rafmacalaba/datause-encoder-data huggingface dataset:
Table 9: Infrastructure Complementarities and Structural Change

|||OLS|||GAM||
|---|---|---|---|---|---|---|
||Agriculture|Manufacturing|Services|Agriculture|Manufacturing|Services|
|∆_Li_|-0.011|-0.769***|-0.735***|-0.005|-0.694***|-0.491***|
||(0.052)|(0.074)|(0.071)|(0.055)|(0.071)|(0.070)|
|∆_Gi_|-0.110***|0.214***|0.274***|-0.131***|0.187***|0.283***|
||(0.010)|(0.015)|(0.014)|(0.012)|(0.015)|(0.015)|
|∆<sup> ̄</sup>_Ii_|-0.046***|0.242***|0.203***|-0.041***|0.187***|0.145***|
||(0.014)|(0.020)|(0.020)|(0.015)|(0.019)|(0.019)|
|∆_Gi ×_∆<sup> ̄</sup>_Ii_|-0.065***|-0.057***|-0.070***|-0.066***|-0.050***|-0.046***|
||(0.009)|(0.013)|(0.013)|(0.009)|(0.012)|(0.012)|
|Num.Obs.|557|557|557|557|557|557|
|R2|0.433|0.569|0.535|0.475|0.677|0.652|
|Std.Errors|IID|IID|IID||||
|_S_(lat, lon)||||0.000|0.000|0.000|

Robust standard errors in parenthesis. * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Panel OLS controls for latitude and longitude. Panel GAM shows semiparametric regressions with thin-plate spline in latitude and longitude, _S_ (lat, lon) shows the approximate p-value of the thin-plate spline.

We use the allocations of the Efficiency + Coordination counterfactual to isolate the effect of complementarities since the fully optimal model has an additional effect through the changes in aggregate budgets. To ease the interpretation, before interacting _Gi_ and _I_<sup> ̄</sup> _i_ , we demean them so that coefficients _β_ 2 and _β_ 3 denote the average effect of _Gi_ and _I_<sup> ̄</sup> _i_ , respectively.

We find that increases in the quality of electricity or roads are associated with reductions in the employment share in agriculture and increases in manufacturing and services. In fact, focusing on the GAM estimates, for a 1 percentage point increase in the quality of electricity (roads), the employment share in agriculture declines by 0.13 (0.04) percentage points while manufacturing and services employment shares increase by 0.19 and 0.28 (0.19 and 0.15) percentage points, respectively. This result is consistent with Szerman et al. (2022), who find strong gains along the intensive margin in Brazilian agriculture when electricity access is improved.

The interacted term is always negative and significant in all sectors. For agriculture, this suggests that the complementarities in infrastructure investment help reduce even further the agricultural employment shares.<sup>23</sup> For manufacturing and services, the estimated coefficient

> 23Through the lens of our model, this complementarity acts through a labor push mechanism (AlvarezCuadrado and Poschke, 2011), as increased productivity in agriculture reduces the employment share in the primary sector. It is also consistent with Vanden Eynde and Wren-Lewis (2021), who find that roads and electricity are complements in dry season agricultural production and suggest the effect is through productivity, which is in line with our results.

34
Table 1: Summary statistics (mean) over time

||1995|2000|2005|2011|
|---|---|---|---|---|
|Intermediate goods|0.536|0.546|0.562|0.573|
|Backward-GVC|0.215|0.239|0.252|0.252|
|Forward-GVC|0.139|0.156|0.159|0.167|
|RTA|0.199|0.301|0.388|0.460|
|SERV|0.150|0.204|0.330|0.423|
|— MFN|0.007|0.044|0.073|0.102|
|— NT|0.139|0.157|0.280|0.369|
|— NonEst|0.123|0.161|0.260|0.349|
|— Move|0.144|0.172|0.278|0.367|

The three GVC-variables are expressed as shares of total gross bilateral trade in manufacturing. _RTA_ , _SERV_ , _MFN_ , _NT_ , _NonEst_ , and _Move_ are dummy variables, and therefore the mean shows the share of country pairs that have the respective agreement or provision.

