rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1.** Descriptive Statistics

||A) Mal<br>|e Sample<br>|B) Fema<br>|le Sample<br>|
|---|---|---|---|---|
||Mean|No. Obs.|Mean|No. Obs.|
|Geographical region at age 12|||||
|West|0.323|7,860|0.350|14,851|
|South|0.114|7,860|0.129|14,851|
|Center|0.142|7,860|0.152|14,851|
|North|0.140|7,860|0.121|14,851|
|East|0.281|7,860|0.248|14,851|
|Type of location at age 12|||||
|Large city (Urban)|0.401|7,855|0.431|14,844|
|Small city (Urban)|0.206|7,855|0.206|14,844|
|Village (Rural)|0.393|7,855|0.362|14,844|

_Notes_ : The female sample is based on 2003 and 2008 waves of TDHS, whereas the male sample is based on only 2008 wave of TDHS because information on the location of residence at age 12 is not available for men in the 2003 survey.

_Source_ : Authors’ calculations based on 2003 and 2008 Turkish Demographic and Health Survey.

45
11 rapidly, soon after the EU market was falling. This changing balance in export destination is shown in Figure 7. Total export volume hovered around 4m metric tonnes during the 1990s, but the share of the EU fell from almost 95 percent in 1999, to less than ten percent in 2005, subsequently reviving somewhat to around 30 percent in 2008. The share of dried cassava (pellets) exports to Korea grew rapidly from merely two percent in 2004 to 16 percent in 2008 (COMTRADE, accessed 03 Dec 2009, TTTA, 2004 and 2009).

**Figure 7: Thai Dried Cassava Export: The Shift in Destinations**

<!-- Start of picture text --> 100% 6<br>90%<br>5<br>80%<br>70%<br>4<br>60%<br>50% 3<br>40%<br>2<br>30%<br>20%<br>1<br>10%<br>0% 0<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Share EU Share China Share Korea Total volume<br>Millions<br>Metric tonnes<br>Share in Thai dried cassava exports<br><!-- End of picture text --> _Source:_ COMTRADE, accessed 03 Dec 2009, TTTA (2004 and 2009) Given different demand patterns in the EU and China, this shift in export destination saw associated changes in the product composition of Thai cassava exports (Figure 8). The major change has been from pellet to cassava chip exports. But an important subsidiary change in Thai cassava export has been the growth of starch exports to China. This grew from six percent of Thailand’s cassava exports to China in 2002 to 27 percent in 2008. However, within starch exports to China, there has been a marked shift from modified to native starches (Figure 9).
# **Non-technical summary**

Apart from a decrease in base wages, firms could use other ways of reducing labour costs when faced by negative external shocks, for example by cutting bonuses and benefits, encouraging earlier retirement and hiring workers at lower wages than those who have recently quit. The adjustment of non-wage labour costs has gained attention in the policy debate due to two main reasons. First, non-wage labour costs represent a substantial part of total compensation. Since firms are primarily concerned with total compensation per employee, an assessment of the flexibility of non-wage labour costs is as important as evaluation of the degree of wage flexibility. Second, in an environment of sticky prices and nominal wages, non-wage labour costs become an important adjustment tool to external shocks, allowing dampening of the effects of negative demand shocks on the firm’s employment.

Using a unique survey of firms from 12 countries of the European Union conducted between the second half of 2007 and the first quarter of 2008 within the framework of the Wage Dynamics Network coordinated by the European Central Bank, we examine the importance and determinants of alternative strategies firms might use to adjust their labour costs. In our survey, we asked firms’ managers directly about their use of these policies in the recent past. In this paper we analyse factual responses about the types of margins firms have used in the last five years preceding the survey. Specifically, we are able to identify the incidence of the following six labour-cost saving strategies: reduce or eliminate bonus payments; reduce nonpay benefits; change shift assignments or shift premia; slow or freeze rate at which promotions are filled; recruit new employees at lower wage level than those who left voluntarily; and encourage early retirement to replace high wage employees by entrants with lower wages.

