rafmacalaba/datause-encoder-data huggingface dataset:
10 that if the principal expends the effort (incurs $ _c_ ) to upgrade his belief _p_ about the good state, it means that either the cost of determining the true state is not onerous or the policymaker’s prior probability _p_ is very low. In either case, it pays the foreign principal to expend $ _c_ and send a message, that is, to lobby.

Thus, the absence of foreign lobbying by a host country is also informative per se and suggests three possible country situations: (i) the principal is an arm of the foreign government and has the power to enforce safety regulations in order to change the state to G, but determines that incurring $ _c_ is too costly (see e.g. fn. 9 about Haiti); (ii) the principal is a private entity and it is beyond its means to determine the true state (therefore only few private entities lobby in the data); (iii) the principal may be the government or a private entity, and finds that the probability that the true state is R (run-down) is too high. In all three cases, _p_ < _c_ /2, in the first two because _c_ is too high and in the third because _p_ is too low.

Second, consider the oft-used lobbying ploy of organizing expense-paid junkets for legislators. Apart from establishing friendly relations with legislators, it provides the opportunity for the legislators to personally undertake an “audit”. This is itself a signal to legislators that the lobby is being truthful with its message (of the true state being G); else why would it reveal the true state at its own expense?<sup>9</sup> Thus, junkets are not cheap talk.

The model is not without limitations. It assumes that all individuals in the foreign country benefit equally from tourism promotion. Otherwise, even if lobbying is informational, it can produce rents for a small set of beneficiaries whose interests are served, possibly at the expense of others. In that case, the welfare effects of informational lobbying may be ambiguous.

# **3.2 Evidence from a Survey**

Available evidence suggests that lobbying in the tourism industry is directed towards reducing information asymmetries and managing the image of the country. In addition to anecdotal evidence in newspaper articles and our own informal conversations with lobbyists that we have footnoted, we present some findings from a survey of Registered Foreign Agents

> 9 Theoretically, a free audit implies these actions. The lobby never lies about the state being R. It will provide a free audit for the legislator only if it is sure the state is A (note the difference between this and implication 2. above). As a result, the legislator updates their personal probability that the state is G to 1.
in GDP, estimated from the IMF’s WEO database at 037 over the period 2000-10. These numbers imply from the budget constraint that the share of spending on other items,  , is 0736. The cash transfer per child, <sup></sup> , is set equal initially to 005, with the total amount of spending taken to be subsumed in other spending, <sup></sup> <sup>,asnoted</sup> earlier.

Dabla-Norris et al. (2011, Table 1.a) estimate the efficiency parameter for public investment in Brazil, , at 078.<sup>28</sup> Thus, according to these numbers, in Brazil more than 20 percent of public spending on infrastructure investment is “wasted,” in the sense that it does not turn into public capital. In the absence of data specific to the education and health sectors, the efficiency parameters for spending on education and health,  and , respectively, are also set at the same value. Using (29), the equilibrium public-private capital ratio can be computed to give  = 0147. Thus, by this measure, public capital remains a relatively scarce factor in Brazil.

Finally, the elasticity of output of health services with respect to public spending on health is set at  = 08. There are no good benchmarks in the literature but this value appears to be a reasonable starting point, given that most of these services are measured based on salaries paid to medical workers, and the correspondence between elasticities and shares in the Cobb-Douglas function with constant returns to scale. By implication, the elasticity with respect to public capital in infrastructure is 02.

The benchmark parameter values are summarized in Table 1. Based on these parameter and initial values–from which it can be established that Π2 = 0289, which implies that the model is indeed stable–the model is solved for the steady-state growth rate of output. A multiplicative constant is also introduced, in order to yield an annual growth rate of marketed output equal to 27 percent, the average rate of growth of real GDP for Brazil over the period 1990-2008.<sup>29</sup>

> 28Dabla-Norris et al. (2011) define their metric on a range of 1 to 4, with a value of 3.12 for Brazil; this value was simply divided by 4 to obtain an indicator bounded by unity.