Table 2: Summary statistics (mean) across income groups

||South-South|North-North|North-South|South-North|
|---|---|---|---|---|
|Intermediate goods|0.550|0.578|0.545|0.550|
|Backward-GVC|0.250|0.225|0.232|0.245|
|Forward-GVC|0.145|0.172|0.154|0.155|
|RTA|0.218|0.495|0.345|0.345|
|SERV|0.145|0.456|0.284|0.284|
|— MFN|0.033|0.076|0.062|0.062|
|— NT|0.114|0.420|0.237|0.237|
|— NonEst|0.106|0.419|0.216|0.216|
|— Move|0.115|0.407|0.249|0.249|

The three GVC-variables are expressed as shares of total gross bilateral trade in manufacturing. _RTA_ , _SERV_ , _MFN_ , _NT_ , _NonEst_ , and _Move_ are dummy variables, and therefore the mean shows the share of country pairs that have the respective agreement or provision. 930, 600, 775, 775 are the number of directional country pairs that are included in South-South, North-North, North-South, and SouthNorth, respectively. Values are averaged across sample years.

in their exports to the developed and developing countries, while forward linkages show the opposite trend. This is intuitive since higher income countries tend to export high value-added inputs to each other (as part of the production process of sophisticated goods), or to developing countries for assembly. On the other hand, developing countries have a high share of foreign-value added in their exports since their participation in GVCs is often characterized by lower value-added activities such as assembly. In terms of trade agreements, high-income country pairs are most likely to have an RTA and a large share of them include a services agreement. A smaller share of developing country pairs has RTAs, and having an RTA for services is even less likely.

18
**Table 11. Interactions: female empowerment**

|Dependent variable||Female en<br>|trepreneur<br>||
|---|---|---|---|---|
||Empowerme<br>Fem. labor part|nt indicator:<br>icipation rate|Empowerme<br>Fem. Primary c|nt indicator:<br>ompletion rate|
||(1)|(2)|(3)|(4)|
|F_cj|-0.628**|-0.571***|-0.218|0.152|
||(0.254)|(0.183)|(0.413)|(0.384)|
|F_cj * Empowerment indicator|0.010**|0.010***|0.001|-0.002|
||(0.005)|(0.003)|(0.005)|(0.004)|
|F_c|0.476||0.462||
||(0.299)||(0.359)||
|F_c * Empowerment indicator|-0.002<br>(0.006)||0.001<br>(0.004)||
|Empowerment indicator|0.002***<br>(0.000)||0.001***<br>(0.000)||
|Firm controls|Yes|Yes|Yes|Yes|
|City BE controls|Yes|Yes|Yes|Yes|
|Country controls|Yes|No|Yes|No|
|Country FE|No|Yes|No|Yes|
|Industry, Year FE|Yes|Yes|Yes|Yes|
|Observations<br>|45,182|45,736|44,069|44,623|
|Adjusted R<sup>2</sup>|0.060|0.085|0.057|0.086|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country-industry level in columns. OLS are used here. Based on non-FDI firms, i.e. we delete FDI firms with 10% or more foreign ownership.

33
The share of BTPs fell to 7 percent of total marketable debt in 1985, although it recovered to 16 percent by 1990. At that time, BOTs accounted for 32 percent of marketable debt and the various types of Treasury certificates for a combined 49 percent.

**Table 2.2:  Composition of Central Government Marketable Debt, 1980-90**

||1980|1985|1990|
|---|---|---|---|
|Treasury Bills|56.9|34.5|31.6|
|Treasury Bonds|13.3|7.2|15.6|
|CCTs|21.7|52.4|40.6|
|CTEs|0.0|1.7|3.8|
|CTOs|0.0|0.0|4.2|
|Other|8.1|4.2|4.2|
|Total|100.0|100.0|100.0|

Source: Scarpelli 2001:30.

