rafmacalaba/datause-encoder-data huggingface dataset:
**Table 13: Regression of the Treatment Variable on Instruments and Exogenous Control Variables**

|_Vibl_|_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|---|---|
|_arae_|_All_|_All_|_< 70 All_|_>= 70 All_|_<70 Males_|_<70 Females_|
|Instrument by year|0.907***<br>(0.034)|0.890***<br>(0.036)|0.643***<br>(0.035)|1.155***<br>(0.075)|0.924***<br>(0.050)|0.887***<br>(0.042)|
||0.951***|0.932***|0.612***|1.287***|0.916***|0.954***|
|Instrument by area|<br>(0.026)|<br>(0.028)|<br>(0.032)|<br>(0.053)|<br>(0.041)|<br>(0.038)|
|Oh b|0.009|0.009|0.010|0.035|0.021|0.004|
|ter uran|(0.028)|(0.024)|(0.016)|(0.040)|(0.031)|(0.034)|
|l|−0.022|−0.033|−0.022|−0.013|−0.028|−0.029|
|Rura|(0.028)|(0.024)|(0.018)|(0.042)|(0.032)|(0.033)|
||<br>0.011***|<br>0.009***|<br>0.012***|<br>0.004***|<br>0.010***|<br>0.009***|
|Age|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.000)|
||0043***|0037***|0054***|0024*|||
|Female|.<br>(0.006)|.<br>(0.006)|.<br>(0.006)|.<br>(0.013)|||
|Sib|<br>−0.005***|<br>−0.005***|<br>−0.003***|<br>−0.006***|−0.006***|−0.005***|
|sen|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|||<br>0049***|<br>0067***|<br>0031*|<br>0062***|<br>0039***|
|Potential beneficiaries, number||.<br>(0.013)|.<br>(0.016)|.<br>(0.019)|.<br>(0.020)|.<br>(0.014)|
|||0.047***|0.063***|0.027|0.071***|0.025|
|Other adults, number||(0.014)|(0.015)|(0.026)|(0.020)|(0.017)|
|||0070***|0054***|0070***|0086***|0058***|
|Minors, number||.<br>(0.016)|.<br>(0.016)|.<br>(0.024)|.<br>(0.021)|.<br>(0.018)|
|||0085***|0116***|0036***|0100***|0077***|
|Females, number||.<br>(0.009)|.<br>(0.007)|.<br>(0.013)|.<br>(0.012)|.<br>(0.010)|
|idd||<br>0.003|<br>0.004|<br>−0.006|<br>−0.005|<br>0.007|
|Wowe||(0.005)|(0.004)|(0.008)|(0.006)|(0.005)|
|||−0031***|−0018***|−0034***|−0033***|−0032***|
|Did||.|.|.|.|.|
|vorce||(0.006)|(0.005)|(0.009)|(0.008)|(0.006)|
|||0.005|0.002|0.010|0.009|0.007|
|Single||(0.006)|(0.005)|(0.009)|(0.009)|(0.007)|
|||−0.034***|−0.014*|−0.057***|−0.030***|−0.039***|
|Primary||(0.007)|(0.008)|(0.013)|(0.011)|(0.010)|
|||−0.096***|−0.042***|−0.207***|−0.052**|−0.125***|
|Secondary||(0.012)|(0.011)|(0.024)|<br>(0.023)|<br>(0.012)|
|||−0.156***|−0.087***|−0.312***|−0.166***|−0.150***|
|Postsecondary||<br>(0.012)|<br>(0.011)|<br>(0.025)|<br>(0.016)|<br>(0.019)|
|Iid||<br>0.008|<br>0.039**|<br>0.022|<br>0.028*|<br>−0.013|
|ncapactate||(0.009)|(0.016)|(0.015)|(0.014)|(0.013)|
|Observations|22,358|22,297|12,093|9,778|9,298|12,892|

_Note:_ The table illustrates a linear probability model on dependent variables indicating receipt of the Colombia Mayor benefit. Standard errors are shown in parentheses.

Moving on to our actual results, we start by presenting the results for the total labor force participation of potential beneficiaries. Table 14 has eight columns. The first four show the ordinary least squares (OLS) regression with municipality and year fixed effects, and the second four present the IV results.

28
The model provides insights into the link between entry barriers, markups, and productivity. It mainly implies that reducing in entry barriers (e.g., through pro-competitive policies) reduces the dispersion in markups, which then generate less concentrated ICT markets. The link to sectoral and aggregate productivity depends on the degree of price dispersion and the elasticities of substitution.

# **4 Empirical Results**

The previous section highlighted the causal link between the regulatory environment, distortions and productivity. This section focuses on microeconomic evidence by mapping exogenous variations in competition regulations (the sectoral laws) and competition enforcement data to firm level data. We estimate the impact of regulations and enforcing regulations on productivity and markups for different ICT sectors (hardware, infrastructure, telecommunications and software).

