rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Working conditions of workers**

||Hazardous job|Air conditioning|Health impact|
|---|---|---|---|
||(1)|(2)|(3)|
|Year 1964|0.014|0.000|0.129|
||(0.043)|(0.035)|(0.187)|
|Year 1965|-0.020|-0.028|-0.090|
||(0.026)|(0.027)|(0.131)|
|Year 1966|0.002|-0.000|0.157|
||(0.026)|(0.033)|(0.171)|
|Year 1967|0.011|-0.031|0.069|
||(0.032)|(0.034)|(0.121)|
|Year 1969 (Year of change)|-0.019|-0.066<sup>*</sup>|0.086|
||(0.030)|(0.032)|(0.119)|
|Year 1970|-0.038|-0.083<sup>**</sup>|0.200<sup>*</sup>|
||(0.036)|(0.038)|(0.097)|
|Year 1971|-0.058<sup>*</sup>|-0.037<sup>*</sup>|0.069|
||(0.030)|(0.019)|(0.115)|
|Year 1972|-0.056<sup>*</sup>|-0.084<sup>**</sup>|0.275<sup>**</sup>|
||(0.028)|(0.030)|(0.122)|
|Year 1973|-0.103<sup>***</sup>|-0.079<sup>***</sup>|0.085|
||(0.032)|(0.024)|(0.118)|
|Year 1974|-0.098<sup>***</sup>|-0.054<sup>*</sup>|0.324<sup>**</sup>|
||(0.029)|(0.030)|(0.127)|
|Year 1975|-0.053|-0.077<sup>***</sup>|0.081|
||(0.032)|(0.024)|(0.109)|
|Year 1976|-0.050|-0.032|0.168|
||(0.037)|(0.026)|(0.120)|
|Year 1977|-0.055<sup>*</sup>|-0.057<sup>*</sup>|0.255<sup>*</sup>|
||(0.031)|(0.031)|(0.130)|
|Year 1978|-0.034|-0.077<sup>**</sup>|0.265<sup>*</sup>|
||(0.022)|(0.033)|(0.135)|
|Year 1979|-0.036|-0.085<sup>***</sup>|0.382<sup>***</sup>|
||(0.031)|(0.026)|(0.112)|
|Year 1980|<br>-0.066<sup>**</sup>|<br>-0.074<sup>**</sup>|<br>0.188|
||(0.024)|(0.026)|(0.120)|
|Year 1981|<br>-0.070<sup>**</sup>|<br>-0.119<sup>***</sup>|<br>0.364<sup>***</sup>|
||(0.027)|(0.030)|(0.102)|
|_N_|10538|10538|10538|
|adj._R_<sup>2</sup>|0.125|0.087|0.172|

Notes: All regressions add birth governorate fixed-effects, household size and gender. Standard errors are clustered at the birth governorate level. *** p<0.01, ** p<0.05, * p<0.1.

# **4.2 Channels**

Education is critical for preparing people to join the labor market and providing them with the skills they need to engage in lifelong learning experiences. Vast research literature provides evidence of the value of investing in education to develop human capital and its contribution to increasing individual earnings (Ashenfelter and Krueger, 1994; Card, 2001; Duflo, 2001; Harmon, 2003; Oreopoulos, 2006; Fiszbein et al. 2007; Clark and Martorell, 2014; Peet et al. 2015; <mark>Silliman and Virtanen, 2022</mark> ). Besides a robust literature examining the impact of schooling on earnings, a large body of literature documents the relationship between educational attainment and labor 7
and overshot, producing a prohibitive<sup>real effective cost of debt in</sup> 1985-1986.