> 29Conceptually, this constant reflects an exogenous productivity factor affecting the production of marketed goods, as defined in equation (10).

25
The second data source we use in this paper is the Turkey Welfare Monitoring Survey (TWMS) baseline data set. The collection of this dataset was mobilized by the World Bank, UNICEF and the Economic Policy Research Foundation of Turkey, TEPAV, in early 2009 and the survey was fielded in May-June 2009. The aim of the survey was to monitor the welfare of Turkish households during the beginning of the Global Financial Crisis of 20082009 and to measure the ways in which households coped with the economic turndown. The survey was fielded in five major cities: Istanbul, Ankara, Izmir, Kocaeli and Adana, accounting for forty percent of the overall urban population in Turkey. It is representative for the combined five-cities and includes 2,102 households.<sup>4</sup> The TWMS includes a number of different questions on health demand behavior, including retrospective questions on the household‘s utilization of health services during the crisis period October 2008 to May 2009. Combined with information on insurance access included in the survey, it is hence possible to track the health utilization behavior of various types of households through the creation of a quasi-panel.<sup>5</sup> The survey distinguishes between the reduction in curative and preventative care services.

Further, we construct a wealth variable at the household level using the TWMS. The asset index constructed for this purpose is based on housing characteristics and household durable goods following (Filmer and Pritchett 2001). We use the asset index to derive both wealth deciles as well as wealth quintiles of the urban sample.

> 4 The survey can be obtained from www.worldbank.org/turkey  or by emailing the World Bank Turkey Country Office turkeywebfdbk@worldbank.org.

> 5 We consider the responses of households to the following two questions in the baseline data:  (i) ― _Have you had to reduce visits to the doctor between October 2008-May 2009_ ?‖   and (ii) ― _Have you had to reduce the utilization of preventive health services from October 2008-May 2009_ ?  Both questions when answered positively involve a reduction in the utilization of health care in the time period t0 to t1.

6
27 in the production of plywood and veneer, and comprises a major obstacle for local firms in meeting the Forestry Code’s 2012 target.

**Table 5: Gabon Log Output Channeled into a Single Chain (at Gabon Wood** **<u>Utilization Rates), Assuming Input of 3,400,000m</u>**<sup>**3**</sup> **lo** **<u><mark>g</mark> s</u>**<sup>**a**</sup>

||**Logs**|**Sawn wood**|**Veneer**<br>**sheets**|**Plywood**|
|---|---|---|---|---|
|Output real(‘000m<sup>~~3~~</sup>)<sup>~~b~~</sup>|3,400|1,380|1,870|1,480|
|Foreign exchange<sup>~~c~~</sup>|1,188|758|1,480|1,329|
|Employment (incl. employment<br>in earlier stages ofprocessing)<sup>d</sup>|6,299|16,967|19,389|34,951|
|Capital cost _($1,000)_<sup>~~e~~</sup>|39,500|43,800|455,000|754,000|
|Producer margins (fob value<br>minus cost) (indexed to ex-forest<br>cost of log)|107|-28|-23|23|

_Source:_ Terheggen’s fieldwork data and, and Odyseé Développement (not published) _Notes:_ a Gabon log production volume rounded average for 2003-2007 b Converted using wood utilization rates in meeting EU buyer requirements regarding species and product specifications, i.e. 2.5:1 (EU sawnwood), 1.82:1 (veneer sheets) and 2.33:1 (plywood) 3 Species weighted average using 2006 prices based on (UNCTAD) (Okoumé logs, sawnwood, and plywood), (ITTO, 2009) (average hardwood log prices Central African species, own calculations), and (FAOSTAT) (veneer sheets export unit price, own calculations) 4 Own calculations based on fieldwork data collected November 2008–March 2009 5 Terheggen’s calculation based on 2003 capital depreciation costs across chains as stated in (Odyssée Développement, 2005) converted into US$ using average 2003 exchange rate (http://data.un.org); excluding environmental costs such as consultancy and certification fees.

