rafmacalaba/datause-encoder-data huggingface dataset:
present the results of firm-level analysis without the product dimension, equivalent to Table 3. The larger coefficients for CU, followed by FTA, confirm our baseline results that positive spillover effects are larger for deeper RTAs. The probability of export entry into a new destination country increases by 0.6 percentage points if a firm exported in the previous year to a country that has an FTA with the new destination. For firms that exported to a country that is part of a CU with the new destination, the probability of export entry increases by 1.6 percentage points. PS agreements, which usually only grant tariff preferences, have a negative impact on export participation and entry.

Table 9: Alternative measure of depth

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Ex|port participa|tion||Export entry||
|Exp Other...(t-1)|||||||
|... CU<br>... FTA<br>... PS|0.038***<br>(0.001)<br>0.010***<br>(0.001)<br>-0.006***<br>(0.000)|0.021***<br>(0.001)<br>0.004***<br>(0.000)<br>-0.002***<br>(0.000)|0.019***<br>(0.001)<br>0.003***<br>(0.000)<br>-0.002***<br>(0.000)|0.016***<br>(0.001)<br>0.006***<br>(0.000)<br>-0.001***<br>(0.000)|0.010***<br>(0.001)<br>0.002***<br>(0.000)<br>0.000<br>(0.000)|0.010***<br>(0.000)<br>0.002***<br>(0.000)<br>0.000***<br>(0.000)|
|... CU x STC||0.002|||0.000||
|||(0.001)|||(0.001)||
|... FTA x STC||0.000|||-0.000||
|||(0.001)|||(0.000)||
|... PS x STC||-0.000**<br>(0.000)|||-0.000**<br>(0.000)||
|... CU x STC (cnt)|||0.030***<br>(0.004)|||0.007***<br>(0.002)|
|... FTA x STC (cnt)|||0.004***<br>(0.001)|||0.001<br>(0.001)|
|... PS x STC (cnt)|||-0.008***<br>(0.001)|||-0.003***<br>(0.000)|
|Observations|20,797,980|164,595,130|164,595,130|20,694,604|164,298,452|164,298,452|
|R-squared|0.467|0.340|0.340|0.179|0.157|0.157|
|Spatial controls|YES|YES|YES|YES|YES|YES|
|Firm-prod-year FE|YES|YES|YES|YES|YES|YES|
|Dest-prod-year FE|YES|YES|YES|YES|YES|YES|
|Firm-prod-dest FE|YES|YES|YES|YES|YES|YES|

Robust standard errors, clustered at the firm-product level, are in parentheses. The dependent variable _yijp,t_ is a dummy variable equal to 1 if firm _i_ exported (or started exporting) product _p_ to country _j_ in year _t_ . STC=1 if a Specific Trade Concern (STC) on product _p_ has been raised in the WTO. STC (cnt) is the incidence of STCs on product _p_ , normalized between 0 and 1. Coefficients on spatial exporting controls are not reported for brevity. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Columns (2) and (4), equivalent to Table 5, show the results for regulation-intensive products. Using the broad classification of RTAs, we do not find statistically significant evidence that deeper RTAs have stronger spillover effects for regulation-intensive goods. This however could be reflecting the shortcomings of this categorical measure of depth, which by construction does not capture the varying content of different RTAs. Using a more informative measure of regulations intensity (continuous STC measure, as in Table 7), we do find evidence that deeper trade agreements have larger effects on regulationintensive goods.

19
# _5.1.1 Innovators and Their Interactions with Government_

In this section, we investigate the interactions between innovating firms and government officials to get a better understanding of whether innovators are victimized by corruption or if they obtain special concessions from government officials. Our surveys provide detailed information on the service interruptions experienced by firms, the delays in obtaining different licenses required for operation of their business and the time spent dealing with bureaucracy.

In Panel A of Table 4, we examine the mean number of days that firms experience service interruptions due to power outages, insufficient water supply, unavailable telephone service and transport failures. We present a test of means between innovators who pay bribes and innovators who don’t pay bribes. Cols. 1 and 2 of panel A show that innovating firms that pay bribes on average lose 17.1, 10.5, and 5.43 days respectively due to power outages, insufficient water supply and unavailable telephone service which are all significantly larger than the 11.2, 6.2, and 2.6 days respectively lost by innovators that don’t pay bribes.

