rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 18<br>140<br>16<br>120<br>14<br>12 100<br>10 80<br>8<br>60<br>6<br>40<br>4<br>20<br>2<br>0 0<br>0 0.05 0.1 0.15 0 0.1 0.2 0.3 0.4<br>Strength of On Board Security Strength of On Board Security<br>(a) 2006 (b) 2009<br>140<br>100<br>120<br>90<br>100 80<br>80 70<br>60 60<br>50<br>40<br>40<br>20<br>30<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.65 0.7 0.75 0.8 0.85 0.9<br>Strength of On Board Security Strength of On Board Security<br>(c) 2010 (d) 2011<br>125<br>100<br>25<br>150<br>7550<br>200<br>225<br>125<br>175<br>125<br>125<br>200<br>250<br>150<br>300<br>225<br>350<br>200<br>150<br>250<br>175<br>100<br>7550<br>125<br>100<br>175<br>25<br>75<br>150<br>50<br>250<br>150<br>225<br>150<br>125<br>100<br>200<br>200<br>225<br>75<br>175<br>225<br>175<br>50<br>150<br>25<br>200 100<br>30<br>50<br>20<br>75<br>75<br>125<br>25<br>175<br>50<br>10<br>175<br>100<br>25<br>20<br>175<br>125<br>150<br>10<br>5<br>5<br>30<br>Number of Navy Patrols Number of Navy Patrols<br>Number of Navy Patrols Number of Navy Patrols<br><!-- End of picture text --> Figure 16: Robustness check: iso-crime curves for all vessels with doubled costs for private security Note: the figures plot the combination of navy patrols and the strength of private security in year _t_ that achieves a certain number of attacks on all vessels in year _t_ + 1. The black line in each graph indicates the optimal combination of navy patrols and private teams that minimizes the total costs for each level of attacks. All the other parameters are set to their benchmark value.

49
Box 3.1 Computing and Plotting a TIP Curve in EViews INCLUDE LYCDF LOAD %0 SMPL @ALL CALL LYCDF({%1}(1), {%1)(2), {%1}(3)) SERIES GAP={%2}-YH SERIES NGAP=GAP/{%2} SERIES DUMGP=O SMPL IF NGAP>O DUMGP=1 SMPL @ALL SCALAR POP=@SUM(POPH) SERIES PH=F_HAT SERIES WNGAP=(POPH*NGAP)/POP SERIES TGAPW=DUMGP*(WNGAP) SMPL @FIRST @FIRST SERIES JPW= TGAPW SMPL @FIRST+1 @LAST JPW=JPW(-1) +TGAPW SMPL @ALL GROUPJC PH JPW FREEZE(TIPC) JC.XY 'END OF PROGRAM Jenkins and Lambert(1997) explain that if all absolute<sup>povertygapswereequal</sup> among the poor, the concave segment of the TIP curve<sup>would becomea straight line with</sup> slope equal z, the poverty line. In addition, there<sup>aremaximumandminimum poverty</sup> situations that set boundaries to the TIP curve in<sup>a manneranalogousto a Lorenzcurve.</sup> The TIP curve would coincide with the horizontal axis if<sup>there were nopoorinthe</sup> population under consideration. If all incomes (or<sup>expenditures)areequalto zerosothat</sup> the relative poverty gaps are all equal to one, the TIP<sup>curvewould bea straightlinefrom</sup> the origin with a vertical intercept equal to 1 at **p=l.** Table 3.4: Poverty in Guinea by Area of Residence

|Poverty|Conakry|Other Urban|Rural|Guinea|
|---|---|---|---|---|
|Measure|||||
|Po|0.07|0.24|0.53|0.40|
|PI|0.01|0.07|0.18|0.13|
|P2|0.00|0.03|0.08|0.06|
|Source:|Computed||||

