rafmacalaba/datause-encoder-data huggingface dataset:
the intersectoral productivity gap fell in the five poorest regions. Among the five richest regions, the intersectoral productivity gap increased in Central and Greater Accra but declined somewhat in Ashanti, Eastern, and Western. Overall, the ratio of intersectoral productivity gaps in industry between the five richest and the five poorest regions roughly doubled from 0.61 in 2003 to 1.3 in 2014.

How closely does this change in the distribution of regional intersectoral productivity gaps relate to recent trends in structural transformation in the country? Between 2003 and 2014, the industrial share of employment and value-added per worker increased, which coincided with the major discovery of oilfields in 2010 (World Bank 2021). Consequently, Ghana’s exports of fuels increased nearly 3.5 times in trade value from 2010 to 2019 (World Bank 2021). More than 76 percent of industrial employment in 2014 is in the four richest regions of Ashanti, Central, Greater Accra, and Western (Figure A3). Given this, the growing importance of extractive sectors is likely to be correlated with the increasing wedge in the intersectoral productivity gap in industry between the five richest and five poorest regions (Figure 13).

# **VII. Conclusion**

The movement of workers from the farm sector to a more productive nonfarm sector has failed to generate a significant gain in labor productivity in recent decades in many developing countries, including in Ghana. We offer a new perspective on the barriers to growth-enhancing structural transformation, by combining structural modeling with recent enterprise census data from Ghana. Specifically, we argue that differences across Ghana’s regions in terms of the productivity gaps between the nonfarm informal and formal sectors impair national productivity gains from structural transformation in the country.

We interpret the disparity in regional intersectoral productivity gaps as reflecting the disparity in regional productivity losses in the intersectoral misallocation of labor, and identify intersectoral misallocation as the output wedge between the informal and formal sectors. Through a series of simulations, we show that a more productive nonfarm informal sector reduces the disparity in regional intersectoral productivity gaps and, in turn, generates higher levels of national nonfarm and aggregate labor productivity as well as a larger level of contribution of structural transformation to national aggregate labor productivity.

Policies that aim to increase labor productivity tend to focus on the reallocation of resources from the informal to the formal sectors. However, limited potential to transition from the informal to the formal sector (de Mel et al. 2010) and high mobility costs (Caselli and Coleman 2001), among other factors, cast doubt on the efficacy of such policies. In contrast, 24
12 control approach associated with directly-managed and budget-financed hospitals toward a mix of accountability mechanisms involving the market (hospitals, as indicated above, are expected to earn part of their income from sources other than the budget) and oversight exercised by the health ministry. This oversight has developed faster in some areas than others. Decree 43 required the development of internal expenditure regulations that lay out explicitly each facility‘s rules for payment of supplementary income or specific spending norms, to be approved by the labor union and leadership of units within the hospital, but also from government health sector administration. By contrast, the accountability mechanism related to the quality and efficiency of care remains weak. The recently passed decree on penalties for administrative violations in medical examination and treatment includes penalties for violations such as ―prescribing use of medical services for profit reasons‖ and ―violating technical professional regulations‖, but it lacks the details needed for operationalization.

# _Social functions_

Finally, in terms of social functions, the relevant concern in Vietnam has been that of ensuring that hospital care remains affordable for the poor. Initially this involved the Commune People‘s Committee issuing letters to specific individuals requesting them to exempt the person from user fees. While intended to be focused on the poor, these exemptions apparently benefitted the nonpoor almost as much, and were not applied consistently across hospitals (World Bank 2001 p.151). These exemptions had to be financed by the hospital – in the form of forgone revenue – and had the scheme persisted concerns would have arisen about the likelihood of the poor being underserved by autonomized hospitals. In any event, this policy was replaced in the late 1990s by a policy of ̳health cards for the poor‘, where local governments either enrolled the poor in the social health insurance
WPS4099