With these high rates of interest,<sup>may lie the solution to the</sup> puzzle posed at the beginning of the<sup>section; namely, where was the</sup> squeeze if credit in agriculture grew by 33<sup>percent between 1984 and</sup> 1987? The explanation we offer is that the additional<sup>credit</sup><sup>**was**</sup> needed to maintain the growing liabilities<sup>of the productive secVor.</sup> As interest accumulated, and the principal<sup>was not repaid, growth of</sup> debt showed up in banks' balance sheets<sup>as additional credit, requiring</sup> an increase of monetary reserves. Loans<sup>to replenish reserves would</sup> come from the Bank of Israel but it supplied<sup>them only reluctantly--</sup> .further-increasing the pressure for higher<sup>interest rates.</sup>

# 6. Provision for Bad Debts"

Here is another facet of inflationary<sup>distortions: provisions for</sup> bad debts are usually around X percent of<sup>total credit balances. In</sup> Israel in the galloping inflation years<sup>of 1975-83, writeoffs for</sup> questionable debts in the banking system<sup>were an insignificant amount,</sup> much lower than the normal 1 percent va4ue. Inflation<sup>and the ample</sup> supply of credit it entailed bestowed a feeling<sup>of economic security</sup> and success even where the underlying structure<sup>was shaky.</sup> Writeoffs increased in 1981-84 in reaction<sup>to difficulties in the</sup> diamond industry, but even then they were<sup>only 0.1-0.3 percent of</sup> credit balances. Substantial provisions for<sup>bad debts appeared in the</sup> banks only after 1985, when many enterprises,<sup>not only in agriculture,</sup> suddenly faced sever financial difficulties.

Bankers were familiar with over capacity<sup>in agriculture, with debts</sup> accumulated to levels that some of the farmers<sup>and some of the moshavim</sup> could not be expected to repay, and with the<sup>lax financial discipline</sup> in the cooperatives--at both the village<sup>and the regional level.</sup> 'Evidently, as revealed by their behavior,<sup>if they did not provide for</sup> bad debts in agriculture, they were fooled<sup>by the appearance of</sup> 9The data in this subsection are frum the<sup>publications of the</sup> Supervisor of Banks.

35
# **D VenRePS Survey**

The survey was administered in two steps to ensure targeting of the forced migrants who fulfilled the characteristics laid out in the sampling frame, and to define (with respondents) the best time to call and administer the questionnaire.

First, forced migrants received a text that introduced the survey team from IPA, described the broad objectives of the research project and the monetary incentive for participation, and mentioned that the team would call them in the next few days to conduct the survey. The text also included a link to the project’s website that had more detailed information.

A few days later, the survey was administered by phone.<sup>16</sup> First, a short screening module was administered to verify the respondent’s eligibility and to obtain informed consent. RAMV migrants were asked if they or other family members had registered in the RAMV census and whether they had PEP. Non-RAMV migrants were asked for their migration date because the targeted migrants had arrived in Colombia between January 2017 and December 2018, were older than 18 years, did not have a different PEP, and did not have a valid passport.<sup>17</sup> Following the screening, the survey was administered with a focus on the family head, partner, or another adult member of the family. The original questionnaire was adjusted and trimmed because of the challenges posed by phone surveys. We decided not to collect data for the entire household roster, and collected the labor module only for the respondent and one other member of the nuclear family.<sup>18</sup> In total, we collected information from 3,455 Venezuelan families living in Colombia. This sample included families with some members who were Colombian, either from birth or

> 16The call was rescheduled when the respondent was not available. When ineligible respondents were called, the team included them in a raffle for 50,000 COP (approximately $USD 18).

> 17As discussed above, PEP was also awarded in previous waves to forced migrants who entered Colombia using a passport and therefore had regular migratory status. By asking if respondents had a Venezuelan passport, the team ensured the exclusion of other PEP holders who were typically wealthier.

> 18The nuclear family includes the household head, partner, children, parents, parents-in-law, daughters-inlaw, and sons-in-law.

57
predicts an inverted U relationship between competition and innovation, in the sense that, innovation initially increases with an increase in product market competition and then declines. The inverted U relationship is particularly applicable to core innovation in countries and industries that are at the forefront of the technology frontier.

In our sample of emerging markets, where innovation is mainly through adaptation or imitation, we examine if there are non-linearities in the relation between competition and innovation by interacting with firm characteristics. We are particularly interested in the interaction between firm organization and competition on innovation. While we used seven indicators of competition in Table 6, in this section, we restrict ourselves to the variable which is available for the largest sample of firms - Percentage of establishment sales that are sold domestically.

