rafmacalaba/datause-encoder-data huggingface dataset:
pattern remains quite stable the five survey years. These findings are summarized in Tables 6 and 7 presenting the predicted probabilities of employment in the various occupations at mean values of the explanatory variables. For example, the first cell in Table 6 indicates that in 1983 the predicted probability of employment in agricultural labor would be about 34.6% if all individuals were scheduled castes or scheduled tribes (with education levels and other characteristics corresponding to the overall average in the population). This probability would fall to 23.4% if their caste status were switched to non-SC/ST. It is important to recognize that these are stylized probabilities – in reality SC/ST’s would have education levels and landholdings well below the national average as well. Table 6 indicates that the effect of caste status on regular non-farm employment probabilities appears to operate indirectly through the differential education and landholdings of SC/ST’s instead of directly. Holding these other characteristics constant (at their national average), predicted employment in regular non-farm employment is not markedly lower for SC/STs. With casual and self-employment in the non-farm sector, evidence of caste differences are more readily discernable. In general, there is little evidence of marked changes in the role of social status in determining occupational status over time.

Table 7 confirms the clear association of education with employment in non-farm activities. Predicted probabilities of regular non-farm employment in all five survey years increase markedly with education levels (at mean values of other characteristics), while they fall sharply in the case of agricultural labor, and more moderately in the case of casual non-farm employment. There is little evidence of a strong role for education in self-employment activities. Once again, this is possibly the consequence of the heterogeneity in the kind of self-employment activities that take place.

Finally, the multinomial models in Appendix Tables 3-7 also suggest that the probability of employment in regular non-farm activities and non-farm self-employment is significantly higher (relative to agricultural labor) for those with higher per-capita landholdings. Lanjouw and Stern (1998) argue, on the basis of a detailed village study in Uttar Pradesh, that information networks and ability to pay bribes are important determinants of access to the better-paying and more attractive non-farm jobs. It is possible that per-capita landholdings are proxying such assets here.

14
# **1. Introduction**

Using a case study, Zambia, and deriving from a data base analysis of contracts from 2008 to 2011, this paper assesses empirically if political interference in a low-governance environment has diminished in the last years, as would be expected after a semi-autonomous agency model was set up ten years ago.

Throughout the world, the road sector has proven particularly prone to major governance issues (Castalia 2009). Since road-related expenditure very visibly illustrates the ‘government at work’, it serves as a particularly effective means of pleasing the public. However, high investments in the road sector are not necessarily synonymous with efficient investments.

Governments usually see road building as an important tool to maintain the political unity of the country and, as a result, road building funds are usually not based on a systematic prioritization with a sound modeling process. Roads are often used as political tools (Flyvbjerg 2009). It is for instance what Ayogu (2002) had also demonstrated in the case of Nigeria, where the interaction of interest-group struggle and the politics of center-state grants involve difficult trade-offs. There are strong incentives for central and local governments to show consideration to local people (and possibly collect funding) by investing in rural roads for instance.

The Kenya Anti-Corruption Commission (2007) pointed out that the risks of inefficient investments in the road sector are significant, especially when considering the major amount of resources being channeled into road infrastructure investments. In Kenya weak strategy, poor planning (lacking feasibility studies, traffic data, and data on road condition) and procurement problems led to costly investments with a very limited positive impact for the local population. Where fraud, collusion and corruption in road projects is common, roads end up costing far more to build than they should, do not last as long as they ought to, and the corruption proceeds often end up polluting a country’s political climate.

To tackle this, there has been a long debate on the need (or not) to earmark funds for the road sector (and give autonomy to the institution in charge of managing earmarked funds)<sup>2</sup> . Proponents of earmarked funds and autonomous agencies in this area emphasize the fact that this scheme reduces bureaucracy (and corruption), which leads to speedy completion of projects and then contributes to reduced construction costs and increased stability in the sector funding.

At the end of the 1990s, when the fight against corruption started to become a priority in the road sector and donors strived to ensure adequate financing for road maintenance, there was a push for the establishment of second generation road funds as well as new road agency institutional setups.

