounts. Figure 5 plots the overnight lending TMM against Paris money market rates and displays the fairly close correlation between the two in recent years. However, because many UMOA banks are persistently in debt to the BCEAO, and because borrowing at the TMM is subject to ceilings and to the discretion of the BCEAO, the TMM can only be considered as an imperfect indicator of the marginal cost of funds to UYIOA banks. In particular it is not clear how closely interbank rates or the wholesale deposit rates offered by banks follow the TMM (no time series on these rates is collected by the BCEAO). Because _cL_ the weak condition of many UMOA (and BEAC) banks, c(afidence is low among wholesale depositors and the interbank markets are very thin. As explained above there is no strong evidence of a high degree of interest rate sensitivity of money demand in UMOA countries, a fact which suggests that only minimum working balances are held in banks in the UMOA, with most portfolio investment already placed in France, Monaco or elsewhere. It is possible therefore that uncontrolled interest rates also deviate from French comparators; but in the absence of reliable series on non-administered interest rates in the CFA zone, we are unable to draw definite conclusions.

4.2 The rand zone: effectiveness of interest rate policy in Lesotho<sup>2 6</sup> We now examine evidence from Lesotho, where data is more readily available on uncontrolled interest rates. The Central Bank of Lesotho sets only the prime lending rate, the minimum savings deposit rate, as well as the Treasury bill rate and the rates on banks' deposits with the CBL. These 26 This section draws on earlier work done jointly with Ignacio Mas.
statistically significant in explaining the likelihood of experiencing poverty for all years and for both Rio Grande do Norte and the Northeast.

The household size is statistically significant and positively correlated with the incidence of poverty. Moreover, larger households are poorer and the effect is concave, indicating that the scaling factor matters for poverty (Figures 12a and 12b). This finding holds for both the Northeast region and for Rio Grande do Norte. However, the adverse impact of a large family on the likelihood of escaping poverty has increased in the 1990s for the region as a whole. In Rio Grande do Norte the impact seems to have been constant.

# **Figure 12a: Impact of household size for Rio Grande do Norte**

<!-- Start of picture text --> Rio Grande do Norte: Household Size<br>1981 1985 1988 1990 1993 1995 1997 1998 1999<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>-0.6<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PNADs 1981 to 1999.

Note: Figures are probit coefficients and confidence intervals for household size.

# **Figure 12b: Impact of household size for Northeast**

<!-- Start of picture text --> North East: Household Size<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>1981 1985 1988 1990 1993 1995 1997 1998 1999<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PNADs 1981 to 1999.

Note: Figures are probit coefficients and confidence intervals for household size.

32
- 49 -

# REEENCES

- Acharya, Sarthi. 1988. _Agricultural Wages In India: Trends and Regional Variations._ (mhnoo) New Delhi: World Bank Resident Mission.

- Ahmed, R., and M. Hossain. 1987. _Infrastructure and Developoent of a Rural_ Economy. IFPRI, Washington, D.C: (mimeographed).

- Bell, C.L.G. and Hazell, P.B.R. 1980. _Measuring the Indirect Effects of An Agricultural Investmnt Project on Its Surrounding RegIon._ American Journal of Agricultural Economics 62:75-86.

- Bhalla, G.S. and Kundu, Amltabh. n.d. _Small and Intermediate Towns In India's Regional Dvelopment._ (mlmeo) New Delhl: Center for the Study of Regional Deveiopment, Jawajarial Nehru University.

- Bhalia, G.S., R.K. Shama, G.K. Chadha and S.P. Kashyap. 1989. _Structural Changes In the PunJab_ Economy; _1969/70 to 1979/80: A Study In Input-Output Analysis._ FPRI, Washington, D.C: (mimegraphed).

- 8halla, G.S.; Sharma, R.K.; Chadha, G.K.; and Kashyap, S.P. 1989. _Structural Changes In the PunJab Economy 1969/70 to 1979/80: A Study In Input-Output Analysis._ (mbneo) Washington DC: International Food Policy Research Institute.

- Bhalia, Shela. 1981. _Islands of Growth: A Note on Haryana Experienc and Some Possibl ImplIcations._ EconomIc and Political Weekly 16(23):1022-1030.

- Binawanger, H.P. and Khandker, S.R. 1988. _The ilpwct of Infrastructure and Financlal Institutions on Agricultural Output and lnvestwnt In India._ (mimeo) Washington DC: World Bank.

- Chadha, G.K. 1986a. _Agricultural Growth and Rural Mon-Fars ActIvitles: An Analysis of Indian Expwrience._ In Yang-Boo Choo and Fu-Chen Lo editors Rural Industrialization and Non-Farm Activitles of Asian Farmers. Seoul: Korea Rural Economics Institute/Asian and Pacific Development Centre.

- Chadha, G.K. 1986b. _The Off-Farm Economic Structure of Agriculturally Growing Regions: A Study of the Indlan Punjab._ In R.T. Shand ed. Off-Farm Employment In the Development of Rural Asia, Volume 2. Canberra: The Australian National University.

- de Janvry, Alain and Subbarao K. 1986. Agricultural Price Policy and Income Dlstribution In India. Delhl: Oxford University Press.

- Doolalkar, A. 1985. _Agricultural Implemnts Industry: A Case Study of Agriculture Unked Growth In PunJab, India._ (mimeo)

- Deshpande, S. and Deshpande, L.K. 1985. _Census of_ 1981 _and the Structure of Employment._ Economic and Polltical Weekly 22(22).