In Table 10, we present interaction regressions with the full set of control variables. The aggregate indicators in Table 10 show that while there is an inverse relationship between the percentage of establishment sales sold domestically and the extent of innovation, this is less likely when the firm is a corporation and more likely when the firm has 50% or more state ownership or is a small/medium sized firm. When we interact percentage of sales sold domestically with other firm characteristics such as age and foreign ownership, we find that none of these firm characteristics have a significant influence on the extent of innovation of exporters versus non-exporters as shown by the aggregate indicators The underlying indicators reveal some differences however. Exporters who are older are more likely to sign joint ventures with foreign partners and those that are foreign owned are more likely to sign joint ventures as well as open new plants.

While the above results show that competition has a significant impact on innovation and varies according to the types of firms, we recognize that inclusion of industry and country dummies may not be sufficient to remove all spurious correlation between competition and the innovation indicators. In the absence of time series data and information on policy reforms across our sample of countries that might have allowed us to correct for the potential endogeneity in the competition measure, we leave the identification issues in this area for future work.

25
**Figure 4 World Shares of GDP, Trade, and International Financial Integration**

# **China**

<!-- Start of picture text --> 6%<br>Trade<br>5%<br>GDP<br>4%<br>3%<br>2%<br>IFI<br>1%<br>0%<br>India<br>2.0%<br>1.5% GDP<br>1.0%<br>Trade<br>0.5%<br>IFI<br>0.0%<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> The GDP shares are calculated as the country's GDP divided by the sum of GDP for all the countries in the dataset. The trade shares are calculated as the country's exports plus imports divided by the sum of exports and imports of all the countries in the dataset. The international financial integration shares are calculated as the sum of the country's foreign assets plus foreign liabilities divided by the sum of these for all the countries in the dataset. Source: Authors' calculations drawing on the dataset constructed by Lane and Milsei-Ferretti (2006).
41 _Table 6. Proceeds and Investment from Privatization Transactions_ (In US$ millions)

|Year<br>_____<br>|**Sale of**<br>.Shares<br>|**Concession**<br>|<br>Transfer<br>C#sio|Other<br>Revenue|TOTAL<br>PROCEED<br>|**Projected**<br>S Investment|
|---|---|---|---|---|---|---|
|1991|$2.59||_|**-**|$2.59|**-**|
|1992|$207.53|<br>**-**||$1.39|$208.92|<br>$706.00|
|1993|$316.70|<br>$20.70|**-**|$6.49|$323.89|<br>$589.34|
|1994|$2,648.57|<br>**-**|$2.21|$2.52|$2,653.30|<br>$3,137.55|
|1995|$1,089.01|<br>$48.76|$1.18|$6.45|$1,145.40|<br>$216.07|
|1996|$2,291.81|<br>$344.20|$1.00|$5.79|$2,642.80|<br>$2,852.20|
|1997*|$67.13|<br>**-**|**-**|$1.32|$68.45|$218.19|
|TOTAL|$6,623.34|<br>$413.66|$4.39|$23.96|$7,065.35|<br>$7,719.35|

