rafmacalaba/data-use-mentions-extended huggingface dataset:
|District effects|No|Yes|Yes|Yes|Yes|
|Year effects|No|Yes|Yes|Yes|Yes|
|Oth. controls|No|No|Yes|Yes|Yes|
|Het. trends|No|No|No|Yes|Yes|
|Observations|1340|1340|1340|1340|1340|

_Source_ : Author’s analysis based on AWI data (for field wages) and program expenditure data for SGRY and NREGA from 2001 to 2010. Program expenditure data is from the Ministry of Rural Development, Government of India. _Note_ : All estimates are of the order 10<sup>_−_2</sup> . The unit of observation is a district-year. The dependent variable in all the regressions is the log of field wages. Columns 1-5 have _sinh_<sup>_−_1</sup> ( _ed,t_ ) as the key regressor. In columns 1-4, program expenditure is for SGRY (b/w 2001 and 2007) and NREGA (b/w 2006 and 2010). In column 5, only expenditure under NREGA is considered (all expenditure values under SGRY are substituted by zero). The Inverse Hyperbolic Sine (IHS) transformation of the expenditure variable sinh<sup>_−_1</sup> ( _ed,t_ ) has the same interpretation as _log_ ( _ed,t_ ). Other controls include _NREGAd,t_ , _Util. Ratiod,t_ , _Raini,t_ , state elections-year indicator, and density of scheduled caste and scheduled tribe population in a district. All regressions are weighted by district-level population. The standard errors reported in square brackets are clustered at the district level and are robust to heteroskedasticity. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01 For both SGRY
|**|
|<br>Drying (GHC)|86|84|94||75|92|***|
|Milling (GHC)|61|71|31|***|51|91|***|
|No. of observations|1778|1344|434||679|665||

_Source:_ Own computation from World Bank Kpong and Weta irrigation scheme survey. Asterisks denote significance of t-tests for equality of means between the preceding columns: *** p<0.01, ** p<0.05, * p<0.1.

18
.38|0.77|0.52|0.36|
|Ethiopia|18.3|0.22|0.42|0.79|0.27|

<mark>Source: Gallup World Poll (various years). See World Bank (2016). Ratios refer to percentages, e.g., percent rural / percent urban.</mark>

**Table 3: Individuals with internet access, 2014**

||All<br>(percent)|Ratio<br>rural /<br>urban|Ratio<br>women /<br>men|Ratio<br>age 45+ /<br>15-24|Ratio<br>bottom40 /<br>upper60|
|---|---|---|---|---|---|
|South Africa|34.1|0.52|0.70|0.32|0.74|
|Botswana|29.0|0.55|0.81|0.23|0.51|
|Kenya|26.3|1.02|0.57|0.54|0.46|
|Nigeria|18.4|0.69|0.59|0.23|0.40|
|Namibia|16.2|0.15|0.76|0.37|0.32|
|Cameroon|14.1|0.11|1.09|0.48|0.48|
|Ghana|12.7|0.61|0.48|0.44|0.29|
|Mozambique|11.1|0.13|0.74|0.27|0.24|
|Uganda|7.9|0.38|0.26|1.92|0.28|
|Rwanda|6
Colima education authorities also attribute their performance to two other innovations: competitive testing for half of all new teachers and an agreement with the teacher union not to rotate teachers during the school year. In other states, such rotations start a chain reaction that can result in some classes having 3-4 different teachers during the academic year.

# **School-Based Management Program in Mexico: “** **_Programa Escuelas de Calidad_ ” (PEC)**

In 2001, the Mexican federal government introduced PEC to empower the school community and promote school autonomy and accountability. The main objective of this initiative was to encourage parents, teachers and directors to design and carry out School Strategic Transformation Plans that responded to the needs of the school and its students. For this purpose, PEC yearly awards a monetary grant and technical assistance to implement these improvement plans in selected schools. While it builds on previous small-scale experiences that decentralized limited decision-making powers to the school, PEC was the most extensive SBM program implemented in Mexico before 2013 (see Schmelkes 2001 for more details on other SBM initiatives in Mexico prior to PEC). PEC also became one of the flagship programs of the Fox administration (2000 - 2006) and it expanded rapidly, from 2,239 schools included in the program in 2001 to around 55,432 schools at 2014. PEC was canceled in 2014, after 14 years of implementation; a new SBM program was initiated called “ _Escuelas al Centro_ ” (Schools at the Center).