Equally concerning from a developmental perspective is the competitiveness of Gabonese producers at various points in the timber value chain. It is clear that Gabon possesses a significant resource rent in tropical hardwoods in general and Okoumé in particular. Table 5 shows that the fob price of a log is approximately double that of the cost of extraction, however, at current prices, and with current levels of processing efficiency the surplus generated in plywood manufacture is much smaller, both as an absolute sum and as a proportion of costs. And at current prices, all of the resource rents are dissipated – indeed more than dissipated – if logs are sawn or transformed and exported as veneer. Thus, the “retrograde” transition from processed to raw timber reflected in the shift from the European to the Chinese market may arguably be developmentally positive, at least in terms of static comparative advantage and at current prices.

But neither of these two conditions are a “given”. With appropriate policy support and in competitive markets, it is possible that processing efficiency in Gabon’s timber value adding sectors (especially in sawmills) could be improved. Moreover, it is also possible that Chinese producers will increasingly have to internalize some of the
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interruptions in power, water, and telephone service in the last year, it serves as a proxy for the overall state of infrastructure.

The European countries clearly have a superior infrastructure, with Poland’s stronger than Moldova’s (table 2). Firms in Poland, on average, experienced only 1.5 power outages during the previous year and those in Moldova 2.3—compared with 37.3 in Tajikistan and 15.8 in the Kyrgyz Republic. Water and telephone outages tell a similar story. Firms in Uzbekistan reported fewer service outages on average than those in the other Central Asian countries, but still substantially more than those in the European countries.

These microeconomic results mirror country-level data. For example, the European countries had the most telephone mainlines per 1,000 people and the most Internet hosts per 10,000 people in 2000, with Poland having the most developed telecommunications sector.

**Table 2 Mean Indicators of Infrastructure by Country**

|Indicator|Kyrgyz<br>Republic|Moldova|Poland|Tajikistan|Uzbekistan|
|---|---|---|---|---|---|
|_Service interruptions in_||||||
|_previous year_||||||
|Power|15.8|2.3|1.5|37.3|7.7|
|Water|2.4|1.0|0.2|1.8|2.0|
|Telephone|1.9|4.3|0.5|6.4|1.8|
|Telephone mainlines per||||||
|1000 people, 2000|77||283|36|67|
|Internet hosts per 10,000<br>people,2000|6.2|4.0|67.2|0.4|0.1|

_Source:_ For service interruptions, World Bank, Business Environment and Enterprise Productivity Extension Survey, 2003; for other data, World Bank, World Development Indicators database.

_Competition_ 13
of the country’s administrative regions (10 regions in total, at the time that the enterprise census was conducted). The productivity gap averaged across the five richest regions is three times larger than that for the five poorest regions. Greater Accra, the country’s richest region, has the largest productivity gap, more than four times larger than the productivity gap averaged across the other nine regions. Greater Accra’s large productivity gap is due mainly to the substantially higher average productivity in its formal sector compared to the formal sectors in other regions.

Simulated model predictions suggest a strong negative relationship between the disparity in regional intersectoral productivity gaps and both the level of national labor productivity and the contribution of structural transformation to national labor productivity. Reducing the standard deviation of regional intersectoral productivity gaps by 50 percent raises national nonfarm labor productivity, national aggregate (nonfarm and farm) labor productivity, and the contribution of structural transformation to national aggregate labor productivity by a little less than 3 percent each. Reducing the standard deviation of regional intersectoral productivity gaps by 90 percent raises national nonfarm labor productivity by 13.8 percent, national aggregate labor productivity by 11.9 percent, and the contribution of structural transformation to national aggregate labor productivity by 19.7 percent.