The program in Box 3.1 was used to produce figure<sup>3.1.It requiresthe user to fillin</sup> the values of three arguments. The WORKFILE containing the relevant data, a<sup>GROUP</sup> containing three series (total expenditure, the household<sup>size and the householdweight),and</sup> a SCALAR equal to the poverty line. The program can<sup>beeasilyeditedto includeformulae</sup> 25
<mark>Bourguignon, Francois, Francisco Ferreira and Phillippe Leite. 2003. “Conditional Cash Transfers, Schooling, and Child Labor: Micro-simulating Brazil's Bolsa Escola Program.”</mark> _<mark>The World Bank Economic Review</mark>_ <mark>, 17(2): 229-254.</mark> <mark>Chaudhury, Nazmul, Jed Friedman and Junko Onishi. 2013. “Philippines Conditional Cash Transfer Program Impact Evaluation 2012.” World Bank.</mark> <mark>Contreras, Diana and Pushkar Maitra. 2013. “Health Spillover Effects of a Conditional Cash Transfer Program.” Working Paper No. 44-13. Monash University, Department of Economics.</mark> <mark>Crost, Benjamin, Joseph Felter and Patrick Johnston. 2014. “Aid Under Fire: Development Projects and Civil Conflict.”</mark> _<mark>The American Economic Review</mark>_ <mark>104(6): 1833-1856.</mark> Cunha, Jesse, Giacomo De Giorgi and Seema Jayachandran. 2015. “The Price Effects of Cash Versus In-kind Transfers.” Staff Reports <mark>735, Federal Reserve Bank of New York.</mark> Cunha, Jesse, Giacomo De Giorgi and Seema Jayachandran. 2019. “The Price Effects of Cash Versus In-kind Transfers.” _Review of Economic Studies_ 86(1): 240-281.

<mark>Currie, Janet, and Tom Vogl. 2013. “Early-life Health and Adult Circumstance in Developing Countries.”</mark> _<mark>Annual Review of Economics</mark>_ <mark>5(1): 1-36.</mark> <mark>de Hoop, Jacobus, Jed Friedman, Eeshani Kandpal and Furio Rosati. 2019. “</mark> Child Schooling and Child Work in the Presence of a Partial Education Subsidy.” _Journal of Human Resources_ 54(2): 503-551.

<mark>Deaton, Angus. 1988. “Quality, Quantity, and Spatial Variation of Price.”</mark> _<mark>The American Economic Review</mark>_ <mark>: 418-430.</mark> <mark>Del Boca, Daniela, Chiara Pronzato, and Giuseppe Sorrenti. 2018. “The Impact of a Conditional Cash Transfer Program on Households' Well-Being.” W</mark> orking Papers <mark>2018-093, Human Capital and Economic Opportunity Working Group.</mark> <mark>Denney, Liya, Imelda Angeles-Agdeppa, Mario Capanzana, Marvin Toledo, Juliana Donohue and Alicia Carriquiry. 2018. “Nutrient Intakes and Food Sources of Filipino Infants, Toddlers and Young Children are Inadequate: Findings from the National Nutrition Survey 2013.”</mark> _<mark>Nutrients</mark>_ <mark>10 (11): 1730.</mark> DSWD. 2015. _Pantawid Pamilyang Pilipino Program_ . Government of the Philippines.

<mark>Elbers, Chris, Tomoki Fujii, Peter Lanjouw, Berk Ozler, and Wesley Yin. 2007. "Poverty Alleviation Through Geographic Targeting: How Much Does Disaggregation Help?."</mark> _<mark>Journal of Development Economics</mark>_ <mark>83 (1): 198-213.</mark> <mark>Ferreira, Francisco H., Deon Filmer and Norbert Schady. 2017.</mark> “Own and Sibling Effects of Conditional Cash Transfer Programs: Theory and Evidence from Cambodia” in. S. Bandyopadhyay (ed.) _Research on Economic Inequality_ 25: 259-298.