SOURCE: COPRI After the elections the concession became less politically desirable because of growing public disillusionment with privatization. Although a new SEDAPAL privatization team was appointed in December 1995, government's interest in the concession was much weaker after the elections (field interviews). As table 4 shows, support for privatization in general peaked among all income groups in 1993 and fell off gradually, then sharply in 1997. The decline in support stemmed from a perception that the benefits were concentrated in a small group of foreign investors and wealthy Peruvians, plus sharp increases in the prices of services, such as telephone and electricity rates. The SEDAPAL concession would also have combined large rate increases and higher returns to investors in the short run, with most consumer benefits coming in the medium term as major investments are completed. Support for privatization of SEDAPAL was never as strong as for other SOEs and fell sharply over time. In 1989, 47
<!-- Start of picture text --> = e<br>(co) ° ° e<br>°<br>°<br>e e e<br>= e 8 3 e<br>ae e e 3 H e<br>it ® e<br>3 ee +48<br>N 8 e<br>‘ e<br>i 1<br>oe+4 e e e ee e e<br>2008 2010 2012 2014 2016<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> oS<br>e<br>io}<br>e<br>s<br>= e e<br>ae e e 8 e<br>wa ° 8 8<br>° e e e 3° $ bd° %<br>e e i<br>- f 8<br>e<br>o48i |e e e:<br>2008 2010 2012 2014 2016<br>Year<br><!-- End of picture text -->
|Indigenous and CommunityBiodiversityConservation Project(GEF|)Robert Ragland Davis|
|---|---|
|Indigenous and CommunityBiodiversityConservation Project(GEF|)Robert Ragland Davis|
|Indigenous and CommunityBiodiversityConservation Project(GEF|)Robert Ragland Davis|
|Indigenous and CommunityBiodiversityConservation Project(GEF|)Robert Ragland Davis|
|Indigenous and CommunityBiodiversityConservation Project(GEF|)Robert Ragland Davis|
|Indigenous and CommunityBiodiversityConservation Project(GEF|)Robert Ragland Davis|
|MILLENNIUM SCIENCE INITIATIVE PROJECT|Lauritz B. Holm-Nielsen|
|MILLENNIUM SCIENCE INITIATIVE PROJECT|Lauritz B. Holm-Nielsen|
|MILLENNIUM SCIENCE INITIATIVE PROJECT|Lauritz B. Holm-Nielsen|
|MILLENNIUM SCIENCE INITIATIVE PROJECT|Lauritz B. Holm-Nielsen|
|MILLENNIUM SCIENCE INITIATIVE PROJECT|Lauritz B. Holm-Nielsen|
|MILLENNIUM SCIENCE INITIATIVE PROJECT|Lauritz B. Holm-Nielsen|
|MILLENNIUM SCIENCE INITIATIVE PROJECT|Lauritz B. Holm-Nielsen|
|National System of Protected Areas|Gabriela Arcos|
|National System of Protected Areas|Gabriela Arcos|
|National System of Protected Areas|Gabriela Arcos|
|National System of Protected Areas|Gabriela Arcos|
|National System of Protected Areas|Gabriela Arcos|
|National System of Protected Areas|Gabriela Arcos|
|National System of Protected Areas|Gabriela Arcos|
|National System of Protected Areas|Gabriela Arcos|
|National System of Protected Areas|Gabriela Arcos|
|Public Sector Reform Adjustment Loan(02)Project|Paolo B. Zacchia|
|Public Sector Reform Adjustment Loan(02)Project|Paolo B. Zacchia|
|Decentralization Adjustment Loan Project|Marcelo Giugale|
|Decentralization Adjustment Loan Project|Marcelo Giugale|
|Gender EquityProject: Generosidad|Maria E. Castro-Munoz|
|Gender EquityProject: Generosidad|Maria E. Castro-Munoz|
|Gender EquityProject: Generosidad|Maria E. Castro-Munoz|
|Gender EquityProject: Generosidad|Maria E. Castro-Munoz|
|Gender EquityProject: Generosidad|Maria E. Castro-Munoz|
|Gender EquityProject: Generosidad|Maria E. Castro-Munoz|
|Gender EquityProject: Generosidad|Maria E. Castro-Munoz|
|Gender EquityProject: Generosidad|Maria E. Castro-Munoz|
|Gas Development and Utilization Technical Assistance Project|Salahuddin Khwaja|
|Gas Development and Utilization Technical Assistance Project|Salahuddin Khwaja|
|Gas Development and Utilization Technical Assistance Project|Salahuddin Khwaja|
|Gas Development and Utilization Technical Assistance Project|Salahuddin Khwaja|
|Gas Development and Utilization Technical Assistance Project|Salahuddin Khwaja|
|Gas Development and Utilization Technical Assistance Project|Salahuddin Khwaja|
|Gas Development and Utilization Technical Assistance Project|Salahuddin Khwaja|
|Gas Development and Utilization Technical Assistance Project|Salahuddin Khwaja|
|Gas Development and Utilization Technical Assistance Project|Salahuddin Khwaja|
|ZhejiangUrban Environment Project|GuangmingYan|
|ZhejiangUrban Environment Project|GuangmingYan|
|ZhejiangUrban Environment Project|GuangmingYan|
|ZhejiangUrban Environment Project|GuangmingYan|
# Should I Stay or Should I Go? Internal Migration