The above data relate to marketable central government debt. However, the government also had recourse to a substantial amount of non-marketable debt, through the issue of postal savings instruments and through direct loans from the banking system. The proportion of nonmarketable debt declined over time (Table 2.3). There was also a small but growing accumulation of debt by other public entities, mostly local authorities. The total general government debt increased over the period from 67 percent of GDP in 1980 to 98 percent in 1990.

**Table 2.3:  Composition of General Government Debt, 1980-90**

||1980|1985|1990|
|---|---|---|---|
|Marketable Central Government Debt|56.8|75.4|81.2|
|Non-Marketable Central Gvt Debt|34.1|20.8|16.9|
|Other General Government Debt|9.1|3.8|1.9|
|Total|100.0|100.0|100.0|
|Total in ITL trillion|228.2|667.9|1284.6|
|Total(% of GDP)|67.2|82.4|98.0|

Source: Scarpelli 2001 and OECD The introduction of new public debt instruments, especially variable cost certificates targeted at households, resulted in significant changes in the relative importance of different types of investors. Treasury bills changed over time from being an instrument of bank liquidity to an instrument of investing savings for Italian households.<sup>9</sup> The substitution of bank deposits with public debt securities in the financial portfolios of households took place almost entirely in the early 1980s. The share of households in Treasury bills rose from 24 percent in 1976 to 53 percent in 1981 and 76 percent in 1985. Their share in all marketable debt securities increased at the same time from 29 to 54 percent, while that of the banking system (including the central bank) fell from 69 to 35 percent (Table 2.4).

> 9 Artoni (1983) pp. 67-71; Zautzik (1984).

14
Figure 2 depicts the flood risk exposure map of Bangladesh, as measured by the highest inundation levels for a 24-hour duration from the simulations, under the baseline and the climate change scenarios respectively. While the duration of a flood is an important metric for determining its impact, models to reliably determine the duration of a flood beyond a few days are not currently available. The choice of 24 hours duration is based on the potential severe damages that road infrastructure, flood embankments and rural houses usually are subject to from floods of such duration.<sup>18</sup> Construction of flood maps of longer duration was not feasible within the resource constraints of the study. (See Figure 2.) The flood risk exposures under the two scenarios are summarized in Table 2. Under the baseline scenario, 45% of land is under at least 0.3 m of water (see Table 2). Under the climate change scenario, the total flooded area increases by 4%; exposing more inhabitants, assets and activities at risk. More importantly, the inundation depth increases in most flooded areas, increasing the level of risk to which these areas are exposed. Areas inundated to depths greater than 0.9m increases from 34% of the total area under the baseline scenario to 40% under the climate change scenario. The distribution of land based on inundation levels at the peak period is approximately the same as those for the 24 hour duration flood.

The number of inhabitants exposed to inundation risk of different depths under the two scenarios is estimated by overlaying the inundation risk map with the population map for 2001 using Geographic Information System (GIS) software. About 23 million people lived in rural areas with estimated inundation depths greater the 0.3 meter in 2001.<sup>19</sup> Urban migration is expected to offset increases in the population by 2050 resulting in a decline in the number of rural inhabitants exposed to inundation risk to 19.2 million. An additional 1.9 million live in the newly inundated areas under the climate change scenario resulting in a total of 21.1 million rural inhabitants that are exposed to risk of inundation greater than 0.3 meter by 2050.<sup>20</sup> This represents a 9% increase

> 18 Urban floods and flash floods are not accounted for in this analysis. 19 The exposed population count is estimated at the thana level, which is the lowest administrative unit, using the population density and the total area that falls into each risk zone.  The total exposed population count is the sum of the exposed population across all thanas.

> 20 The rural and urban population growth rates to 2050 are applied uniformly to existing rural and urban areas respectively.