35
they see the market speci...c demand shocks, vUS and vEU; drawn from distributions Hj (v) ; j 2 fEU; USg ; where the draws for each ...rm are independent across markets. This assumption is convenient; it allows us to separate the decisions on entry made by a ...rm in each market.<sup>22</sup> It is also consistent with the facts: the correlation between the estimates of demand shocks in Demidova, Kee and Krishna (2012) is close to 0: If ...rms decide to sell in market j, they incur a ...xed cost of production, f; as well as transportation costs of the iceberg form � BD;j > 1; so that marginal costs are increased by this factor. Given wage w, the marginal cost of a ...rm with productivity ' is w� BD;j=': If ...rms further choose to meet ROOs, they pay in addition d<sup>j</sup> ; the documentation cost of meeting ROOs.

A ...rm’s decision on whether to sell in a market or not depends on its value of ' and v in the market. As all varieties are symmetric, while productivities and demand shocks di¤er across ...rms, we can drop ! from our notation, keeping only ' and v: A Bangladeshi ...rm with productivity ' and market demand shock vBD;j in market j will charge price pBD;j (') and sell qBD;j (') units, so its revenue will be where RBD;j = PBD;jXBD;j is the total sales from Bangladesh and tBD;j is the tari¤ on Bangladeshi exports by country j: The ad-valorem tari¤ tBD;j is levied on the price so the ...rm receives (1 � tBD;j)pBD;j per unit sold at the price pBD;j. As the demand shock is multiplicative, it does not a¤ect the price set by a ...rm, so that a ...rm’s price depends only on its productivity. The pro...ts earned by a ...rm are It is easy to see that ...rms set consumer prices as if their marginal costs were '(w�1�BDtBD;j;j )<sup>,</sup> while receiving only (1 � tBD;j) of their variable pro...ts. As usual, due to the CES framework,

> that costs may be per country.

> 22If demand shocks were correlated, a ...rm may enter just to get information on the state of demand.

15
fact that local governments shelter certain industries in the region from competition) on 1.i.t **i..** Their results show that industries with high past profit margins and high shares of state ownership are less geographically concentrated.' Lu and Tao **(2009)** address that same question using(Iiii. **%..** industry-level data from 1998 to **2005** and show that there is less geographic concentration in industries with higher shares of employment in state-owned enterprises. Fujita and Hu (2001) show an increasing income disparity between China's coastal and interior regions and attribute it to the clustering of manufacturing and the selfselection of FDI into coastal Special Economic Zones. Ge **(2009)** demonstrates that access to foreign trade and FDI is a driving force of unbalanced spatial distribution in China. In particular, industries dependent on foreign trade and FDI are more likely to locate in regions with better access to foreign markets.

Martincus and Sanguinetti **(2009)** estimate the impact of the interaction between tariffs and distance to the capital city on the regional employment shares of manufacturing industries in Argentina (for years 1974, **1985,** and 1994). They find that lower tariffs were associated with the de-concentration of manufacturing activities away from Buenos Aires and its surrounding region, which they interpret as indicative that with the opening to trade, demand and cost linkages weakened and **1.in-.tii** diseconomies such as high commuting costs or high land rents prevailed.<sup>9</sup> Using measures of openness based on trade volumes rather than trade policies, Martincus (2010) estimates a similar econometric specification to explain the distribution of Brazilian industries across states and shows that more open **industries** tended to locate in states near the country's largest trading partner Argentina, and this tendency increased with the trade liberalization of the 1990s. These studies demonstrate that industrial policies can have large and long term impacts on spatial concentration and, potentially, on interregional wage and income inequality. In the sections below, we describe the policies that were in place in India prior to the reforms and whose liberalization we expect

> 'Thei mechanism behind their findings is that in the fiscally decentralized Chinese regime local governments have an incentive to protect industries with high profit margins and state-owned enterprises because they are able to expropriate some of their profits through ad-hoc taxes and fees. 'This finding is obtained in specifications that control for comparative advantage and input-output (IO) linkages factors through interactions of industry characteristics (e.g., intermediate input in, ) and region characteristics (e.g., natural resource endowments).