The underlying idea was that if the staff of an autonomous agency were better paid, corruption would be much more limited and political interference curbed. Road agencies were then put under the authority of Boards where road users were represented in order to create accountability.

> 2 For references on this debate, see McCleary (1989).

2
Equation (1) is the core of the basic methodology.<sup>The key thing to noteabout this</sup> equation is that the first-order percentage<sup>changein welfarecan be calculated usingonly</sup> information on the income shares of different<sup>income sources,the budgetshares of</sup> different items of expenditure, and the percentage<sup>changesin pricesexperienced. Such</sup> information is readily available from many<sup>householdsurveysmakingtheapplication of</sup> this methodology relatively straightforward in<sup>a largenumber of countries.</sup>

# 3. **Application of the Basic Methodology**

Given the above methodology, the impact<sup>of a price change upon a household will clearly</sup> depend on two things: which prices change<sup>(and by how much); and the natureof the</sup> household. We consider each issue in turn.

# _Determining price changes_

The easiest way to "determine" price changes<sup>is toassumethem.That is,the above</sup> methodology can be used to explore the potential<sup>impact upon different groups of</sup> households of a set of possible price changes.<sup>Thisis particularly valuable wherethe</sup> price changes likely to result from the implementation<sup>of a reformare not known with</sup> any degree of accuracy (or where the analysis<sup>of howthepolicy reforms might change</sup> prices is complex, costly or simply has not<sup>yetbeen undertaken). Evenwhere price</sup> changes have been predicted by some other<sup>model, assuminga set of exogenous price</sup> changes allows policymakers to conduct<sup>sensitivityanalysis on the poverty impact of the</sup> models predictions.

Rather than assuming exogenous changes in the<sup>pricesfaced by households,one might</sup> instead wish to assume exogenous changes in<sup>apolicy related priceand some</sup> transmission mechanism between the policy<sup>related priceand the pricefaced by the</sup> household. For example, if one is interested<sup>in the poverty impact of a 10 percent</sup> increase in the rice tariff we could write down<sup>the price of tradablegoodsas a functionof</sup> the various taxes and costs which incur<sup>between theborderand the household.i.e.</sup> _ph =_ **_pW.(1_** _-_ t) for output prices and _p = p_ **_W.(_**<sup>**_1_**</sup> _+_ t) for the price of tradeable inputs<sup>and consumptiongoods</sup> (2) where ph is the price experienced by the household,<sup>**_pW_**is the world price and t is the</sup> tariff, tax or unit cost between the border<sup>andthe household.If we knowthe world price</sup> and the tax then we can calculate the percentage<sup>change in the household pricefor any</sup> change in the tax. 1 l Alternatively one could<sup>treat the tax as endogenousand use the unit</sup>

> " In the simple case shown in equation (2) the percentage<sup>changein the householdprice will be equal to</sup> _at /(1 +_ t) where a is the percentage change<sup>in the tariff on an input;or-at</sup><sup>_/(1-_t) for anoutput.</sup>

_5_
We use household-level total expenditure (as a proxy for consumption) and total income using the Centre for Monitoring Indian Economy’s Consumer Pyramids Household Survey (CPHS). These data are collected through stratified multi-stage surveys of 2011 census district classification in India, covering 28 states and union territories and 514 districts.<sup>7</sup> The CPHS is conducted every four months with every household surveyed in every round. The latest CPHS wave was conducted during June 2020. The monthly time-series is created by seeking data on income and expenses from households for each of the four months preceding the month of the survey. By putting together all the data from multiple surveys, we create a monthly time-series of income and expenses of households. We correct for response bias in CPHS using weights for non-response, which was a concern during the lockdown period.

All other data are either from Covid19India.org, the 2011 Census, the SHRUG data set (Asher et al. 2019), or the Reserve Bank of India Database on the Indian Economy. We aggregate daily district-level information on infections from Covid19India.org which collates information from the central and state governments, and verifies it against media reports, to monthly data. Since the 2011 district boundaries used by the Census and the SHRUG database are different from the 2020 boundaries used for the zone classification, we convert information from these sources using official orders for rearranging district boundaries and 2011 data on population in the relevant sub-districts. From the Census, we compute the average age for each district. From the SHRUG database, which consolidates village and urban ward characteristics from a large number of official sources (Asher et al. 2019), we obtain the population density and the fraction of workers in the services sector. We also use quarter-end outstanding aggregate credit for scheduled commercial banks from the Reserve Bank of India’s Quarterly Statistics on Deposits and Credit of Scheduled Commercial Banks (Database on Indian Economy). We use per-capita credit to measure access to finance.