10
18 years, these records show that<sup>the officialworker remittances have increasedat a steady</sup> 3.86 percent per year for the<sup>24 study countries.16This annualrate of increasefor official</sup> worker remittances is higher<sup>than that recorded for the annual rate of GDP growth (1.61</sup> percent per year) in 119 low and<sup>lower middle-incomedevelopingcountries over thelast</sup> twenty years.

The second finding from this study<sup>is that with respect tolegal migration,</sup> international migration definitely<sup>involves the movementof the educated.In both the</sup> United States and the OECD,<sup>individuals with a primary education accountfor less than</sup> 30 percent of total immigrants.<sup>The vast majority of immigrantsto both regions(67</sup> percent in the United States and<sup>88 percentin the OECD) havea secondary(highschool)</sup> education or higher.<sup>17</sup> From an<sup>educationalstandpoint,internationalmigrants represent</sup> an elite that is much better educated<sup>than the rest of the populationin their countryof</sup> origin.

The third finding is that while<sup>migrantsare well-educated,international migration</sup> does not tend to take a very high<sup>proportionof the best educated (tertiary)population in</sup> labor-exporting countries.<sup>For 22 of the33 countriesin which educational attainment</sup> data can be estimated,<sup>18</sup> less<sup>than10 percent of thetertiary-educatedpopulationof labor-</sup> exporting countries has migrated<sup>to the United Statesor the OECD.Part of the reason for</sup> this finding is that large labor-exporting<sup>countriesare alsotypically large population</sup>

> 16 If the figures for remittance flows<sup>which occur outsideof official bankingchannels could be included,it</sup> is likely that total remittances -<sup>officialand unofficial -would have increased at an even faster rate over</sup> the last twenty years.

> " As emphasized in the paper, these<sup>figures do not include the large numbers of illegal (and presumably</sup> less educated) immigrants<sup>to the United Statesor the OECD.</sup>

**18** Table 5 presents 20 countries<sup>with educationalattainmentdata, and Table 7 presents13 countries.</sup>
# **When Elephants Make Peace:**

**The Impact of the China-U.S. Trade Agreement on Developing Countries**<sup>**1**</sup>

Caroline Freund, Maryla Maliszewska, Aaditya Mattoo, Michele Ruta World Bank _JEL codes_ : F1, F6 _Key words_ : Trade wars, managed trade, preferential agreements 1 A joint product of the Chief Economist Office of East Asia and Pacific and the Trade and Regional Integration Global Unit. Claudia Hofmann assisted with the legal analysis of the text in section 2 and Maria Pereira with the simulations in section 4. We are grateful to Chad Bown, Erik Churchill, Bert Hofman, Antonio Nucifora, Martin Raiser, Martin Rama and Chunlin Zhang for helpful suggestions and discussions. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
**TABLE 2: SUMMARY OF HEALTH FACILITIES INCLUDED IN THE IMPACT EVALUATION**

|District|||Num<br>|ber of health facil<br>|ities<br>|||
|---|---|---|---|---|---|---|---|
||CSI<br>Public|CMA<br>Public|District<br>Hospital|Confessional<br>CSI/ CMA/<br>Hospital|For-<br>profit/<br>Para-<br>public|Total|Private<br>(%)|
|Abong-Mbang|14|2|1|4|2|23|27%|
|Doume|9|1|1|2|1|14|23%|
|Lomie|7|2|1|2|0|12|18%|
|Messamena|9|1|1|2|0|13|17%|
|Nguelemendouka|5|0|1|1|0|7|17%|
|Kette|9|0|1|0|0|10|0%|
|**Total in East**|53|6|6|11|3|79|19%|
|Kumbo East|17|2|1|6|4|30|34%|
|Nkambe|11|2|1|4|2|20|32%|
|Ndop|12|2|1|8|4|27|46%|
|Fundong|9|3|1|12|3|28|56%|
|**Total in North West**|49|9|4|30|13|105|43%|
|Mamfe|11|1|1|1|0|14|8%|
|Kumba|10|1|1|5|1|18|35%|
|Buea|10|3|1|0|9|23|41%|
|Limbe|10|1|1|1|7|20|42%|
|**Total in South West**|41|6|4|7|17|75|34%|
|Pilot Zone total|**143**|**21**|**14**|**48**|**33**|**259**|**33%**|