# **Measuring and Reducing the Impact of Corruption in Infrastructure**

## **Charles Kenny**<sup>**1**</sup>

## **Abstract**

This paper examines what we can say about the extent and impact of corruption in infrastructure in developing countries using existing evidence. It looks at different approaches to estimating the extent of corruption and reports on the results of such studies. It suggests that there is considerable evidence that most existing perceptions measures appear to be very weak proxies for the actual extent of corruption in the infrastructure sector, largely (but inaccurately) measuring petty rather than grand corruption. Existing survey evidence is more reliable, but limited in extent and still subject to sufficient uncertainty that it should not be used as a tool for differentiating countries in terms of access to infrastructure finance or appropriate policy models. The paper discusses evidence for the relative costs of corruption impacts and suggests that a focus on bribe payments as the indicator of the costs of corruption in infrastructure may be misplaced. It draws some conclusions regarding priorities for infrastructure anticorruption research and activities in projects, in particular regarding disaggregated and actionable indicators of weak governance and corruption.

## World Bank Policy Research Working Paper 4099, December 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Senior Economist, World Bank. Thanks to Antonio Estache, Jonathan Halpern, Laszlo Lovei, Gregory Kisunko, Todd Moss, Tina Soreide and Richard Messick for comments, and in particular Jim Anderson both for detailed comments and for pointing out a significant error in an earlier draft.

1
Policy Research Working Paper 6702

# **Abstract**

Poor sanitation and open defecation are thought to be a major cause of diarrhea and intestinal parasite infections among young children. In 1999, India launched the Total Sanitation Campaign with the goal of achieving universal toilet coverage in rural India by 2012. This paper reports on a cluster-randomized, controlled trial that was conducted in 80 rural villages in Madhya Pradesh to measure the effect of the program on toilet access, sanitation behavior, and child health outcomes. The study analyzed a random sample of 3,039 households and 5,206 children under five years of age. Field staff collected baseline measures of sanitation conditions, behavior, and child health, and re-visited households 21 months later. The analysis finds that implementation of the program activities was slower than the original timeline (only 35 percent of villages were triggered more than six months before the follow-up survey).

Nevertheless, the Total Sanitation Campaign successfully increased toilet coverage by 19 percent in intervention villages compared with control villages (41 percent v. 22 percent), while reported open defecation decreased by 10 percent among adults (74 percent v. 84 percent). The intervention also led to some improvements in water quality and protozoan infection, but consistent improvements were not observed across multiple child health outcomes (diarrhea, helminth infections, child growth). However, the exposure period was likely to have been too short to result in any benefit of the sanitation interventions on child health. Given the large improvements in toilet construction documented, an additional follow-up survey with a longer period of exposure would yield valuable information on the effects of improved sanitation conditions on health outcomes.

This paper is a product of the Water and Sanitation Program (WSP), Sustainable Development Network. WSP is a multidonor partnership created in 1978 and administered by the World Bank to support poor people in obtaining affordable, safe, and sustainable access to water and sanitation services. The paper is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The corresponding author may be contacted at srpatil@neerman.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
**Table A5.** Decomposition results of the emissions changes and contributions of various driving forces in the **HI countries** over the early period (1980-1990), late period (20052015), and entire period (1980-2015).

|**Period**|**1980-1990**|**2005-2015**|**1980-2015**|
|---|---|---|---|
|**Emissions change (Mt)**||||
|Overall change|338.0|-1,165.1|1,357.6|
|_EC_|-3.5|-267.2|40.5|
|_ECS_|-733.9|-610.3|-1,737.7|
|_EI_|-2,053.6|-2,285.2|-6,184.4|
|_PCG_|2,304.7|928.2|6,081.7|
|_P_|824.2|1,069.4|3,157.5|
|**Contribution (%)**||||
|Growth rate|3.2|-9.0|12.9|
|_EC_|-0.1|-2.1|0.4|
|_ECS_|-7.0|-4.7|-16.6|
|_EI_|-19.6|-17.6|-58.9|
|_PCG_|22.0|7.1|57.9|
|_P_|7.9|8.2|30.1|

**_Note:_** _EC_ , _ECS_ , _EI_ , _PCG_ , and _P_ refer to emission coefficient, energy mix, energy intensity, income, and population, respectively.