The survey also has information on the delay experienced in obtaining a telephone line, electricity connection, water connection, construction permit, import license, and operating license from the day the firm applied for the license to when they received the approval/service. When we look at innovators that bribe versus those that don’t, we find that innovators that pay bribes experience significantly longer delays in obtaining telephone connections, electrical connections, construction permits, import licenses and operating licenses than innovators that don’t pay bribes. One caveat though is that the sample sizes for firms reporting data on water connections, construction permits, import and operating licenses is less than 500. Note that while these results suggest that innovating firms that pay bribes are not benefitting through reduced service interruptions, and may be extorted by actual service interruptions, we are cautious in our interpretations since we do not have information on the timing of the bribe payments relative to that of the service interruptions.

In Panel C, we look at the average number of days innovators spend in inspections and mandatory meetings with officials of different government agencies in the context of regulation of their business. We find that innovators that pay bribes spend significantly more time dealing with officials from all agencies expect in the case of fire and building safety and environmental 25
# **Annex V-B: Exogenous Accounts for Growth and Welfare Inference under FixedPrices Multipliers Assumptions**

|Account Rubric|Account<br>Number(*)|Account Name|
|---|---|---|
|Current Accounts of<br>Economic Agents|13<br>14<br>15|Central Bank<br>Central Government<br>Rest Of the World|
|Capital Accounts of<br>Economic Agents|45<br>46<br>47|Central Bank<br>Central Government<br>Rest Of the World|
|Change in Assets<br>and Liabilities|50<br>52<br>53<br>54<br>55<br>56<br>57<br>58<br>59|Special Deposits to Central Bank<br>Capital Shares<br>Credits allowed by Commercial Banks<br>Bills issued by Government<br>Advances of Central Bank to the Treasury<br>State Consolidated Credits allowed by the Central Bank<br>Commercial Bank Refinancing allocated by the Central Bank<br>External Engagements<br>External Financial Resources|

**(*)** _Accounts have the same numbering as in the financial SAM (see Annex I)._

49
4 inform about the possibility of profitable intensification, not on the particular interventions or intervention packages needed to broker it. Limited profitability of intensification has been raised regularly as a potential reason for low technology adoption in SSA (Burke et al., 2017; LiverpoolTasie et al., 2017; Sheahan et al., 2013). Knowing that profitable intensification is possible is a precondition to the design of any intervention package.

The empirical application concerns intensification among smallholder rice growers’ households in Côte d’Ivoire. Rice is an important staple crop for the majority of households in SSA, with rapidly expanding demand following income growth and urbanization continuing to exceed more gradually growing domestic supply. As a result, SSA continues to import around 40 percent of its rice consumption. It accounts for about one-third of the global rice trade. Rice production in SSA remains largely rainfed, including in lowlands. This also holds in Côte d’Ivoire. Lagging production levels have been explained by delays in sowing time due to lack of mechanization, limited access to modern inputs, poor agronomic practices and lack of working capital among the supply chain actors (Komatsu et al., 2022; Niang et al., 2017; Tanaka et al., 2015, Wopereis, et al., 2013).

In particular, baseline data from low-land rice farmers in 60 villages from three different agroecological regions in Côte d’Ivoire<sup>4</sup> are used to investigate the productivity and profitability effects of four different, currently practiced, production technology packages: (a) a traditional production package (TP) involving manual land preparation, without any chemical inputs, (b) a semitraditional production package (STP) including manual land preparation and the use of chemical inputs, (c) a semi-modern production package (SMP) involving animal traction for land