35
# U -

||4. Lead<br>....................|....................<br>33|
|---|---|---|
||- Effeet of Lead on Blood Pressure in Adults................<br>- Hypertenion..................<br>- PematureMortality................................<br>-Effcts<br>of Blood Lead on Neurodevelopment in Children.........|...34<br>35<br>..37<br>....38|
||5. Nitrogen Diadde<br>.<br>...............................<br>6. Carbon MonOide ..............................|......<br>40<br>41|
||7. Carbon Dioide .42||
|IV.|APPLUCATION TO JAKARTA, INDONESIA .......................|43|
||A. Badcroud<br>Information..................<br>|43|
||B.FstimatedHealth Effects.47||
|V.|IMPROVING THE ESTIMATES OF AIR POLLUTION DAMAGES<br>UNCERTAINTIES AND FUTRE NEEDS ------------------|..<br>As48|
|REF|ERENCES -54||
Sons' Primary Completion Estimates by Birth Year <!-- Start of picture text --> Intercept Linear Coefficient Qudratic Coefficient<br>4 .05 .006<br>.004<br>2 0 yn<br>><br>? .002<br>><br>><br>o j—-- te — -.05 bf f<br>> on-rT4te ooo<br>><br>-.002<br>-.2 -.1<br>59 60 61 62 68 69 70 71 72 59 60 61 62 68 69 70 71 72 59 60 61 62 68 69 70 71 72<br><!-- End of picture text --> Birth Years Omitted Birth Year 1957-58 © Comparison Cohort @ Fullly Exposed Cohort
# Table B.2: **Effects of Inpres Schools on Intergenerational Educational Mobility: Comparison Cohorts: 1958-1962**

**Panel A: Dependent Variable: Primary Completion**

||**Linear**|**Model**|**Quadra**|**tic Model**|
|---|---|---|---|---|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|0.205***|0.215***|0.196***|0.199***|
||(0.041)|(0.043)|(0.042)|(0.046)|
|Father’s Edu_×_Born 1968-72_×_Inpres|-0.022***|-0.014**|-0.039***|-0.019**|
||(0.006)|(0.006)|(0.011)|(0.010)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|||0.002***<br>(0.001)|0.001**<br>(0.001)|
|R2|0.105|0.170|0.116|0.186|
|Observations|1190828|837014|1190828|837014|

# **Panel B: Dependent Variable: Years of Schooling**

||**Linea**|**r Model**|**Quadra**|**tic Model**|
|---|---|---|---|---|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|1.597***|0.551|1.534***|0.521|
||(0.384)|(0.462)|(0.381)|(0.488)|
|Father’s Edu_×_Born 1968-72_×_Inpres|-0.123**|0.054|-0.234***|-0.014|
||(0.050)|(0.061)|(0.085)|(0.092)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|||0.013**<br>(0.006)|0.008<br>(0.007)|
|R2|0.321|0.408|0.322|0.408|
|Observations|1190828|837014|1190828|837014|

Notes: Robust standard errors in parentheses clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). Quadratic model is adopted following Becker et al. (2015). Sample corresponds to children born between 1958 and 1962, or 1968 to 1972. Covariates include birth district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Primary completion takes the value of 1 if the child has completed primary and 0, otherwise. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

65
**-4-**

terms of their lag structure. Pinell-Siles' .Ady of private investment in India highlights the dampening effect that the tax system has on capital formation because of the ,ailure to adjust taxable income for inflation.<sup>4</sup> Using panel data on Colombian firms, Bilsborrow found that the availability of foreign exchange to implement planned capital formation and the internal flow of funds were the most important determinants of investment.<sup>5</sup> The importance of cash flow effects reflect the uncertainty, informational constraints, and weak capital markets faced by Colombian entrepreneurs.'