The results show that competition enforcement by either Indecopi (the National Institute for the Defense of Free Competition and the Protection of Intellectual Property) or Osiptel (the Supervisory Body of Private Investment in Telecommunications) has a positive impact on revenue total factor productivity (TFPR). Assuming that we account for variations in markups correctly, this indicates that there is a real productivity improvement. Interestingly, and as Edmond et al. (2018) would have expected, this result holds mainly for the most productive firms, the leaders of the productivity distribution. It appears that middle-aged, but more productive firms benefit most from enforcement of competition regulations, however, inconsistent validation by robustness checks raises doubts about the significance of firm age. Markups seem to not be affected by competition enforcement cases.

When isolating each ICT branch, we observe sector-specific patterns.

## **4.1 Data Description**

Firm-level data from the Annual Economic Survey (EEA) is collected by the National Institute of Statistics (INE) for 2007-17. The EEA draws its sample from a directory of formal firms with annual sales above 150 Tax Units,<sup>3</sup> based on administrative tax records. The survey stratifies the sampling frame by economic activity and firm size proxied by annual sales to ensure that it is nationally representative of Peruvian firms at the four-digit ISIC code level. For each year, given their sectoral importance, firms with annual sales above a certain threshold are selected using forced inclusion sampling (inclusion forzosa) to form

> 3150 Tax Units amounted to 517,500 in 2007 and 607,500 in 2017. The tax is called Unidad Impositiva Tributaria.

12
# **<mark>Table C.4a: Comparison of modal class assignments across the 4-class and the 5-class models for relations with suppliers</mark>**

|Read by rows: from 4-class (row) to|5-class (<br>Class<br>1|column)<br>Class<br>2|Class 3|Class 4|Class 5|Total|
|---|---|---|---|---|---|---|
|Pure bilateralism|**89.04**|0.84|0.29|0|9.83|100|
|Bilateralism with private support|9.23|**88.93**|0.03|0|1.81|100|
|Bilateralism with legal support|0|1.06|**88.53**|0.01|10.4|100|
|Strong comprehensive governance|0|0|0|**100**|0|100|
|Total|60.69|14.88|14.32|1.62|8.49|100|
|Read by columns: from 5-class (colu|mn) to 4<br>|-class (ro<br>|w)<br>||||
||Class<br>1|Class<br>2|Class 3|Class 4|Class 5|Total|
|Pure bilateralism|**97.58**|3.77|1.34|0.07|77.06|66.52|
|Bilateralism with private support|2.42|**95.09**|0.03|0|3.4|15.91|
|Bilateralism with legal support|0|1.13|**98.63**|0.09|19.54|15.95|
|Strong comprehensive governance|0|0|0|**99.84**|0|1.62|
|Total|100|100|100|100|100|100|
|Estimated class membership probab<br>|ilities and<br>|standard<br>|errors<br>||||
|Pure bilateralism|0.657|0.0243|Class 1|0.5932|0.0273||
|Bilateralism with private support|0.166|0.0218|Class 2|0.1655|0.0244||
|Bilateralism with legal support|0.160|0.0171|Class 3|0.1496|0.0171||
|Strong comprehensive governance|0.017|0.0043|Class 4|0.0165|0.0043||
||||Class 5|0.0751|0.0204||

Appendixes, page 20
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 32. Electricity and water subsidies that reach the poor**

<!-- Start of picture text --> a. Electricity b. Water<br>Nigeria Senegal<br>Gabon CAR<br>Congo Gabon<br>Côte d'ivoire Congo<br>Nigeria Kaduna<br>Cape Verde<br>Togo<br>Togo DRC<br>Sao Tome Nigeria FCT<br>Senegal Cameroon<br>Cameroon Côte d'ivoire<br>Mozambique Niger<br>Ghana Chad<br>CAR Cape Verde<br>Burundi<br>Guinea<br>Burundi Malawi Blantyre<br>Ghana<br>Burkina Guinea<br>Chad Uganda<br>Malawi Malawi Lilongwe<br>Uganda Burkina<br>Rwanda Rwanda<br>0 0.5 1 0 0.2 0.4 0.6 0.8 1<br>Omega Omega<br><!-- End of picture text --> _Source_ : Banerjee and others 2008a.

_Note_ : Omega is a measure of distributional incidence, or the share of subsidies received by the poor as a percentage of their share in the population. The higher the value of omega, the better the distributional performance of the subsidy. Values of omega below 1 denote a regressive subsidy and values above 1 denote a progressive subsidy. CAR = Central African Republic; DRC = Democratic Republic of Congo.