**9**
36

# Table 7. Expenditures on subsidized food by expenditure quintile group in rural Egypt, 1997

|||Mean pe<br>on sub|r capita ex<br>sidized fo|penditures (<br>od per wee|in LE)a<br>k by:||
|---|---|---|---|---|---|---|
|Subsidized food|Lowest<br>Quintile<br>[1]|Second<br>[2]|Third<br>[3]|Fourth<br>[4]|Highest<br>Quintile<br>[5]|Average|
|Baladi bread|0.42|0.44|0.48|0.54|0.54|<br>0.49|
|Baladi wheat flour|0.64|0.54|0.52|0.54|0.53|<br>0.55|
|Edible oil|0.06|0.08|0.09|0.09|0.10|<br>0.09|
|Sugarb|0.07|0.09|0.10|0.11|0.11|<br>0.10|
|Total expenditures on subsidized|||||||
|food by quintile|1.19|1.15|1.19|1.28|<br>1.28|<br>1.23|
|Percent of total expenditures on<br>subsidized food by quintile|19.54|<br>18.88|19.54|<br>21.02|<br>21.02|<br>100.00|
|Percent of total population in quintile|20.00|20.00|20.00|20.00|<br>20.00|<br>100.00|

# Source: IFPRI Egypt Integrated Household Survey, 1997.

Notes: N = 1,278. Rural population is divided into quintile groups on the basis of total per capita fitted expenditures.

a In 1997, 1 Egyptian pound (LE) = US$0.295. All expenditure figures are in nominal terms. b Includes expenditures on fully and partially subsidized sugar.
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the GFC (Table 9). Whereas the amount of cross-border bonds and cross-border syndicated loans per firm-quarter declined by 67 percent and 34 percent during the GFC relative to the pre-crisis years, debt issued through domestic bonds and domestic syndicated loans increased by 74 percent and 38 percent, respectively. Because cross-border syndicated loans witnessed the largest decline in financing while domestic bonds saw the largest increase, the aggregate patterns show a switch from syndicate loan to bond financing. In contrast with the patterns for advanced economies, the results for emerging economies show a stability in debt financing both at the economy-industry level and within firms, explained by the movement toward domestic debt financing that fully offset the decline in crossborder issuances.

Debt maturity also changed as firms switched across debt markets during the GFC. The movement from international toward longer-term domestic debt explains the observed longer syndicated loan maturities, as debt maturities remained relatively stable within each of these syndicated loan markets. But the overall debt maturity at issuance remained stable because bond maturities tended to decline. In particular, the average maturity of bonds shortened by about 2.5 years during the GFC, whereas that of syndicated loans lengthened by about 3 years (Figure 4, Panel B). The total debt maturity remained relatively stable during the GFC relative to the pre-crisis period both at the economy-industry level and within firms, while the maturity of bonds and syndicated loans moved in opposite directions (Table 10, Panel A).

The movement away from international toward domestic debt markets during the GFC also translated into higher shares of debt denominated in domestic currency (Figure 4, Panel C). The regression results show a statistically significant decline of 22 p.p. in foreign currency debt financing during the GFC relative to the pre-crisis period (Table 10, Panel B). The results show a similar decline (of about 17 p.p.) for firms that raised debt before and during the GFC. The increase in domestic 27
migrants abroad may reduce remittances.<sup>8</sup> This should reduce , and be a push factor increasing the desire to migrate. pp<sup>GG</sup> II<sup>GG</sup> + RR<sup>GG</sup> Offsetting this, there is likely to also be a reduction in the pull factor to migrate, as COVID-19 increases unemployment and potentially lowers incomes to be earned in both Senegal and Europe. That is, , and should fall. The net change in expected earnings gain from migrating is therefore uncertain since both income at home and income abroad are likely to have pp<sup>SS</sup> II<sup>SS</sup> + RR<sup>SS</sup> pp<sup>EE</sup> II<sup>EE</sup> + RR<sup>EE</sup> fallen. The Gambia and Senegal have experienced lower COVID-19 infection rates than the main destinations for Gambians in Europe, and lockdowns have been imposed more stringently in Europe. Evidence from both the U.S. (Borjas and Cassidy, 2020) and Europe (Fasani and Mazza, 2020) suggests that immigrants in both regions have been particularly at risk of unemployment, suggesting that the income to be earned from migrating to Europe is likely to have fallen. In contrast, COVID-19 is expected to have had less effect on the income gains from migrating to Senegal.