> 4 The data were collected in the context of a World Bank inclusive smallholder rice value chain development pilot (https://www.jobsanddevelopment.org/cote-divoire-country-pilot/).
Table S.34: Impacts on children’s educational achievement: mid-term examination results <u>(OLS)</u>

|Dependent variables:|||GP|A z-scores|||
|---|---|---|---|---|---|---|
||Bengali|Bengali|English|English|Math|Math|
||(a)|(b)|(c)|(d)|(e)|(f)|
|**June 2014**|||||||
|All products|0.048|0.048|-0.018|0.019|-0.017|-0.006|
||(0.185)|(0.151)|(0.141)|(0.134)|(0.143)|(0.141)|
|One product|0.157|0.100|0.029|0.035|-0.036|-0.042|
||(0.170)|(0.171)|(0.112)|(0.126)|(0.151)|(0.170)|
|All = one (p-val.)|0.550|0.775|0.754|0.922|0.897|0.836|
|All + one = 0 (p-val.)|0.511|0.585|0.957|0.787|0.840|0.858|
|Mean _y_ (control)|-0.042|-0.042|-0.034|-0.034|0.028|0.028|
|R-squared|0.293|0.391|0.393|0.440|0.355|0.363|
|No. of obs.|284|284|284|284|283|283|
|Dependent var.|General<br>science|General<br>science|Islam<br>studies|Islam<br>studies|Bangladesh<br>general<br>studies|Bangladesh<br>general<br>studies|
||(g)|(h)|(i)|(j)|(k)|(l)|
|**June 2014**|||||||
|All products|-0.392*|-0.397*|0.010|-0.023|0.041|0.034|
||(0.201)|(0.191)|(0.172)|(0.175)|(0.161)|(0.164)|
|One product|-0.281|-0.363*|0.068|-0.006|0.062|0.046|
||(0.205)|(0.182)|(0.136)|(0.144)|(0.189)|(0.216)|
|All = one (p-val.)|0.371|0.808|0.730|0.933|0.897|0.955|
|All + one = 0 (p-val.)|0.101|0.042|0.768|0.908|0.745|0.809|
|Mean _y_ (control)|0.165|0.165|0.014|0.014|0.001|0.001|
|R-squared|0.213|0.319|0.329|0.393|0.219|0.241|
|No. of obs.|283|283|284|284|284|284|
|Baseline control|No|Yes|No|Yes|No|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

79
survival in Table 3 reflecting that some individuals would have been employed as wage workers had they not started or continued with their firms. Columns 2 and 6 consider total employment in the firm, which is the sum of the owner’s employment in the firm, the number of wage and salary workers, and the number of casual workers in the firm. The average control group firm among new applicants has 3.7 workers by the time of the third survey, with the treatment effect of 5.2 workers more than doubling this average. The average control group firm has 5.6 workers by the time of the third survey, with the treatment effect of 4.4 workers representing an 80 percent increase. Impacts are larger in the second and third years once all the grant had been received than in the first year. The treatment effect is larger in year 3 than year 2 for existing firms, but this appears to be largely a result of the impact of the grant on firm survival (appendix table 6). Appendix table 7 shows these increases in employment come from both more wage and salary workers, and from more casual and daily workers. Our survey data asks more detailed questions on the three most recently hired workers each year. Only 5 percent of these are related to the owner, 33 percent are female, 45 percent have post-secondary education, and their average wage is 22,400 Naira/month (approximately US$143).

Columns 3 and 6 then examine the extent to which winning the competition has enabled firms to surpass the 10 worker threshold.<sup>15</sup> Amongst new firm applicants, we see that only 11 percent of the control group had reached this size three years after applying, with treatment increasing this by 22.9 percentage points. Among existing firms, 17 percent of the control group were at this size after 3 years, with the treatment taking a further 20.6 percent to this level. Few firms have grown to the size of having 25 workers, but columns 4 and 8 of Table 4 show that by the third round the treatment has had a statistically significant 2.5-2.7 percentage point increase in this likelihood, relative to a control mean of only 1.4 percent.

The propensity score matching estimates show larger impacts on employment for the national and zonal winners, with an estimated impact of 7.0 workers per firm for new applicants, and 7.3 workers per firm for the existing applicants. Figure 1 shows a separation at the top of the

> 15 Note this variable was not included in the pre-analysis plan. It was motivated by the definition of 10 workers as a cutoff between micro and small businesses in many countries, and by the work of Hsieh and Olken (2014) noting this threshold as one few firms in developing countries exceed. The CDFs in Figure 1 show that the results would be similar if we instead chose another binary threshold under 15 or 20 workers.