Our contribution to the literature is threefold. First, we document comparable information on labour cost adjustment practices beyond base wages for a large set of EU countries and sectors. This allows us to discuss the relative importance of each individual strategy across countries characterised by different sets of laws and institutions governing their labour markets. Our survey shows that firms fairly commonly use strategies to reduce labour costs other than reducing base wages – 63% of the firms’ managers said they had used at least one other margin of adjustment in the recent past, and 58% had used at least one of the six margins explicitly identified in the survey. We find substantial heterogeneity in the use of these strategies across countries and firms, depending on firm characteristics and labour market institutions.

2
The state (and national) government in Mumbai also plays a significant role. The city’s management is divided among the Municipal Corporation of Greater Mumbai, the state of Maharashtra, and the government of India (Patel 2007). The rail network is run by the Railway Board of the Indian Railways. The state government is very much involved in economic development, land policy, housing, and law and order and has established autonomous public sector corporations such as for housing (Patel 2007).

Although provincial/state or national takeover of regional services may effectively address the provision of services that exhibit externalities, the provision of services by a senior level of government also raises concerns about local responsiveness. It may be less appropriate for a senior level of government to provide services because it is further removed from local residents, making it difficult to determine the quality and quantity of output to provide in each municipality. Senior levels of government are likely to be less responsive and less accountable to local residents than a local government.

# **3. Observations on Governance in Metropolitan Cities around the World**

A review of metropolitan cities around the world reveals that there is no one model that stands above the rest. This finding is consistent with other reviews of metropolitan governance (see, for example, Lefèvre 2003; Divay and Wolfe 2002; Bird and Slack 2004a; OECD 2006). The appropriate structure for metropolitan areas depends on several factors that include the roles and responsibilities of local governments, their sources of revenue, service delivery conditions in terms of geography and topography, the homogeneity of preferences in the region, the availability of skilled workers, the political strength of local leaders, the variability in service delivery across the country, the relationship with other levels of government, and other factors (Fox and Gurley 2006).

Although there is not one model that stands above the rest, a review of different models in different cities around the world reveals a number of important findings about the governance of metropolitan areas: 25
Development Office (FCDO) are known in the past decade to have withdrawn resources from developing countries that have become richer (e.g., Vietnam) to focus more on the poorer countries (e.g., Sub-Saharan Africa). An accurate evaluation of a (poor) country’s NSS statistical capacity is thus quite helpful for the donor and the government to discuss alternative aid strategies when this country raises its income level.

More generally, ongoing technological advances with computing and data storage power have made various data sources more accessible. The cycle of data collection activities has also significantly been shortened, due to decreasing costs of mobile devices and faster internet connections. In this regard, the roles of NSS may increasingly transition from the more basic tasks of data collection and dissemination to the more advanced tasks of data analysis and interpretation. Alternative methods of producing data, such as employing statistical data imputation methods, have been found to substitute for the need to collect data at least to some extent (Dang, Jolliffe, and Carletto, 2019). Consequently, future updates of the SPI can include more indicators for an NSO’s analytical capacity to produce various statistical products.

Finally, a question can be raised on how often the SPI’s framework (including its indicators) should be updated? For example, should it be on a five-year basis or a shorter basis? Past experience with the SCI suggests that this index has served multiple objectives well in the past two decades. But this took place in a less changing data landscape, which used to have less technology development and more expensive data equipment. Just in the past few years, we have seen all the constraints loosened with fast technological progress as discussed earlier. While we do not offer conclusive thoughts on this topic, it may be useful to open up the discussion with various stakeholders on the best ways to ensure that the SPI remains useful and relevant in a fast changing world.

19
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Modebadze and Eroyants (2010) and our construction of a balanced input-output table for Armenia is explained in appendix G.