# and Household Welfare in Ghana

Vasco Molini<sup>1</sup> Dan Pavelesku<sup>2</sup> Marco Ranzani<sup>3</sup> JEL Classification: E21, O55, R23.

Keywords: Internal migration, consumption, Ghana.

> 1 Poverty and Equity Global Practice, The World Bank, 20433 Washington D.C. - USA. E-mail: vmolini@worldbank.org. 2 Poverty and Equity Global Practice, The World Bank, 20433 Washington D.C. - USA. E-mail: dpavelesku@worldbank.org. 3 Poverty and Equity Global Practice, The World Bank, 20433 Washington D.C. - USA. E-mail: mranzani@worldbank.org.

> The authors acknowledge financial support from the World Bank. We thank David McKenzie, Pierella Paci, and Manjula M. Luthria for comments on an earlier version of the manuscript.
Am TICAL 9W OF Tulllrt CT S1wIIS 0 lNEEW EIJT VRtUALES GNrH RTE --------w **-** -----------------------------------------------------CIWRA - CALaIATION REPTAL SECTOAL OR wiMtAs PET ISTICS Of P31W AMD _AD_ ECONUCTIUII sOCIoPURPW TYPE AD S1M ANLYSIS MOIUUTAL POICY PRDUCTIO POLITICAL OF SWr tfCE OMltIB PERIOD UIAES VAILES Mol FACTS VRMILES _tS ixpct of to OP 20 csitries 10 Vas Tamtion rate Poapittion 0.78 **(** R-aqr 4 0.88 (190) du cn (Wrld aC" pt1 (197X-9) -v th ram of 1_ O o rs), n.s. Sib ple pr t nad of exclicnr 0PEC Irctnt regraealian prodictivity nd ccmtriee PoUUY plus sAtiple with Nbrkettru + regresaion 10 high-tax costlasf **-v-** 10 lowta cantries 10 low inom castria **-V.** 10 hlsh-incoI . ... ........... .................. .. ..... .... ..... ........ ..... ..... .................................................. NMAiN Iqpct of NP aid 30 Cs Av. anal Export Export growth Foreign arou Exasnoa first (1) _OpWt_ orP nwt itcluirHg rate in tobility in rats rate the ipmct of ir tobility an of sparta 18 Latin An. 22 12. 10 coatnt WI (current a/c apart sawing **rate** wd (scic. not I Africn yaMc n.s. deficit) inst bilIty on arcuth **rat** pcff lobd 8 Asian 1954-75 or + prodictivity 1954-6 Inntobillty of Dom0etic graith (function 1%6-75 _apart_ pricea ,wrl **rate** of productlin) ad volum + than the gltbal * cr -or ns. Lobar force Iqats, inla. (different grcwth rate those s fartad muareonmts) + thrcush the avIg _rate (bV_ triple least )qam ............. ....................... .. .. .. ...... ....... .............................................................
duction for most of empirical economists. In short, this commonly and frequently used index is a composite score of about 15 (may be different each year) polls of experts, expressing their opinion about the level of corruption in a given country. This measure may obviously be subject to certain perceptional and ideological biases, image of a country in mass media, etc. The advantage of it, however, is it’s availability for a large sample of countries (174 in 2005, 169 in 2007), and over a long period of time (annually from 2001 to 2007, also for 1996, 1980-1985, 1988-1992).