22
the right of non-establishment significantly increase both gross- and GVCtrade flows. Country pairs tend to trade 12.4 percent more when they have a services agreement that allows services exports without local establishment, compared to when they only have an RTA covering goods. The effect of a services agreement without a non-establishment clause is absent. Furthermore, the positive impact of the non-establishment right is larger at 18.4 percent for trade in intermediate goods, 14.5 percent for backwardGVC, and 12.1 percent for forward-GVC trade, hinting at the particular importance of non-establishment rights as GVCs become more prevalent. Provisions on the movement of natural persons do not show significant effects on GVC trade but one could speculate that this issue is often dealt with outside the scope of RTAs, such as separate visa arrangements or migration policies.

Table 5: Heterogeneous impact of services RTAs by provisions

||(1)|(2)|(3)<br>GVC-exports|(4)|
|---|---|---|---|---|
||Gross exports<br>(total)|Intermediate<br>goods|Backward<br>(sourcing)|Forward<br>(selling)|
|RTA|0.126<sup>_∗∗_</sup>|0.119<sup>_∗_</sup>|0.145<sup>_∗∗_</sup>|0.0891|
||(0.0422)|(0.0476)|(0.0507)|(0.0482)|
|SERV|-0.0662|-0.0334|-0.0573|-0.0840|
||(0.0767)|(0.0954)|(0.0644)|(0.118)|
|SERV*MFN|-0.115<sup>_∗_</sup>|-0.0838|-0.192<sup>_∗_</sup>|0.0256|
||(0.0573)|(0.0634)|(0.0750)|(0.0615)|
|SERV*NT|0.135|0.139|0.155|0.105|
||(0.0811)|(0.0802)|(0.0821)|(0.0820)|
|SERV*NonEst|0.183<sup>_∗∗∗_</sup>|0.202<sup>_∗∗∗_</sup>|0.193<sup>_∗∗_</sup>|0.198<sup>_∗∗∗_</sup>|
||(0.0536)|(0.0559)|(0.0638)|(0.0567)|
|SERV*Move|-0.160|-0.224|-0.165|-0.147|
||(0.109)|(0.119)|(0.104)|(0.138)|
|Obs.|12221|12221|12221|12221|

Standard errors clustered by country pairs in parentheses. All specifications include exporter-year, importer-year, and country pair fixed effects, and are estimated using PPML. _MFN_ =1 if the services agreement contains an MFN clause. _NT_ =1 if the services agreement contains a national treatment clause. _NonEst=1_ if the services agreement explicitly grants the right of non-establishment (i.e. if it allows the provision of services without local presence). _Move_ =1 if the services agreement allows the movement of natural persons in the provision of services. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 The intuition for why the non-establishment clause should matter for GVC-trade is straightforward. The requirement of local presence is one of the large fixed costs of building the services links that are needed for multiple production blocks. The relaxation of proximity requirements, therefore, 23
# **2.2. Modal Mix in the Transport Sector**

One potential factor driving transport sector CO2 emissions growth could be modal shifting, from less emission intensive modes (in terms of CO2 emissions per passenger/freight kilometer), such as railway and water transportation, to more emission intensive modes, such as commercial airplanes and private road vehicles.