Our argument that the variation in regional intersectoral productivity gaps acts as a barrier to growth-enhancing structural transformation weakens if differences in the pace of structural transformation across regions naturally equalizes regional intersectoral productivity gaps over time. Using short-term net employment creation rates in informal and formal enterprises in services and industry across regions as a proxy for the differing pace of structural transformation across regions, we examine the extent to which structural transformation contributes to the change in regional intersectoral productivity gaps. We find that the differing pace of structural transformation across regions in fact has the opposite effect, increasing the disparity in regional intersectoral productivity gaps.

The rest of the paper is organized as follows. Section II provides an overview of national and regional patterns and trends in incomes, structural transformation, and informal nonfarm employment. Section III describes the enterprise data we use in our analysis and provides estimates of regional intersectoral productivity gaps. Section IV presents the model for deriving regional productivity losses from intersectoral misallocation of labor and compares these regional losses to regional intersectoral productivity gaps. Section V discusses the simulation results of the effects of hypothetical adjustments to the disparity in regional intersectoral productivity gaps on the level of national labor productivity and on the contribution of structural transformation to national labor productivity. It also discusses the robustness of the 5
This idea of social exclusion has long been echoed in arguments for relative poverty lines. In a recent example, a commentator on Japan’s (recently introduced) national poverty line (of around $15 a day) argued that:<sup>26</sup> “Poverty in a prosperous society usually does not mean living in rags on a dirt floor. These are people with cellphones and cars, but they are cut off from the rest of society.” The social roles of certain forms of consumption have long been noted by anthropologists, who have pointed to the social roles played by festivals, celebrations and communal feasts in poor countries; see, for example, Geertz (1976) and Fuller (1999, Chapter 6). Rao (2002) documents the importance of celebrations to maintaining the social networks that are crucial to coping with poverty in rural India. Banerjee and Duflo (2007) report seemingly high expenditures on celebrations and festivals by very poor people in survey data for a number of developing countries. In Yemen, participants at “qat sessions” discuss local economic and social affairs while chewing this mild stimulant; these sessions serve an important social role—and no less so for poor people—such that “refusing to take qat is tantamount to accepting ostracisation” (Milanovic, 2008, p.684). Clothing can also serve a social role. Friedman (1990) describes how poor Congolese acquired clothing with a conspicuous “designer label,” which he interpreted as status-seeking behavior. A field experiment by van Kempen (2004) revealed that poor people in Bolivia were willing to pay a premium for a designer label, which (he argues) serves as a symbolic expression of social identity for the poor (van Kempen, 2004, p.222).<sup>27</sup> However, neither the relative deprivation nor the social inclusion arguments tell us that the poverty line should be a constant proportion of the mean—implying that the elasticity of the poverty line to the mean is unity. Consider the relative deprivation argument and recall equation (3.1). It is obvious from equation (3.2) that only if own income has no utility value independently of relative income will it be the case that (3.1) can be written in the special form: _zi_  ( _pi_ , _xi_ , _uz_ ) _~~y~~ i_ (8) In other words, strongly relative lines require that utility (at given _x_ ) depends solely on relative income. While we might agree that relative considerations matter, at least above some level of mean income, it is hard to accept that absolute levels of living do not matter independently of relative position.

> 26 The commentator is Masami Iwata, as quoted by Fackler (2010, p.A6). 27

> For a more general discussion of the social-symbolic roles that consumption can play see Khalil (2000).

16
20 formal and informal sector pay (the greater is market segmentation), the more likely is it that effective minimum wage laws will result in a fall in rural sector wages. The fact that the urban informal sector has grown in Mexico (from 49.4 percent of urban employment in 1984 (Roberts, 1991) to 50.5 percent in 1988 (calculations here) reinforces the possibility of such an effect on rural wages.

In sum, detailed examination of the household sector data reveal interesting differences in pay between formal and informal sector workers, with the largest differences concentrated among part-time and female workers. The fact that very few male formal sector workers report wages at or near the minimum wage provide only weak validation for the firm level averages (in which more than two thirds of the unskilled workers are male), because they are based on both skilled and unskilled individual wages. In any case, the largest differences in the measured effects of minimum wages in the two data sets arise from the inclusions of lower paid informal workers in the household data.

