rafmacalaba/datause-encoder-data huggingface dataset:
33 supplier. The extensive powers of Marcos after martial law made it easier for his cronies to carry out more of their transactions inside the Philippines and avoid damaging international scrutiny.

The picture that emerges for the period 1978-83 is that of a telecommunications company with friendly ties to the administration that is given the opportunity to earn high profits, take over the assets of other companies, and borrow heavily. Because it is a publicly traded company controlled by minority shareholders, its profits are channeled to more exclusive accounts. And because the company is concerned about the stability of the administration, it hesitates to expand the local network, except where the expansion facilitates long-distance services.

As the disintegration of the Marcos regime accelerated in 1984-85 and the economy collapsed, PLDT's investment came to a complete halt, and attempts to secure capital abroad intensified. The evidence on capital flight in this period is extensive (Sison 1986), and there is no disputing that PLDT's managers deliberately failed to remit foreign exchange earnings in 1985 (Friedland 1988).35 This effect is also reflected in the sudden decline in international long-distance revenue as a share of total revenues in 1985 (see figure 4.12).

_Regime shift, political turmoil, and regulatory activism, 1986-89_ The anti-Marcos coalition that took over in 1986 included a broad range of interests with many differences to settle. During the period of turmoil that ensued, political activism among the nonelite increased. Regulatory conflict would be expected to arise between PLDT and the new government (Implication 8) and investment to come to a halt in the local network (Implication 5), though not necessarily in international circuits (Implication 11). In fact, these are precisely the events that transpired in the early years of the new Aquino administration.

PLDT's first skirmish came less than three weeks after the inauguration, shortly after the establishment of the Presidential Commission on Good Government. The commission moved quickly to sequester PLDT's assets and to appoint a supervisory committee, headed by attorney Luis F. Sison, to oversee its management. A few days later, the committee's task was specified as the investigation of the company's connections with Marcos and its past business practices. Numerous allegations were 35 PLDT declared some of the underreported foreign earnings later in 1986. Some of Sison's (1986) other findings were disputed by a subsequent PCGG study by Mario Locsin and Benjamin Guingona. When preparing their report, Locsin and Guingona were the commission's representatives on PLDT's board of directors. A senior Aquino government official has suggested that the latter study was orchestrated by the Cojuangco family (Friedland 1988).
that there is (of course) a variance in the PPP revision at a given level of consumption per capita. (For example, China, is a not one of the countries used to set the international poverty line but it had one of the highest PPP revisions.) Nonetheless, Proposition 1 is suggestive that the pure effect of the PPP revisions, for a given set of national lines, will be tend to reduce the aggregate poverty count. As we will see, this is confirmed by our empirical results in section 5. Against this effect, the upward shift in the national poverty lines implied by the RCS data will tend to increase the poverty count. The balance of these effects is an empirical question.

# **4. Household surveys and poverty measures**

We have estimated all poverty measures ourselves from the primary sample survey data rather than relying on pre-existing poverty or inequality measures. The primary data come in various forms, ranging from micro data (the most common) to specially designed grouped tabulations from the raw data, constructed following our guidelines. All our previous estimates have been updated to assure internal consistency.

We draw on 675 surveys for 115 countries; a full listing is found in Chen and Ravallion (2008b).<sup>20</sup> The surveys are nationally representative. Taking the most recent survey for each country, about 1.23 million households were interviewed in the surveys used for our 2005 estimate. The surveys were mostly done by governmental statistics offices as part of their routine operations. Not all available surveys were included; a survey was dropped if there were known to be serious comparability problems with the rest of the data set.<sup>21</sup>

**_Poverty measures._** Following past practice, poverty is assessed using household per capita expenditure on consumption or household income per capita as measured from the national sample surveys.22 Households are ranked by consumption (or income) per person. The distributions are weighted by household size and sample expansion factors. Thus our poverty counts give the number of people living in households with per capita consumption or income below the international poverty line.

> 20 We had data for 116 countries but Zimbabwe had to be dropped drop due to the difficulty in converting local currency to PPP due to the high inflation rate.

> 21 Also, we have not used surveys for 2006 or 2007 when we already have a survey for 2005—the latest year for which we provide estimates in this paper.