Figures 2(a)-2(d) illustrate the evolution of the modal mix for passenger transport activity in China, Korea, Vietnam and Pakistan, respectively, in terms of passenger kilometers. Road and rail are the main modes of passenger transport in China from 1990 to 2006, but it is clear that there has been a shift from rail towards road (see Figure 2(a)). There has also been a steady increase in the reliance on air transport for passengers, while the use of waterways has declined considerably. It should be noted that international aviation is included in the modal data for China. Road is by far the dominant mode of passenger transport in Korea, although the share of subway in passenger transport has increased over the period 1994-2006 (see Figure 2(b)). On the other hand, the share of aviation in passenger transport has waned only slightly, but the share or rail has declined significantly. Air transport has gained a substantial share of passenger transport in Vietnam from 1990 to 2006, largely at the expense of road transport, which, nonetheless, remains dominant (see Figure 2(c)). The share of rail and navigation has also gone down, especially sharply in 2005-2006, when road regained some of the share it had been losing in previous years. In Pakistan, where there is virtually no water transport, and aviation only accounts for a miniscule and declining share, road is overwhelmingly the main mode 8
||year spanned more than one calendar year, the<br>inflation figure is the weighted average over<br>the calendar years covered, where the weights<br>used are the proportion of months covered in<br>the calendar year.<br>Source: World Development Indicators,<br>World Bank.|
|---|---|
|Gross Tertiary Enrollment Rate|Gross enrollment rate in tertiary education.<br>Values of the variable were computed in two<br>steps. In the first step, gross tertiary enrollment<br>rate values for the fiscal year covered by the<br>Enterprise Survey were taken. In cases where<br>the fiscal year spanned more than one calendar<br>year, the enrollment figure is the weighted<br>average over the calendar years covered,<br>where the weights used are the proportion of<br>months covered in the calendar year. In the<br>second step, annual values were taken from the<br>first step and averaged over the three years<br>prior to the (final) year covered by the<br>Enterprise Survey. Three years average was<br>taken to avoid too many missing values of<br>tertiary enrollment rate. The final figure is<br>divided by 100 to avoid too small coefficients<br>in the regressions.<br>Source: World Development Indicators,<br>World Bank.|
|Gross Primary Enrollment Rate|Gross enrollment rate in primary education.<br>Values of the variable were computed in two<br>steps. In the first step, gross primary<br>enrollment rate values for the fiscal year<br>covered by the Enterprise Survey were taken.<br>In cases where the fiscal year spanned more<br>than one calendar year, the enrollment figure is<br>the weighted average over the calendar years<br>covered, where the weights used are the<br>proportion of months covered in the calendar<br>year. In the second step, annual values were<br>taken from the first step and averaged over the<br>three years prior to the (final) year covered by<br>the Enterprise Survey. Three years average<br>was taken to avoid too many missing values of<br>primary enrollment rate. The final figure is<br>divided by 100 to avoid too small coefficients<br>in the regressions.|

58
simulation results to the case of a differing pace of structural transformation across regions. Section VI explores the change in the distribution of regional intersectoral productivity gaps, in conjunction with recent patterns of structural transformation in Ghana. Section VII concludes.

# **II. Background**

# A. _Regional incomes_

Regional GDP estimates are unavailable for Ghana. Consequently, to obtain a picture of trends in income across regions, we use trends in average consumption (per adult equivalent) as a proxy. Figure 1 plots average consumption for nine of the country’s 10 regions benchmarked to Greater Accra (the richest region) in 2005/06 and in 2016/17, based on data from Ghana Living Standard Survey (GLSS) rounds. The GLSS are multi-topic, national household sample surveys administered by the Ghana Statistical Service (GSS) with data representative at the region level.<sup>3</sup> The regions are organized from the poorest (Upper West) to the second richest (Ashanti).

Regions have generally observed a growing gap in average consumption relative to Greater Accra. In addition, this growing gap is generally larger (in percent terms) for poorer regions than for richer regions. The pattern in trends suggests that poorer regions are falling behind Greater Accra at a faster rate than richer regions.

# B. _National and regional sectoral employment shares_

Figure 2a plots trends in employment shares by sector (agriculture, industry, and services) at the national level between 1960 and 2018 (almost a 60-year period). Ghana did not exhibit any strong signs of structural transformation during the subperiod from 1960 to 2000 (during the first 40 years of the full period); sectoral employment shares showed some fluctuation during this subperiod but no sustained upward or downward trends. Across this subperiod, agriculture accounted for, on average, about 55 percent of overall employment, followed by services (about 30 percent) and industry (about 15 percent).

Since the early 2000s, the employment share in agriculture began to decline noticeably, with the trend accelerating from 2005 after Ghana opened its economy to global trade

> 3 See GSS (2008) and GSS (2019) for survey design and implementation details for the 2005/06 and 2016/17 GLSS rounds, respectively.

6