**Figure 33. Affordability of electricity and water in Cameroon and in other low-income countries**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2 4 6 8 10 12 14 16<br>Monthly utility bill (US$)<br>Cameroon<br>LIC<br>Average cost of power is 4 $/month per 25 kwh or 8 USD/Month per 50 kwh subsistence consumption<br>Average cost of water is 3.28 $/month per 4 m3 or 8.2 USD/Month per 10 m3 subsistence consumption<br>Power cost-recovery tariff is 0.16 $/kwh<br>Water cost-recovery tariff is 0.82 $/m3<br>household budget<br>Share of urban households whose utility bill would be less than 5 percent of the monthly<br><!-- End of picture text --> _Source:_ Banerjee and others 2009. _Note:_ LIC = low-income countries; kWh = kilowatt-hour.

44
- 22 -

For comparison pi -poses, similar crosstab analysis is conducted for nonsocial projects utilizing the OED data on the project rating (unsatisfactory versus satisfactory)."3 The relationship between policies and project performance is not very different from those for projects in the social sectors. In addition, we tested through F-test whether the bi-variate relationship between economny-wide policies and the probability for project failure was statistically different for projects in the social sectors from those for other types of projects. In fact, the null hypothesis that the impact of policies on the likelihood of project failure is the same for social and nonsocial projects could not be rejected using F test. This is particularly telling, given the recent evidence on how large the impact of policies on the economic rate of return of nonsocial (tradeable and non-tradeable) projects is.<sup>14</sup>

# VI.B Results on longer-term project sustainability

As indicated in Section V, the project performance variable (sat/unsat) is likely to be subject to measurement error. In particular, the universe of projects rated as satisfactory is likely to contain projects which are not fully satisfactory. Furthermore, the sat/unsat rating does not filly incorporate an assessment of project benefits. While the qualitative nature of the results indicating a positive correlation between the quality of economy-wide policies and project performance is unlikely to be affected, it is warranted to explore an alternative data set. In this subsection, we use the limited information on sustainability of social projects, which is available for only _56_ projects evaluated in 1989 and 1990. For these years, only the black market premia variable has a sufficient number of observations for matching the policy index with the project performance variable. A bivariate crosstabulation analysis is thus performed between the black 31 The cross-tabs for health sector and for nonsocial projects are available from the authors upon request. 14 Projects outside the social sectors implemented **in** an undistorted macroeconomic and total regime are found to have economic rates of return which are almost twice (or 7-10 percentage points) as high as projects implemented under a distorted policy environment (Kaufinann 1991).
When such proxy indicators are used for targeting instead of explicit poverty estimates, there is mis-targeting both due to the targeting errors described above and due to problems with the proxy welfare index at the community level.<sup>3</sup> In recent years there has been growing experience with the development of “poverty maps” that explicitly estimate consumption or income-based welfare outcomes at the local level.<sup>4</sup> This approach involves imputing consumption or income at the unit-record level into the population census based on a set of regression models estimated from household survey data. The imputed household-level income or consumption estimates are then aggregated into welfare indices (including but not confined to poverty measures) at different levels of geographic aggregation. While there is inevitably a degree of statistical error associated with these welfare indices, this uncertainty can be quantified, and it is not uncommon to find that the method produces reliable estimates of poverty for communities comprised of only 1,0005,000 households on average.<sup>5</sup> This paper asks to what extent the high degree of spatial disaggregation offered by such poverty maps in three different countries can help to improve targeting schemes aimed at reducing poverty. In this sense, the paper closely builds on the earlier analysis in Ravallion (1993) who finds that spatial disaggregation to the broad regional level in Indonesia – the lowest level at which household survey data provide reliable estimates of poverty – improves targeting but only to a modest extent. As in Ravallion (1993), we consider the distribution of a hypothetical budget to a country’s population, assuming that we have no information about the poverty status of this population other than the geographic location of residence and the level

> another in terms of the degree of inequality they exhibit and that there should be no presumption that inequality is less severe in poor communities.

> 3 For an example of the latter, see Hentschel et al. (2000).

> 4  Hentschel et al, 2000, and Elbers, Lanjouw and Lanjouw, 2002, 2003 describe the basic methodology.  At present, the methodology has been implemented or is in the process of being implemented in some 30 developing countries.  For further details see the website:

5
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE OECD financiers account for around 4 percent of capital spending. Their presence is most pronounced in the transport sector.

**Table 16. Financial flows to Cameroon’s infrastructure, 2001-2006**

<u>$ millions per year</u>

||O&M<br>Public sector|Public<br>sector|Ca<br>ODA|pital expenditure<br>Non-OECD<br>financiers|<br>PPI|Total<br>CAPEX|Total<br>spending|
|---|---|---|---|---|---|---|---|
|ICT|90|100|3|0|76|179|270|
|Irrigation|3|0|0|0|0|0|3|
|Power|173|8|15|2|60|84|258|
|Transport|175|32|53|13|0|98|273|
|WSS|48|36|23|1|19|79|127|
|Total|490|176|93|16|156|440|930|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ Financial flows are annual averages between 2001 and 2006. O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development; WSS = water supply and sanitation; ICT = information and communication technology.