33
# **6 Tables and Figures**

Table 1: Montreal Protocol Substances

|**Gas**|**Introduction**<br>**0% Date**:<br>**nonArticle**5|**0% Date**:<br>**Article**5|**Common Uses**|
|---|---|---|---|
||**Annex A**<br> <br>|||
|CFCs|Montreal Protocol (1987)<br>1996|2010|Refrigerant, propellant|
|halons|Montreal Protocol (1987)<br>1994<br>**Annex B**|2010|Fire extinguishants|
|“other” CFCs<sup>_~~∗~~_</sup>|London Amendment (1990)<br>1996|2010|Not in Wide Use|
|CTC|London Amendment (1990)<br>1996|2010|Feedstock, solvent|
|TCA|London Amendment (1990)<br>1996<br>**Annex C**|2015|Solvent|
|HCFCs|Copenhagen Amendment (1992)<br>2020|2030|CFC Replacement|
|HBFCs|Copenhagen Amendment (1992)<br>1996|1996|Not in Wide Use|
|BCM|Beijing Amendment (1999)<br>2002<br>**Annex E**|2002|Not in Wide Use|
|MB|Copenhagen Amendment (1992)<br>2005<br>**Annex F**|2015|Fumigant, Pesticide|
|HFCs|Kigali Amendment(2016)<br>2036(85%)|2045 (85%)|HCFC Replacement|

24
**Table 1. The sample and its distribution across administrative categories**

||N|Margin of<br>error|Prior estimate on<br>the probability of<br>being urban|n*|
|---|---|---|---|---|
|Capitals and Municipal Corporations|147|0.05|0.97|34|
|Municipalities|905|0.05|0.90|120|
|Industrial Towns|31|0.05|0.90|25|
|Other Towns|2,769|0.05|0.80|226|
|Census Towns|3,710|0.05|0.71|292|
|Outgrowths|935|0.05|0.60|264|
|Villages|553,127|0.03|0.10|315|
|Total|561,624|||1,277|
|Mean probability of being urban|||0.11||
|Relative standard error|||0.08||

In equation (1) the term e� is the margin of error allowed for administrative category i , and z indicates the boundaries of the chosen confidence interval. For a conventional 95 percent confidence, the value of z is 1.96, and e� is equivalent to 1.96 times the standard error of p� . We assume that the standard error of the prior probability of being urban is much smaller for villages than for the seven other administrative categories. Therefore, e� is set at 3 percent for villages and at 5 percent for all other strata.

Based on these assumptions, the total sample size needs to be 1,277 or higher: 405 units statutorily urban; 557 units urban in terms of the census; and 315 villages. For the full sample we achieve a relative standard error of 8 percent, which is below the 10 percent threshold required for a survey design to be considered good (UN 2005).

In the second stage, each of the eight strata is divided into three substrata defined by average consumption per capita. Consumption data are from the household consumer expenditure module of the 68th round of National Sample Survey of India (NSSO 2012). The data quality is considerably better for the 21 largest states and union territories than for the smaller ones. The monthly household expenditure is divided by household size to estimate consumption per capita. To account for the spatial variation in prices, the result is deflated by a year‐specific price index that differs across states and union territories and between rural and urban areas. The 21 states and union territories are ranked on the basis of their average consumption per capita, and they are divided into three groups of equal size: richer, intermediate, and poorer.

The sample for each administrative category is split evenly across these three groups of states and union territories. For example, of the 264 units retained in the outgrowth stratum of the sample, 88 would be from richer states and union territories, another 88 units from the intermediate group, and the remaining 88 from the poorer group.

The sampling process is completed by conducting random draws without replacement for each of the 24 substrata. Our final sample with 1,277 administrative units is relatively evenly distributed across India’s 7
conservatism,<sup>9</sup> use of social media, exposure to the local feminist movement,<sup>10</sup> as well as a subsample of questions from the Crowne-Marlowe social desirability scale (for robustness checks as in Dhar et al., 2022; Mehmood et al., 2022). The Online Appendix contains a full list of these items.