Appendix Table 1 describes the summary statistics. In Panel A, the number of observations is 3,665, which reflects 733 districts across India each observed over five months (March 2020 to July 2020). Red zone districts have much higher levels of nighttime lights compared to Orange and Green districts. The levels of mobility are similar across zone types. Simultaneously, Red zone districts had 1,324 average infections, compared to 260 in Orange districts, and 89 in Green districts. These patterns are consistent with higher infection rates in large urban areas with greater international connectivity and lower distancing. Correspondingly, Red zone districts are more populous with greater density, have higher bank credit and a larger fraction of the population employed in the services sector. The mean age in Red, Orange and Green 7 CPHS does not cover Arunachal Pradesh, Nagaland, Manipur, Mizoram, Andaman and Nicobar Islands, Dadra & Nagar Haveli and Daman & Diu. 7
22

**Table 6 PRODUCTIVITY EQUATIONS** - **I**

_In(Q/K),O a,+r(Xk_ 1 _ln(L/K)e_<sup>**_I_**</sup> **_2N_** _(MIK)eNllnK+er_

||Eq.:<br>**X,**=**fmat,, levy.]**|Eq. 2:<br>**XN**=**(matt, ley,,,**liqj|Eq. 3:<br>**X1,=[dfitb,,,mktbtbft,Iev,,Iiqj**|
|---|---|---|---|
|mat2|.156(4.30)|.148(4.26)||
|dfitb|||.080(2.97)|
|mktbtb|||-.037(-1.38)|
|lev|-.120(-2.64)|-.120(-3.18)|-.039(-1.17)|
|liq|_<br>__|_<br>.063(2.81)|.077(3.44)|
|In K|.056(2.75)|.057(3.23)|.076(4.34)|
|In (L/K)|.128(4.13)|.135(5.19)|.180(7.27)|
|In (M/K)|.918(45.32)|.907(54.79)|.882(55.19)|
|constant|-.050(-5.26)|-.052(-5.65)|*.053(-5.72)|
|waldl(d.f)|4592.14(5)|6381.02(6)|11143.0(6)|
|wald2(d.f)|81.29(9)|102.16(9)|248.55(8)|
|2nd order<br>serial corr.<br>(p value)|-1.70<br>(.089)|-1.66<br>(.097)|-1.59<br>(.112)|
|Sargan(d.f.)|187.30(170)|219.16(204)|248.6(238)|
|R<sup>2</sup>||_||
|obs.|10305|10305|10305|
|period|1981-89|1981-89|1981-89|

Notes: (I) **su,** = a + AA,;(ii) **waid]** is for **_the_** slope coefficients; (iii) wald2 is for the time dummies, (iv) t statistics in parentheses are based on robust standard errors; (vi) ninimum lag length for instrmments used is **2.**
<!-- Start of picture text --> Sichuan* (province) 2000‐2008<br>1200<br>1000<br>800<br>600<br>400 2008<br>200<br>2000<br>0<br>2000 20000<br>Prefecture GDP per capita (RMB, log scale)<br>Education spending per capita (2008)<br>Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance<br>441.18 441.18 261.91 1.68 197.68 0.45<br>Education spending per capita (2000)<br>Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance<br>74.43 186.79 40.06 4.66 35.59 0.48<br>per capita (RMB)<br>Prefecture education spending<br><!-- End of picture text -->

- The two points within the circled area contain prefectures, Aba 阿坝藏族羌族自治州 (population 894,077)and Ganzi 甘孜藏族自 治州 (population 975,207).  Each prefecture attributes to 1% of the total population of 81,331,778. c