- Economic Itelligence Service. 1963. Agricultural Productlon In Indla, State-Wlse and Crop-Wlse Data: 1949-50 to 1981-82. Bombay: Contre for MonItoring the Indian Economy.

- Economic intellIgence Service. 1985. ProfIles of DistrIcts, Part I: A-K. Bombay:<sup>Centre</sup> for iAonitorlng the Indian Economy.
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of economic data, cross-country comparisons of the saving rate should probably be viewed with caution. Bearing that in mind, however, it appears that, with the exception of Malawi, the adjustment programs have not had a marked negative impact on the domestic savings performance. This observation would contradict the hypothesis that the additional foreign assistance accompanying the adjustment process leads to a substitution of foreign for domestic savings. In the case of Tanzania, the comparison underlines that, after the data adjustments described in Section 111.2 above, Tanzania's domestic savings performance has not deteriorated during the adjustment period, and in fact, is better than in all other countries in the sample except Kenya.

# **V3.4** Export Growth

The relative performance of merchandise exports in the five countries in shown in Figure 11.' It shows that the export performance of the countries under review has been mixed. In Ghana, Kenya, and Tanzania, exports had been declining prior to reform. The introduction of reform led to a marked improvement in export performance, especially in Ghana and Tanzania. Kenya also experienced a reversal in export performance, though not as dramatic as that in Ghana and Tanzania. However, in Malawi and Uganda, both of which had positive export growth rates prior to reform, the situation was reversed: these countries experienced falling exports during the period of reform. In Uganda, the reversal is quite severe, with growth rates of exports declining from an average of 8.3 percent per annum to -0.4 percent per annum.

8 Export volume data for Ghana reflect only cocoa exponu, while for Uganda they only capture coffee exports. For lack of information, export volume data for Tanzania include only officially recorded exports.
# **2.2 Trade data**

Our export and import data is a monthly product-country data series obtained from the Philippine Statistics Authority (PSA). The data ranges from January 2019 to December 2020. Products are classified at 10-digit Harmonized System (HS) level.

Table 1 displays descriptive statistics for our trade data. The average monthly productcountry of destination exports from the Philippines between January 2019 and December 2020 was US$ 553,417. The average monthly product-country of origin imports to the Philippines was US$ 232,370 over the same period. Whereas the average monthly exports exceeded imports, reflecting a surplus trade balance, the Philippines has perennially had a trade imbalance. Thus, our surplus could indicate a massive drop in imports than exports during the lockdown period.

Table 1 also shows that the average lockdown dummy for both exports and imports was 43%. Statistics of the level of stringency are also presented in Table 1. It is a binary variable that is constructed from the aforementioned government stringency index which ranges from 0 to 100. Following Majune (2020), a country is classified as having high stringent measures if its average score is greater than the median (53 in our case) and vice versa for a low stringent country. We find that over half of the Philippines’ trading partners had strict lockdown policies with export destinations being slightly sterner than importing countries.

**Table 1:** Descriptive Statistics

|**Variable**|**Obs.**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|**Export trade**||||||
|Export value|243,526|553,417|5,570,730|25|458,000,000|
|Lockdown dummy|243,526|0.43|0.50|0|1|
|Stringency index|243,526|0.53|0.50|0|1|
|**Import trade**||||||
|Import value|848,983|232,370|2,402,721|25|294,000,000|
|lockdown|848,983|0.43|0.50|0|1|
|Stringency index|848,983|0.50|0.50|0|1|

Source: Authors’ compilation using PSA data 7
2 clothing imports fror long Kong. The central f£sture of his methodology is that "... the price of rights to export textiles from Hong Kong measures the g;ap between import price and unit cost in Hong Kong. The rationale is that textile quotas are openly traded in Hong Kong so that the market price for transfers Is expected to reflect the value of the price-cost difference."<sup>2 ',</sup> Hamilton (1986) also uses Hong Kong quota prices to measure rent income. In addition, he uses specially-compiled sets of clothin, import statistics to calculate f.o.b. unit values for Hong Kong exports to tha US, which<sup>serve</sup><sup>**as**</sup> proxies for tile rent-inclusive export prices. Dividing rent incone by the rent-inclusive US value of exports, he arrives at the US export tax equivalent rate of textile and apparel quotas on Hong Kong, which he then converts to the import tariff equivalent (MTE) rate by taking the ratio of c.i.f. and f.o.b. values of clothing imports from Hong Kong. He calculates this MTE rate as 9 per cent in 1981 and 37 per cent in 1982.

Trela and Whalley (1988, 1990) employ a general equilibrium model to analyze the effects of bilateral quota restrictions imposed by the US, Canada and the EC on 14 product categories from 34 developing countries (including Hong Kong). Like Morkre, their methodology for obtaining the supply price of quota restricted products involves the use of data on Hong Kong quota prices: they compute the Hong Kong supply price by subtracting the quota price from the US price, then they compute the production costs of quota restricted products in other exporting countries by multiplying the unit cost in Hong Kong with the ratio of the exporting country's relative wage rate in the textile and apparel industry compared to Hong Kong. Using 1986 data, they estimate both global and national welfare costs of the KFA. Their results suggest global gains from the elimination of quotas and tariffs of more than