The national PEC intervention has been subject to evaluation. Murnane et al. (2006) use nationally representative data and difference-in-difference estimation techniques to compare PEC schools – defined as schools that joined the program in its second year of operation (2002) – to non-PEC schools. They validate the equality of pre-intervention trends between both sets of schools. However, the authors reject the equality of trends in the pre-period when schools that received benefits during the first year of operation of the intervention are included in the treatment group. These schools were improving outcomes
** the types of legal structures that can be used to secure obligations (e.g. security interest, pledge, mortgage, etc.); the types of transactions that should be considered within the scope of the law (loans secured with movable property, retention of title, financial leasing, assignment of receivables, consignments, etc); the types of movable property that may be used as security; and the types of debtors who may give security in movable property.

- **Creation:** the legal requirements for giving and taking a right against movable property to secure an obligation.

- **Priority:** the rules that determine the relative rights among conflicting claims against movable property.

- **Publicity/registration of security interests:** the means of making a claim against movable property transparent to third parties, commonly provided by registration in a public registry, by taking possession or control of the movable property, by direct notice,

> 45 See ―Private Credit in 129 Countries‖, Simeon Djankov, Caralee McLiesh and Andrei Schleifer, 2006.

33
As noted above, the earlier (RV-85 and RV-98) tables for females are constructed more subjectively than the RV-04 tables for both sexes and the RV-98 table for males. Whilst the shape and level of the mortality tables is still a matter for some debate in Chile, the RV-04 table seems to be the most scientific and robust for both sexes.

Each of these three tables is published as a period table, requiring adjustments in order to convert them into cohort tables. Cohort results were initially developed using two alternatives, namely, national population projection rates, and the rates of improvement between the RV-85 and the RV-04 tables. The first method was ultimately judged as superior and has been adopted here. The basic reason was the high degree of arbitrariness involved in the construction of the RV-85 table. In particular, it is clear that the female improvement rates derived from these tables continue to be open to greater uncertainty and are well above the observed and assumed population estimates.

> 5A standard battery of statistical tests is set out in Benjamin and Pollard (2001) and has been applied to the RV-04 tables separately for male and female tables. In the case of each test, the representative table used in these calculations is found to pass the test – that is, the table reflects the underlying mortality experience.
## of our four different measures.

With regard to these two input variables, our results are well in line with what we have obtained so far. In particular, as indicated by both the ATEs and the ATTs, state-owned firms with most similar characteristics employ more workers as compared to private enterprises. To give one example, the average subsidiary or independent state-owned firm employs 46.7% more workers than it would employ as privatized company. Similarly, the ATT including both types of firms indicates that for the same level and growth rate of TFP and all other matching variables, the capital input of state-owned enterprises is roughly double of that in private firms. This reinforces our argument that not only labor hoarding but also overinvestment is important in the Belarusian machine building industry.

# **7. Concluding remarks**

This paper evaluates the economic performance of state-owned versus non-state-owned firms in the Belarusian machine building industry. Empirically, the focus is on the firm growth performance and on total factor productivity. We apply standard modern methods from the empirical firm growth literature and our results indicate that firm growth (in terms of employment) in state-owned firms might be unsustainable and driven by inefficient resource allocation. Our results also indicate that vertical production chains might be particularly important to explain the source of such misallocation of resources. We use matching techniques to investigate the instruments used to enable such resource misallocation. Soft budget constraints appear to be in place in the sector. The group of state-owned subsidiaries of vertical networks tends to benefit most from such soft budget constraints.

From an economic point of view, these unsustainable firm growth trends can have very relevant effects on firm productivity dynamics. Using estimates of total factor productivity (TFP), which account for the simultanity bias, we unambiguously reveal that state-owned firms are less efficient than their private counterparts. Moreover, TFP convergence equations show that the dynamics and long-run level of productivity is substantially lower for this group of firms. labor 28
limited model for the CB component in columns 4 and 6, which is also the case for the DR component for the digital sector in column 5.