# **3.2.2 Treatments**

The experiment had two treatment arms: T1 only provides information on gifting. T2 first corrects respondents’ beliefs about public reform support and then provides information on gifting. After the baseline survey, all respondents were asked about their beliefs regarding public support for inheritance law reform:

**[Second-Order Beliefs - all respondents:]** Now let’s talk about the opinions of _other_ Tunisians:

1. Out of 10 _women_ chosen at random from Tunisia, how many do you think believe that daughters should inherit as much as sons?

2. Similarly, out of 10 _men_ [...]?

3. In your opinion, how many of these _(answer)_ women would support a legal reform making gender equality in inheritance the legal norm in Tunisia?

4. Similarly, how many of these _(answer)_ men [...]?

After these questions, respondents were randomly allocated to T1, T2 and C in roughly equal shares. The interviews of respondents who were allocated to C directly continued with the outcome measures. Only individuals in T2 were informed about the true level of support for legal reform:

> 9Respondents were asked whether the sale of pork and alcohol should be legal in Tunisia, whether banks should be allowed to charge interest, whether religious leaders such as sheiks and imams should have a say in Tunisian politics and whether Islamic inheritance law forms part of Tunisian national identity. I construct a measure of religious conservatism based on these items using principal component analysis. The individual items as well as the index measure are highly correlated with self-reported religiosity.

> 10The baseline survey included questions asking respondents whether they knew Halima Jouini, Yosra Frawes and Bochra Belhaj Hmida, three prominent Tunisian feminists, #EnaZeda, the local offspring of #MeToo, and whether they had a family member or friend who identified as a feminist. All survey items are highly correlated among each other and I construct a measure of exposure to feminism with principal component analysis based on these items.

14
**Table 9:  Exploratory Analysis of the Association between School Autonomy and Accountability and Test Scores (PISA 2009)**

|**Variables**|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|**9**|**10**|**11**|**12**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Overall SABER Score||||||||||||11.6*|
|Autonomy: Budget|19.2**|9.4*|||||||||15.0**||
|Autonomy: Personnel|||10.6**|9.5**|||||||3.9||
|School Council|||||9.0*|4.8|||||1.6||
|Student Assessment|||||||15.4**|5.3|||9.3||
|School Accountability|||||||||9.7|1.0|-0.8||
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Constant|363.4***|367.3***|392.8***|361.5***|396.3***|384.5***|367.1***|379.6***|393.3***|396.3***|331.1***|367.8***|
|R2|0.043|0.430|0.0190|0.440|0.015|0.410|0.020|0.430|0.012|0.430|0.055|0.391|
|F|10.15**|23.9***|4.37**|24.7***|3.41*|23.4***|4.68**|23.2***|2.62|23.0***|2.55**|28.3***|
|N|225|225|225|225|225|225|225|225|225|225|225|225|

Source: Estimated by the authors Notes: Significance: * = 0.1, ** = 0.05, *** = 0.01; PISA scores have a mean of 500 and a standard deviation of 100; controls: total school enrolment; student-teacher ratio; quality school resources; index academic selectivity; public school 23
Figure 6: Marginal impact of the expected public debt-to-GDP ratio on the interest rate (Baseline) <!-- Start of picture text --> IT PSTR GPSTR<br>12 12 12<br>Advanced economies Advanced economies Advanced economies<br>Emerging markets Emerging markets Emerging markets<br>10 10 10<br>8 8 8<br>6 6 6<br>4 4 4<br>2 2 2<br>0 0 0<br>-2 -2 -2<br>-4 -4 -4<br>0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50<br>Share of the foreign private investors(%) Share of the foreign private investors(%) Share of the foreign private investors(%)<br>basis points basis points basis points<br><!-- End of picture text --> Notes: Shadow area refers to 90 percent confidence interval computed with Driscoll and Kraay (1998) standard errors. Circles and triangles correspond to observation for private foreign share. Broken lines represent location parameters.