> 22 The use of a “per capita” normalization is standard in the literature on developing countries. This stems from the general presumption that there is rather little scope for economies of size in consumption for poor people. However, that assumption can be questioned; see Lanjouw and Ravallion (1995).

14
Table 9: IV estimates of the effect of ICT use on the share of white/blue-collar workers and wages: Manufacturing

|(1)<br>**Dependent variable:** ∆**white/blue-collar workers**|(2)|
|---|---|
|∆Share of labor that uses computers<br>0.0024***|0.0018**|
|(0.0008)|(0.0008)|
|F- frst stage<br>40.43|38.71|
|Sargan p-value<br>0.8068|0.9983|
|∆Share of labor that uses Internet<br>0.0018***|0.0014**|
|(0.0006)|(0.0007)|
|F- frst stage<br>73.39|70.25|
|Sargan p-value<br>0.1221|0.1475|
|∆ln(stock of computer equipment/worker)<br>0.0785***<br>(0.0230)|0.1211***<br>(0.0402)|
|F- frst stage<br>26.34|11.11|
|Sargan p-value<br>0.4247|0.7757|
|**Dependent variable:** ∆**white/blue-collar wages**||
|∆Share of labor that uses computer<br>-0.0239***<br>(0.0062)|-0.0235***<br>(0.0063)|
|F- frst stage<br>40.43|38.71|
|∆Share of labor that uses Internet<br>-0.0201***|-0.0197***|
|(0.0049)|(0.0050)|
|F- frst stage<br>73.39|70.25|
|Sargan p-value<br>0.3811|0.3912|
|∆ln(stock of computer equipment/worker)<br>-0.9751***|-1.8169***|
|(0.2073)|(0.5389)|
|F- frst stage<br>26.34|11.11|
|Sargan p-value<br>0.5499|0.9405|
|**Controls**||
|∆ln(capital/worker)<br>No|Yes|
|Observations<br>11,438|11,438|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

33
We develop a model to derive productivity losses from the misallocation of labor between the informal and formal sectors. The model is adapted from the framework developed by Brandt et al. (2013), which extends the Hsieh and Klenow (2009) firm-by-sector level model, to examine factor misallocation and its effect on total factor productivity at a more aggregate level, between sectors, provinces, and China’s nonfarm economy. Under Hsieh and Klenow’s model, maximum allocation efficiency is achieved when the marginal revenue products of enterprises within a sector become equal.<sup>12</sup> Consider a static allocation problem in an economy consisting of two sectors ( ), formal ( ) and informal ( ), and regions ( ), . Employment in a year is given. We model ss the allocation of labor across sectors within a region. We however do not model allocation of ff ii nn rr rr= 1,2 ... nn labor across regions. Supporting this decision, based on GLSS 2016/17, we find that internal migration (across regions) by workers is limited in Ghana. Only 8.2 percent of workers have moved from their birth region to another region due to self-reported employment-related reasons.<sup>13</sup> More importantly, the distribution of workers between the informal and formal sectors in regions is unchanged by the extent of migration.

We assume a linear production technology at the sector level with labor as the only input. Outputs produced in both sectors are gross substitutes with some degree of product differentiation. Average productivity equals marginal productivity in a linear production function. As a result, the condition for allocative efficiency in our model can be expressed in terms of average labor productivities. The production function in sector and region exhibits constant returns to scale (CRS): ss rr r r r , (3) YY = AA LL where r and r are output and employment in sector and region , respectively, and r is r labor productivity in sector YY LL and region , i.e., r = ss r ~~.~~ Total output in region rr ( AA) is a YY rr constant elasticity of substitution (CES) aggregate of sectoral outputs: ss rr AA LL rr YY

> 12 A large body of literature adopts the model by Hsieh and Klenow to estimate misallocation across enterprises (for example, see Daude and Fernández-Arias 2010, Busso et al. 2013, Crespi et al. 2014, Nguyen et al. 2016, Chuah et al. 2018, Bastidas and Acosta 2019, and Cirera et al. 2020).

> 13 Self-reported employment-related reasons comprise seeking employment or establishing a business; they also include job relocation or job transfers. If we relax the restriction on the reason for movement, the share of workers who have moved from their birth region to another region increases to 25.6 percent.