<!-- Start of picture text --> Sichuan** (province) 2000‐2008<br>700<br>600<br>500<br>2008<br>400<br>300<br>200<br>2000<br>100<br>0<br>2000 20000<br>Prefecture GDP per capita (RMB, log scale)<br>Education spending per capita (2008)<br>Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance<br>387.74 387.74 261.91 1.48 96.91 0.25<br>Education spending per capita (2000)<br>Highest/ Standard Coefficient<br>M ean Highest Lowest lowest deviation of variance<br>65.97 125.74 40.06 3.14 22.37 0.34<br>per capita (RMB)<br>Prefecture education spending<br><!-- End of picture text -->

- ** This graph excludes thetwo prefectures, Aba 阿坝藏族羌族自 治州 and Ganzi 甘孜藏族自治州 .

|0<br>200<br>400<br>600<br>800<br>1000<br>20<br>**Prefecture education**<br>**spending per capita (RMB)**|Educati<br>00<br>**Prefe**<br>**Liao**|on spendin<br>**cture GDP p**<br>**ning (provi**|g per capita<br>20000<br>**er capita (R**<br>**nce) 2000‐**|<br> (2008)<br>**MB, log scal**<br>**2008**<br>2000|<br>**e)**<br>2008|
|---|---|---|---|---|---|
|M ean|Highest|Lowest|Highest/<br>lowest|Standard<br>deviation|Coefficient<br>of variance|
|541.27|867.12|352.55|2.46|186.83|0.35|
|M ean|Highest<br>Educati|Lowest<br>on spendin|Highest/<br>lowest<br>g per capita|Standard<br>deviation<br> (2000)|Coefficient<br>of variance|
|117.39|202.75|67.49|3.00|42.48|0.36|

Source: Provincial Statistical Yearbooks 36
by surveys at highway sites report no change or even a slight increase in the total number of empty trips.

Under the conceptual framework, the data in the above hypothetical case is consistent with a setting where search frictions are important. After search frictions were alleviated in both the fronthaul and backhaul locations, more matches occurred as predicted. However, demand imbalances, regulatory barriers, or returns to scale in the matching technology meant that the proportional increase in matches was higher in the fronthaul market than in the backhaul market. As a result, even though each carrier is more likely to find a backhaul, the overall number of empty trips may remain constant or increase.

For confirmation, the conceptual framework suggests that measuring backhaul probabilities through surveys may show benefits from the platform even if counting empty trips does not. In addition, the number of idle carriers in the fronthaul location may have decreased, which provides another source of economic efficiency.

**Case: United Kingdom** McKinnon and Ge (2006) document that between 1973 and 2003, the share of empty trips fell by 7 percentage points, as can be seen in Figure 5. They attribute the trend to changes in the structure of shipping, improvements in load matching, and the growth in “reverse logistics” demand such as shipping demand for recycled products. Under the framework, these changes map to simultaneous decreases in search frictions and demand imbalances, which predicts a larger decrease in the share of empty trips than either change alone. To tease out which effect dominated, one could have measured the change over this period in idle vehicles as well as empty trips. If the change in idle vehicles was small, this suggests that reverse logistics may be more important, while if the change in idle vehicles was large, then the improvement in load matching may be more important.

**Case: Colombia** Between 1950 and 2005, Gonzalez-Calderon et al. (2012b) report that the percentage of empty trips in Colombia fell by about 0.66 percentage points every five years. The periods where this percentage fell the most, between 1976 and 1980 and between 1991 and 2005, were also periods where the total number of trips increased significantly.

Under the conceptual framework, the pattern in Colombia is consistent with a setting where search frictions exhibited returns to scale. When the market expanded in the late 1970s and the 1990s, the total number of carriers and shippers may have increased and thereby alleviated the existing search frictions. However, this is also consistent with a story where demand in the backhaul direction increased, alleviating the demand imbalance. Additional data on the direction of shipments, and the percentage of empty trips along different origin destination pairs, would clarify which effect dominated.