# **II. Estimation of the Tariff Equivalence of the Regulatory Barriers in Services**

Estimates of the ad valorem equivalents of the regulatory barriers in services are crucial to the results. In order to make these estimates, we first need to assess the regulatory environment in the services sectors in our model. We commissioned a 112 page survey of the regulatory regimes in key Armenian business services sectors, namely, insurance, banking, fixed line and mobile telecommunications services and air transportation services. We supplemented that information with research by regional experts into the relevant sector.<sup>4</sup> This questionnaire and research provided us with data and descriptions and assessments of the regulatory environment in these sectors.

Modebadze (2010) then estimated the ad valorem equivalents of barriers to foreign direct investment in fixed line and mobile telecommunications, banking, insurance and maritime transportation services. The process involved converting the answers and data of the questionnaires into an index of restrictiveness in each industry. Modebadze followed the methodology of Kimura, Ando and Fujii (2004a, 2004b, 2004c) to generate these estimates. The methodology involves classification of the possible restrictions into separate categories with unique weights summing to one, where the weights are determined based on the significance of each category. Next, Modebadze assigned a score to each potential restriction, where the score reflects the level of restriction imposed by the economy. Modebadze estimated two indices: an index of ―regulatory barriers‖ (RB index) where the regulatory barriers impose costs on both domestic and multinational firms in a non-discriminatory manner; and an index of discriminatory barriers against multinational service providers, which we call the foreign discriminatory index (FDR index).<sup>5</sup> This methodology further involves building on the estimates and methodology explained in the volume by C. Findlay and T. Warren (2000), notably papers by Warren (2000), McGuire

> 4 We thank Karine Eroyants and Grigol Modebadze for this research.

> 5 In order to obtain the estimated score for each restriction, the assigned score is multiplied by the corresponding weight. Finally, the estimated scores for all categories are summed to obtain the restrictiveness indices.

8
6 In table 2-3 it is evident that the proportions of the principal components of FDI--new equity, reinvested earnings, and FDI-related borrowing--have also shifted over time. The new equity component had thus increased to over 65 percent of total FDI flovs to developing countries by 1988 -up from 23 percent in 1977 and 46 percent in 1982.9 Meanwhile, the proportions for both reinvested earnings and FDI-related borrowing had decreased. In 1988 rsinvested earnings were S percent and borrowing 25 percent of the total flows. There were important individual country exceptions to these trends, however. For instance, although the relative importance cf reinvested earnings in Brazil exhibits substantial year-to-year fluctuations, there has not been the same downward trend over the past decade that the worldwide data reveal.

Table 2-3: Components of FDI Flows to Developing Countries, 1975-88 (percent)

|Year|Equity|Reinvested<br>earnings|Long-term<br>borrowing|Short-term<br>borrowing|
|---|---|---|---|---|
|1975|27.1|18.3|54.4|0.2|
|1976|12.9|30.4|59.3|-2.6|
|1977|23.4|28.1|50.2|-1.6|
|1918|36.9|29.5|34.5|-0.9|
|1979|42.0|23.1|39.0|-4.2|
|1980|52.5|24.7|23.6|-0.8|
|1981|46.3|19.7|34.6|-0.6|
|1982|45.5|22.6|30.7|1.2|
|1963|44.0|15.8|40.9|-0.7|
|1984|57.5|14.2|26.0|2.3|
|1985|53.6|17.4|33.3|-4.3|
|1986|54.3|20.0|26.0|-0.6|
|1987|62.0|14.5|23.1|0.4|
|1988|66.7|9.1|24.2|0.4|

Nute: These are -:et figures, for example, new equity less divestment. See IMF (1977), p.140, para. 419-20.

Source: IMF.
-4-

- Ministry of Finance which sets prices biased on the average cost of production for producers and sets fuel prices to ensure costs of all fuels to motorists are covered by the revenue received from motorists

- DNC _(Departwmento Nacional de Combustdve)_ estimates domestic ethanol demand

- PEIROBRAS which is the state owned distributor, marketer and virtually exclusive importer of automotive fuels including ethanoL

Based on estimates of domestic sugar and ethanol demand production quotas are set for all mills and distilleries by the SDR. Only- whin1 iesel quotas are filled are mills and distileries entitled to export their over-quota production. Export licenses are used to control exports. The production quotas are set separately for two regions: North/Northeast and CentrallSouth; based on regional demands for each product. A mill or distllery which increases production for export in any one year has a high proportion of that increase progressively switched to quota in subsequent years and all other units have their quotas marginally decreased. So the incentives for increasing production for export are gready diminished.