**These** earlier studies by Behrman, Bilsborrow, and Pinell-Siles were followed by a number of more ambitious multi-country analyses that highlighted the role of government policy, particularly public investment, on private capitdl formation in developing economies.<sup>7</sup> The theoretical framework adopted was sometimes ad hoc or some combination of neoclassical and flexible accelerator models with additional variables to capture the effects of government policies. Fry estimated investment functions for sixty-one developing countries using demand, relative prices, the exchange rate, and the availability of domestic credit.<sup>8</sup> His estimates find significant effects on the investment ratio from the growth rate of GDP, the ratio of foreign exchange receipts to GDP, the ratio of domestic credit to GDP, the purchasing power of exports, the ratio of actual to expected prices, and the lagged investment ratio. Tun Wai and Wong's estimates of

> 4Pinell-Siles, 1979.

5Billsborrow- 1977.

'More recent work by Dailami on Colombia found that the high real marginal cost of capital, especially for small- and medium-size firrs, served to constrain the expansion of capacity in the private sector. Dailarni, 1989.

7Fry, 1980; Sundararajan and Thakur, 1980; Tun Wai and Wong, 1982; Blejer and Khan, 1984. aFry, 1980.
**Table 2** Impact of Treatments on Intimate Partner Violence <u>(IPV)</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|A. Whole Sample|||||
|IPV often index|||||
|treatment x post|-0.229**<br>(0.100)|-0.298***<br>(0.108)|0.000|5,182|
|treatment x post x Goal|0.110<br>(0.173)||||
|IPV in last year index<br>treatment x post|-0.150*|-0.198*|-0.000|5,182|
||(0.078)|(0.112)|||
|treatment x post x Goal|0.114<br>(0.140)||||
|Psych. abuse often|||||
|treatment x post|-0.011|-0.012|0.026|5,182|
||(0.012)|(0.013)|||
|treatment x post x Goal|-0.024<br>(0.020)||||
|Physical abuse often|||||
|treatment x post|-0.018**|-0.020*|0.019|5,182|
||(0.008)|(0.011)|||
|treatment x post x Goal|0.003<br>(0.018)||||
|Force sex often|||||
|treatment x post|-0.028**|-0.035***|0.023|5,182|
|treatment x post x Goal|(0.011)<br>0.035**<br>(0.014)|(0.013)|||
|Psych. abuse in last year|||||
|treatment x post|-0.029|-0.024|0.086|5,182|
||(0.024)|(0.030)|||
|treatment x post x Goal|-0.020<br>(0.042)||||
|Physical abuse in last year<br>treatment x post|-0.037*|-0.019|0.062|5,182|
||(0.019)|(0.026)|||
|treatment x post x Goal|0.007<br>(0.034)||||
|Force sex in last year<br>treatment x post|-0.028*|-0.059**|0.045|5,182|
||(0.017)|(0.024)|||
|treatment x post x Goal|0.057**<br>(0.028)||||

_Notes._ This table presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 1. For each outcome, the coefficients from a single regression are presented in two sub-rows, with estimates of _β_ 1 in column 1 and _β_ 2 in column 2, labeled treatment x post, and the estimate of _γ_ 1 in the second of the two rows, labeled treatment x post x Goal. Column 3 presents the outcome mean at endline among females in ELA only communities who were not assigned to the _Goal_ treatment, and column 4 shows the number of observations in the model. All specifications include controls for highest grade attended, whether the female’s household owns the house she lives in, whether the female talks to her mom about sexual reproductive health topics, age of the female, and club fixed effects. IPV often (in last year) index is generated by taking the unweighted mean of the three indicators for each frequency set after they have been standardized to the mean and standard deviation among females in ELA only communities who were not assigned to the _Goal_ treatment at baseline and endline separately. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Female baseline and endline data, balanced panel.

49