A second channel that COVID-19 has affected is the cost of migrating and the ability of individuals to pay this cost. The Senegal-Gambia border was officially closed, and European countries also closed their borders. This reduces legal avenues for migration. Sanchez and Achilli (2020) note that border controls redirect migrants “into more perilous and deadly landscapes, where the likelihood of receiving humanitarian support and rescue is slim at best”. This increases the costs of migrating, _C_<sup>_S_</sup> and _C_<sup>_E_</sup> _._ At the same time costs are increasing, wealth levels in The Gambia are likely to have fallen as households need to use their assets to smooth consumption in wake of lower home earnings. Likewise the earnings and wealth of other community members who might be willing to lend money to help pay these costs is also likely to have fallen. More individuals will thus hit the budget constraint that prevents them from being able to migrate even if they would gain income from doing so.

> 8 Avdiu and Meyer (2021) report that remittances as reported in the balance of payments showed record growth in 2020, whereas household surveys show a decrease in remittances. In their surveys, between March and August 2020, 84.6 percent of households reported a decline of international remittances, and only 1.3 percent witnessed an increase. The increase in remittances in the macro data likely reflects a shift from informal to formal channels, whereas it is the decline in total remittances from formal and informal sources that should affect households in our model.

7
_COTE D’IVOIRE: Competitiveness, Cocoa, and Real Exchange Rates_

# **Productivity by Sectors**

## **Figure 17: Percent Value Added by Sector**

<!-- Start of picture text --> Percent of GDP Valuue Added<br>By Sector, Cote d'Ivoire Agriculture<br>Industry<br>Services<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Years 1965-2003<br>Added<br>Percent of Total Value<br>1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br><!-- End of picture text -->

**Figure 18: Distribution of Value Added by Sector**

<!-- Start of picture text --> Distribution of GDP Value Added<br>By Major Sector in Cote d'Ivoire<br>100%<br>Services<br>90% Industry<br>Agriculture<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Years 1965-2003<br>Percent<br>1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br><!-- End of picture text --> 21
|Min. score in HS GPA or national entry test is an admission requirement|0.50|0.50|0.46|0.50|0.60|
|---|---|---|---|---|---|
|**Panel B. Student,program, field of study, and institution characteristics**||||||
|_Student body characteristics_||||||
|Percent of students that are full-time|61.01|40.41|65.47|39.01|0.39|
|Academic deficiencies||||||
|Mathematics is lacking in incoming students|0.67|0.47|0.81|0.40|**0.02**|
|Reading is lacking in incoming students|0.50|0.50|0.51|0.50|0.96|
|Writing is lacking in incoming students|0.68|0.47|0.54|0.50|**0.02**|
|Oral expression is lacking in incoming students|0.71|0.45|0.58|0.50|**0.03**|
|_Program characteristics_||||||
|Program duration (semesters)|5.05|0.60|5.06|0.73|0.85|
|Program age (years)|10.54|9.28|12.80|9.27|**0.06**|
|Total number of students in the program last year|162.88|291.68|393.95|613.07|**0.00**|
|_Institution characteristics_||||||
|HEI is public|0.40|0.49|0.25|0.44|**0.01**|
|Program has high quality accreditation|0.26|0.44|0.45|0.50|**0.00**|
|Number of programs in the HEI|4.96|3.31|6.18|4.56|**0.02**|
|_Field of study_||||||
|Health|0.04|0.19|0.08|0.27|0.17|
|Education, Arts, Humanities, and Social|0.49|0.50|0.27|0.44|**0.00**|
|Economics, Management and Accounting|0.22|0.42|0.27|0.45|0.41|
|Agronomy and Vet, Engineering and Architecture|0.24|0.43|0.38|0.49|**0.02**|
|**Panel C. Noise controls**||||||
|Number of attempts to complete the survey|9.85|3.46|9.39|3.95|0.34|
|Survey completed by phone|0.22|0.42|0.18|0.39|0.42|
|**Observations**|1|53|9|2||