The other main instrument of policy is the price fLxing arrangements. To ensure ethanol quotas are filled ethanol prices to producers are set to cover the cost of production of ehanol. To reduce incentives to switch sugarcane from ethanol to sugar, domestic sugar prices are also set in line with the ethanol price and the costs of producing sugar.

Consumer prices for ethanol do not bear a direct reMonship to producer prices in the same way as sugar prices. The monopoly status of DETROBRAS makes this possible. The consumer price of ethanoi is influenced by the total cost to PET'ROBRAS for refining and supplying all automotive fuels. Generally profits on the sale of gasoline are used to subsidize losses on other fuels.

Exports of sugar and ethanol receive the world prices less a fixed export tax-currently around 20 percent on the cane price or about 12 percent on the domestic sugar price. The export tax is based on a percentage of the fixed cane price and so it does not vary with the world price of sugar or ethanol.

Export licenses are used to remove the incentive to divert sugar or ethanol to world markets when the world price of either product is above the domestic price and before domestic requirements are meL In most years since 1985 world sugar prices have exceeded those of domestic Brazian sugar and ehanol prices for producers. The indicator world ethanol price also appears to be above the Brazilian producer price for ethanol although the. world ethol market is small. The imported oil price (measured in sugar equivalents and adjusted for its efficiency advantage compared to ethanol) has
# Table 8 Wage growth

Dependent: Monthly earning growth Sample: Age started>=15

|Experience 0.0356|-0.0091 -0.0172 -0.0099 -0.0128|
|---|---|
|(5.80)|(0.49) (0.89) (0.50) (0.63)|
|Experience * years of schooling|0.0052 0.0056 0.0055 0.0054|
||(2.54) (2.64) (2.56) (2.47)|
|Age started -0.1664|-0.2141 -0.2037 -0.1902 -0.1843|
|(2.23)|(2.76) (2.64) (2.33) (2.25)|
|Age started squared 0.0039|0.0051 0.0047 0.0047 0.0044|
|(2.04)|(2.56) (2.38) (2.22) (2.08)|
|Female 0.0085|-0.0090 -0.0133 0.0192 0.0200|
|(0.31)|(0.32) (0.47) (0.65) (0.68)|
|Asset 2000|3.27E-08 3.08e-08|
||(2.93) (2.48)|
|Mother/guardian schooling|0.0031 0.0036|
||(1.72) (1.96)|
|Mother/guardian age|-0.0089 -0.0033|
||(1.91) (0.67)|
|Province dummies yes|yes yes|
|School dummies|yes yes|
|Number of observations 1692|1692 1692 1692 1692|
|Number of provinces 49|49 49|
|Number of schools|101 101|
|R squared 0.0973|0.1035 0.1101 0.1182 0.1229|

Numbers in parentheses are absolute t values with Huber robust standard errors. Current age is age started first job plus experience (years).