Table 8: Panel results following equation (3) using ITPD-E data

||**(1)**<br>**Digital+**|**(2)**<br>**Digital**|**(3)**<br>**Business**|**(4)**<br>**Digital+**|**(5)**<br>**Digital**|**(6)**<br>**Business**|
|---|---|---|---|---|---|---|
|MODEL**CB**OP|-0.020|0.325|0.048|-0.083|0.176|-0.167|
||(0.971)|(0.604)|(0.940)|(0.853)|(0.776)|(0.730)|
|MODEL**CB**CT|0.476|0.009|0.235|0.304|0.026|0.187|
||(0.329)|(0.986)|(0.643)|(0.413)|(0.959)|(0.625)|
|MODEL**CB**LT|-0.973|0.311|-1.026|-1.235***|-0.435|-1.651***|
||(0.169)|(0.686)|(0.165)|(0.009
POLICY RESEARCH WORKfNG PAPER 1725

# Summary findings

Recent theory increasingly emphasizes the association of short-term debt with higher-quality firms and better incentives. The possibility of premature liquidation, for example, may serve as a disciplinary device to improve firm performance. At the same time the role of longterm debt, especially when it is heavily subsidized, is being rethought because so many development banks are plagued with nonperforming loans and doubts about the selection criteria used in allocating funds.

Jaramillo and Schiantarelli explore empirical evidence about the structure of debt maturity in Ecuadorean firms. They discuss how it has been affected by government intervention in credit markets, and by financial liberalization. Using firnm-level panel data, they investigate the determinants of access to long-term debt in Ecuador. Finally, they provide evidence about how the maturity structure of debt affects firms' performance, particularly productivity and capital accumtulation. They find that: Long-term debt is very unevenly distributed. Almost

- 30 percent of firms never have access to it during the

period studied.

- **L** arge firms are more likely to have access to long term debt than small firms. The former are on average more profitable.

- Conditional on size, operating profits do not increase the probability of receiving long-term credit and may actually decrease it. This suggests that the mechanism used to allocate long-term resources in Ecuador mav be flawed.

- The allocation problem was worse for directed credit. There is some evidenct that, after financial liberalization, the problem was less severe.

- There is a strong positive association between asset maturity and debt maturity, a matching of assets and liabilities.

- Shorter-term loans are not conducive to greater productivity while long-term loans may lead to inmprovements in productivity.

- While long-term loans may positively affect the quality of capital accumulation, they do not have an impact on the amount of fixed investment.

This paper - a product of the Finance and Private Sector Development Division, Policy Research Department - was prepared for the conference "Firm Finance: Theory and Evidence," Finance and Private Sector Development
positive total asset position only after recovering from the Gulf War. The total foreign assets of these countries rose sharply during the late 1990s; that growth slowed, particularly in the past five years, in what seems to be a response to the volatility of and decline in oil prices and the uncertainty caused by the Arab Spring.

Non-oil-producing countries (the Arab Republic of Egypt, Jordan, Morocco, and Tunisia) maintained positive net total liabilities positions over the past two decades. Total liabilities in these countries rose sharply over this period, with total assets falling, widening the gap between total liabilities and total assets to 3: 1 over the past 10 years.

**Figure 3 Net foreign assets as percent of GDP, by country, 2007 and 2015**

<!-- Start of picture text --> by country<br>2007 2015<br>AREBHRCOM DJIDZAEGYIRQJORKWTLBNLBYMARMRTOMNQATSAUSDNSYRTUNWBGYEM<br>6<br>4<br>2<br>0<br>-2<br><!-- End of picture text --> Source: Authors, based on data from Lane and Feretti 2015.

**Figure 4 Current account as percent of GDP, by Figure 5 Capital account as percent of GDP, by country, 2007 and 2015 country, 2007 and 2015**

<!-- Start of picture text --> by country by country<br>2007 2015<br>2007 2015<br>AREBHRCOMDJIDZAEGYIRQJORKWTLBNLBYMARMRTOMNQATSAUSDNSYRTUNWBGYEM AREBHRCOMDJIDZAEGYIRQJORKWTLBNLBYMARMRTOMNQATSAUSDNSYRTUNWBGYEM<br>.5 .15<br>.1<br>0 .05<br>0<br>-.5 -.05<br><!-- End of picture text --> _Source_ : Authors, based on data from Lane and Feretti 2015.

_Source_ : Authors, based on data from Lane and Feretti 2015.