# V. Conclusions

This paper has used diverse data to examine the impact of minimum wage legislation on wages and employment in Mexico and Colombia. Divergen **t** rends in the behavior of the real minimum wage in the two countries in the 1980s provides an interesting backdrop from which to study the effects of minimum wages. Indeed, the effects of minimum wages on the two economies in the 1980s reflect in some measure these divergent trends.

In the case of Mexico, minimum wages have had virtually no effect on wages or employment in the formal sector. The key explanation for this lies in the position of the minimum wage relative to the wages of formal sector workers-- the minimum wage is not an effective wage in either set of data for a majority of firms or workers. Concerning employment, the time series and more reliable panel data estimates both yield elasticities of low skilled employment with respect to the minimum wage which are statistically insignificant and near zero over this period.

Where the minimum wage is more likely to have had a notable effect is in the Mexican informal sector, where data suggest that significant numbers of workers are paid at or below minimum wages. Understanding better the impact of minimum wage laws over time on estimated formal and informal
|**Paternal Education (REF= Less t**<br>|**han primar**|**y)**|||||
|---|---|---|---|---|---|---|
|Less than primary|455|16.11|64.44|(15.33, 16.91)|0.40||
|Primary|311|16.60|66.40|(15.70, 17.49)|0.45|0.158|
|Secondary or greater|741|17.66|70.64|(17.11, 18.21)|0.38|<0.001|
|**Parental Distress Sum Score**||||||0.047|
|No parental distress|704|16.54|66.16|(15.97, 17.12)|0.29||
|Some parental distress|803|17.30|69.20|(16.73, 17.88)|0.29||
|**Cut the size of meals or skip m**<br>|**eals since M**<br>|**arch 2020 (REF=**<br>|**Not at all)**<br>||||
|Not at all|586|16.97|67.88|(16.33, 17.62)|0.32||
|Very little/Somewhat|593|16.76|67.04|(16.09, 17.44)|0.51|0.252|
|A great extent|259|16.90|67.60|(15.90, 17.90)|0.45|0.536|
|**School Enrollment (36-72 mont**|**hs) (REF= O**|**ut of school)**|||||
|Out of School|621|12.58|50.32|(11.99, 13.16)|0.30||
|Enrolled in ECE|508|19.79|79.16|(19.25, 20.33)|0.27|<0.001|
|Enrolled in Primary|193|21.85|87.40|(21.21,22.50)|0.33|<0.001|

_Note: All estimates weighted. Psychosocial stimulation ranges from 0 to 14. Parental distress sum score ranges from 0 to 5. The age parents believe children should begin school is open ended and represents child year of age. Linearized standard error is based on the weighted sum score variation._

# Risk Factor Prevalence

Risk factor prevalence is similar in households with children 0-35 months and those with children 36-72 months. The three most prevalent risk factors in all households were: experiencing some level of food insecurity, living in a rural area, and parental distress (Figures 2 & 3). Households with children aged 0-35 months, compared to 36-72 months, experienced much higher levels of parental distress (57 percent to 53 percent, respectively) and less psychosocial stimulation (12 percent compared to 3 percent experience no psychosocial stimulation, respectively). Households with children 36-72 months experienced slightly higher levels of food insecurity (61 percent to 60 percent, respectively), lived in rural areas at higher rates (66 percent to 61 percent, respectively), and were more likely to have a mother with low levels of maternal education (49 to 44 percent, respectively), but none of these age-group differences were statistically significant. Approximately 43 percent of 36-72-month-old children are currently not enrolled in any type of ECE or primary school.

There was also a high level of co-occurrence of risk factors in our sample overall. For example, as Figure 3 indicates, 64 percent of households lived in a rural area; of those, for 13 percent, living in a rural area was an isolated risk factor, whereas the remaining 87 percent of those households also experienced parental distress, experienced food insecurity, or both.

## **Figure 2. Prevalence of Risk Factors by Child Age (N=2,896)**

18