# _Source_ : Own calculations using WBSCPS and administrative data.

_Notes_ : This table shows descriptive statistics for the 245 surveyed programs in Ecuador. The unit of observation is a program. Columns (1) and (2) refer to the 153 programs that do not match to students in the individual-level dataset and have at least two students; columns (3) and (4) refer to the 92 programs that match (with 2+ students per program); and column (5) presents the p-value for the t-test of the difference in means between columns (1) and (3). P-value is in boldface 80
bribe payments but controlling for past profitability, innovation is still significantly associated with increased bribe payments.

Second, we check whether our results are robust to the use of alternate bribe variables. The 2002 and 2005 BEEPS surveys have two other questions on bribes that could serve as alternate dependent variables. Firms were asked to report on a scale of 1 to 6 whether it is common for firms in their line of business to have to pay some irregular “additional payments/gifts” to get things done ” with regard to customs, taxes, licenses, regulations, services etc, with 1 being Never and 6 being Always. In addition, firms were asked to report on a scale of 1 to 6 whether firms in their line of business usually know in advance about how much this ‘additional payment/gifts’ with 1 being Never and 6 being Always. Using either of these alternate dependent variables, **Bribes_Common** and **Bribes_Known,** in a linear regression model,<sup>32</sup> we find our results unchanged – Innovating firms are more likely to say that it is common to pay bribes and to also report knowing how much this additional bribe payment is. This is consistent with our interpretation that, in our sample, corruption is a fee imposed on firms by government employees.

# **7. Conclusion**

A key policy issue in development finance is to design institutions that promote innovation and economic growth. In many countries there is considerable illegality in the relations between government officials and firms. The very institutions designed to promote commerce and ensure a level playing field become platforms that permit state employees to hold up firms opportunistically. On their part, many firms underreport revenues to the state. In this paper, we use a sample of 25,000 firms in 57 countries to study how the corruption by public employees affects innovation and firms’ own dealings with the state’s revenue authorities. We have four major findings:

> 32 We treat the Likert-scale measures as ordinal data and use OLS since Menard (1995) suggests that a linear regression is appropriate with ordinal dependent variables that have a large number of categories if we treat the variables as though they were measured on an interval scale. In the sociology and marketing literature, where use of ordinal variables from survey data is ubiquitous, it is common practice to treat ordinal variables as being continuous and to use ordinary least squares (OLS) estimation when the number of outcomes for the categorical dependent variable is greater than four. The assumption behind this is that when the number of cut-off points is greater than four, they may be considered to be approximately the same distance from each other.

32
<!-- Start of picture text --> Average plant size (output) Labor productivity<br>2 15<br>al 7 a<br>5 wast,i = 0 1 2 4 5 6 0 |<br>cit -0.5 ~ ee<br>15 “A<br>Young sTM="Old -:-:-UL(Young) -------LL(Young) -:---UL(Old) -------LL (Old) Young “=="Old -:-:-UL(Young) -------LL(Young) ~-:-*-UL(Old) -------LL(Old)<br><!-- End of picture text -->

<!-- Start of picture text --> TFPR<br>0.2<br>0.15 ae<br>, s— ye es<br>0.1<br>—=—Young s==Old -:-:-UL(Young) -------LL(Young) -:-:-UL(Old) -------LL(Old)<br><!-- End of picture text -->