In order to understand the dynamics of the foreign sector in the oil versus the non-oil sector, we also look at changes in NFA and its key components over the period 2007
through which resources can hinder development. The purchase of arms may indeed explain why growth in newly oil-rich countries often fails to materialize. Public money could be spent on health, education, and infrastructure instead of arms. This arms-imports effect also adds to our understanding of the relationship between oil and conflict as arms imports may fuel future conflicts (Pamp et al., 2018) as well as political repression (Blanton, 1999). This anti-development spending choice and the consequences on conflict may be an indirect consequence of balance-of-payment preferences in arms exporters (Chan, 1980; Snider, 1984) or of a handful of greedy businesspersons. The import of arms may thus be one reason why oil-rich governments perpetuate the curse of oil. And since SIPRI data on arms transfers are publicly available and updated yearly from a wide range of sources from online news reports to parliamentary records, it provides a potential way of spotting arms import surges early. This is certainly valuable if we think the presource curse plays out over a few years after discoveries.

24
the classical “wasteful” approach. In particular, it would be interesting to investigate the performance of the proposed method for a multidimensional poverty measure constructed from the data available in a typical LSMS survey and a typical DHS or MICS survey. This performance can be assessed in contexts where the full multidimensional poverty measure can be constructed, e.g., those considered in Table 6 in Evans et al. (2023). Similarly, it could be interesting to study simple ways of extending the proposed method to multidimensional poverty measures for which the monetary dimension is not captured by a dichotomous status (i.e., monetary poor or not monetary poor) but by a trichotomous status (i.e., extremely, moderately or not monetary deprived) or by a continuous variable.

# **3.4.2 Imputing incomes into the non-monetary survey**

The method mentioned here is a more elaborate variant of the method proposed in the Section 3.4.1. It also makes assumptions on the missing joint distribution between monetary and non-monetary outcomes. The idea is to impute consumption or income into the non-monetary survey, say DHS or MICS.<sup>49</sup> One way of doing it is to use survey-to-survey imputation techniques, as pioneered by Elbers et al. (2003).<sup>50</sup> These techniques build an imputation model based on the common variables in the two surveys, e.g., demographic variables and maybe some common outcomes. This model is trained in a similar context

> 48See Section 3.2 for one alternative quantification of deviations.

> 49Another possibility is to impute non-monetary outcomes in an LSMS survey.

> 50See Dang and Lanjouw (2023) for a recent review.

27
43 <!-- Start of picture text -->
1.024<br>1.022<br>1.02<br>1.018<br>1.016<br>1.014 W(R=0.965)/W(R=0.985<br>1.012 )<br>1.01<br>1.008<br>1.006<br>0.85 0.9 0.95 1<br>Rm<br><!-- End of picture text -->

> B. Average ratios of welfare when _R_  0.965 to welfare when _R_  0.985,

> assuming  0.001 and a proportional cost of 1.5%

Figure 12: Average ratios of welfare under repression to welfare with no repression.

# 5.2 Changing the distribution of potential income

In the above calculations we used the US income distribution in 2011. But the income distribution changes over time and across countries. How will changes in the income distribution affect our measures of the welfare cost and the benefits from regulations? In an attempt to get a partial answer to this question we try two additional distributions. The first, denoted by _teta1_ , is obtained from the income distribution in Table 2 ( _teta_ ) in the following way. We assume that the potential income ( <sup>2</sup> ) of the most productive agent (the agent indexed 1) is 0.046 instead of 0.146. The remaining 10% of aggregate potential output are distributed evenly over the rest of the population. In Figure 13, _teta1_ is in red and is very close to _teta_ (in blue) except that the agent index 1 has now  0.21 rather than  0.38 that he had before the change. The second income distribution, denoted by _teta2_ , is constructed as follows. We take the index
Table C. City Size Equations<sup>a,b</sup> (standard errors corrected for spatial dependence in parentheses)

||(1)|(2)|
|---|---|---|
||Spatial GMM|Spatial OLS|
|Ln(ruralpop. supply)|1.706***|1.216***|
||(0.635)|(0.386)|
|Ln(rural income opportunities)|-3.317***|-1.999***|
||(0.864)|(0.462)|
|Ln(marketpotential)|2.322***|1.426***|
||(0.660)|(0.468)|
|Average Schooling|0.181*|0.231**|
||(0.099)|(0.112)|
|Ln(inter-citytransport costs)|-1.346***|0.081|
||(0.280)|(0.083)|
|State capital dummy|-0.211|1.091***|
||(0.330)|(0.187)|
|time dummies|Yes|Yes|
|Observations|369|369|
|Hansen J statistic<br>(overidentificationtest)|1.659||