11
rural areas and their heavy reliance on unskilled wages elsewhere, these are the key industries when it comes to any poverty assessment. The strongest world price increases are for the heavily subsidized farm products: rice and other grains, cotton, dairy products and beef. The ranking of the price rises arises from the composition of cuts – both across the three sets of agricultural distortions and across countries. The other important point made in this paper is that, given the increasingly differentiated nature of traded products, there is no one “world price” and careful attention must be paid to bilateral patterns of trade and country-specific price changes.

Finally, Hertel and Ivanic outline the methodology for transmitting the generated price and volume changes to the national case studies. This turns out to be quite complex, and represents an important innovation in the linking of global economic outcomes with national impacts.

# **3. Price Transmission**

Our analysis of the country case studies is structured around the conceptual framework laid out by Winters (2002) and Winters, McCulloch and McKay (2004). This begins with the question of price transmission – namely how much of the world price shock is transmitted to producers and consumers?

With a majority of the poor in most countries located in rural areas – often poorly served by transportation and communication infrastructure, it is important to ask whether developments in global markets will really have an impact on these households. Of course, this is an empirical question, subject to econometric investigation, and this is precisely what Alessandro Nicita (2004) does in the case of Mexico. He shows that 13
||(0.009)|(0.009)|(0.008)|(0.008)|(0.006)|(0.007)|(0.016)|(0.006)|(0.007)|(0.015)|
|---|---|---|---|---|---|---|---|---|---|---|
|Child dependency ratio|0.056|0.030|0.016|-0.005|-0.004|-0.040|0.055|-0.010|-0.042|0.032|
||(0.049)|(0.045)|(0.044)|(0.041)|(0.032)|(0.033)|(0.073)|(0.033)|(0.035)|(0.072)|
|Log of cultivated land (acres)|||-0.351***|-0.322***|-0.720***|-0.717***|-0.737***|0.202***|0.229***|0.132***|
||||(0.014)|(0.014)|(0.014)|(0.017)|(0.028)|(0.015)|(0.017)|(0.031)|
|Export Orientation Index||1.413***||1.292***|0.904***|0.941***|0.825***|0.867***|0.935***|0.713***|
|||(0.055)||(0.051)|(0.040)|(0.047)|(0.084)|(0.042)|(0.047)|(0.089)|
|Log of agricultural input costs<sup>a</sup>|||||0.482***|0.482***|0.491***|0.519***|0.512***|0.544***|
||||||(0.012)|(0.014)|(0.023)|(0.012)|(0.014)|(0.024)|
|Simpson Index of Diversification|||||0.591***|0.603***|0.550***|0.630***|0.619***|0.668***|
||||||(0.048)|(0.051)|(0.140)|(0.050)|(0.053)|(0.143)|
|Number of unpaid ag family<br>|||||0.046**|0.035*|0.113*|0.045**|0.031|0.154**|
|workers|||||(0.018)|(0.019)|(0.065)|(0.020)|(0.020)|(0.067)|
|Incidence of crop subsidy|||||-0.718***|-0.713***|-0.444|-0.835***|-0.790***|-0.595*|
||||||(0.095)|(0.101)|(0.312)|(0.097)|(0.103)|(0.324)|
|Log of crop subsidy|||||0.061***|0.063***|0.015|0.077***|0.074***|0.038|
||||||(0.011)|(0.012)|(0.039)|(0.011)|(0.012)|(0.041)|
|Tractor|||||0.088***|0.075***|0.138|0.071**|0.054*|0.114|
||||||(0.025)|(0.025)|(0.105)|(0.029)|(0.030)|(0.097)|
|Access to finance|||||0.127***|0.132***|0.068|0.119***|0.122***|0.064|
||||||(0.029)|(0.033)|(0.064)|(0.027)|(0.030)|(0.064)|