30
Unfortunately, since one does not observe all relevant individual circumstances in practice, this measure of IO serves only as a lower bound.<sup>6</sup> Finally, IO can be estimated either non-parametrically (Lefranc et al., 2008, and Rodríguez, 2008) or parametrically (Bourguignon et al., 2007, and Ferreira and Gignoux, 2011). The non-parametric approach makes minimal assumptions, but requires a sufficient amount of data. When the data are limited there is a premium for imposing additional structure by assuming a functional form for the function _g_ . This would be the parametric approach. We will adopt the former, the non-parametric approach, as this takes full advantage of the fact that the database covers between 1 and 5 percent of the total population.

# **3. The database and variables**

# **3.1. The IPUMS USA database and sample design**

We use the IPUMS USA database, which contains the largest sample of US   population between 1 and 5 percent of the total population that covers a period of 50 years at regular decennial intervals: 1960, 1970, 1980, 1990, 2000 and 2010.<sup>7</sup> The data are representative at the US state level. The obvious advantage of working with such a large data set is that sampling errors are reduced to a minimum. For their special features we do not consider the states of Alaska, Hawaii, District of Columbia and Puerto Rico in this analysis.

Total yearly income is obtained by aggregating incomes from 8 different sources: ( _i_ ) wages, salary, commissions, bonuses or tips; ( _ii_ ) self-employment income; ( _iii_ ) interest, dividends, net rental income, or income from estate/trusts; ( _iv_ ) social security or railroad retirement; ( _v_ ) supplemental security income; ( _vi_ ) public assistance or welfare payments; ( _vii_ ) retirement, survivor or disability pensions; and ( _viii_ ) other regular sources of income, such as veterans payments, unemployment compensation, child support or

> 6 Using the MLD index, the within-group inequality component could be seen as a proxy of inequality due to individual effort. Unfortunately, this measure contains residual elements arising from non-observed circumstances, luck and other measurement errors, which prevents us from using this term in the empirical analysis. 7 The data cover 1 percent of the population for the years 1960-70 and 2010, and 5 percent of the population for the years 1980-2000.

6
# FIGURE 11 Central bank credibility and pass-through

- A. Central bank independence and inflation targeting frameworks

<!-- Start of picture text --> Number of countries Index<br>30 7<br>6<br>20<br>5<br>10<br>Inflation-targeting (IT) frameworks 4<br>Central bank independence (RHS)<br>0 3<br>2000 2004 2008 2012 2016<br><!-- End of picture text --> B. Central bank independence and exchange rate pass-through from monetary policy shocks <!-- Start of picture text --> Ratio<br>0.6 Interquartile range Median<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Low High Low High<br>Advanced economies EMDEs<br><!-- End of picture text --> C. Central bank independence and exchange rate pass-through from monetary policy shocks in EMDEs

- D. Central bank independence and average exchange rate pass-through in EMDEs

<!-- Start of picture text --> EMDEs 16<br>16 14<br>14 12<br>12 10<br>10 8<br>8 6<br>6 4<br>4 2<br>2 0<br>0 -0.1 0 0.1 0.2<br>0 0.2 0.4 0.6 Pass-through, ratio<br>Pass-through, ratio<br>Note: The central bank independence index is computed by Dincer and Eichengreen (2014). An increase in the index<br>means greater central bank independence. Pass-throughs are defined as the ratio between the one-year cumulative<br>impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change<br>estimated from factor-augmented vector autoregression models for 29 advanced economies and 26 EMDEs over 1998-<br>2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the<br>interquartile range and markers represent the median across countries. EMDEs = emerging market and developing<br>economies; ERPTR = exchange rate pass-through ratio; IT = inflation targeting.<br>B. Low and high central bank independence are defined as below or above the sample average.<br>C.D. The sample only includes EMDEs with floating exchange rate regimes according to the IMF classification.<br>D. Shock-specific pass-throughs are aggregated using shares of currency movements accounted for by each type of shock<br>as weights.<br>Central bank<br>Central bank independence, index<br>independence, index<br><!-- End of picture text --> Note: The central bank independence index is computed by Dincer and Eichengreen (2014). An increase in the index means greater central bank independence. Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change estimated from factor-augmented vector autoregression models for 29 advanced economies and 26 EMDEs over 19982017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers represent the median across countries. EMDEs = emerging market and developing economies; ERPTR = exchange rate pass-through ratio; IT = inflation targeting.

33