The state of corruption in the world based on our 5 measurements is shown in Table 3. As expected, developed countries are the least corrupted based on any measure, while African and former Soviet Union countries are among the most corrupted. There is big variation in level of corruption in the world. For example, bribe burden in Singapore is virtually 0, while in Senegal people pay up to 14% of GDP in bribes. In Spain firms spend 0.1% of their annual sales on unofficial payments to governmental officials, while in Paraguay this figure reaches 10%. In Kyrgyzia about 85% contacts of the firms with governmental officials end up in paying bribes, while in Namibia it is only 1.2%.

Table 3 also shows the distribution of the countries by income and region (see World maps) for each measure. Comparing the distributions with the one for corruption perception, which is available for almost all countries and can be considered representative, one can see that GCB sample (variables paid bribe and bribe burden) is somewhat skewed towards high and upper middle income countries, with most representation in Europe, CIS, Latin America and East Asia. At the same time, the WBES sample (variables paid bribe and bribe burden) is somewhat skewed to lower middle and low income countries, mostly in Africa, CIS, Eastern Europe and South America. Analysis of all 4 corruption variables would therefore provide the most objective picture.

# 5 Econometric specifications

Our goal is to identify empirical effect of decentralization on corruption, where the population of interest is the world’s countries. Our general approach is to regress: corruption = β0 + β1 ∗ decentralization + other controls. (4) β1 is coefficient (coefficients if we use several variables for decentralization) we are interested in, provided the other factors which may affect the relationship between decentralization and corruption are properly controlled for. To cover as many appearances of corruption in a society as possible as well as to make our research robust to small sample bias and sample selection we use 5 different variables to measure corruption. They are described in detail in Section 7. The distribution of these variables drives several different econometric specifications of the equation 4.

In the case of paid bribe variable we have binary responses of individuals from different countries, 1 meaning that they paid bribe in the last 12 months, 0 meaning that they did not. Therefore, we are estimating the probability of an individual i 17
Uganda) and even higher (Botswana and Niger). From the utility’s perspective, underpricing can amount to losses valued as much (and even more than) 100 percent of the utility’s turnover (see annex 14 for details).

Because electricity subsidies are typically justified by the need to make services affordable to lowincome households, a key question is whether subsidies reach such households. Results across a wide range of African countries show that the share of subsidies going to the poor is less than half their share of the population, indicating a very pro-rich distribution (figure 21). This result simply reflects the fact that connections to power are already highly skewed toward more affluent households. In SSA as a whole, access to power among the bottom three quintiles of the budget distribution is no more than 12 percent on average compared to 72 percent in the top budget quintile.

To put these results in perspective, one must compare them with the aims achieved by other forms of social policy. Estimates for Cameroon, Gabon, and Guinea indicate that expenditures on primary education and basic health care reach the poor better than do power and water subsidies (Wodon 2007) Figure 21. Extent to which electricity reaches the poor <!-- Start of picture text --> Nigeria<br>Gabon<br>Congo<br>Côte d'ivoire<br>Cape Verde<br>Togo<br>Sao Tome<br>Senegal<br>Cameroon<br>Mozambique<br>Ghana<br>CAR<br>Guinea<br>Burundi<br>Burkina<br>Chad<br>Malawi<br>Uganda<br>Rwanda<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9<br>Measure of distributional incidence<br><!-- End of picture text --> _Sources_ : Banerjee and others 2008; Wodon 2007. _Note_ : A measure of distributional incidence captures the share of subsidies received by the poor, divided by the proportion of the population in poverty. A value greater than 1 implies that the subsidy distribution is progressive (pro-poor), because the share of benefits allocated to the poor is larger than their share of the total population. A value less than 1 implies that the distribution is regressive (pro-rich).

A key message is that power subsidies will always be highly regressive as long as access is highly regressive. The distributional score presented above (figure 21) can be decomposed into access and subsidy design factors (figure 22). The access factor is related to the availability of electricity in the area where the household lives and to the household’s choice to connect to the network if service is available. The subsidy design factor relates to who is targeted to receive the subsidies, rates of subsidization, and consumption levels. As for the overall distributional score, values higher (lower) than one for access and subsidy design factors are indications that those factors are progressive (regressive).

32