16
Table 4: **Effects of Inpres Schools on Relative Mobility in Primary Completion of Children: Relative Mobility is Measured by Intergenerational Marginal Association (IGMA)**

|||**S**|**ons**||||**Dau**|**ghters**||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)||(4)|(5)|(6)|(7)|(8)|
||**Highest**|**Intensity(=1)**|**Mean Inte**|**ns**|**ity(=0.215)**|**Highest**|**Intensity(=1)**|**Mean In**|**tensity(=0.215)**|
|||**Normalized**||**N**|**ormalized**||**Normalized**||**Normalized**|
|∆_IGMA_0|-0.038<br>(0.011)|-157.95%|-0.008<br>(0.002)||-33.96%|-0.017<br>(0.010)|-70.66%|-0.004<br>(0.002)|-15.19%|
|∆_IGMA_6|-0.013<br>(0.005)|-54.04%|-0.003<br>-0.00108||-11.62%|-0.004<br>(0.004)|-16.63%|-0.001<br>(0.001)|-3.57%|
|∆_IGMA_9|-0.000<br>(0.003)|0.00%|0<br>(0.001)||0.00%|0.002<br>(0.003)|8.31%|0.000<br>(0.001)|1.79%|
|∆_IGMA_12|0.013<br>(0.004)|54.04%|0.003<br>(0.001)||11.62%|0.009<br>(0.005)|37.41%|0.002<br>(0.001)|8.04%|
|∆_IGMA_16|0.030<br>(0.008)|124.70%|0.006<br>(0.002)||26.81%|0.018<br>(0.010)|74.82%|0.004<br>(0.002)|16.09%|

Notes: Robust standard errors are in parentheses, clustered at the district of birth(*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). IGMA is the slope of Conditional Expectation Function (CEF). ∆ _IGMAy_ = _θ_ 7 + 2 _θ_ 11 _Eiy_<sup>_p× Inpres_, where</sup><sup>_E_</sup> _iy_<sup>_p_represents father’s years of</sup> schooling for _y_ = 0 _,_ 6 _,_ 9 _,_ 12 _,_ 16. The IGMA values are based on coefficients reported in Table 3. The variable Inpres intensity measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. The Normalized IGMA is the IGMA value relative to IGMA of children for father’s with average years of education (5.57 years), comparison cohort (born between 1957 to 1962), and zero Inpres intensity for the full sample (combined sample of sons and daughters). Normalized IGMA values are reported in percentage. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

41
# **1 Introduction**

Rural-to-urban migration and non-farm income play an increasingly important role in sustainable development and poverty reduction in rural areas (FAO, 1998; Morrison et al., 2008; OECD, 2005; The World Bank, 2007). It can be considered as an important way to increase overall rural economic activity and employment. In many developing countries, non-farm activity often accounts for as much as 50% of rural employment and a similar percentage share of household income (Lanjouw, 1999a). Average non-farm income share of the total is about 42% in Africa, 40% in Latin America and 32% in Asia (The World Bank, 2000). In China, the rural-tourban migration and the development of the rural non-farm sector strongly modified rural household income structure. Non-farm activities gradually became an important source for rural household incomes. In 2004, the share of non-farm income reached 46% of the total rural income (National Statistics Bureau of China 2005).

Shortage of arable land is a binding constraint of agricultural productivity in China. Per capita farm income has always been low due to the limited marginal labor productivity. Conflicts between shortage of land and surplus of labor are more serious in poor areas. Peasants have a strong incentive to leave land for better job opportunities. The economic reforms, in particular the implementation of the Household Responsibility System (HRS), in the late 1970s not only stimulated the incentive of the farmers and contributed to the sharp increase of the agricultural productivities, but also legitimized the rural redundant labor to leave land ( _litu_ ) and countryside ( _lixiang_ ). Since then, rural non-farm sectors and urban sectors have played an increasingly important role in absorbing surplus agricultural labor, enhancing rural income and reducing rural poverty. In only 21 years (1980 - 2001), the incidence of rural poverty fell from 76% to 13%, and rural income Gini increased from 0.25 to 0.37.<sup>1</sup> Whether the decline in poverty was principally due to farm income growth or due to non-farm income growth , and whether the rising share of non-farm income in total rural household income was the leading cause of the sharp increase in rural inequality, have been key issues of debate.

Some studies suggest that the rise in rural inequality in China since the beginning of the economic reforms has been largely due to the increasing share of non-farm income in total

> 1 See Ravallion and Chen (2004); Ravallion (2003).

3
Subject to (1) CC<sup>dd</sup> ≤WW All of the inputs into this decision problem may differ by individual. Differences in risk preferences and in how individuals trade off income, health, and other amenities will result in differences in migration intentions even when facing the same income-earning opportunities, costs, and health conditions. But individuals will also differ in the incomes they can earn at home and abroad, and potentially also in the costs they face of migrating, or in other parameters.