Cameroon’s current spending on infrastructure amounts to around 5.6 percent of GDP (figure 28), slightly less than the average spending in other resource-rich countries. Relative to this peer group, Cameroon is more heavily reliant on public spending in the ICT sector, but less reliant on public spending in the other infrastructure sectors. In comparison to peers, ODA plays a much more pronounced role in transport, while the power sector receives more private investment (figure 29). ICT, power, and _Source: Note:_ transport in Cameroon each receive 28–29 percent of total infrastructure spending, and the water sector receives the remaining 14 percent.

**Figure 28. Cameroon’s infrastructure spending in regional context, 2001–06**

<!-- Start of picture text --> As percentage of GDP<br>SSA<br>Capex O&M<br>LIC-Nonfragile<br>MIC<br>LIC-Fragile<br>Resource-Rich<br>Zimbabwe<br>Ethiopia<br>Uganda<br>Kenya<br>Central African Republic<br>Botswana<br>South Africa<br>Cameroon<br>Congo, Dem. Rep.<br>0 5 10 15 20<br>% of GDP<br><!-- End of picture text --> _Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ Financial flows are annual averages between 2001 and 2006. LIC = low-income country; MIC = middle-income country; ECOWAS = Economic Community of West African States; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M= operations and maintenance; CAPEX = capital expenditure.

41
Policy ReseaRch WoRking PaPeR 4732

# **Abstract**

This paper uses a new, 2005/06 nationally-representative household survey to analyze the impact of internal remittances (from Ghana) and international remittances (from African and other countries) on poverty and inequality in Ghana. To control for selection and endogeneity, it uses a two-stage multinomial logit model with instrumental variables focusing on variations in migration networks and remittances among various ethno-religious groups in Ghana. The paper finds that both internal and international remittances reduce the level, depth, and severity of poverty in Ghana. However, the size of the poverty reduction depends on the type of remittances received. In general, poverty in Ghana is reduced more by international than internal remittances. For households receiving international remittances, the level of poverty falls by 88.1 percent with the inclusion of remittances; for households receiving internal remittances, poverty falls by 69.4 percent with the inclusion of remittances. The paper also finds that both types of remittances increase income inequality in Ghana. For households with internal remittances, the inclusion of remittances causes the Gini coefficient to rise by 4 percent, and for households with international remittances, the inclusion of remittances causes the Gini to increase by 17.4 percent.

This paper—a product of the Development Prospects Group, Development Economics Department and the Africa Region—is part of a larger effort in the department to understand the impact of migration and remittances on poverty and inequality in the developing world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at radams@worldbank.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
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26
# **Introduction**

Improving agricultural productivity is widely regarded as a channel for reducing poverty and food insecurity in Sub-Saharan Africa (SSA). This view is based on the heavy reliance that poor and food insecure households place on agriculture. Unfortunately, agricultural productivity has been very low in SSA: since the 1960s, average per capita annual growth in agricultural productivity has been less than 1% for the continent as a whole, and – at times – negative for some sub-regions (FAO statistics, 2013). The lagging agricultural growth in SSA has often been explained by low utilization of inorganic fertilizer (for example, see Morris et al., 2007). Morris et al (2007) observed that when countries and crops in similar agro-ecological zones are compared, the rate of fertilizer application is much lower in SSA than in other developing regions of the world. Meanwhile fertilizer use is viewed as particularly important in SSA because the continent’s soils are inherently poor in nutrients, and over the last several decades, land-use practices have further worsened soil fertility through leaching, nutrient mining by crops and inadequate erosion control (Henao and Baanante, 2006).

The low use of inorganic fertilizer in crop production in SSA can be attributed to both demand-side and supply-side factors. The first and perhaps most obvious demand-side factor that could potentially explain the low use of fertilizer in Africa relates to profitability. Farmers’ demand for commercial fertilizer is weak because fertilizer use is probably unprofitable or only barely profitable to most farmers. Incentives to use fertilizer are often undermined by the low fertilizer response rate, high variability of crop yields, and high fertilizer prices relative to crop output prices. Demand for fertilizer is further weakened by lack of information about the availability and cost of fertilizer, the inability of farmers to raise resources needed to purchase fertilizer at planting when they are paid for their output months later at harvest (credit constraint), and lack of knowledge on the part of many farmers about how to use fertilizer efficiently (Morris et al., 2007; Kelly et al., 2