23
Table 13: Intensive margin: Existing exports

|**Dependent Variable:**Exports_f pjt_|||||
|---|---|---|---|---|
|<br>Big Firm: exports > median in||HS6 product-Year|HS2 sector-Year|Year|
||(1)|(2)|(3)|(4)|
|Log (Tariff_i_,_j_,_p_,_t_)|-0.0351|-0.0024|-0.0036|-0.2244|
||(0.036)|(0.093)|(0.085)|(0.107)**|
|Log (Tariff_i_,_j_,_p_,_t_) x Big Firm_f _,_t_||-0.0332|-0.0314|0.1962|
|||(0.090)|(0.090)|(0.108)*|
|SPS_i_,_j_,_p_,_t_|0.0021|-0.0840|-0.1102|0.0374|
||(0.048)|(0.097)|(0.067)|(0.114)|
|SPS_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.1022|0.1256|-0.0300|
|||(0.115)|(0.079)|(0.126)|
|TBT_i_,_j_,_p_,_t_|-0.0971|-0.2802|-0.2091|-0.0552|
||(0.044)**|(0.078)***|(0.099)**|(0.160)|
|TBT_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.1452|0.1136|0.1566|
|||(0.071)**|(0.060)***|(0.073)**|
|Pre-Shipment_i_,_j_,_p_,_t_|0.0388|-0.0929|-0.0327|0.1404|
||(0.053)|(0.060)|(0.077)|(0.062)**|
|Pre-Ship._i_,_j_,_p_,_t _x Big Firm_f _,_t_<br>||0.1676|0.0797|-0.1017|
|3||(0.064)***|(0.087)|(0.073)|
|QuantityControl_i_,_j_,_p_,_t_|-0.5739|-0.5769|-0.7281|-0.7879|
||(0.158)***|(0.134)***|(0.151)***|(0.150)***|
|Quantity Control_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.267|0.1658|0.2286|
|||(0.103)***|(0.082)**|(0.100)**|
|PriceControl_i_,_j_,_p_,_t_|-0.0483|-0.1686|-0.0706|-0.0782|
||(0.040)|(0.059)***|(0.053)|(0.100)|
|Price Control_i_,_j_,_p_,_t _x Big Firm_f _,_t_||0.1442|0.0173|0.0242|
|||(0.072)**|(0.055)|(0.097)|
|Log (Import Demand_f _,_p_,_j_,_t_)|0.0324|0.0319|0.0322|0.0323|
||(0.006)***|(0.006)***|(0.006)***|(0.006)***|
|Observations|253212|253212|253212|253212|
|Adjusted_R_<sup>2</sup>|0.96|0.96|0.96|0.96|
|Fixed Effects|_f _−_p _−_j_, _f _−_p _−_t_, _j _−|_t_<br>_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_|_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_|_f _−_p _−_j_, _f _−_p _−_t_, _j _−_t_|

This table estimates equation 4 for the intensive margin of Colombian firm-level exports. Columns (1-4) show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis.

> ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
# **Table A1: Senegal: % of currently separated or divorced women, by age group**

||||||||**Ag**|**e group**||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Year**|[15-19]|[20-24]|[25-29]|[30-34]|[35-39] [|40-44]|[45-49]|[50-54]|[55-59]|[60-64]|[65-69]|[70+]|[70-74]|[75+]|_Sources_|
|1960-1961|1.0|2.5|4.6|3.2|4.3|3.4|6.0|4.5|5.3|3.8|4.1|2.6|2.5|1.3|ED|
|1970-1971||1.0|3.1|3.6|3.4|3.1|3.2|4.1|4.5|5.0|3.9|3.0|2.7|3.3|ED|
|1978||1.9|4.7|3.8|2.8|4.0|2.8|2.7|||||||WFS|
|1986||0.6|2.0|2.8|2.0|2.7|2.4|1.7|||||||DHS|
|1988|0.1|0.9|1.6|2.0|2.2|2.1|2.1|2.1|2.4|2.5|2.5|2.7|3.3||Census|
|1992-1993||0.4|1.8|2.4|2.1|2.4|2.0|1.8|||||||DHS|
|1997||0.4|1.6|2.9|3.2|3.3|2.6|1.6|||||||DHS|
|1999||0.7|2.3|4.7|4.7|5.6|5.7|4.4|||||||DHS|
|2000||0.4|1.2|1.8|2.1|2.3|2.2|1.4|1.5|0.9|0.6|1.0|0.4|0.5|MICS|
|2002|0.1|0.5|1.5|2.4|3.1|3.1|2.8|2.4|1.8|1.6|1.1|1.0|0.9|0.7|Census|
|2005||0.6|1.7|2.3|3.1|3.1|3.7|3.3|||||||DHS|
|2010-2011|0.0|0.7|1.3|3.0|3.8|3.7|4.2|3.6|4.4|6.0|3.5|3.6|2.2|2.9|DHS-MICS|
|2012-2013||0.9|2.8|4.9|5.2|5.3|6.4|6.2|3.0|4.0|1.2|2.0|3.1|0.4|DHS|
|2012-2014||0.7|2.7|4.4|4.8|6.7|6.8|6.6|4.2|2.9|3.0|2.1|2.7|0.3|DHS|
|2014||0.5|2.6|4.0|4.4|8.0|7.2|7.0|5.4|1.9|4.9|2.2|2.3|0.1|DHS|