Figure 7: Marginal impact of the share of foreign private investors on the interest rate (Baseline) <!-- Start of picture text --> IT PSTR GPSTR<br>200 10 10 200 200<br>7.5 7.5<br>150 5 5 150 150<br>2.5 2.5<br>0 0<br>100 100 100<br>-2.5 -2.5<br>-5 -5<br>50 50 50<br>-7.5 -7.5<br>-10 -10<br>0 0 0<br>0 10 20 30 40 50 0 10 20 30 40 50 0 10 20 30 40 50<br>Share of the foreign private investors(%) Share of the foreign private investors(%) Share of the foreign private investors(%)<br>-10 -10<br>-7.5<br>-7.5 -5<br>-2.5<br>-10<br>-7.5<br>-10<br>-5<br>0 2.5<br>5<br>-2.5<br>-5<br>7.5<br>10<br>2.5<br>5<br>10<br>7.5<br>7.5<br>-2.5<br>10<br>5<br>0<br>2.5 0<br>-7.5<br>-2.5<br>2.50<br>-5<br>-7.5<br>5<br>-7.5<br>7.5<br>-5<br>10<br>-2.5<br>10<br>7.55<br>0<br>2.5<br>-7.5<br>-2.5<br>0<br>-5-2.5<br>Expected public debt to GDP ratio(%) Expected public debt to GDP ratio(%) Expected public debt to GDP ratio(%) 02.55<br><!-- End of picture text --> Notes: Basis points.
Table 7: One factor model: Do direct links equalize farm size?

|||(1)|(2)|(3)|(4)|
|---|---|---|---|---|---|
|Regressor<br><br>|Coefcient|Kinshipy|Kinship|Neighbor|Neighbor|
|(_ai−_<br>_a_)<br>_Nv_<br><br>|_α_1||0.021**<br>(0.010)||0.025<br>(0.022)|
|(<br>_a__~~−~~aj_)<br>_Nv_<br>|_α_2||0.021**<br>(0.010)||0.025<br>(0.022)|
|(_ai−aj_)<br>2<br>_Lij_|_α_5|0.002**|0.001|0.007*|0.005*|
|||(0.001)|(0.001)|(0.004)|(0.003)|
|Constant|_α_0|0|0|0|0|
|Observations||54,144|54,144|21,266|21,266|

Note: The dependent variable is the amount of land transferred from _i_ to _j_ measured in hectares. Standard errors clustered by village are shown in parentheses.*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Source: Author’s own calculations on the Gambia Networks Data 2009.

owners: even smaller land owners share land across ethnic or kinship boundaries.

Next, direct kinship links between households _L_<sup>_K_</sup> _ij_<sup>are introduced, and model (5) is estimated.</sup> Results show that _α_ �5 is positive and statistically significant, suggesting that land transfers between directly related households tend to equalize their respective land areas (see column 1 of Table 7). The point estimate is very small, however: approximately 0.2% of the transfers that would be necessary to equalize landholdings between related households. Once village regressors<sup>_<u>ai</u>_</sup> _N_<sup>_−_</sup> _vav_ and _<u>avN−vaj</u>_ are added, the finding disappears, as shown in column 2. Again, the interpretation is that land transfers are better predicted by differences from the village average than by pairwise differences across related households.

In the last two columns of Table 7 geographical proximity _L_<sup>_D_</sup> _ij_<sup>isconsidered.Asexplained</sup> earlier, data on compound neighbors only exist for a subset of households, hence the drop in the total number of observations. When the only regressor is the pairwise land difference between neighbors, a positive and significant _α_ 5 estimate is found, suggesting that land transfers between neighbors on average go in the direction of equalizing their respective land areas. Unlike in the case of related households, this finding survives adding village regressors<sup>_<u>ai</u>_</sup> _N_<sup>_−_</sup> _vav_ and _<u>avN−vaj</u>_<sup>:pointestimatesfor</sup><sup>_α_1and</sup><sup>_α_2remainlargeandcomparabletothoseobtainedin</sup> � column 2, but they are not statistically significant. In contrast, _α_ 5 remains statistically significant, weakly suggesting that land transfers serve more the role of land equalization between 27