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

- a. The instruments are semi-arid area dummy, port dummy, illiteracy rate (1970), ln(industry capital per worker, 1970), ln(distance to state capital)*ln(market pot. agric. land availability, 1970), ln(humidity), ln(avg. temperature), ln(rural pop. supply, 1970)
(0.214)||||(0.534)|||
|**2016**|0.479|||2016|1.648|||
||(0.991)||||(0.961)|||
|**2017**|0.683*|||2017|0.339|||
||(0.203)||||(0.509)|||
|**2018**|0.835*|||2018|0.656|||
||(0.291)||||(0.461)|||
|**2019**|0.261<br>(0.300)|||2019|0.064<br>(0.507)|||
|**ATT**|0.449*<br>(0.192)||0.263<br>(0.190)|ATT|0.464<br>(0.370)||0.503<br>(0.474)|

_Source_ : Authors’ estimation based on the ACC 2012, 2016, and 2019 survey data. _Note_ : P-value for a pre-test of parallel trends assumption is 0.230 for maize and 0.278 for wheat. Comparison group: “never treated.” Anticipation periods: 0. Estimation method: Doubly Robust. Standard errors are reported in parenthesis. * _p <_ 0.10.

The results from the event-study (dynamic) aggregation on the quantity of maize and wheat seed purchased by farmers show coefficients similar to those from the cohort and survey year aggregations. DSM led to a 46 percent increase in the quantity of maize seed purchased per hectare, while no statistically significant impact was observed for wheat seed purchases (Figure 2).

18
<br>10%<br>10.0%<br> $3,000<br>5%<br> $2,500<br>3.5%<br>2.3% 0%<br> $2,000<br>-5%<br> $1,500 1,252<br>-10%<br> $1,000 747 809 809<br>506<br> $500 -15%<br>-15.6%<br> $- -20%<br>GDP Female Trad. Mod. Trad. Mod.<br>Female Male<br>Source: author’s calculations<br>Millions<br>Growth (in %)<br><!-- End of picture text --> Fertility rates equal to the net reproduction rate of 1.0 would increase GDP by 3.1 percent (Figure 3). Overall, Liberian women have 4.47 children over the course of their reproductive years.<sup>23</sup> We initially investigated to what extent a fertility rate that is equal to an instant-replacement level alters the economic

> 21 These point estimates were drawn from ILO 2010, constructed to allow for 5-year cohort averages.

> 22 Estimates on these benchmarks are available upon request to the authors.

> 23 This figure is drawn from the World Population Prospects, which estimates population size, in five- year increments, over the century. These rates are slightly higher than the estimates derived from the recently released DHS 2019/2020, reported earlier in the paper. This number decreased from 6.8 in 1970-1975 and it is projected to reach 3.9 in 2025-2030 (United Nations, 2015). There is significant variation in the average fertility rate across regions, education levels, and wealth quintiles (DHS 2019/2020).

17
a model which incorporates a realistic transportation sector into a broader trade model<sup>8</sup> . Using this model, they estimate the magnitude of search frictions. Demirel et al. (2010) develop a simple, two-location model to study the role of matching frictions for backhauls. In a frictionless market, when there are more carriers than shippers interested in a backhaul trip, the price of backhaul trips should fall to zero. Since these prices are empirically positive, Demirel et al. (2010) argue that matching frictions must be present, and calibrate a matching model with data from Rhine shipping.

The magnitude of search frictions depends on the effectiveness of matching platforms and information technology. A series of papers have focused on Electronic Vehicle Management Systems (EVMS) adoption in North America. Conceptually, EVMS reduces communications costs and therefore makes search and matching easier. Hubbard (2003) found that U.S. trucks with EVMS drove 12.7 percent more loaded miles. Looking at Canadian trucks, Barla et al. (2010) found that while EVMS reduced empty miles on backhaul segments, empty miles on fronthaul segments increased. They argue that falling search costs may induce more search, or encourage carriers to travel empty to more profitable markets. The relationship between search costs or information technology and empty trips may not be monotonic.

In an earlier setting, Mansell (2001) finds that freight exchanges reduced empty trips in the United Kingdom by 5 percentage points, from a baseline of 28 percent empty trips. Heilmann (2020) studies the effect of the Uber Freight platform on matching jobs and reducing empty trips. He finds that deadhead miles in the United States fall by 22.6 percent, or 4 percentage points, and that utilization increases from 81.6 percent to 85.8 percent. However, the effect of matching platforms may not be monotonic. Rosaia (2020) considers the effect of competing platforms within the NYC taxi market. He finds that competing platforms can exacerbate search frictions