Note: ED : Enquête Démographique ; WFS : World Fertility Survey ; DHS : Demographic and Health Surveys; MICS: Multiple Indicator Cluster Surveys Source: United Nations. Department of Economic and Social Affairs. Population Division (2015). World Marriage Data 2015 (POP/DB/Marr/Rev2015).

48
**Table 4: Percent Changes in Maize Exports from Tanzania Due to Historical Production Deviations from Trend under Regimes 1 and 2**

|||**Regim**|**e 1: Basel**|**ine**||**Regim**|**e 2: Tan**|**zania Expo**|**rt Restri**|**ction**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Ri**|**1995**|**1971**|**1980**|**1982**|**1983**|**1995**|**1971**|**1980**|**1982**|**1983**|
|**egon**|**+**||**0**|**-**||**+**||**0**||**-**|
||**I**|**II**|**III**|**IV**|**V**|**VI**|**VII**|**VIII**|**IX**|**X**|
|Argentina|194|59|38|-66|-7|0|0|0|-64|-2|
|Brazil|131|59|20|-65|-20|0|0|0|-63|-15|
|China|182|38|-7|-64|-18|0|0|0|-62|-13|
|East Asia|153|51|-3|-59|-1|0|0|0|-57|0|
|Eastern<br>Europe &<br>Former USSR|205|135|12|-69|-63|0|0|0|-68|-60|
|Latin America<br>& Caribbean|179|51|24|-61|31|0|0|0|-59|0|
|Malawi|118|168|17|-68|-61|0|0|0|-67|-58|
|Mexico|222|65|-5|-66|107|0|0|0|-64|0|
|Middle East &<br>North Africa|216|78|9|-63|-26|0|0|0|-61|-22|
|Mozambique|107|77|1|-51|-43|0|0|0|-49|-40|
|Oceania|225|45|7|-67|7|0|0|0|-65|0|
|Rest of East<br>Africa|194|67|63|-68|-37|0|0|0|-66|-33|
|Rest of North<br>America|206|59|-6|-68|47|0|0|0|-66|0|
|Rest of Sub-<br>Saharan<br>Africa|115|82|10|-55|-41|0|0|0|-53|-38|
|South Asia|120|105|-13|-42|-57|0|0|-7|-39|-54|
|Southern<br>Africa|137|74|1|-58|-30|0|0|0|-56|-26|
|Uganda|62|3722|929|-51|-36|0|0|0|-46|-29|
|USA|180|52|-4|-65|5|0|0|0|-48|-33|
|Western<br>Europe|204|83|9|-65|-51|0|0|0|-63|0|
|Zambia|43|432|170|-45|-50|0|0|0|-63|-47|
|Zimbabwe|142|101|7|-57|-53|0|0|0|-42|-47|
|Total|175|79|7|-62|-28|0|0|0|-60|-29|

Source: Authors’ simulations 16
23 Figure 1: Impacts of UC and VF on probability of being too ill to work <!-- Start of picture text --> UC scheme VF scheme<br>-20 -10 0 10 20 -20 -10 0 10 20<br>Quarters since UC implementation Quarters since VF implementation<br>.04 .04<br>.03 .03<br>.02 .02<br>.01 .01<br>0 0<br>-.01 -.01<br>-.02 -.02<br>Effect of scheme on prob. of being too ill to work<br>-.03 -.03<br>-.04 -.04<br><!-- End of picture text -->