Per-capita GDP in 2019 was $778 in The Gambia, about half that in Senegal ($1,447), and 1/42 the level in Italy ($33,228).<sup>5</sup> While GDP differences overstate the income gains migrants can achieve, reported earnings of Gambian migrants in Italy of 2-3 euros per hour would still enable migrants to earn as much in a week as a construction worker earns in Dakar in a month (6,0009,000 Gambian dalasi<sup>6</sup> ), and about as much in a day in Italy as mean monthly income earned by youth in our sample villages (1,044 Gambian dalasi).<sup>7</sup> While earnings were lower in Senegal than in Europe, the cost and ease of migration were also much less. A bus ticket from our villages to Dakar costs approximately 1,200 GMB ($24), whereas the cost of traveling the backway to Europe is typically in the $2,000 to $5,000 range. Migration via the backway is also risky, with the chance of getting attacked, robbed, and death during the journey, and of being deported. In our model these could be captured as reducing the likelihood of finding employment abroad, _p_<sup>_E_</sup> _,_ and in lowering the health conditions associated with migration to Europe, _H_<sup>_E_</sup> _._ Prior to COVID-19, this calculation still resulted in many youth having the intention to migrate, as our data will show in the next section. We now discuss how COVID-19 may affect this decision.

# **2.2 How might COVID-19 change these intentions?**

There are several channels through which the COVID-19 pandemic can influence these migration intentions. First, the economic consequences of the pandemic in The Gambia are likely to lower the income-earning opportunities at home, while lower earnings for household members who are

> 5 Source: World Development Indicators, <u>https://data.worldbank.org/indicator/NY.GDP.PCAP.CD</u> [accessed 20 January, 2021].

> 6 At the time of our baseline survey in 2019, the exchange rate was 56 Gambian Dalasi (GMD) = 1 Euro, and 50 GMD = 1 USD.

> 7 Differences in the cost-of-living across locations will reduce these gaps, but the relative rankings will still hold, and _I_ can then be interpreted as the income net of living costs.

6
There is no straightforward way of signing the expression (9).) Our empirical results suggest that the quadratic and cross-derivative terms are zero. If in addition _T"_ = 0, we have **_dp p 8w_** which turns out to be positive for the range of parameter values considered in this paper. Hence, the efficiency wage curve is upward-sloping.

# _e) An Extension: Vacancies and Matching_

The efficiency wage model developed so far shares one important feature with traditional search models,"<sup>2</sup> namely an endogenously determined labor turnover rate. However, it also differs from those models in important ways. First, in the efficiency wage model firms set wages in contrast to the Nash bargaining solution usually deployed by the search literature. In a sense, the efficiency wage model is the limit case of the bargaining model in which firms have all the bargaining power and are therefore in a position to make a take-it-or-leave-it offer to workers. Second, the search literature deploys a matching (hiring) function relating the number of hires, _hl,_ to the number of vacancies, v, and the number of unemployed I - 1. This matching function describes the efficiency of the job reallocation process. In this section, we briefly discuss the possibility of incorporating a matching function into the efficiency wage model developed here.

For the sake of concreteness, consider the case in which the matching function is of the Cobb-Douglas type, h _I_ = va (1 - 1)1 **_-a_** where we assumed for simplicity that all unemployed (selfemployed) workers are searching for a formal-sectorjob.'<sup>3</sup> Suppose further that the only adjustment cost is the cost of posting a vacancy and that the unit cost of a vacancy is a constant, c. Hence, the total cost of changing the employment level is T = _c_ v. Eliminating the number of vacancies, v, yields a total adjustment cost _T = c (hl)l1a_ ( _1_ - _1)(1 -1/a)_ and a cost of adjustment per employee of "The sufficiency conditions for the firm's optimization problems do not guarantee an upwardsloping curve.

1<sup>2</sup> See Blanchard and Katz (1997) and Rogerson (1997) for recent surveys.

"<sup>3</sup> Recall that in our notation h is the hiring rate and _hI_ is total hires of one firm. For simplicity, we have set the number of firms to one: _N_ = 1.

11